Revolution of Alzheimer Precision Neurology.
Passageway of Systems Biology and Neurophysiology
Harald Hampel, Nicola Toschi, Claudio Babiloni, Filippo Baldacci, Keith
Black, Arun Bokde, René Bun, Francesco Cacciola, Enrica Cavedo, Patrizia
Chiesa, et al.
To cite this version:
Harald Hampel, Nicola Toschi, Claudio Babiloni, Filippo Baldacci, Keith Black, et al.. Revolution
of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology. Journal of
Alzheimer’s Disease, IOS Press, 2018, 64 (s1), pp.S47 - S105. 10.3233/jad-179932. hal-01910402
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J Alzheimers Dis. Author manuscript; available in PMC 2018 June 19.
Published in final edited form as:
J Alzheimers Dis. 2018 ; 64(Suppl 1): S47–S105. doi:10.3233/JAD-179932.
Revolution of Alzheimer Precision Neurology: Passageway of
Systems Biology and Neurophysiology
Author Manuscript
Harald Hampela,b,c,d,*, Nicola Toschie,f,g, Claudio Babilonih,i, Filippo Baldaccia,b,c,d,j, Keith L.
Blackk, Arun L.W. Bokdel, René S. Buna,b,c,d, Francesco Cacciolam, Enrica Cavedoa,b,c,d,n,
Patrizia A. Chiesaa,b,c,d, Olivier Collioto, Cristina-Maria Comana,b,c,d, Bruno Duboisp, Andrea
Duggentoe, Stanley Durrlemanq, Maria-Teresa Ferrettir,s, Nathalie Georget, Remy Genthonp,
Marie-Odile Habertu,v, Karl Herholzw,x, Yosef Koronyok, Maya Koronyo-Hamaouik,y, Foudil
Lamariz, Todd Langevinaa, Stéphane Lehéricyab,ac, Jean Lorenceauad, Christian Neriae,
Robert Nisticòaf, Francis Nyasse-Messenep, Craig Ritchieag, Simone Rossiah,ai, Emiliano
Santarnecchiah,aj, Olaf Spornsak,al, Steven R. Verdooneram, Andrea Vergalloa,b,c,d, Nicolas
Villainb,c,d, Erfan Younesian, Francesco Garacie,ao, and Simone Listaa,b,c,d,* for the
Alzheimer Precision Medicine Initiative (APMI)
aAXA
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Research Fund & Sorbonne Université Chair, Paris, France bSorbonne Université, AP-HP,
GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de
l’hôpital, F-75013, Paris, France cInstitut du Cerveau et de la Moelle Épinière (ICM), INSERM U
1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France dInstitut de la Mémoire et
de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière,
AP-HP, Boulevard de l’hôpital, F-75013, Paris, France eDepartment of Biomedicine and
Prevention, University of Rome “Tor Vergata”, Rome, Italy fDepartment of Radiology, “Athinoula A.
Martinos” Center for Biomedical Imaging, Boston, MA, USA gHarvard Medical School, Boston,
MA, USA hDepartment of Physiology and Pharmacology “Vittorio Erspamer”, University of Rome
“La Sapienza”, Rome, Italy iInstitute for Research and Medical Care, IRCCS “San Raffaele
Pisana”, Rome, Italy jDepartment of Clinical and Experimental Medicine, University of Pisa, Pisa,
Italy kDepartment of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai
Medical Center, Los Angeles, California, USA lDiscipline of Psychiatry, School of Medicine and
Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland mUnit of
Neurosurgery, Azienda Ospedaliera Universitaria Senese, Siena, Italy nIRCCS “San Giovanni di
Dio-Fatebenefratelli”, Brescia, Italy oInserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris,
France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du
Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de
Recherche de Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière,
Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Paris, France pSorbonne Université,
Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie,
Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de
*
Correspondence to: Harald Hampel, MD, PhD, MA, MSc, Simone Lista, PhD, AXA Research Fund & Sorbonne Université Chair,
Sorbonne Université, Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer, Institut du Cerveau et de la
Moelle Épinière (ICM), Pavillon François Lhermitte, Hôpital Pitié-Salpêtrière, 47 Boulevard de l’hôpital, 75651 Paris CEDEX 13,
France, Phone: +33 1 42 16 75 15, Fax: +33 1 42 16 75 16, harald.hampel@icm-institute.org, simone.lista@icm-institute.org.
Hampel et al.
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l’hôpital, F-75013, Paris, France qInserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris,
France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du
Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de
Recherche de Paris, France rIREM, Institute for Regenerative Medicine, University of Zurich,
Zürich, Switzerland sZNZ Neuroscience Center Zurich, Zürich, Switzerland tSorbonne Universités,
UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de
la Moelle Épinière, ICM, Ecole Normale Supérieure, ENS, Centre MEG-EEG, F-75013, Paris,
France uDépartement de Médecine Nucléaire, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris,
France vLaboratoire d’Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm
U 1146, CNRS UMR 7371, Paris, France wDivision of Neuroscience and Experimental
Psychology, University of Manchester, Manchester, UK xDivision of Informatics, Imaging and Data
Sciences, University of Manchester, Wolfson Molecular Imaging Centre, Manchester, UK
yDepartment of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
zAP-HP, UF Biochimie des Maladies Neuro-métaboliques, Service de Biochimie Métabolique,
Groupe Hospitalier Pitié-Salpêtrière, Paris, France aaFunctional Neuromodulation, Ltd Boston,
MA, USA abCentre de NeuroImagerie de Recherche - CENIR, Institut du Cerveau et de la Moelle
Épinière - ICM, F-75013, Paris, France acSorbonne Universités, UPMC Univ Paris 06 UMR S
1127, Inserm U 1127, CNRS UMR 7225, ICM, F-75013, Paris, France adInstitut de la Vision,
INSERM, Sorbonne Universités, UPMC Univ Paris 06, UMR_S968, CNRS UMR7210, Paris,
France aeSorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, CNRS UMR
8256, Institut de Biologie Paris-Seine (IBPS), Place Jussieu, F-75005, Paris, France afDepartment
of Biology, University of Rome “Tor Vergata” & Pharmacology of Synaptic Disease Lab, European
Brain Research Institute (E.B.R.I.), Rome, Italy agCentre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, UK ahDepartment of Medicine, Surgery and Neurosciences, Unit of
Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN
Lab.), University of Siena, Siena, Italy aiDepartment of Medicine, Surgery and Neurosciences,
Section of Human Physiology University of Siena, Siena, Italy ajBerenson-Allen Center for
Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center,
Harvard Medical School, Boston, MA, USA akDepartment of Psychological and Brain Sciences,
Indiana University, Bloomington, IN, USA alIU Network Science Institute, Indiana University,
Bloomington, IN, USA amNeuroVision Imaging LLC, Sacramento, California, USA anITTM
Solutions, Esch-sur-Alzette, Luxembourg aoCasa di Cura “San Raffaele Cassino”, Cassino, Italy
Abstract
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The Precision Neurology development process implements systems theory with system biology
and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven
investigation of systems dysfunction within distinct molecular, cellular and large-scale neural
network systems in both animal models as well as through tests for the usefulness of these
candidate dynamic systems biomarkers in different diseases and subgroups at different stages of
pathophysiological progression. This translational research path is paralleled by an “omics”-based,
hypothesis-free, exploratory research pathway, which will collect multimodal data from
progressing asymptomatic, preclinical and clinical neurodegenerative disease (ND) populations,
within the wide continuous biological and clinical spectrum of ND, applying high-throughput and
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high-content technologies combined with powerful computational and statistical modeling tools,
aimed at identifying novel dysfunctional systems and predictive marker signatures associated with
ND. The goals are to identify common biological denominators or differentiating classifiers across
the continuum of ND during detectable stages of pathophysiological progression, characterize
systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems
biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate
endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate
development of early and effective individualized treatment of ND, such as Alzheimer’s disease
(AD).
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The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as
the Paris based core of the Sorbonne University Clinical Research Group “Alzheimer Precision
Medicine” (GRC-APM) were recently launched to facilitate the passageway from conventional
clinical diagnostic and drug development towards breakthrough innovation based on the
investigation of the comprehensive biological nature of aging individuals. The APMI movement is
gaining momentum to systematically apply both systems neurophysiology and systems biology in
exploratory translational neuroscience research on ND.
INTRODUCTION
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A dementia syndrome is caused by a range of neurological disorders; Alzheimer’s disease
(AD) is the most common disease causing dementia, accounting for 50–70% of cases.
Increasing age is the most important risk factor for AD and other dementias, and as life
expectancy increases and demographic ageing occurs in populations around the world, the
number of people with dementia is expected to continue to exponentially grow. In 2015,
almost 47 million people worldwide were estimated to be affected by dementia, and the
numbers are expected to reach 75 million by 2030, and 131 million by 2050, with the
greatest increase expected in low-income and middle-income countries [1].
On May 29, 2017, at the 70th session of the World Health Assembly in Geneva, the World
Health Organization (WHO) has unanimously adopted a global plan on dementia – the
Global Plan of Action on the Public Health Response to Dementia 2017–2025 – that
includes targets for the advancement of dementia awareness, risk reduction, diagnosis, care
and treatment, support for care partners and research (available at https://www.alz.co.uk/
news/global-plan-on-dementia-adopted-by-who).
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Recent years have witnessed an increasing understanding of the molecular mechanisms
related to AD. The pathogenesis of this complex polygenic neurodegenerative disease (ND)
involves sequentially interacting pathophysiological cascades, including both core events –
i.e., accumulation of the forty-two-amino acid-long amyloid beta (Aβ42) peptide into
amyloid plaques and self-aggregation of hyperphosphorylated tau protein to form
intraneuronal neurofibrillary tangles – and downstream processes, such as generalized
neuroinflammation [2, 3]. These events induce axonal degeneration [4–6] and disruption of
synaptic integrity [7, 8], thus leading to synaptic dysfunction and, ultimately, deterioration of
physiological neural connectivity [9].
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In spite of such advancements in understanding the disease, AD is characterized by a high
degree of heterogeneity in its manifestation, progression, response to treatment, as well as
susceptibility to risk factors. Phenotypic variability is currently considered one of the biggest
challenges in clinical science and clinical trial design [10]. On the one hand, the same
syndrome can be caused by substantially different pathophysiological mechanisms. In order
to ensure more precise and definitive AD diagnosis, biomarkers are crucially needed to
detect and track disease processes in the brain. On the other hand, similar pathophysiology
can present itself with distinct symptomatology across patients, suggesting that additional
factors can influence disease manifestation and progression. The identity and impact of such
additional factors (including genetic, epigenetic, life-style, and phenotypic traits) deserve
further investigation. Particularly, a growing body of evidence demonstrated that a factor
such as an individual’s sex can modulate disease phenotype and drug response [11], thus
substantially contributing to clinical heterogeneity. In AD patients, sex differences have been
reported in the rate of cognitive deterioration [12, 13] and brain atrophy [14], in the absence
of clear differences in amyloid or tau burden [15]. In addition, sex-genotype interaction in
AD have been shown to affect both risk of onset and conversion [16] as well as response to
pharmacological treatment [17, 18]. The socio-economic construct associated with the
female and male position in the society (i.e. gender) can also influence disease onset and
progression, as it affects education, salary, pension plans, and caregiving burden [19].
Therefore, sex and gender appear to be central drivers of phenotypic variability in AD and
their role should be carefully considered when designing strategies for prevention, detection
and treatment of the disease. Analysis of sex and gender effects – both alone and in
combination with a variety of genetic, epigenetic, and phenotypic traits – should be the first
step towards a more personalized and patient-centered approach to AD.
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THE PRECISION NEUROLOGY PARADIGM IN ALZHEIMER’S DISEASE
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Breakthrough conceptual shifts have recently commenced to emerge in the field of AD and
other ND, highlighting the presence of risk and protection factors and the non-linear
dynamic continuum of complex pathophysiologies along a wide spectrum of multi-factorial
brain proteinopathies. Substantial advancements in detecting, treating, and preventing AD
are expected to evolve through the generation and the systematic implementation of a
strategy based on the precision medicine (PM) paradigm [20, 21], whose establishment
requires the implementation of an array of integrated disciplines and technological
developments such as the “omics” approaches, neuroimaging modalities, cognitive
assessment tests, and clinical characteristics. These converge to several domains that need to
be analyzed according to the systems theory paradigm [22]. This allows for the
conceptualization of novel and original models to elucidate all systems levels – assessed by
systems biology and systems neurophysiology (Figure 1) – and the different types of
spatiotemporal data characterizing the genetically, biologically, pathologically, and clinically
heterogeneous construct of “AD” [21]. Thus, systems biology and systems neurophysiology
permit to delineate the multivariate and combinatorial profiles of genetic, biological,
pathophysiological, and clinical markers reflecting the heterogeneity of this condition.
Thanks to fundamental advances in research technology, we got new and better performing
analysis tools to register and create comprehensive brains maps and record dynamic patterns
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across different systems: from molecules, neurons to brain areas. Particularly, systems
neurophysiology will aim at showing how computational network models can elucidate the
relationship between structure and dynamic function in brain networks, as demonstrated by
recent findings in time-dependent functional connectivity measured with non-invasive
neuroimaging techniques.
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The transition to PM from the traditional model does not occur overnight. But the more we
build innovative and interdisciplinary networks with partners, the faster and more effectively
we can see the changes happening. To fulfill on the promise of PM, there needs to be a new
ecosystem with partnerships of multiple stakeholders who collaborate to find creative and
novel solutions. Such a new ecosystem – comprised of academic and community providers,
industry, professional societies, government, consumers, and patient advocacy groups —
could advance the following pilot initiatives on a local, national and potentially international
scale.
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In order to advance the development of the PM paradigm in AD, the international Alzheimer
PM Initiative (APMI) and its planned Cohort Program (APMI-CP) (Figure 2) have been
recently launched by our consortium and thematically linked to the U.S. Precision Medicine
Initiative (PMI) (available at https://www.whitehouse.gov/precision-medicine) and the U.S.
“All of Us Research Program” – evolved from the U.S. PMI Cohort Program (available at
https://www.nih.gov/research-training/allofus-research-program) (Table 1). Four pioneering
translational neuroscience research programs – “MIDAS”, “PHOENIX”, “POSEIDON”, and
“VISION” – have been developed and launched in an interdisciplinary local network by our
group at the APMI and APMI-CP initiation site Paris, France, at the Sorbonne University
(Sorbonne Université) and at the Pitié-Salpêtrière University Hospital, Institute for Memory
and Alzheimer’s Disease (Institut de la Mémoire et de la Maladie d’Alzheimer, IM2A) and
the Brain and Spine Institute (Institut du Cerveau et de la Moelle Épinière, ICM) in Paris to
organize, combine, and integrate the components of systems biology and neurophysiology in
order to facilitate the development of PM in AD, a model approach for other
proteinopathies/ND of the brain. In this regard, following the APMI conceptual framework,
mono-center pilot APMI subcohorts spanning from early asymptomatic preclinical
populations to prodromal to dementia late stage populations – namely INSIGHT-preAD,
Predict-MA PHRC, RESPIR, and SOCRATES – have been established at our central clinical
recruitment site, the IM2A. These pilot APMI cohorts allow for the standardized academic
university-based expert center inclusion of both cognitively intact individuals at risk for AD
and patients with a full range of ND and provide an assortment of unique heterogeneous and
multidimensional data. The research using these pilot AMPI cohorts is performed under the
structural framework of the newly established Sorbonne University – “Clinical Research
Group in Alzheimer Precision Medicine” (GRC n° 21), Sorbonne Université – “Groupe de
Recherche Clinique - Alzheimer Precision Medicine”) (GRC-APM). The major objective of
the Sorbonne Université GRC-APM is to accelerate the reformation of traditional
Neurology, Psychiatry, and Neuroscience embracing the PM paradigm, based on complex
systems theory, using systems biology and systems neurophysiology, big data science, and
biomarker-guided integrative disease modeling (IDM) to improve detection, classification,
and therapy development in AD and other ND.
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The implementation of PM in AD is expected to result into a novel, original scientific
taxonomy and a distinguished working lexicon and terminology (see Table 2) for realitybased medicine, which detects evidence from real-life scenarios.
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An appropriately integrative understanding of AD will be propelled by advances in
molecular technology and data processing that will allow generating, analyzing, interpreting,
and storing huge amounts of heterogeneous and multidimensional data, termed big data. Big
data in AD can be used to improve our current mechanistic understanding of the disease
through the application of different computational and data science methods, under the
theoretical framework of IDM [23]. Multimodal big data integration is essential to
understand the link between elements from large-scale neurobiological systems such as
protein interaction and genetic regulatory networks, synaptic connections and anatomical
projections among brain areas. Usually, these data come from multiple levels of
organizations or involve different domains of biology and data types (Figure 3).
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To be effective, PM needs to exploit advanced tools for collecting/managing/examining big
data. Particularly, thanks to outstanding progresses in information technology, the
development and implementation of electronic health records (EHRs) enable gathering/
preserving longitudinal health-care records and clinical data at highly limited costs.
Furthermore, the adoption of personal mobile technologies – namely phones, apps,
wearables, in-home devices – as innovative ways to collect health information (mobile
health or “m-health”) is becoming a common practice. These devices allow the accumulation
of clinically relevant information in a more ecological/natural environment and the
improvement of patient care. High-volume and dense data generated from progressively
more sophisticated software applications can enrich self-reported information on both
lifestyle and environment, thus providing researchers with a well-defined vision of these
factors, previously difficult to obtain.
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Being rooted in a multidimensional data-driven approach, PM is expected to upgrade the
prevention and treatment of AD to a higher level of individualization, promoting a shift
towards every single preclinical participant at risk rather than late stage patients and disease
in general. This goal will be achieved mainly through the identification and validation of
reliable biomarkers, which will allow better classifying patients by their probable disease
risk, prognosis and/or response to preventive measures and treatment [20, 21]. To date, PM
(in general) and biomarker-guided therapeutic strategies (in particular) have witnessed their
broadest applications in the field of oncology. The Food and Drug Administration has
recently approved for the first time a cancer treatment based on the presence of specific
molecular aberrations rather than on the tumor’s anatomical origin. Pembrolizumab (a
humanized antibody used in cancer immunotherapy) has been granted approval for adult and
pediatric patients with metastatic or unresectable, microsatellite instability-high (MSI-H) or
mismatch repair deficient (dMMR) solid tumors [24]. The implementation of PM in ND
currently impels researchers to envision a cross-trans-fertilization from such more advanced
fields of medicine. In this setting, the repurposing of some previously approved mechanistic
anticancer drugs for ND may offer the potential to reduce both the cost and time to achieve
licensed approval status. For instance, tyrosine kinase inhibitors like bosutinib [25] and
masitinib [26] (which represent a standard approach for anticancer treatment) have shown
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promising clinical results in patients with amyotrophic lateral sclerosis and can also exert
neuroprotective actions in other ND through the activation of autophagy. The search basin
for anticancer drugs repositionable for neurodegeneration will ultimately require data-driven
approaches grounded on specific biomarker data; such a strategy is aimed at identifying
pathophysiological commonalities, potentially common molecular alterations between
cancer and ND [26].
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Apart from treatment, another important aim of PM in AD will be the preclinical detection
of pathophysiology at its earliest stage and related early disease initiation and the
implementation of preventive interventions at the individual level. This goal may be
achieved through an integrated analysis of genetic, biomarker, imaging, and clinical
characteristics that distinguish one individual from others. To achieve this goal, the
availability of reliable multimodal biological indicators – biomarkers – will be required [27–
34]. In this regard, several potential biological markers have been identified across the full
spectrum of AD, from preclinical to prodromal to clinical stages [35–41]. This includes
different categories, as follows: 1) neurogenetics/neuroepigenetics markers [42–45], 2)
neurochemistry markers [4, 46–48], including both cerebrospinal fluid (CSF) [49–55] and
blood (plasma/serum) markers [56–63], 3) markers derived from structural/functional/
metabolic neuroimaging [64–68], and 4) neurophysiology/neurodynamic markers [69].
Moreover, opinions of regulatory agencies and industry stakeholders in AD biomarker
discovery area are regularly in discussion and development [70, 71]. The integration and
recomposition of the experimental information obtained from biomarker studies through the
systems biology and systems neurophysiology paradigms will ultimately allow to improve
patient care and clinical outcomes through the PM paradigm [72] in line with the Institute of
Medicine (IOM) Committee Recommendations for Advancing Appropriate Use of
Biomarker Tests (companion diagnostics) for Molecularly Targeted Therapies [73].
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Starting from these premises, PM can be conceptualized as a biomarker-guided medicine.
According to the Food and Drug Administration (FDA) and the NIH Biomarkers, Endpoints,
and other Tools (BEST) Resource, biomarker categories can be categorized as follows: 1)
susceptibility/risk biomarker, 2) diagnostic biomarker, 3) monitoring biomarker, 4)
prognostic biomarker, 5) predictive biomarker, 6) pharmacodynamic/response biomarker,
and 7) safety biomarker [74]. Unfortunately, any attempt to provide such a clear-cut
classification in the AD field remains problematic. For example, “amyloid positivity” is
widely considered both a diagnostic and predictive biomarker; however, this may not be the
case at an individual level [74]. To target “individual variability” will ultimately require
analyzing multiple biological pathways inexpensively, quickly, and sensitively. The
increasing adoption of next generation sequencing in clinical practice has been recently
driven by reducing costs and high-throughput analytical methods. In this setting, unbiased
whole-genome sequencing (WGS) and whole-exome sequencing (WES) represent major
milestones in the area of genomic medicine since they allow the complete elucidation of the
genomic determinants of a specific AD patient’s heritable make-up, and thus are among the
most comprehensive tools for future clinical applications [74, 75]. Moreover, upcoming
commercially available genetic tests, e.g. gene-based assays, implementing polygenic risk
scoring for assessing AD onset risk, are currently in late stage clinical development. In
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particular, a 90% maximum prediction accuracy via polygenic risk scoring can be
accomplished by predictors of genetic risk based on genomic profiles [76].
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It is generally acknowledged that an individual’s health, response to environmental and
lifestyle factors, susceptibility to pathophysiology/syndromes/diseases and tolerability/
response to treatments are indeed impacted to a varying degree by their own unique
biological (genetic/genomic/molecular) profile. Thanks to progress in the area of personal
genomics, it is possible to identify the genetic/genomic predisposition of an individual for
some common diseases, carrier status for inherited diseases, and adverse reactions to
common drugs. Personal genomics provides support in predicting the likelihood that an
individual will be affected by a disease and may help personalize drug selection and
treatment delivery to get the best possible care, thus playing a key role in predictive and
personalized medicine, in the framework of the PM paradigm [77]. In this regard, the
23andMe Personal Genome Service (PGS) Test (available at https://www.23andme.com/engb/) uses a qualitative in vitro molecular diagnostic system used for detecting variants in
genomic DNA isolated from human adults specimens (saliva) that will provide information –
i.e. delivering and interpreting genetic health risk (GHR) reports – to users about their
genetic risk of developing a disease to inform lifestyle choices and/or conversations with a
healthcare professional. Specifically, GHR reports have already been authorized by the FDA
for Late-onset AD and Parkinson’s disease and the following diseases: hereditary
thrombophilia, alpha-1 antitrypsin deficiency, Gaucher disease, Factor XI deficiency, Celiac
disease, G6PD deficiency, hereditary hemochromatosis, Early-Onset primary dystonia
(available at https://www.accessdata.fda.gov/cdrh_docs/pdf16/DEN160026.pdf). Based on
the gene expression profiles generated by GenomeDx Biosciences Decipher Genomics
Resource Information Database (Decipher GRID®), a recent analysis showed that the
genomic signature PAM50, normally applied to breast cancer patients to determine their risk
of reappearance, can be used in prostate cancer as well for predicting which individual may
take advantage from early initiation of post-operative androgen deprivation therapy (ADT),
thus delivering a potential clinical tool to customize the treatment of prostate cancer. This
personalized selection of patients will ameliorate treatment outcomes and prevent many
patients from unnecessary risks of toxicity [78].
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Differently from the invariable genetic/genomic information, an individual’s proteomics/
peptidomics and metabolomics/lipidomics profile may be modified and vary over time.
Figure 4 provides an up-to-date summary of currently available “omics” technologies –
genomics, transcriptomics, miRNomics, proteomics, metabolomics – and how they can be
used to disentangle different systems biomarker categories [79]. At present, the majority of
the documented candidate biomarkers originate from genomic and proteomic disciplines.
This might be due to the higher stability of the signal and standardization achieved by using
genomic and proteomic tools compared to other available “omic” methodologies. In
addition, the better stability of proteins versus mRNAs might account for the greater
availability and progress in discovery and validation of proteomic markers compared to e.g.
transcriptomic approaches [79]. The appropriate interpretation of the obtained highthroughput data in the context of the disease molecular pathophysiology and its specific
treatment is considered the rate-limiting step in the biomarker discovery and validation
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process. As a result, “omics” data sets need to be rigorously identified, extracted, and
interpreted in order to deliver valuable biological information [79].
Within the PM framework, it has been proposed to screen and detect unsuspected age-related
neurodegenerative diseases as early as possible in cognitively healthy potentially preclinical
affected adults. As far as AD is concerned, it has been hypothesized that such a screening
program – based on WGS combined with whole-body magnetic resonance imaging (WBMRI), metabolomics screening, constant heart monitoring, pedigree analysis, microbiome
sequencing, and standard laboratory tests – could identify people at risk of developing
clinical AD decades in advance. Controversies still exist, however, regarding both the high
costs inherent to this approach and the potential risks of false-positive results and
overdiagnosis [80].
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Very recently, a pilot study has been conducted to investigate the impact of WGS in healthy
subjects examined within a primary care context. Although several potentially pathogenetic
variants were identified, only a fraction of the carriers demonstrated overt clinical signs or
symptoms, indicating that the expected clinical phenotype would develop later during
progression of pathophysiology. Although integrating genome sequencing and other
sequencing methods into the day-to-day practice will undoubtedly provide unprecedented
preventive opportunities, a careful sample size determination will be necessary for achieving
a sufficient statistical power to detect a clinically meaningful effect size [81].
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To aid PM fully coming to life in the field of ND, the interplay of “omics”-based techniques
and sequencing methods is paramount, since the availability and increasing standardization
of high-throughput big data will, through adequate IDM supported by advances in data
science, allow creating new biomarker-guided targeted preventive and therapeutic
opportunities [20, 21]. Therefore, the use of advanced sequencing methods and of “omics”based screening of pathophysiological disease states is anticipated to result in enhanced
personalized and precise – both preventive and therapeutic – interventions by disclosing
accurate patterns of pathophysiological biomarkers and molecular signatures underlying the
biological mechanisms progressing non-linear dynamic in specific disease states in
individual patients [82]. Extensive efforts are presently performed to explicate gene-protein
links, key molecular pathways functions, protein-protein and signaling network
organization, and organism-level responses via high-throughput biological data at different
time points (e.g. global gene expression and comprehensive proteomic data) [83].
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In this context, it is important to note that, so far, a major obstacle to our understanding and
to the development of possibly novel stratification approaches for AD is, as mentioned, the
fragmentation of previous research (single-center, single-method studies). Neuroscience has
been highly productive, but its progress can also be somewhat unsystematic and remote to
clinical practice. That said, so far conventional “big data” analytics techniques have failed to
provide the qualitative change which is indispensable to provide a mechanistic (and not only
statistical) understanding of AD pathophysiology, which in turn is instrumental to
formulating personalized treatment strategies. A first step, as mentioned above, is the
integration of complex and high-dimensional information from hundreds or thousands of
patients contained in “big data” repositories. However, this alone is not sufficient; “big data”
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need to be turned into “smart data” by injecting not only novel methodologies but also
expert knowledge and targeted clinical hypotheses. This poses a major analytics challenge,
as neither single national-level studies nor single biomedical or technical disciplines can
tackle the problem on their own. A number of potentially disease-modifying clinical
development programs in AD have failed so far [84], and in addition we are in serious need
of novel out-of-the box preclinical models that can generate actionable knowledge, either in
research or, eventually, therapy. This is why, while computational and statistical modeling
are increasingly invaluable in AD research, it is necessary to go beyond purely descriptive
data-analysis techniques (e.g., techniques that identify associations between certain data and
phenotypes). Additional efforts are needed to inject specific domain competencies which can
be formalized mathematically into predictive models that can disclose how specific
components of pathogenic pathways interact within complex brain networks, across
molecular to cellular and systems scales. Such predictive models should, as far as possible,
include realistic representations of neurobiological processes and mechanisms that allow
direct comparison to experimental settings and, ultimately, pave the way to discover new
strategies for targeted control and intervention. In this respect, it is also essential to form
additional private-public partnerships with a strong focus on data sharing and pathway-based
analysis. With this type of integrative approach, successful real-world examples of advanced
simulation have already generated tangible support for clinical trials in AD.
SYSTEMS BIOLOGY OF ALZHEIMER’S DISEASE
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The polygenic multifactorial nature of AD and other complex proteinopathies of the brain
with progression to ND is widely recognized. Although several mechanisms have been
identified that may have a role in the pathogenesis of AD and other ND, the molecular and
temporal dynamics of the biological processes that lead to onset and progression of diseases
such as AD remain to be well-understood on a system level. Complex chronic diseases such
as AD are thought to result from an interplay between environmental, genetic, and
epigenetic factors. State-of-the-art “omics” techniques such as genomics, epigenomics,
transcriptomics, proteomics, and metabolomics offer remarkable promise as research tools to
decipher the dynamics and biological nature of the pathogenesis ultimately leading to
neurodegeneration and a spectrum of clinical neurological phenotypes for which predictive
markers and selective therapeutic tools are needed. Breakthrough advances in genetic and
genomic technologies are making global genome sequencing possible, affordable and
clinically practical through advanced NGS technologies. New genetic technologies,
however, provide a crucial basis to the understanding of the complex pathophysiological
pathways involved in proteinopathies/ND.
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The concept of complex multiscale systems (consisting of macromolecules that reciprocally
interact with each other in dynamic modular complexes and networks) as the underlying
foundations of life has been first proposed more than 50 years ago [85]. Over the past
decades, we have gained detailed insights into the structure, regulation, and function of
different molecular and cellular systems, which are currently viewed as building blocks or
inventories of working parts. However, the main challenge ahead is to clarify how these
single agents are reciprocally associated by multiple interactions across distinct system
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protein; protein-protein; protein-metabolite networks, interactomics). Major challenges exist
for the development of reliable holistic models that are based on unbiased data-integration
workflows and that could highlight the properties of complex biological structures, for
which the whole is often greater than the sum of their parts. In this context, the main goals
of systems biology in the field of ND research are as follows: 1) to characterize complex
systems and/or networks in a straightforward, viable manner, by probing key layers of
molecular regulation and expression on a genome-wide level and 2) to integrate different
genome-wide data sets in a multidimensional manner – that is, across different layers of
molecular regulation, timescales, cell types and so on – in order to generate comprehensive
in silico models of ND that show the best balance between coverage and selectivity, reduce
model space down to manageable numbers of highly-prioritized testable hypotheses, and are
biologically precise. This will shed more light on how complex diseases may be
conceptualized as a result of altered networks states [86] caused by multifactorial
perturbations, which is expected to foster marker and target discovery. Under this theoretical
framework, the dynamics and biology of ND processes scrutinized by systems modeling and
systems biology can be more comprehensively understood. This may be achieved via a twostep approach consisting of initial animal studies followed by confirmation and validation in
clinical cohort programs [87] or via an approach consisting of molecular and clinical studies
in cohorts, for example the search for predictive marker signatures, followed by studies in
experimental models of ND of biological and therapeutical significance associated to such
marker signatures. Numerous disease conditions in humans (including proteinopathies/ND,
cardiovascular disorders, malignancies, the metabolic syndrome, and diabetes) have a highly
complex biological nature that cannot be entirely and adequately captured through the
investigation of single linear molecular alterations. Besides being multifactorial, such
diseases are primarily caused by altered essential networks required for the correct
functioning of basic physiological pathways. Such disease processes are fundamentally
nonlinear dynamic, being the results of an evolving interplay between homeostatic defense
mechanisms and impaired physiological networks through space and time [88]. Since cell
survival mechanisms under the control of stress response factors may also be those that
trigger cell death depending on the pathophysiological context in which they operate [89]
identifying the critical phases that, at the molecular, cellular, or system levels, are associated
with the dynamics of ND processes and could modify the capacity of individuals to maintain
function and resist ND is essential for clinical discovery and therapeutic developments,
especially in the context of the growing needs for PM.
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Recent years have witnessed significant advances in our understanding of how human
diseases are routed in altered molecular and cellular networks. Several genetic alterations
and pathophysiological mechanisms – mainly involving the amyloid precursor protein (APP)
processing and tau related networks – are considered to be significant aspects in the
pathogenesis of AD [90]. Such network derangements can cause either loss or gain of
specific molecular functions and an increased formation of neurotoxic molecular species
(e.g., toxic amyloid or protein aggregates) that can in turn adversely affect supra-cellular
levels. Another important factor that should not be overlooked in the conceptualization of
complex diseases is the crucial counteracting role of homeostatic networks. In this regard,
the interest into the potential protective role of resilience factors against neurodegeneration
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(e.g., autophagy, proteostasis, endolysosomal networks, protein folding chaperone networks,
disaggregates, and other stress-protective and clearance networks) is currently gaining
momentum [90].
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The causative pathways that lead to the onset of AD and its clinical phenotypes at the
individual level are thought to consist of genetic/epigenetic susceptibility and/or protection
coupled with a continuing dynamic interplay between altered brain networks and
counteracting neural mechanisms of resilience. Integrative systems biology-based
approaches are crucial to disentangling this intricate interplay. First, simple model organisms
mimicking the main features of AD need to be developed in order to extensively apply
different “omics” techniques. This approach may offer invaluable data to shed more light on
the conserved pathways that modulate the onset and progression of AD, being ultimately
useful for testing potential strategies that could delay and/or modify the natural course of
disease [90]. However, the regulation of gene expression and pathway activity might differ
between simple model organisms and humans, which calls for integrated use of simple
model organisms and higher-order models such as mouse models and human cell models,
e.g. induced-pluripotent-stem-cells coaxed into neurons or neurons obtained by direct
conversion of fibroblasts [91].
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New evidence from preclinical models needs to be duly replicated, with a special focus on
subtle initial network alterations that can be visualized by neuroimaging, which could
potentially become the targets of early therapeutic interventions [92–95]. Neuroimaging and
biomarker data should be fully integrated and analyzed in a longitudinal manner through
computational and integrative network biology tools within a systems biology-based
framework. The increasing trend towards high-throughput techniques in AD research will
generate multifactorial data that will require integration in a standardized, efficient, costeffective, and secure manner. The vast amount of data generated will cause new challenges
for data science – mainly in terms of data storage, processing, and mining. As we are
entering into the “era of big and deep data” in AD, computational systems biology
approaches are continuously being optimized in order to support the approximate modeling
of biological systems [90].
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A holistic systems biology-based research strategy in AD research will likely rely on
generating large and rich data sets, applying multi-layer network approaches for integration
and comparative assessments of different datasets, and reckoning on the information
generated for discovery of novel disease markers and targets. A translational approach from
preclinical studies to bedside (complemented by reverse translational approaches) will be
required to integrate and implement fundamental aspects of the systems theory and the
systems biology concept into clinical practice – i.e., translational systems medicine – in the
upcoming future [96–99]. Key to the success of these approaches is the use of robust data
integration methods. There is a large array of methods that enable complex data sets
collected in experimental models of ND or human cohorts to be analyzed and integrated on a
system level [100, 101]. Methods based on graph theory (that is network approaches) such
as spectral decomposition of the signal [102] weighted gene co-expression network analysis
[103] and Bayesian causal inference [104] and those based on formal concept analysis [105]
and tree induction [106, 107] likely hold strong promises for generating comprehensive in
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silico models that accurately select for biological rules, disease targets, and risk factors with
potential for clinical exploitation.
Application of systems biology in AD cohorts. The example of the European Prevention of
Alzheimer’s Dementia (EPAD) Consortium
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Implementation of systems biology into clinical and research practice requires a number of
steps. First, molecular tests and biomarkers for matching individuals/patients to clinical
trials and/or targeted therapies will require continuous refinements and validation of highthroughput techniques, systems-level approaches, and computational tools. Second, all
molecular tests to be used for AD, as well as all patient care-related molecular analyses,
need to be performed using assays that are highly reproducible, accurate, and satisfy the
U.S. Food and Drug Administration (FDA) clinical trials guidelines, with adherence to
principles of Good Clinical Practice (GCP) (available at http://www.fda.gov/
regulatoryinformation/guidances/ucm122046.htm), the European Medicines Agency (EMA)
(http://www.ema.europa.eu/ema/), and the European Clinical Trials Database (EudraCT)
(https://eudract.ema.europa.eu/). In this scenario, the Alzheimer’s disease neuroimaging
initiative (ADNI) and the Dominantly Inherited Alzheimer Network (DIAN) will provide
collaborative large-scale longitudinal data on AD associated autosomal dominant mutation
carriers that will be invaluable to systematize and make explicit the translation of
neuroimaging and biochemical markers into clinical guidelines. Third, the era of big and
deep data generation and the availability of comprehensive repositories has brought the need
for collaboration, sharing, integration, normalization, and analysis of both data and
metadata, with the ultimate goal to make effective translational use of this new knowledge.
In this scenario, several clinical trials may benefit from the holistic approach provided by
systems biology. Among them, interest in the European Prevention of Alzheimer’s Dementia
(EPAD) program is gaining momentum.
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The EPAD program [108] is a pan-European initiative that will establish a shared platform to
design and conduct phase 2 Proof-of-Concept (PoC) clinical trials specifically aimed at
developing new treatments for the secondary prevention of AD. To investigate different
agents in the pre-AD population in the most efficient manner, a Bayesian adaptive design
that learns from data accrued as the trial progresses will be used. Clearly disappointing
results of recently completed phase 3 AD therapy trials may be explained by their
exploratory (rather than confirmatory) nature, mostly caused by an incomplete exploration
phase throughout phase 2 [109]. Hopefully, the EPAD program will be helpful to overcome
previous pitfalls in the field by assuming that a correctly designed phase 2 trial can take
several years to be completed. Other common issues that the EPAD Longitudinal Cohort
Study (LCS) (available at https://clinicaltrials.gov/ct2/show/NCT02804789) will address
include: 1) the high screen failure rates, 2) the unwillingness or inability to implement an
adequate patient stratification, and 3) the lack of a pre-randomization run-in period. The
EPAD LCS is expected to provide reliable disease models of the preclinical and prodromal
periods of AD before the final implementation of a clinical trial. The EPAD LCS will be
conducted in a large cohort of 5,000 subjects who had undergone a thorough assessment in
terms of cognition [110, 111], neuroimaging, core CSF biomarkers (Aβ42, total tau [t-tau],
and hyperphosphorylated tau [p-tau]), clinical outcomes, and genotyping. Annual
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assessments will be performed with the goal of identifying different disease trajectories to
provide an optimal stratification for trial inclusion. Risk stratification groups with similar
biological underpinnings will be helpful to identify specific classes of subjects to be
included (or excluded) from the clinical trial according to the PM paradigm.
The development of an EPAD site network across the European Trial Delivery Centers will
be critical to the initiative success. Site certifications, continuing training, and commitment
to the EPAD program is expected to reduce study site heterogeneity and will hopefully
provide highly accurate estimates of treatment effects. Each TDC will assess approximately
200 research participants, of whom 100 will be included in the clinical trial. This effort is
unprecedented, as previous clinical trials involved numerous centers (up to 200), each
enrolling a handful of patients. Conversely, the traditional methodology will be overturned
by EPAD, inasmuch as a few centers will enroll numerous patients.
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In general, the correct implementation of phase 3 trials preliminary requires more robust
phase 2 outcomes. The EPAD program will improve the study methodology, ultimately
favoring an optimal disease modelling and a better patient stratification before embarking on
phase 3 confirmatory trials. The EPAD LCS was started in May 2016 at six sites, with a total
of 400 participants having already been recruited. Disease modelling work is expected to be
introduced as soon as an enrolment goal of 500 subjects will be achieved. It is anticipated
that the EPAD PoC Study Platform trial will begin in 2018.
SYSTEMS NEUROPHYSIOLOGY OF ALZHEIMER’S DISEASE:
UNDERSTANDING NEUROPHYSIOLOGY AND NEURODYNAMICS BEHIND
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AETIOLOGY
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During the last two decades, the neuroscience field has entered a rapid phase of expansion
characterized by the development of a large proportion of methodologies allowing the
recording of neural data obtained from a wide range of modalities, from metabolic pathways
to optical imaging to functional magnetic resonance imaging (fMRI). These data are
collected through different spatiotemporal domains (Figure 5). Most of these techniques
have been so far used one at a time [112, 113]. Recently, there is an attempt towards data
integration in order to create comprehensive maps and record dynamic patterns across
multiple levels of organization (neurons, circuits, systems, whole brain) and involving
different domains of biology and data types (such as anatomical and functional connectivity,
genetic/genomic patterns [112, 114]). This effort is in line with the new paradigm of systems
neurophysiology aiming at integrating “big neuroscience data” recorded in a multimodal
fashion to understand the role of the complex web of interconnections among several
elements of large-scale neurobiological systems [115–118]. The ultimate goal of systems
neurophysiology is to clarify how signals are represented within neocortical networks and
the specific roles played by the multitude of the heterogeneous neuronal components. The
new interdisciplinary field of network neuroscience proposes to overcome these enduring
challenges by approaching brain structures and functions via an explicitly integrative
perspective [112]. Here, we will present scientific advancements related to single
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methodologies utilized by system neurophysiology, within wider context of the PM
paradigm in AD.
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An increasingly important integrative component in this endeavor is connectomics the
emerging science of brain networks, which comprises studies of both anatomical and
functional brain connectivity, across modalities and methodologies. The rise of
connectomics has triggered several national and international consortia devoted to mapping
patterns of brain connectivity across large subject cohorts, including the Human
Connectome Project funded by the U.S. National Institutes of Health [119]. These projects
have pushed the boundaries of data sharing, neuroinformatics and computational analysis.
Similar connectomics efforts are underway to track lifespan development [120] as well as
address patient populations, including people with ND. To deal with the mounting volume of
connectome data, the field is developing basic network science tools and methodology that
can be applied to brain data [121]. So far, broad exploratory analysis has revealed a number
of architectural principles that underpin macro- and meso-scale maps of brain connectivity,
including modular organization and the existence of prominent hub regions. Much is still to
be learned about the contributions of connectome architecture to human brain function and
its role in pathophysiological processes. Systems neurophysiology in combination with
connectomics and computational network models has great promise to illuminate the
relation of structure to dynamics in brain networks as shown, for example, in recent findings
on time-dependent functional connectivity as measured with non-invasive neuroimaging
techniques.
CONTRIBUTION AND ROLE OF STRUCTURAL MAGNETIC RESONANCE
IMAGING (MRI)
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Magnetic resonance imaging (MRI) is a widely, non-invasive, relatively non-expensive and
versatile technology. Among MRI modalities, structural or anatomical MRI, using threedimensional T1-weighted sequences, is the most widely used [122, 123] and validated [124,
125]. Structural MRI allows visualization and measurement of atrophy which is a
macroscopic correlate of neurodegeneration, in particular of neuronal and dendritic loss. The
progression of atrophy in AD approximately follows that of neurofibrillary tangles found in
post-mortem AD cases and described by Braak and colleagues [126] and Delacourte and
colleagues [127]. Moreover, previous studies showed that structural MRI alterations
correlate with tau deposition, as described by Braak stages, and CSF tau biomarkers [128].
On the contrary, not all structural MRI measures are well correlated to measures of betaamyloid deposition, and atrophy patterns do not follow those of amyloid deposition [129,
130]. Due to these reasons, it should be noted that brain atrophy in AD is descriptive of brain
structural changes but not specific for underlying AD pathophysiology. Indeed, a given
atrophy pattern can be associated with different pathophysiological processes. However,
MRI atrophy measures are well correlated with cognitive and clinical functions [131, 132],
and highly correlated with the concurrent rate of clinical decline [133–135]. Therefore, they
constitute attractive tools to track disease progression and to monitor the effect of treatment.
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Automated image analysis approaches allow measuring distributed patterns of atrophy
across the whole brain, using either region-of-interest measurements, voxel-based maps of
gray-matter or cortical thickness measurements [136, 137]. Machine learning algorithms
applied to whole-brain atrophy maps can automatically identify patients with AD and
thereby support diagnosis [138–141].
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The most widely studied and accepted structural MRI marker of AD is atrophy of the medial
temporal lobe [142, 143]. Assessment of medial temporal atrophy can be performed in
clinical routine using visual scales [144]. However, such approach is observer-dependent and
only semi-quantitative. On the other hand, fully-automated segmentation approaches provide
objective, quantitative, volumetric measurement of hippocampal atrophy [145–149].
Hippocampal volumetry can discriminate AD patients from controls with high sensitivity
and specificity [150]. Moreover, numerous studies have shown that patients with higher
hippocampal atrophy are at higher risk of rapid cognitive decline [151–155]. However,
atrophy of the hippocampus was found in other types of dementia, suggesting low specificity
of this marker for the identification of AD [156, 157]. Recent developments of ultra-high
field MRI (7 Tesla and higher) allow the study of anatomical alterations with an
unprecedented level of detail. In particular, using 7T MRI, it is possible to distinguish
between different cellular layers and anatomical subregions within the hippocampus. Its
application in AD has demonstrated that hippocampal subregions and layers are
differentially affected by atrophy [158, 159]. These advanced techniques have the potential
to provide more sensitive measures than global hippocampal volumetry.
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Another region of interest for AD is the basal forebrain cholinergic system (BFCS) since it
represents the region with the majority of cholinergic nuclei efferent to the cerebral cortex
[160, 161]. The measurement of BFCS nuclei has been developed and validated as a highly
relevant and robust region of interest for automatic structural MRI assessment of atrophy
rate of change from the preclinical to the clinical AD stages [160, 162–167]. Evidence
indicates that the BFCS may even degenerate before medio-temporal lobe structures, as
early as at the preclinical stage [163, 168]. In contrast to the hippocampal volume, the
atrophy of BFCS was significantly correlated to in vivo brain amyloid load in AD and nondemented elderly individuals [169, 170].
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Machine learning approaches based on whole brain atrophy patterns have been developed to
predict the evolution of patients, in particular the progression to dementia of individuals with
mild cognitive impairment (MCI) [171–173]. Nevertheless, most of these approaches have
been validated on a single research dataset, most often provided by the ADNI. Therefore,
their ability to generalize across datasets as well as their performance in a clinical routine
context remain unclear and larger-scale validation studies are needed.
Its ability to track progression makes structural MRI also attractive to monitor the effect of
treatment [29]. Of all outcome measures (including clinical, cognitive and fluid biomarkers),
structural MRI measures seem to have the highest measurement precision [135]. They are
thus an attractive outcome measure for clinical trials, as well as to monitor the effect of
treatment in a clinical context. It should be noted that different types of treatment seem to
result in different effects on atrophy measures. In a randomized placebo-controlled trial,
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patients treated with donepezil, an acetylcholinesterase inhibitor, have a significantly lower
rate of annual hippocampal atrophy and cortical thickness compared to those receiving
placebo [174, 175]. Moreover, the treatment group demonstrated a significantly decreased
annual rate of atrophy of the BFCS compared to MCI individuals that received placebo
[176]. The BFCS complements hippocampal volumetry in assessing structural progression
in AD and provides a promising outcome measure for clinical trials [161] Anti-amyloid
therapies, however, seem to result in increased rate of atrophy [177]. Nevertheless, it may be
hypothesized that such accelerated atrophy only occurs at the beginning of treatment,
perhaps caused by a reduction in microglial activation associated with plaques, and that a
reduction of atrophy may occur in the longer term. Overall, structural MRI remains an
attractive tool to study the morphological effects of treatment, in particular if new molecules
targeting other aspects of AD pathophysiology (e.g. anti-tau or neuroprotective treatments)
become available. Furthermore, structural MRI plays an important role in monitoring safety
of treatments. Indeed, microbleeds and transient cerebral edema (respectively called ARIAH
and ARIAE) occur in some patients treated with active Aβ immunization [178].
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In summary, structural MRI is an attractive marker for tailoring therapeutic interventions. Its
most attractive features are its ability to precisely track cognitive decline, its potential for
monitory the effect of treatment and to predict the evolution of patients. For prediction, the
most promising avenue is that of machine learning approaches from whole-brain
measurements. Such approaches require larger scale validation using multiple clinical
routine cohorts. The integration of structural MRI analysis tools with other techniques such
as those from functional MRI, electroencephalography (EEG), magnetoencephalography
(MEG) or diffusion tensor imaging (DTI), in a multimodal fashion, will enable the
investigation of temporal and topographical relationships between numerous pathological
alterations and neurobiological systems related to AD. Such big data integration, will
improve our understanding of the in vivo interacting pathophysiological mechanisms across
brain related systems characterizing AD, as envisioned by the PM concept.
CONTRIBUTION AND ROLE OF DIFFUSION TENSOR IMAGING (DTI)
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Diffusion tensor imaging (DTI), which employs a Gaussian approximation to model the MR
signal attenuation due to net water molecule displacements in a de facto restricted cellular
environment. This technique has become the mainstream strategy for examining white
matter microarchitecture, connectivity as well as integrity both in an investigative and in a
clinical setting, and it has been widely employed in studies focused on AD, MCI [179–181]
as well as several other pathologies [182–185]. The apparent water diffusion tensor (which is
termed apparent precisely because intracellular water diffusion is not truly free) can be
estimated in brain parenchyma based on relatively fast echo planar imaging (EPI) techniques
[186] which only pose moderate demand in terms of in-scanner subject time. From these
tensor estimates, white matter tract-specific orientation information can be obtained through
deterministic (based on the orientation of the main DT eigenvector) or probabilistic
approaches [187]. Also, model free tractography approaches exist, a promising development
of which is constrained spherical deconvolution [188–191], which has lately been extended
to incorporate multi-tissue models anatomically based filtering [188, 189] (Figure 6).
Further, scalar indices derived from the diffusion tensor are rotationally invariant and are
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Nevertheless, a recent meta-analysis indicates high variability in both the anatomy of regions
studied and DTI-derived metrics [206] – a partial contribution to which may be the intrinsic
limits of the DTI techniques. Determining the most robust acquisition parameters and
processing strategies for DTI for a multicenter setting is still an active area of research, and
initial clinical and physical phantom data, i.e. scans obtained from a volunteer as well as a
physical object with defined diffusion properties, suggest that the variability of DTI-based
diffusion metrics across a range of MRI scanners is at least 50% higher than that of
volumetric measures [207]. For prediction of conversion from MCI into AD dementia, DTI
reached an accuracy of about 77% – 95% at 2 to 3 years follow up [205, 208, 209] in
monocenter studies, prediction accuracy for multicenter studies still needs to be studied.
Also, all diffusion weighted imaging protocols suffer from the relatively low signal-to-noise
ratio inherent in the necessarily fast EPI techniques. In this respect, the increase in signal-to-
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well known to be sensitive, albeit not specific, indicators of microstructural alterations. The
single tensor eigenvalues as well as Mean Diffusivity (MD – mean of eigenvalues) and
Fractional Anisotropy (FA – normalized variance of eigenvalues [192]) can aid in
quantifying fiber integrity through region of interest (ROI), voxel- or Tract-Based Spatial
Statistics based approaches [180]. A decrease in FA (possibly accompanied by an increase of
MD or other directional diffusivities) is typically the hallmark of unspecific bundle
degeneration, as seen in AD and MCI [193, 194]. Importantly, correlations between DTIderived indices in white matter (WM) and AD disease severity have been reported [195,
196], suggesting that DTI measures may be used as indexes of disease progression. DTI may
therefore provide unique information about WM integrity [66] in AD patients and MCI
subjects. Indeed, several studies have demonstrated early WM changes within the
parahippocampus, hippocampus, posterior cingulum, and splenium already at the MCI stage
[197–200]. However, the majority of DTI studies indicate that the uncinate fasciculus, the
entire corpus callosum and the cingulum tract are most involved in pathogenesis in both
MCI and AD. In a recent study on AD and MCI subjects [201] the interpretation of a
selective increase in FA in the MCI group was aided by the introduction tensor mode (MO)
[202], a third invariant which distinguishes the type of anisotropy (planar, e.g. in regions of
crossing or kissing fibers versus linear, in regions which exhibit one predominant
orientation). This, in turn, led to the detection of a relative preservation of motor-related
projection fibers crossing the association fibers of the superior longitudinal fasciculus in the
early-stage MCI subjects before they degenerated to AD. Also, recent DTI data seems to
point towards a reconstruction of the trajectory of progressive white matter degeneration in
AD as it spreads with aging. In agreement with this so called retrogenesis model (cortical
regions that mature earliest in infancy tend to degenerate last in AD) it has been shown that
white matter abnormalities in specific brain regions such as prefrontal cortex white matter,
inferior longitudinal fasciculus and temporo-parietal areas [180, 197, 203, 204] appear
earlier. Also, DTI has been able to offer insight into asymptomatic “preclinical” at risk
stages such as subjective cognitive decline, where DTI based scalar markers of diffusion
properties were significantly associated with rates of cognitive decline and hippocampus
atrophy at clinical follow up, with odds ratios up to 3 [205], and DTI indexes invariants were
seen to be more sensitive than CSF biomarkers in predicting cognitive decline and medial
temporal atrophy in subjective cognitive decline and MCI subjects [205].
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noise ratio afforded by moving to ultra-high field imaging (at e.g. 7T) is somewhat
counteracted by the rapid shortening of transverse (T2) relaxation times with increasing field
strength and consequent signal loss. Nevertheless, while ultra-high field diffusion weighted
imaging therefore poses significant challenges, improved distortion correction techniques
[210] coupled with monopolar acquisition schemes which allow a significant (about 30%)
shortening of echo times, and the additional use of simultaneous multislice excitation
strategies [211] may allow in vivo diffusion-weighted imaging to finally advance towards
sub-millimeter imaging at ultra-high field. Accordingly, ex-vivo studies have already defined
white matter lesions in aging and AD at 11.4T [212], and 7T imaging has been helpful in
discriminating Parkinson’s disease [213] and amyotrophic lateral sclerosis [214]. Finally, it
is well known that the assumption of a Gaussian propagator (which is at the root of DTI) is
insufficient in regions with more intricate fiber architecture such as mixed tissue types
and/or kissing or crossing fibers [215]. To this end, more advanced protocols such as
Diffusion Spectrum Imaging [216], Diffusional Kurtosis Imaging [217–221], higher order
tensor models [222], compartment models [223–225] and anomalous diffusion [226, 227],
which can be optimized in order to enhance their suitability in a clinical setting [228], have
been already been successfully employed in augmenting information about tissue
degeneration in several ND, including AD [229–232].
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Another avenue for DTI-based methodology is the construction and subsequent analysis of
brain-wide maps of anatomical connections that can be summarized as structural networks
or graphs [115]. Basically, these efforts proceed by first dividing the brain into a set of
internally coherent gray matter parcels or regions (the nodes of the network) and then
estimating the strengths of anatomical projections between these nodes (the edges of the
network). While the reconstruction of such maps faces significant methodological issues, the
resulting structural networks have been validated against classical histological techniques in
non-human species. Human structural networks capture individual differences that relate to
genetics [233] and various phenotypic variables, including indices of cognitive performance
[234]. They also exhibit characteristic changes across the life span [120], during normal
aging [235] and in the course of brain disorders [236]. For example, the loss of connectivity
associated with the progression of AD results a loss of links between dense clusters of
functionally-related regions and hence a decreased capacity for integration [237, 238].
CONTRIBUTION AND ROLE OF FUNCTIONAL MAGNETIC RESONANCE
IMAGING (fMRI)
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Using fMRI in a PM-based paradigm to tailoring therapeutics for patient treatment would be
a very innovative approach from current methods to developing therapeutics for patients.
The diagnosis and classification of patients would be based on clinical criteria, where a
patient would be classified according to predetermined criteria. Implementation of a PM
paradigm would use fMRI as a biomarker of functional brain changes that would be part of
defining the patient’s phenotype in combination with the other modalities. Thus, it would
seek to integrate fMRI-based biomarkers within a systems neurophysiology context to
provide an integrated picture of the patient’s status [21]. The biomarkers within a systems
neurophysiology approach would inform the treatment approach that a patient would
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receive. Given the complexity of AD and the other ND, the fMRI-based biomarkers would
be integrated within a systems biology and neurophysiology approach with the other
modalities (genetic, clinical, behavioral, cognitive, etc.) where the different biomarkers
would reflect disease mechanisms, pathophysiology, clinical history and permit patient
stratification for treatment [20, 21].
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fMRI can be used to measure the vascular response to local neuronal activation due to
stimuli or a cognitive task [239]. There are two broad approaches that may be utilized with
fMRI data in defining PM-based biomarkers for AD detection and diagnosis – one would
examine brain activation data in response to a stimulus or cognitive paradigm whereas
another approach would examine the intrinsic connectivity networks measured using resting
state fMRI. The first approach would lead to biomarkers that would be associated with the
cognitive paradigm or stimulus class whereas examination of the intrinsic connectivity
networks would provide a search for biomarkers over all brain networks.
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In terms of a PM approach with tailoring therapeutics, the use of a cognitive task or stimulus
would be a form of ‘stress test’ to a specific network, for example in asymptomatic at risk
stages for preclinical and clinical AD, a memory task would typically activate the
hippocampus, ventral- and dorsal-prefrontal regions, posterior cingulate regions [240–249],
and a working memory task would primarily activate dorsal and ventral frontal regions and
inferior and superior parietal regions [250–255]. A limitation of the cognitive paradigm
approach is that the patient must be able to perform the task, and variability in task
performance would alter the activation pattern [256–261]. An alternative approach in AD
would be to implement cognitive paradigms outside of the memory domain that individuals
may still be able to perform such as visual perception, attentional tasks or passive stimuli
[262–273]. The changes found using this approach would be applicable to patients that may
be clinically more advanced, but also provides an approach to measure the ‘downstream’
effects of the pattern of disease-related neuropathology. Current studies examined the
differences between patients and healthy controls or among different risk groups by
quantifying the average difference between the groups, where the groups are defined by
clinical-descriptive phenotypes or risk groups based on genetics or family history. The
proposed PM paradigm would instead examine the variability among the subjects to define
phenotypes that are data-driven and may not necessarily reflect the underlying
pathophysiology and clinical phenotypes. There is evidence of significant variability in brain
activation from healthy status to MCI to mild AD stage, for example a using a face-name
association paradigm, there was a nonlinear response in hippocampus, with higher activation
in MCI subjects compared to healthy controls and AD dementia patients [242, 249, 274].
Similarly with the visual perception task the activation levels varied along the dorsal visual
pathway as disease severity increased [262].
In addition to measuring brain function one would need to integrate the above biomarkers
with results from fMRI studies of the mechanisms of action of the potential therapeutics –
most studies have examined cholinergic drugs over an extended treatment period in either
MCI subjects or mild AD patients (see for example [273, 275–278]). Another potential
approach to be used within a PM paradigm is to measure the effects of a single dose [279–
282] and investigate the predictive power of the single dose over the effectiveness of the
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therapeutic strategy for the biomarkers-characterized patient. The single dose approach has
the potential to inform the tailoring of the therapeutic intervention by providing information
about potential medium to long term effects of any treatment.
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The various fMRI-based paradigms described above would provide information about a
specific brain network or set of brain regions and any data-driven approach would be limited
to data from the brain network or regions activated during the task. An alternative approach
utilizing fMRI would be to use whole-brain resting state fMRI to measure so-called restingstate networks or intrinsic connectivity networks (ICNs) [283–286]. These ICNs have been
shown to be highly reproducible across individuals [287], exhibit characteristic dynamic
fluctuations [288] as well as patterns of change across development, life span and in the
course of brain disorders [236]. The topography of ICNs resembles other networks, such as
those engaged during human behavior and cognition (for example, see [289–293]), derived
from gene co-expression [294, 295], disease phenotypes and disease progression (for
example [246, 296–303]), as well as brain activation level and cognitive performance (for
example [293, 304–306]. The structure of ICN networks can be probed with a variety of
network tools to reveal individual differences in their internal coherence and their mutual
interactions. In combination with these advanced analytics, ICNs can potentially provide a
rich set of biomarkers of brain function, including insights into which ICNs are specifically
disturbed as a result of pathophysiology, and thus yield a more integrated perspective on
system-wide changes within a patient. The tailoring of therapeutics could benefit from
associations between biomarkers and the presence of the disease pathophysiology. Given the
variability that is present in AD patients and MCI subjects, the ICN-based biomarkers and
their relation to genetic profiles [68] may be able to provide an improved systems biology
characterization of brain function. The use of ICNs for tailoring therapeutics still needs
considerable development work, and there is currently only limited work on the effects of an
AD-related drug on ICNs [307]. It should be noted that while the task-free design of resting
fMRI lends itself to application in clinical cohorts, the sensitivity to motion artifacts and
ongoing temporal fluctuations in the network structure of ICNs entail greater reproducibility
as scan lengths are increased (for example, see [308]).
The potential of fMRI to assist in the PM-oriented targeting of therapeutics for AD patients
is strong but also will require very significant development work. The integration of fMRI
with the other domains such as genetics, cognition, clinical measures has so far mostly been
attempted within a group analysis context, and a PM paradigm would need development of
new statistical models to define potential therapeutic strategy on a single individuals basis
[309].
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CONTRIBUTION AND ROLE OF ELECTROENCEPHALOGRAPHY (EEG)
Candidate topographic neurophysiological (neurodynamic) biomarkers of AD can be derived
from resting state eyes-closed electroencephalographic (rsEEG) rhythms recorded in
subjects relaxed in quiet wakefulness (eyes closed, no sleep) with their mind freely
wandering [310]. These rsEEG markers are non-invasive, cost-effective, available
worldwide, and repeatable even in severe dementia. They may probe the neurophysiological
“reserve” in AD patients, as one of the dimensions of the brain reserve [311]. This
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neurophysiological “reserve” may reflect residual mechanisms for 1) “synchronization” of
neural activity in a given cortical region and 2) the coupling of activity between nodes of a
given brain neural networks as a sign of functional cortical “connectivity” [310, 312].
RsEEG markers in AD at the group level reflect the neurophysiological reserve of the
disease over time and after cholinergic therapy
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Previous rsEEG studies using “synchronization” markers showed that compared with groups
of normal elderly (Nold) subjects, AD groups with dementia (ADD) exhibited lower power
density in posterior cortical alpha (8–12 Hz) and beta (13–30 Hz) rhythms [313–319]. There
was also higher power density in widespread delta (<4 Hz) and theta (4–7 Hz) rhythms
[320–325]. Finally, ADD, dementia due to Parkinson’s (PDD), and dementia with Lewy
bodies (DLB) groups were characterized by abnormally lower posterior alpha source
activities [326]. The effect was dramatic in the ADD, marked in the DLB, and moderate in
the PDD [326]. There were also abnormally higher occipital delta source activities with
dramatic effects in the PDD group, marked in the DLB group, and moderate in the ADD
group [326].
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Concerning “connectivity” markers, ADD groups were characterized by abnormally lower
spectral coherence in alpha and beta (13–20 Hz) rhythms between posterior electrode pairs
[316, 327–339]. These effects were observed in temporo-parieto-occipital electrode pairs in
some studies [316, 327, 333, 337] and in frontocentral electrode pairs in others [329, 332,
340]. Other studies reported either a global decrease [327, 334] or increase [337, 341] of
delta and theta coherences between electrode pairs in ADD groups. Another investigation
pointed to a complex topographical pattern of coherence increase and a decrease in those
groups [342]. Alternative techniques of “connectivity” unveiled a decrement of
synchronization likelihood between electrode pairs in frontoparietal alpha rhythms in ADD
and its prodromal stage of amnesic MCI [319, 343]. Finally, there were reduced cortical
connectivity and “small-worldness” in ADD groups as revealed by graph theory indexes
[344–347].
RsEEG rhythms deteriorate across time (e.g. about 12–24 months) in groups of aMCI
subjects and ADD patients (see for a review [348]): 1) increased delta-theta and increased
alpha-beta power density at parieto-occipital electrodes [349]; 2) increased theta power
density, decreased beta power density, and decreased mean frequency at the temporal and
temporo-occipital electrodes [316, 350, 351]; 3) increased delta and increased alpha 1 in
parieto-occipital sources [352, 353]; and 4) reduced cortical connectivity as revealed by
graph theory indexes [347].
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In groups of ADD patients, Acetylcholinesterase inhibitor drugs (i.e. enhancing the
cholinergic tone) showed beneficial or protective effects in delta [320, 354–356], theta [321,
356, 357], and alpha rhythms [355, 358]. When observed at short-term, these effects
predicted longer-term therapy efficacy [357, 359, 360](for a review see [352]). However,
some contradictory findings suggest future more controlled cross-validation studies [361,
362].
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Abnormal posterior cortical delta rhythms in ADD patients might reflect an upregulation of
their generation mechanisms in quiet wakefulness, possibly due to cortical blood
hypoperfusion and synaptic dysfunction in the same regions [363–366] and atrophy in the
posterior cortex [312, 352, 367–369]. Furthermore, reduced posterior cortical alpha rhythms
in ADD subjects might be due to an unselective tonic cortical excitation in populations of
cortical pyramidal, thalamo-cortical, and reticular thalamic neurons generating those
rhythms [370–372]. Such cortical over excitation might induce a background noise in the
neural information processing interfering with vigilance and cognition [310].
RsEEG markers in AD at the individual level: classification accuracy and predictions
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RsEEG markers allowed the discrimination of ADD patients from Nold individuals and
others with neurodegenerative dementing disorders such as PDD and DLB persons. Global
delta and alpha coherences between electrode pairs successfully classified ADD compared
with DLB people with 0.75–0.80 (e.g. 1 = 100%; [373]). Furthermore, twenty discriminant
scalp rsEEG power density and coherence variables showed a classification accuracy of 0.90
in the discrimination of ADD versus Nold and ADD versus PDD subjects [374]. Another
study in small populations of ADD, PDD/DLB, and frontotemporal dementia patients
reached a classification accuracy of 1.0 using 25 discriminant scalp rsEEG power density
and functional cortical connectivity (i.e. Granger causality) variables [375]. In another study,
combining quantitative rsEEG variables (including those of functional cortical connectivity)
with neuropsychological, clinical, neuroimaging, cerebrospinal fluid, and visual EEG data
reached “only” a classification accuracy of 0.87 in the discrimination between ADD, PDD,
and DLB persons [376]. Concerning cortical source space, resting state delta and alpha
sources classified Nold subjects versus ADD/DLB/PDD patients and ADD versus PDD
patients with 0.85–0.90 [326]. Milder classification effects were observed in PDD and ADD
individuals with MCI [377].
RsEEG markers predicted cognitive decline in aMCI individuals at about 6–24 months (see
[348] for a review). The main effects are summarized as follows: 1) combined alpha-theta
power density and mean frequency from left temporal-occipital regions [316]; 2) anterior
localization of alpha sources [315]; 3) high temporal delta sources [378]; 4) high theta power
density [379]; and 5) low posterior alpha power density [380].
Concluding remarks on EEG implementation
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Overall, it is suggested that resting state cortical delta and alpha rhythms might unveil more
compromised neurophysiological reserve in AD, at the group and the individual level. These
rsEEG markers predicted and tracked the AD progression as neurophysiological endpoints
for therapeutic interventions. Future multi-centric longitudinal studies should provide a large
open access database for a systematic comparison of rsEEG markers of “synchronization”
and “connectivity” markers for a better definition of “neurophysiological reserve” for
clinical applications and research.
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CONTRIBUTION AND ROLE OF MAGNETOENCEPHALOGRAPHY (MEG)
Magnetoencephalography (MEG) allows recording the magnetic signals of the order of
10−12 Teslas, which are produced at the scalp surface by the activity of neuronal assemblies.
It may provide information complementary to EEG for uncovering new neurodynamic
biomarkers of AD, particularly in its very early asymptomatic at risk and preclinical stages,
therefore before the prodromal and clinical stages..
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MEG can be used to investigate cognitive functions in a way very similar to EEG. With this
approach, impaired brain functional activities were characterized in AD and MCI stages
during memory tasks for instance. Walla and colleagues [381] used a recognition memory
task in which they manipulated the depth of encoding of verbal information. They showed
alteration of temporo-parietal event-related responses to old—previously encoded—versus
new items in AD patients relative to controls, after deep encoding. The mismatch negativity
(MMN) was also shown to be a potential AD marker. The mismatch negativity is a wellknown component of the event-related potential response, which is associated with the
detection of deviant stimuli in a stream of standard, repeated stimuli—classically in the
auditory modality, hence allowing the assessment of the quality of sensory processing,
memory, and predictive coding [382, 383]. Its magnetic counterpart, the MMNm, was shown
to be delayed in latency in AD compared to healthy elderly controls [384] (see also [385]).
Most interestingly, using memory tasks in pre-clinical stages of AD, e.g., in APOE ε4
carriers, some studies pointed to the capacity of MEG for revealing neurophysiological
markers of subjects’ decline, potentially predictive of pathology emergence [386, 387]. In
sum, MEG can be used in the same way as EEG to investigate cognitive functions during
various task performance; both these methods provide highly convergent and temporally
detailed data on information processing and cognitive functions in normal and pathological
aging.
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However, the most unique potential of MEG for uncovering pathophysiological mechanisms
and providing new neurodynamic biomarkers in the field of AD may lie in the study of
functional brain networks, particularly of resting state networks (for review, [388]). As
mentioned above, fMRI studies have shown that, in the absence of task demand, the resting
brain exhibits spontaneous and highly structured, often oscillatory, fluctuations in activity
[389]. MEG and EEG provide a richer view of these networks in the time and frequency
domains [390–395]. Resting state networks are usually studied using time-frequency
decomposition of MEG (or EEG) signals. This allows identifying a rich set of resting state
networks in distinct frequency bands (e.g. [390, 392, 393, 396]). It was shown that AD
patients show altered resting state network activity. This was revealed at the level of
oscillatory activity characteristics, pointing to an overall slowing of brain rhythms with
particular abnormalities in the delta (<4Hz) and beta (~20Hz) frequency ranges [397–402].
Moreover, alteration of resting state networks, correlated with memory impairment, was
recently shown using a graph-theoretical approach applied to neuromagnetic data [403].
Important questions are: When do these changes emerge in the course of the disease and
which changes are predictive of or specific for the development of molecular and clinical
AD? There is particular potential in EEG and MEG methods to provide such a surrogate
biomarker for clinical outcome. Moreover, there is evidence that some MEG markers of
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functional brain networks may be predictive of the conversion from MCI to AD dementia
[397, 400, 404].
On a practical note, it is important to underline that resting state studies have the advantage
to be particularly adapted for elderly patients, because they require no cognitive effort and
require relatively modest data acquisition time. It is worth mentioning that MEG – in
comparison to most EEG systems – requires only a short time of subject’s preparation for
recording. The whole-head MEG systems that are available at present comprise about 300
sensors that are fixed in a rigid helmet. After head shape numeration and the installation of a
few reference sensors, individuals are comfortably seated with their head placed in the
helmet. The installation time takes as little as 20 minutes. Moreover, the total “innocuity” of
MEG allows close follow-up and detailed longitudinal assessment of disease progression.
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The recent development and promising results of neuromagnetic imaging methods has led to
the Magnetoencephalography International Consortium of Alzheimer’s Disease (MAGICAD) initiative. This initiative aims at advancing the use of MEG for AD and pre-AD
research, combining data from resting state and simple memory and MMN tasks, in a multicentric study [405]. While still in its burgeoning with regard to clinical applications, MEG
has the potential to provide new tools for patient stratification – in order to better target
patient population for clinical trials – and for treatment evaluation [406, 407], and to shed
new light on the neurodynamic pathophysiological mechanisms of AD. It allows to foresee
the identification of individualized signatures of disease progression in the form of temporal
profiles of early adaptive, compensatory and decompensatory brain network changes.
Moreover, it is clear that the full power of MEG will come from its combination with other
methods to allow multimodal assessment of individuals and IDM of multi-modal big data.
For example, the combination of genetic data, such as the APOE polymorphism
characterization with MEG resting state analysis has revealed promising in identifying MCI
subjects at high risk of conversion to AD dementia as well as asymptomatic subjects at high
risk of developing significant cognitive deterioration [408]. Multifactorial characterization of
MCI subjects, including neuropsychological assessment, structural and functional brain
measures, APOE genotyping, demonstrated very high sensitivity and specificity for
predicting conversion to AD [409].
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In conclusion, the advances in the characterization of the dynamics of functional brain
networks based on MEG stands the chance to provide new insights into the
pathophysiological mechanisms of AD. In doing so, it shall constitute a powerful tool to
bridge the gap between what is known from the cellular and molecular pathways of the
disease – its start and its progression – and the cognitive dysfunctions constituting its clinical
and behavioral hallmark. This is likely to be key for developing new biomarker-guided
targeted treatments and PM, based on the characterization of the individual genetic patterns
and pathophysiological pathways towards neurodegeneration and dementia.
CONTRIBUTION AND ROLE OF NEUROMODULATION
Neuromodulation refers to forms of more or less invasive targeted and reversible electrical
stimulation of discrete brain regions; it usually assists – but not replaces – traditional
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pharmacological treatments, with the aim to induce long-lasting changes of firing neural
properties, both in the target region and connected networks, thereby modifying behavior or
diseases’ symptoms. Therefore, neuromodulation fits well with the broad paradigm of PM
that is the customization of healthcare tailored on the individual patients’ demands and
disease’s pathophysiology.
Invasive neuromodulation in AD
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Neuromodulation through deep brain stimulation (DBS) is an emerging opportunity in AD,
being already an established therapy for advanced neurological and psychiatric diseases
[410]. Several subcortical and cortical targets of stimulation have experimentally shown
improvements in learning and memory, reinforcement of synaptic strength and restoring of
physiological patterns of oscillatory brain activity, especially in the theta band, a rhythm that
is functional to memorization [411]. DBS of the entorhinal cortex [412] enhanced memory
of spatial information when applied during learning. DBS of the nucleus basalis of Meynert
was studied in six patients with mild to moderate AD in a 12-month pilot study [413]. DBS
was well tolerated and 4 of 6 patients were considered stable or improved at 12 months
based on cognitive scores. The fornix – a deep white matter tract interconnecting
hippocampus with mammillary bodies, and a central node of the Papez circuitry which is
integral to memory function [411] – has been the most investigated, human DBS target for
AD [414–417].
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A 12-month follow-up of the first implanted 6 patients in the bilateral fornix showed a
possible slowing of cognitive decline in some of them, accompanied by increase of
metabolism in memory-related neural network structures [418], and by a reversal of the
usual hippocampal atrophy found in AD [416]. These promising results prompted the first
multicenter, 12-month, double-blind, randomized, controlled study of bilateral DBS of
bilateral fornix in 42 patients with mild probable AD [419, 420]. The study showed no
differences between those patients who received stimulation compared to controls who were
not stimulated in cognitive measures. However, patients who received stimulation showed an
increase in glucose metabolism in pre-selected brain regions at 6 and 12 months whereas
those who were not stimulated showed decreased metabolism as expected. In a post-hoc
regression analysis age was associated with outcome. Patients with late onset disease (≥65
years old) receiving stimulation showed a slowing of decline in cognitive measures when
compared to those not stimulated. Improvement in glucose metabolism in this subgroup was
greater in magnitude compared to the group as a whole. Stimulation of the fornix appeared
to be safe. The overall perioperative adverse effects of the procedure, despite the cortical
atrophy and the trans-ventricular trajectories of the electrodes towards the deep target, were
comparable in DBS in other ND and there was no evidence of mortality or neurological
morbidity at three months from the implant [419].
Non-invasive neuromodulation in AD
A different, non-invasive yet still experimental in AD, research approach for
neuromodulation is the targeting of neocortical regions relevant to AD pathophysiologythrough the scalp by applying repetitive transcranial magnetic stimulation (rTMS) or weak
currents via transcranial direct current stimulation (tDCS), in repeated daily sessions of
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stimulation [421]. Mechanisms of action are different, as rTMS makes cortical neurons to
fire trans-synaptically [422], while tDCS shifts the level of their firing probability in a
polarity-dependent manner [423]. Both stimulation techniques induce controllable excitatory
or inhibitory after effects: high-frequency rTMS and anodal tDCS generally increase cortical
excitability, while low-frequency rTMS and cathodal tDCS do the opposite [424, 425]; these
effects are either local or involve the cortico-subcortical network to which the targeted
region belongs [426]. In case of AD, the mere “stimulation” of a cortical target, even if
prolonged for several daily sessions, does not help so much in preventing the decline of
memory and other cognitive functions [421]. However, there are few controlled studies for
rTMS in AD and even less for tDCS, for a total of a few dozens of patients treated so far
[421]. What is emerging as a possible role for non-invasive neuromodulation is the coupling
of stimulation with cognitive therapy, with the aim to promote plastic associative learning
mechanisms to synergically improve the effects of cognitive rehabilitation only [427–429].
This approach, while still in need of quantitative characterization [430–432] seems
promising only in mild AD, when the severity of neurodegeneration makes still available a
residual neural substrate to possibly intervene on [433].
From the bench to the patient: a future way of non-invasive neuromodulation?
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Physiological cerebral activity is composed of oscillatory activity across a wide range of
frequencies, ranging from 0.05 up to 500–600 Hz: oscillations in the 30–80 Hz range are
known as “gamma” activity. A relative attenuation of gamma activity is a consistent finding
in patients with AD [315]. Moreover, dysregulation of hippocampal theta/gamma coupling
may precede amyloid deposit activity in animal models of AD [434]. A seminal recent study
in pre-symptomatic and amyloid pre-depositing AD mice, showed that exogenously-induced
flickering lights oscillating at 40 Hz reduce Aβ concentrations and amyloid plaques, as well
as tau concentrations, in a mouse model of AD [435], preventing subsequent
neurodegeneration and behavioral deficits, thus suggesting that gamma induction may
represent a novel therapeutic approach for AD. This opens translational perspectives, as the
possibility of modulating gamma activity in humans, potentially leading to the same
beneficial effects observed in mouse models. The possibility of modulating brain oscillatory
patterns in AD patients has been recently shown, with EEG changes in brain connectivity in
the gamma band following the administration of antiepileptic drugs [436].
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A viable way to interact with brain oscillations is transcranial alternating current stimulation
(tACS), where low intensity (max 2 mA) alternating sinusoidal currents are applied via scalp
electrodes. Due to the safety [437] and controllability (in terms of stimulation frequency and
the possibility to target almost any cortical region) of the procedure, tACS has gained
consensus as one of the most promising techniques to modulate brain oscillations in the
healthy and pathological brains. Empirical evidence using neurophysiological markers,
demonstrate that tACS modulates brain oscillatory activity via network resonance,
suggesting that a weak stimulation at a resonant frequency could cause large-scale
modulation of network activity and amplify endogenous network oscillations in a frequencyspecific manner [438–441]. The application of tACS in the gamma band (specifically 40Hz)
has been shown effective in transiently modulating various abilities in humans, including
those related to higher-order cognition [442, 443] and sensorimotor performance [444]. The
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repeated administration of tACS in AD patients, if individually tailored on cortical regions
with higher concentration of Aβ, might constitute a timely, disease-transforming,
personalized therapeutic application worth to be tested in patient populations.
CONTRIBUTION AND ROLE OF POSITRON EMISSION TOMOGRAPHY (PET)
Positron Emission Tomography (PET) has the potential to make a major contribution to
selection for treatment in AD. This is of particular interest at very early asymptomatic stages
of the disease, when clinical symptoms are still absent. In addition, it may also turn out as
important at later stages as it is increasingly being recognized that several distinct
pathophysiological processes can contribute to the development and manifestation of first
symptoms and dementia. They vary considerably among patients, and one would therefore
want to target the leading cause in individual patients.
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At preclinical or prodromal disease stages identification of fibrillary amyloid deposits by
PET currently is of obvious importance as an approved imaging biomarker for clinical trials.
Use of a conservative cut-point has been suggested to minimize inclusion of elderly subjects
with beginning amyloid deposition but without subsequent worsening [445]. Depending on a
positive outcome of trials, amyloid PET might become a theragnostic procedure to select
patients for anti-amyloid treatment.
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In individuals with manifest dementia, differential diagnosis between AD and other diseases,
such as FTD and vascular dementia, is important for selecting symptomatic treatment.
18F-2-fluoro-2-deoxy-D-glucose PET (18F-FDG-PET) has repeatedly been demonstrated to
provide reliable differentiation between AD and FTD [446]. Beyond its relevance in the
differential diagnosis, 18F-FDG-PET is a topographic marker of AD that can be used to
measure disease progression and help identifying clinical subtypes [447]. Thus, it has a
mediational effect between the neuropathological hallmarks of the disease (NFT and Aβ)
and the cognitive symptoms [448]. It has also been used successfully to study mechanisms
underlying cognitive reserve, which delays the onset of dementia [449]. Identification of in
vivo AD pathology has also proven to be relevant in disease identification. Indeed, some AD
clinical phenotypes can be underlain by several neurodegenerative disorders (e.g. primary
progressive aphasia, corticobasal syndrome), including the classical amnestic AD [450]. In
such cases amyloid PET can identify fibrillary amyloid as an indicator of AD. Fibrillary
amyloid can also coexist with other pathologies, which is frequently the case in patients with
DLB and vascular dementia (which might be termed mixed dementia), but is also possible
with FTD and may possibly contribute to more rapid progression [451, 452]. Thus, if antiamyloid therapy did eventually show clinical benefit in AD patients, patients with non-AD
dementia and positive amyloid PET might also benefit.
Amongst the large variety of possible pathophysiological contributors to AD, many are
accessible by specific PET tracers. The most prominent are fibrillary tau deposits. The
current generation of PET tau tracers has been demonstrated to reflect the pathological
staging of tau deposits in AD, but there is also evidence of some off-target binding that
complicates the interpretation of scans. Next generation tracers are being developed to
overcome these limitations [453].
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Neuroinflammation is another major factor which has been shown to accelerate disease
progression. It is associated with activation of microglia, which can be imaged by PET using
the translocator protein (TSPO) tracers. 11C-(R)-PK11195 has been the first of those, and in
spite of some limitations due to a relatively high level of non-specific binding is still widely
used. A large number of second generation tracers with higher specificity has been
developed but their binding is subject to a genetic polymorphisms that blurs the advantage of
these tracers [454]. Nonetheless, beyond these limitations, the development of these tracers
could provide relevant biomarkers and offer new insights in the variability of evolution of
AD [455]. There are also tracers for imaging of astrogliosis, and markers for cytokines and
inflammatory endothelial changes are being developed. Further translational research will
investigate the molecular characteristics and the effects of targeted interventions on
microglial and astrocytic activation.
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Deficits in cholinergic transmission play a major role for deficits in memory and attention in
patients with dementia. Tracers have been developed for nicotinic and muscarinic receptors,
for vesicular transporters and acetylcholinesterase. Clinical studies have provided
preliminary evidence that such tracers could be used to identify responders to
acetylcholinesterase inhibitor therapy, and further research into this issue is required [456].
There are well established Single Photon Emission Computed Tomography (SPECT) and
PET tracers for identification in dopaminergic transmission, which is most severely affected
in DLB. This is providing a useful diagnostic tool for differentiation between AD and DLB,
while research is ongoing to identify the cognitive deficits associated with that deficit and
potential targeted therapeutic interventions [457].
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There is also current research into PET imaging of glucose energy metabolism,
mitochondrial damage, glutamatergic and GABAergic dysfunction, blood-brain barrier
damage and defects in transcriptional regulation and protein synthesis. They may play an
important role in AD pathophysiology and offer windows for targeted intervention.
In conclusion, there is a huge potential of PET to contribute development of the PM
paradigm in AD. Currently, amyloid imaging has been progressed most as a biomarker in
clinical trials towards that goal. 18F-FDG-PET and tau-PET imaging are also involved in
multiple trials, while a large variety of other tracer for specific targets in AD
pathophysiology are still at earlier stages of translational research.
CONTRIBUTION AND ROLE OF RETINAL IMAGING
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Over the past three decades, growing evidence indicates that AD is not confined to the brain
but also affects the eye. Patients with AD and subjects with MCI experience a wide spectrum
of visual deficits [458–464], sleep disturbances [465–471], and ocular abnormalities [472]
[466, 472–489]. Historically, these visual and circadian rhythm disturbances were attributed
to pathology in the brain yet are now being revisited and explored as a potential direct
outcome of ocular pathologies. Among ocular tissues, studies have shown that the retina is
massively impacted by AD [466, 472, 474–479, 482, 484, 486, 487, 490–507]. The retina of
MCI subjects and AD patients displays a host of abnormalities including nerve fiber layer
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(NFL) thinning, optic nerve and retinal ganglion cell (RGC) degeneration, macular volume
changes, retinal angiopathy involving reduced blood flow and vascular structural alterations,
astrogliosis, and abnormal electroretinogram patterns [472]. Given these findings, it is no
surprise that attention has begun shifting towards the neuro-retina as a site of AD
manifestation.
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As a CNS tissue derived from the embryonic diencephalon, the retina shares many structural
and functional features with the brain [508], including the presence of neurons, astroglia,
microglia, pericytes, microvasculature with similar morphological and physiological
properties, and a blood barrier [509–511]. Axons of the optic nerve directly connect the
retina and brain, facilitating vesicular transportation of APP synthesized in RGCs [512].
Further, retinal neurons and glia secrete proteins associated with the amyloid cascade
including γ-secretase, BACE1, Apolipoprotein E, and clusterin [511, 513, 514]. However,
the skull-encased brain is shielded by bone, whereas the retina is accessible for direct, noninvasive high-resolution imaging.
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The converging evidence denoting retinal abnormalities related to nerve degeneration and
vascular changes, common to various neurological and ocular diseases, have long been
described in MCI subjects and AD patients. Yet, the AD-specific pathophysiological
hallmark, Aβ plaques, was only recently identified in post-mortem retinas of AD patients
and early-stage cases [490]. Subsequent studies corroborated these findings of retinal Aβ
deposits and further indicated the presence of p-tau in retinas of AD patients [466, 485, 489,
515, 516]. These studies provided evidence for elevated retinal Aβ40 and Aβ42 peptides
using biochemical assays on whole retinal extracts and revealed diverse retinal Aβ plaque
morphology in flatmounts, often associated with blood vessels or co-localized with sites of
cell degeneration (Figure 7A–H) [466, 485, 489, 490, 515, 516]. Recent data showed that
retinal Aβ deposits were found in clusters and frequently mapped to peripheral regions in
the superior quadrant in AD patients (Figure 7C and 7F). The load of Aβ42-containg retinal
plaques in the superior quadrant was substantially elevated by 4.7-fold in patients compared
to age- and gender-matched controls (Figure 7C–D) [485]. While two groups were unable to
detect Aβ or p-tau in the human AD retina [489, 517], they relied on analysis of cross
sections prepared from narrow strips spanning horizontally from nasal to temporal quadrants
- regions scarce in Aβ pathology. In contrast, a recent study provided in-depth
characterization of retinal Aβ deposits in larger cohorts of definite AD patients via scans of
large retinal areas in flatmounts and in cross sections derived from geometrical regions
abundant with Aβ pathology [485]. The discovery of classical, dense-core (compact), and
neuritic-like plaques in these patients, albeit smaller in average size compared to plaques in
the brain, along with neurofibrillary tangles, Aβ42 fibrils, protofibrils, and structures
resembling oligomers, suggests that the specific signs of AD are shared between the retina
and the brain (Figure 7G). A correlation analysis in a subset of patients has validated
positive relationships between retinal and respective cerebral Aβ plaque burden, with a
tighter association to plaques in the primary visual cortex (Figure 7H) [485]. Notably, retinal
regions in AD patients where abundant Aβ pathology was detected – the periphery of the
superior quadrant and the innermost retinal layers – also showed a significant decrease in
retinal neuronal cells (Figure 7E–F), in agreement with previous studies showing a marked
RGC loss and NFL thinning in the superior quadrants [466, 476, 484, 491, 498, 502, 518,
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519]. A recent clinical study identified circadian abnormalities in AD patients along with a
significant loss of melanopsin RGCs (mRGCs), photoreceptors known to drive circadian
photoentrainment [520], and discovered Aβ accumulation within and around these
degenerating cells. The loss of mRGCs may therefore result from their increased
susceptibility to toxic Aβ forms and offers a plausible retina-based explanation for sleep
disturbances in AD [466].
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In line with the above findings, numerous studies examining the retina of transgenic and
sporadic animal models of AD have reported Aβ deposits, vascular Aβ, p-tau, and paired
helical filament-tau (PHF-tau), often in association with RGC degeneration, local
inflammation (i.e. microglial activation), and impairments in retinal structure and function
[472] [485, 490, 515, 516, 520–537]. These investigations, which included a variety of
transgenic rat and mouse models (ADtg) as well as the sporadic rodent model of AD, O.
degus, demonstrated abundant Aβ deposits, mainly in the GCL and NFL [490, 516, 521,
525, 528, 530, 533]. Furthermore, several publications have described positive responses to
therapies in reducing retinal Aβ plaque burden in ADtg mice, often reflecting the reactions
observed in the respective brains [490, 524, 527, 528, 532, 536].
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To visualize retinal Aβ pathology in live subjects, a non-invasive retinal amyloid imaging
approach was initially developed in ADtg mice, utilizing curcumin as a fluorescent probe
[490, 527]. Curcumin is a natural and safe fluorochrome that crosses the blood-brain and retinal barriers and binds to Aβ fibrils and oligomers with high affinity [490, 527, 538–551],
with the ability for ex vivo and in vivo visualization when specifically bound to retinal Aβ
plaques (Figure 7A–B) [485, 490, 527]. This approach enabled non-invasive detection and
monitoring of desecrate retinal Aβ deposits in live animal models of AD [490], including the
capability to track the dynamic appearance and clearance of individual plaques and their
substantial reduction after glatiramer acetate (GA) immunotherapy [527, 552, 553].
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In a proof-of-concept clinical trial, the safety and feasibility to non-invasively detect and
quantify retinal amyloid deposits in live human patients was demonstrated using a modified
scanning laser ophthalmoscope and a proprietary oral curcumin formulation (Longvida®)
with increased bioavailability (Figure 7I–M) [485]. Corresponding to the pattern reported in
histological examinations, retinal amyloid deposits in living AD patients were frequently
concentrated in the mid- and far-periphery of the superior hemisphere (Figure 7K). A
significant 2.1-fold increase in retinal amyloid index (RAI), a quantitative measure
developed to assess numerical value of amyloid burden in the retina of living patients, was
revealed in AD patients versus matched controls (Figure 7L–M) [485]. Recent studies
applying non-invasive retinal imaging in live AD patients, which detected NFL thinning
[466, 477], increased inclusion bodies [554, 555], reduced blood flow, microvasculature
alterations, and oxygen saturation in arterioles and venules [479, 556, 557], and importantly,
hallmark Aβ deposits [485], are encouraging first steps towards the development of practical
tools for predicting disease risk and progression. Since the retina in other ND such as
multiple sclerosis, ischemic stroke, and Parkinson’s disease also exhibits pathophysiological
processes similar to those detected in the brain [501, 558–561], retinal imaging may also
facilitate differential diagnosis for different proteinopathies, neurodegenerative and
neurological diseases.
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As research exploring AD in the brain, the possibility that the easily accessible retina may
faithfully reflect AD neuropathology warrants further investigation. The preliminary
evidence of retinal Aβ accumulation in early-stage cases together with the indication of
amyloid-related neurodegeneration in the AD retina [466, 485, 490] suggests that AD is both
a cerebral and an ocular disease, and may support retinal imaging as a screening tool even
during the asymptomatic at risk stage. Future studies are needed to assess the nature of the
relationship between cerebral and retinal amyloid burden in larger cohorts and in specific
anatomical regions, and perhaps also to determine the potential link among cerebral amyloid
angiopathy and retinal vascular amyloid. Given that retinal amyloid pathology could foretell
brain disease and cognitive decline, it may prove essential for early detection of AD,
predicting disease progression, and monitoring response to therapy.
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In addition, non-invasive functional tests of pupil reactivity to light may complement the
characterization of retinal abnormalities with imaging techniques [562]. Indeed, pupil
responses to light stimulations are abnormal in AD patients [563], who show hypersensitive
pupil-dilation to tropicamide, an acetylcholine receptor antagonist, as well as a diminished
pupil light reflex [564, 565]. Although the retinal abnormalities mentioned above could
account for these pupillary effects, the Edinger-Westphal nucleus, a major relay involved in
pupil control where early signs of AD (cell loss and amyloid plaques) have also been
observed, could also contribute to pupillary abnormalities. Conducting focal tests in different
regions of the visual field to probe the pupil response can help identifying the functional
consequences of the retinal amyloid imaging results. If the results of retinal imaging and
functional tests were strongly correlated, pupil reactivity could be used as a proxy for AD
severity, with the advantage that functional tests of pupil reactivity are easy, cheap and fast
to perform, do not require a strong involvement of the patients, and can routinely be
conducted to detect and track the evolution of AD, as well as the response to therapy.
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In this regard, the “VISION” pilot translational neuroscience research program – belonging
to the previously mentioned Sorbonne Université GRC-APM (GRC n° 21) – has been
developed and launched in an early asymptomatic preclinical population to assess retinal
amyloid imaging for 1) screening of amyloid and tracking its progression as well as 2)
predicting pathophysiological disease progression, cognitive decline, and conversion to
prodromal AD. The non-invasive nature, easy accessibility and generalizability are
appealing features regarding a potential context of use.
SPATIOTEMPORAL MODELING OF MULTIMODAL LONGITUDINAL DATA
ANALYSIS
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Nowadays, deepening our understanding of AD pathophysiology is made possible by the
following biomarkers that can be derived in-vivo from the subject: “fluid” from blood (e.g.,
genetic risk factors) and CSF (e.g., abnormal Aβ42 and p-tau dosing); “structural” (e.g.,
brain atrophy as a sign of neurodegeneration) and “functional” (e.g., brain disconnection
syndrome) from MRI, “molecular” (e.g., brain hypometabolism and deposition of Aβ42 and
p-tau) from PET, and “neurophysiological” (e.g., abnormal cortical neural synchronization
and coupling). Furthermore, fine neuropsychological and clinical scales allow a detailed
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measurement of cognitive impairment, self-care, independence in living in a community, and
mental disorders (e.g. anxiety, mood, psychosis, and behavior). All these measurements
allow a personalized evaluation of cerebral residual capacity and function over time by the
repetition of the recording sessions.
Keeping in mind this premises, a major issue is the identification of the best statistical and
mathematical procedures, from computational neurosciences, weighting the information
value of the above biomarkers and clinical indices for early diagnosis (even in preclinical or
prodromal stages preceding dementia), monitoring, therapy response, and prediction of the
disease evolution.
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To this aim, digital brain models have been developed in recent years, as a way to synthetize
a 3D geometrical model summarizing the anatomical invariants in a group of subjects [566–
569]. This model has been extended recently to functional data [570, 571]. The main interest
of such models is that they do not only illustrate the effects of the AD on brain structure and
function at the group level but also include information about individual variability allowing
the computation of the difference between a given patient and the reference groups of
healthy subjects and patients with other dementing disorders to provide diagnostic
information as sensitivity (detection of AD patients), specificity (detection of healthy
subjects or patients with other diseases), and global classification accuracy.
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These diagnostic models are based on the Bayesian inference of non-linear mixed-effects
models, which complement the usual linear mixed-effects models typically used in
biostatistics [569, 572]. This combination of statistical and geometric approaches accounts
for the inherent structure in the data such as the specific organization of the brain anatomy as
prior knowledge. It allows the rendering of the inter-individual variability as a realistic and
interpretable change of the 3D model. Individual characteristics are summarized by a
multivariate descriptor, which may be used in turn to explore the distribution of the
individuals in different clusters, to correlate it with external factors, or to use as input in
machine learning algorithms to make individual predictions [568].
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Ideally, such a static model should be adapted to account for the disease progression over
time and provide prognosis of clinical evolution in individual AD patients. Digital models of
brain ageing are constructed as dynamical models showing the complex spatiotemporal
patterns of changes in the above biomarkers while the disease progresses. Inter-individual
variability is expressed in terms of changes in individual spatiotemporal trajectories. The
construction of such models of disease progression results from several key components
[570, 571, 573–576]: 1) artificial intelligence approaches that are used to combine several
short-term data sequences in longitudinal data sets to synthetize a long-term scenario of
disease progression; 2) different data modalities that are integrated in the model by
converting them into a common abstract mathematical space – called a Riemannian
manifold – where statistical distributions of spatiotemporal trajectories may be rigorously
defined; 3) variability in trajectories accounting for the direction of the trajectories and the
dynamics at which these trajectories are followed.
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Each individual disease trajectory is now positioned in a spatiotemporal coordinate system,
where a multivariate descriptor encodes the variability in the direction of the trajectory, and
dynamical parameters encode for the variability in age at disease onset and pace of disease
progression. Given the observation of a new subject at one or few time-points, one may
personalize the scenario of disease progression by adjusting model parameters, thus
transferring the knowledge gained from the automatic analysis of a longitudinal data set to
this new individual. This personalized model may be utilized then to predict the future state
of the subject, for instance the time to the onset of a specific symptom. We have employed
such an approach to predict the time-to-diagnosis in mild cognitive impaired subjects using a
model of cognitive decline from neuropsychological assessments [577], and to predict the
future map of cortical thickness for the same subjects using structural imaging [571]. This
approach opens up the way to build efficient decision support systems for monitoring
disease progression and selecting patients in clinical trials with a specific biomarker-based
diagnosis of AD, at a specific disease stage (e.g. preclinical, prodromal or manifest
dementia) and with an expected pattern of progression.
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In addition, such a personalized scenario may offer a new way to assess treatment efficacy
by evaluating to which extend it changes the disease trajectory, that is the complex nonlinear spatiotemporal patterns of changes. This approach evolves the standard procedure
based on annual percentage rate of an outcome measure since: 1) it does not assume a linear
variation of the outcome at all disease stage but account for the non-linear dynamics of
changes across disease stages, and 2) it makes use of a multivariate descriptor of disease
trajectory and not only a univariate outcome measure.
THE EMERGING FIELD OF SYSTEMS PHARMACOLOGY IN ALZHEIMER’S
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DISEASE
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The consequences of the highly complexity of AD pathophysiology can be clearly observed
in the results of drug development pipeline for the disease: out of 413 clinical trials
conducted during the 2002 to 2012 period, 99.6% failed [578]. Moreover, a review of AD
drug development pipeline in 2016 showed that although the pipeline has increased in size, it
is significantly smaller compared to the cancer field, and that the most common target (76%)
is still amyloid, reflecting the urgent need for deeper understanding the pathophysiology of
the disease [579]. In fact, disappointing results of anti-amyloid drug candidates can be
attributed to three major factors relating to drug discovery and development, namely 1) interspecies mechanistic differences between animal models and human, 2) complex biology of
amyloid-beta in relation to disease staging, and 3) ignorance of non-amyloid pathways.
Thus, it is imperative to delineate the complexity of AD pathophysiology using systems
biology-based approaches, which take advantage of computational analysis and modeling of
both quantitative (e.g. “omics”-based) and qualitative (e.g. literature-based) data. The goal of
systems biology methods is to aid researchers develop hypotheses regarding the disease
system and gain better mechanistic insights into the pathophysiology and progression of
disease across multiple biological scales and time. Mechanistic systems models are either
mathematical representations of pathophysiologic processes or computable cellular networks
but the latter has gained more attention for analysis of drug action [580]. Since these models
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use networks instead of single transduction pathways, complex patterns of drug action
within the target biological context can be studied in more details, a field that has emerged
as systems pharmacology.
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According to the American Association of Pharmaceutical Scientists (AAPS), systems
pharmacology is “the science of advancing knowledge about drug action at the molecular,
cellular, tissue, organ, organism, and population levels” (available at http://www.aaps.org/
Systems_Pharmacology/). To obtain full understanding of drug action at the systems level,
we need to combine disease mechanism, pharmacodynamics and pharmacokinetic data into
a single model. However, incorporation of quantitative parameters and measurements
increases the model complexity so that special mathematical techniques are required to
reduce the number of parameters without affecting the behavior of the system; thus, disease
mechanistic models are considered as the first substrate for building full-fledged systems
pharmacology models [581]. Disease mechanistic models are molecular and cellular
networks that aim to elucidate the impact of therapeutics or new drug candidates on
impaired biological functions under disease conditions. The key to usefulness of disease
models is context-sensitivity, meaning that disease network models should represent the
real-world context in terms of cell and tissue type (spatial dimension), disease sub-type
(functional dimension), and progression stages (temporal dimension). It is only in the right
context that correct inferences, interpretations, and predictions can be made out of the
model. The focus of earlier models was to relate drugs to proteins in the form of drug-target
networks where protein-protein interaction networks were used as the fundamental model
for interpretation of drug mode-of-action [582]. Interestingly, these models also revealed an
important aspect of systems pharmacology paradigm, which was conceptualized and coined
as “polypharmacology” [583]. This concept changed the single-target approach to designing
new drugs in the discovery phase because topological analysis of drug targets in network
models demonstrated that a compound binds to multiple targets. As a consequence, a drug
hits additional targets, known as off-targets, which leads to side effects. Campillos and
colleagues (2008) used drug-drug and drug-target networks enriched with side-effect
phenotype information for all approved drugs across many disease indications and based on
side-effect similarities predicted and experimentally validated novel drug-target relations
[584]. This approach enables researchers to predict off-targets and thereby probable side
effects for candidate drugs in preclinical settings. The so-called structural systems
pharmacology aims at modeling energetic and dynamic modifications of genomic
macromolecules including proteins, DNA, and RNA by drug candidates [585]. This strategy
has been implemented by Nikolic and colleagues (2016) to predict both primary target and
off-target profiles of several anti-neurodegenerative compounds based on their chemical
structures [586]. Their analysis resulted in identification of novel compounds that hit
multiple targets and inhibited acetylcholinesterase, butyrylcholinesterase, monoamine
oxidases A and B in the context of AD pathophysiology. Moreover, knowing which drug
properties distinguishes Central Nervous System drugs from others can help drug designers
select those properties in the new drug candidates that confer the least side effects and the
best efficacy. To this end, Shahid and colleagues (2013) developed a computational method
that identified and classified neurodegenerative drugs from non-neurodegenerative drugs
with 80% accuracy [587]. DrugGenEx-Net is a computational platform that predicts disease-
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specific drug polypharmacology based on multi-tiered network analysis of drug-target,
disease-target, pathway-target and target-target interactions [588]; the model revealed that
Sunitinib, an approved drug for renal cell carcinoma, hits multiple targets associated with
AD pathways and thus can be considered for repurposing.
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With advancements in systems biology modeling languages – such as Systems Biology
Markup Language (SBML) and Open Biological Expression Language (OpenBEL) – drugmode-of-action can now be investigated in a context-sensitive, rich environment that goes
beyond simple representation of protein-protein interactions by including various types of
biological entities covering genotype to phenotype scales. For instance, Fujita and
colleagues (2014) developed a comprehensive molecular interaction map of Parkinson’s
disease that included major signaling pathways in Parkinson’s disease, modeled and
presented in SBML format; however, they did not include drug information in their model
[589]. AlzPathway is the result of an early initiative that attempted to systematically collect
AD-related signaling pathways from literature and bring them together within the first map
of cellular AD signaling pathways, represented in SBML [590]. Recently, Iyappan and
colleagues (2016) identified all signaling pathways reportedly involved in the human ND,
mapped them back onto their corresponding anatomic sites on the human brain, and used
these pathways for explaining the mode-of-action of the AD approved drug, Rasagiline
[591].
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In the past years, with the availability of increasing amount of data and knowledge on the
one hand, and emergence of new computational biology methods on the other, the IDM
framework has increasingly drawn more attention by academic and pharmaceutical research
groups. The models generated by this approach combine data-driven and knowledge-driven
models into a single integrative model and represent signaling pathways with cause and
effect relations [23]. However, a major challenge for this approach is integration of
heterogeneous datasets and information that come from various data sources. For instance,
the ADNI provides big neuroimaging data along with genetic and biomarker data from AD
and MCI subjects [592]. If integrated into predictive models, ADNI data will have maximal
impact on the AD drug research. But, the first step towards IDM is standardization and
harmonization of different datasets so that they are semantically compatible. Ontologies are
semantic frameworks that provide a reference for standardization and harmonization of
diverse datasets. For instance, AD ontology (ADO) has been developed to provide such a
reference for AD knowledge domain [593]. ADO was used by Kodamullil and colleagues
(2015) to represent scientific findings in a computable, cause-and-effect model of AD
pathology, which was designed and coded in Open Biological Expression Language
(available at http://openbel.org/) [594]. This model contains causal and correlative
relationships between biomolecules, pathways, and clinical readouts and was used for
model-guided interpretation of genetic variation data for a comorbidity analysis between AD
and type 2 diabetes mellitus. Similarly, drug-target interactions and drug mode-of-action can
be investigated and predicted using these models. Indeed, integrative models that encompass
data from genome to phenome across biological scales from cells to clinical outcomes,
enable us to predict the mode-of-action of candidate drugs within the right
pathophysiological context and in a multidimensional space of human biology. Perhaps one
of the most fundamental works in this area is the study by Emon and colleagues (2017) who
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systematically analyzed the brain chemical space and identified drug candidates for
repositioning in AD [595]. They first generated a large model in BEL containing genes,
proteins, drugs and chemicals, biological processes, and disease concepts in the context of
neurodegeneration. Then, by mechanistic analysis of this model, they not only suggested
Donepezil as repurposing candidate for amyotrophic lateral sclerosis, but also found a
mechanism of action by which Riluzole, a drug used in amyotrophic lateral sclerosis, could
be predicted to interfere with several pathophysiological pathways in AD. Moreover, the
mode-of-action analysis of other drugs in the context of AD using this model predicted that
Cyclosporine, a drug used for treatment of rheumatoid arthritis, which shares common
targets with 5 approved drugs for AD, can exert neuroprotective effects. Several lines of
evidence that experimentally proved its anti-AD effects supported this prediction.
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Currently, several initiatives have undertaken the effort to facilitate systems pharmacology
studies in the field of ND in general and AD in particular. The AETIONOMY project,
funded by the Innovative Medicine Initiative (see http://www.imi.europa.eu/), has already set
up a specialized knowledgebase for ND with focus on AD and Parkinson’s diseases, and
takes an integrative modeling approach to computationally predict and clinically validate
mechanistic signatures that stratify AD and Parkinson’s patients (see http://
www.aetionomy.eu/). The mission of this project is to lay foundation for development of
new drugs targeting patient subgroups and thus promoting personalized medicine. The Brain
Health Modeling Initiative (BHMI) is another project that takes advantage of integrative
mechanism-based computational models and simulations using big data with the aim of
matching right targets and biomarkers for optimal drug design in AD [596]. The European
commission-funded project SysPharmAD proposes a systems pharmacology approach to the
discovery of novel therapeutics in AD using an integrative network model that combines
“omics” data with stage-specific clinical data. The aim of this project is to design and
validate a systems pharmacology strategy based on AD staging that helps researchers
identify synergistic multi-targeting compounds modifying the disease path (available at
http://cordis.europa.eu/project/rcn/185567_en.html).
CONCLUSIONS
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The multidimensional nature of all ND, AD included, is well established to-date, along with
the fact that their onset and progression arise from dysregulation processes which evolve at
both intracellular and extracellular levels. At the cellular level, ND are characterized by
dystrophic neuronal structural changes leading to loss of function and, eventually, cell death.
These phenomena spread in a “cell-to-cell” fashion in which intraneuronal protein
misfolding affects structural plasticity in a nearby neuron by self-propagation of pathogenic
protein aggregates. This, in turn, leads to decreased dendritic spines and synaptic sites
density, and, eventually, loss of brain connections.
At the subcellular and molecular level, the core pathophysiological phenomenon is
represented by failure of proteostasis cellular pathways [597, 598], from protein misfolding
and aggregation to decreased clearance, mitochondrial dysfunction, loss of cell homeostasis,
and, consequently, enhanced cell signaling pathways related to apoptosis. Therefore, ND are
initially characterized by several alterations of subcellular frameworks, mostly concerning
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proteostasis, on which both the anatomy and physiology of neurons and glial cells are
founded.
The genome, through mutual interactions with endogenous and exogenous factors, leads to a
wide spectrum of variations at the level of proteome and metabolome that, incontrovertibly,
account for both intracellular and extracellular integrity. As a result, the systems biology and
systems neurophysiology paradigms can provide a conceptual model where structural and
functional networks are dynamically interconnected across different dimensional levels into
accounting a multiscale dynamical system which has already been seen to manifest also into
peripheral branches like the autonomic nervous system in health and disease [599, 600].
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At present, there is an urgent need to identify a large array of reliable biomarkers to in vivo
identify the above mentioned interacting multidimensional levels which characterize ND.
Such biomarkers need to be able to chart the spatio-temporal trajectories of complex brain
pathophysiological mechanisms, at the same time taking into account interindividual
variables. Complex, time varying higher order statistics as well as structural model should
also be considered within the systems neurophysiology modeling approach [601–604].
Pathophysiological biomarkers are required to track the pathophysiological mechanisms
underlying ND (Figure 8). For instance, cerebral amyloid-PET is commonly considered as a
molecular proxy of the Aβ metabolism impairment rather than a conventional biomarker of
neocortical deposition of neuritis plaques. In this context, biomarkers are the appropriate
tools for developing receptor-tailored drugs, as already demonstrated and currently practiced
in the field of oncology. Both structural and functional brain markers are expected to
elucidate the link between clinical phenotypes and molecular pathophysiological
mechanisms.
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Notably, cerebral 18F-FDG-PET is commonly used as prognostic indicator in several clinical
trials on AD and other ND. Indeed, the early recovery of specific brain functions or
networks is crucial to identify downstream effects of disease therapies, even before
measuring the clinical benefit. As another example – in the context of identifying brain
biomarkers from non-invasive imaging within a more individually tailored, PM-based
approach – recent developments have pointed out the concept and added value of “dense
sampling of individual brains” [605–607]. This interesting development is based on the
realization that, while a large body of research is accustomed to averaging neuroimaging
data across individuals and, hence, implicitly assuming a high degree of functional
homology, by definition there must be a finer scale at which this homology breaks down possibly the scale which encodes the individual idiosyncrasies at the base of a unique
individual’s disease trajectory and/or therapy response. By sampling relatively few brains for
several hours, the authors demonstrate how individual differences in well-known networks,
e.g. the default mode and the salience network, are clearly visible. Therefore, it is possible
that future developments in neuroimaging will shift more toward longer (several hours/days)
sampling of individual brains/patients, thus providing more solid bases for the
implementation of the “precision neuroscience” paradigm that will likely be needed to
understand ND.
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Interestingly, functional and topographic biomarkers could also be employed in identifying
the adequate target. In particular, they could be valuable in detecting specific brain areas for
potential trials of targeted neuromodulation, thus providing comprehensive information on
regional atrophy, impaired connectivity, metabolic alterations, and regional decrease of
cerebral blood flow. Finally, both clinical examination and full psychometric evaluation still
remain the first-line approach in identifying pathological phenotypes supporting the whole
diagnostic workout. For instance, to date, the identification of hippocampal-like amnestic
impairment supports the clinical diagnosis of AD, thus justifying an anticholinesterase
inhibitor-based treatment. Notably, in the context of a systems biology- and systems
neurophysiology-based interpretation of ND phenotype, clinical markers should be
considered the highest level “descriptors” of the disease and represent the ultimate measures
to identify effective therapies.
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In summary, the future implementation of the systems biology and systems neurophysiology
paradigms – based on the integrated analysis of big and deep heterogeneous data sources –
will be crucial to reach a deeper understanding of the pathophysiology of AD and other ND.
The main challenges ahead will certainly lie in the development of analytical applications
capable of processing massive quantities of stored laboratory and clinical data. Against this
backdrop, the big data approach should be leveraged to maximize the information that can
be extracted from preclinical and clinical records, ultimately augmenting our knowledge
regarding the molecular, cellular, and systems processes underlying AD development. As we
unravel the dynamic and longitudinal changes of the biomarker landscape in AD, we will
make a further step towards a holistic understanding of the natural course of the disease.
Integrating different sources of information will enable researchers to obtain a new
integrated picture of the pathophysiological process of the disease that will span from
molecular alterations to cognitive manifestations. In this scenario, the Big Data Research
and Development Initiative (available at https://obamawhitehouse.archives.gov/blog/
2012/03/29/big-data-big-deal), promoted by the previous Obama Administration under the
“Big Data is a Big Deal” motto, is expected to accelerate progress towards a new era of PM
in AD. This ultimate mission will be accomplished by assembling, linking, and harmonizing
big data to facilitate high-impact, multidisciplinary, and collaborative research efforts. After
a decade of failed clinical trials in AD, the adoption of “big data science” within an IDM
theoretical framework by the international APMI allowed us to enter into a transformative
research scenario. It is currently expected that PM will underpin most, if not all, of the
prevention and treatment advances yet to come. Significant breakthroughs in our
understanding of the early phases of AD and other ND and the rapid advent of new
laboratory technologies are providing unprecedented opportunities to make a major impact
on the natural history of AD at the earliest preclinical asymptomatic stage [608]. We are
currently standing at the edge of a new frontier that will thoroughly explore the molecular
and cellular events that drive the development of the disease before cognitive symptoms are
evident. New preventive approaches and therapies developed through PM may improve
compliance and increased level of trust and confidence among all stakeholders and reduce
the number of failures. In this context, we are expected to move swiftly from the traditional
“one-size-fits-all – magic bullet therapies” scenario to a personalized PM-based approach.
The unprecedented effort promoted by the APMI is ultimately tailored to implement a
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paradigm shift in AD research which will be backboned by large, international, and
interdisciplinary collaborative academic, private and industry networks.
The field of PM does not lack for enthusiastic, dedicated pioneers who are moving forward
expeditiously to clinical adoption. As the evidence base supported by the APMI expands,
much more can and should be done to accelerate the process for the benefit of individual
patients, the healthcare system, and society overall.
Acknowledgments
Dr Harald Hampel is supported by the AXA Research Fund, the “Fondation partenariale Sorbonne Université”
and the “Fondation pour la Recherche sur Alzheimer”, Paris, France. Ce travail a bénéficié d’une aide de l’Etat
“Investissements d’avenir” ANR-10-IAIHU-06. The research leading to these results has received funding from the
program “Investissements d’avenir” ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Agence Institut
Hospitalo-Universitaire-6).
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Dr Arun L.W. Bokde received funding from the Meath Foundation, Ireland.
Dr Olivier Colliot received funding from the program “Investissements d’avenir” ANR-10-IAIHU-06 (Agence
Nationale de la Recherche-10-IA Agence Institut Hospitalo-Universitaire-6), from the European Union H2020
Program (project EuroPOND, grant number No 666992), and from the joint NSF/NIH/ANR program
“Collaborative Research in Computational Neuroscience” (project HIPLAY7, grant number ANR-16NEUC-0001-01). He supported by a “Contrat d’Interface Local” from Assistance Publique-Hôpitaux de Paris (APHP).
Dr Stanley Durrleman is funded by the European Research Council (ERC) under grant agreement No 678304, the
European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 666992, and the
program “Investissements d’avenir” ANR-10-IAIHU-06.
Dr Maria-Teresa Ferretti is the President of Women’s Brain Project and is supported by a research fellowship by
the Synapsis Foundation - Alzheimer Research Switzerland (ARS).
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Dr Nathalie George is supported by the French program “Investissements d’avenir” under Grant numbers
ANR-10-IAIHU-06 (IHU-A-ICM) and ANR-11-INBS-0006 (France Life Imaging).
Dr Maya Koronyo-Hamaoui is supported by NIH/NIA AG044897 and AG056478 Awards and by The Saban and
The Marciano Private Foundations.
Dr Karl Herholz is supported by research grants from GE Healthcare (GEHC) and GlaxoSmithKline (GSK).
Dr Olaf Sporns acknowledges support from the U.S. National Institutes of Health (R01-AT009036).
Dr Andrea Vergallo is supported by Rotary Club Livorno “Mascagni”/The Rotary Foundation (Global Grant No
GG1758249).
Abbreviations
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18F-FDG-PET
18F-2-fluoro-2-deoxy-D-glucose
Aβ42
42-amino acid-long amyloid beta peptide
AD
Alzheimer’s disease
ADD
Alzheimer’s disease dementia
ADNI
Alzheimer’s Disease Neuroimaging Initiative
ADO
Alzheimer’s disease ontology
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PET
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APMI
Alzheimer Precision Medicine Initiative
APMI-CP
Alzheimer Precision Medicine Initiative Cohort Program
APP
amyloid precursor protein
BFCS
basal forebrain cholinergic system
CSF
cerebrospinal fluid
DBS
deep brain stimulation
DLB
Dementia with Lewy bodies
DTI
diffusion tensor imaging
EEG
electroencephalography
EHRs
electronic health records
EPAD
European Prevention of Alzheimer’s Dementia consortium;
EPAD LCS
EPAD Longitudinal Cohort Study
EPI
echo planar imaging
FA
fractional anisotropy
FMRI
functional magnetic resonance imaging
FTD
frontotemporal dementia
ICNs
intrinsic coherent networks
IDM
integrative disease modeling
MCI
mild cognitive impairment
MD
mean diffusivity
MEG
magnetoencephalography
MMN
mismatch negativity
MRI
magnetic resonance imaging
ND
neurodegenerative diseases
NFL
nerve fiber layer
p-tau
hyperphosphorylated tau
Nold
normal elderly subjects
PDD
dementia due to Parkinson’s
PET
Positron Emission Tomography
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PM
Precision medicine
PMI
Precision Medicine Initiative
PoC
Proof-of-Concept
RGC
retinal ganglion cell
ROI
region of interest
RTMS
repetitive transcranial magnetic stimulation
SBML
Systems Biology Markup Language
SPECT
Single Photon Emission Computed Tomography
t-tau
total tau
TACS
transcranial alternating current stimulation
TDCS
transcranial direct current stimulation
WB-MRI
whole-body magnetic resonance imaging
WES
whole-exome sequencing
WGS
whole-genome sequencing
WM
white matter.
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Appendix
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Sorbonne Université Groupe de Recherche Clinique (GRC n° 21)
“Alzheimer Precision Medicine (APM)”
Établissements Publics à caractère Scientifique et Technologique (E.P.S.T.)
Alzheimer Precision Medicine Initiative (APMI)
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CONTRIBUTORS TO THE ALZHEIMER PRECISION MEDICINE INITIATIVE –
WORKING GROUP (APMI-WG)
Principal Investigator and Speaker: Harald Hampel.
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Aguilar LF (Montréal), Babiloni C (Rome), Baldacci F (Pisa), Benda N (Bonn), Black KL
(Los Angeles), Bokde ALW (Dublin), Bonuccelli U (Pisa), Broich K (Bonn), Bun RS
(Paris), Cacciola F (Siena), Castrillo J† (Derio), Cavedo E (Paris), Ceravolo R (Pisa), Chiesa
PA (Par-is), Colliot O (Paris), Coman CM (Paris), Corvol JC (Paris), Cuello AC (Montréal),
Cummings JL (Las Vegas), Dubois B (Paris), Duggento A (Rome), Durrleman S (Paris),
Escott-Price V (Cardiff), Federoff H (Irvine), Ferretti MT (Zürich), Fiandaca M (Irvine),
Frank RA (Malvern), Garaci F (Rome), Genthon R (Paris), George N (Paris), Giorgi FS
(Pisa), Graziani M (Roma), Haberkamp M (Bonn), Habert MO (Paris), Hampel H (Paris),
Herholz K (Manches-ter), Karran E (Cambridge), Kim SH (Seoul), Koronyo Y (Los
Angeles), Koronyo-Hamaoui M (Los Angeles), Lamari F (Paris), Langevin T (MinneapolisSaint Paul), Lehéricy S (Paris), Lista S (Paris), Lorenceau J (Paris), Mapstone M (Irvine),
Neri C (Paris), Nisticò R (Rome), Nyasse-Messene F (Paris), O’Bryant SE (Fort Worth),
Perry G (San Antonio), Ritchie C (Ed-inburgh), Rojkova K (Paris), Rossi S (Siena),
Santarnecchi E (Siena), Schneider LS (Los Angeles), Sporns O (Bloomington), Toschi N
(Rome), Verdooner SR (Sacramento), Vergallo A (Paris), Villain N (Paris), Welikovitch L
(Montréal), Woodcock J (Silver Spring), Younesi E (Esch-sur-Alzette).
DISCLOSURES
Author Manuscript
Dr Harald Hampel serves as Senior Associate Editor for the Journal Alzheimer’s &
Dementia; he received lecture fees from Biogen and Roche, research grants from Pfizer,
Avid, and MSD Avenir (paid to the institution), travel funding from Axovant, Eli Lilly and
company, Takeda and Zinfandel, GE-Healthcare and Oryzon Genomics, consultancy fees
from Jung Diagnostics, Cytox Ltd., Axo-vant, Anavex, Takeda and Zinfandel, GE
Healthcare and Oryzon Genomics, and participated in scientific advisory boards of Axovant,
Eli Lilly and company, Cytox Ltd., GE Healthcare, Takeda and Zinfandel, Oryzon Genomics
and Roche Diagnostics; and he has patents, but receives no royalties.
Dr Keith L. Black, Yosef Koronyo, Dr Maya Koronyo-Hamaoui, and Steven R.
Verdooner are founding members of NeuroVision Imaging (NVI).
Author Manuscript
Dr Olivier Colliot declares no conflicts of interest related to the present article. During the
past 2 years: he has received lecture fees from Roche. His laboratory has received funding
from Air Liquide Medical Systems, Qynapse SAS and MyBrainTechnologies SAS (paid to
the institution). Prior to 2 years ago: he has received lecture fees from Lundbeck and
consulting fees from Guerbet. His laboratory has received funding from EISAI (paid to the
institution).
Dr Bruno Dubois reports personal fees from Eli Lilly and company.
Dr Marie-Odile Habert has received consultant’s honoraria from GE Healthcare, AVIDLILLY and PIRAMAL.
J Alzheimers Dis. Author manuscript; available in PMC 2018 June 19.
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Dr Karl Herholz reports consultancy for PMOD Technologies, Zurich, Switzerland.
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Dr Simone Lista received lecture honoraria from Roche.
Dr Nicola Toschi, Dr Claudio Babiloni, Dr Filippo Baldacci, René S. Bun, Dr Francesco
Cacciola, Dr Enrica Cavedo, Dr Patrizia A. Chiesa, Cristina-Maria Coman, Dr Andrea
Duggento, Dr Stanley Durrleman, Dr Maria-Teresa Ferretti, Dr Remy Genthon, Dr
Foudil Lamari, Dr Todd Langevin, Dr Stephane Lehéricy, Dr Jean Lorenceau, Dr
Christian Neri, Dr Robert Nisticò, Dr Francis Nyasse-Messene, Dr Olaf Sporns, Dr
Craig Ritchie, Dr Simone Rossi, Dr Emiliano Santarnecchi, Dr Andrea Vergallo, Dr
Nicolas Villain, Dr Erfan Younesi, and Dr Francesco Garaci declare no conflicts of
interest.
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Figure 1. Cohorts stratified according to different neuroimaging modalities and methods are
integrated in the disease modeling for classification and prediction of subsets of AD and other
ND patients
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The paradigm of systems neurophysiology aims at studying the fundamental principles of
integrated neural systems functioning by integrating and analyzing neural information
recorded in multimodal fashion through computational modeling and combining datamining methods. This paradigm may be used to decode the information contained in
experimentally-recorded neural activity using analysis methods that are able to integrate the
recordings of simultaneous, single-modality brain cell activity such as fMRI or EEG to
generate synergistic insight and possibly infer hidden neurophysiological variables. The
ultimate goal of systems neurophysiology is to clarify how signals are represented within
neocortical networks and the specific roles played by the multitude of different neuronal
components.
Abbreviations: AD, Alzheimer’s disease; DTI, diffusion tensor imaging; EEG,
electroencephalography; MEG, magnetoencephalography; fMRI, functional magnetic
resonance imaging, sMRI, structural magnetic resonance imaging; ND, neurodegenerative
diseases; PET, positron emission tomography; TMS, transcranial magnetic stimulation
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Figure 2. Translational bench-to-bedside data flow within the conceptual framework of the
Alzheimer Precision Medicine Initiative (APMI)
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The IDM-based “Data Sciences Lifecycle” takes advantage of both data-driven and
knowledge-driven approaches so that both quantitative data (biomolecular, neuroimaging/
neurophysiological, and clinical data) and qualitative data (collected from scientific
literature and on-line media) – generated through the application of systems biology and
systems neurophysiology paradigms – are represented in a harmonized, standardized format
to be prepared for proper management within an integrative computational infrastructure.
Indeed, the resulting heterogeneous, multidimensional big and deep data are harmonized,
standardized, and integrated via computational and data science methods in the form of
mechanistic disease models, according to the IDM conception.
Disease-specific integrative computational models play a key role in the IDM paradigm and
represent the foundations for “actionable” P4M measures in the area of AD and other ND.
As a result, the integrative disease models are anticipated to support decision making for: 1)
early diagnosis of brain disease progression with mechanistic biomarkers (predictive), 2)
screening populations and stratifying individuals at high risk of developing ND based on
mechanistic co-morbidities in order to reduce the likelihood of disease and disability
(preventive), 3) tailoring treatment to the right patient population at the right time
(personalized), and 4) optimizing “actionable” plans for the benefit of patients based on
patient-oriented information gathered in EHRs and on patients’ feedback reported in social
media. Internet has greatly enabled the participation of individual patients in the healthcare
through sharing their experiences in various social media and other online resources
(participatory). The output is anticipated to be an “actionable” model that permits the
prediction of the trajectory of individual patient-centric detection or treatment within the
implementation of the P4M paradigm.
Abbreviations: APMI, Alzheimer Precision Medicine Initiative; EHRs, electronic health
records; IDM, integrative disease modeling; ND, neurodegenerative diseases; P4M,
Predictive, Preventive, Personalized, Participatory Medicine. Modified from [21].
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Figure 3. Model of non-linear dynamic temporo-spatial progression of neural network
disintegration and complex brain systems failure in relation to pathophysiology of AD. Four
dimensions of pathophysiological processes in AD
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Dimension 1 occurs at the level of neuronal networks (coded green to red). Dimension 1 can
begin extremely early in form of synaptic dysfunction and/or synaptotoxic molecular agents,
thus altering the balance of the neuronal network.
Dimension 2 & 3 can be regarded as the temporal and spatial spreading from almost
exclusively default mode to episodic memory networks to temporal, parietal and frontal
neocortical associative areas responsible for working memory, language and/or visual
processes. Every one of these complex systems can experience a variable degree of
decompensation (see Dimension 1), from adaptation to compensation to massive
decompensation and widespread dysorganisation.
Dimension 4 is essentially the integration of Dimensions 1 and 2 and 3 into late-stage
clinically symptomatic and syndromatic cognitive and later behavioral and
psychopathological dysfunction and decline. It is therefore clear how this complex, multiscale and multilayer association of networks can be partially robust to “insults” if sufficient
compensatory mechanisms are in place, but also extremely and randomly fragile if
adaptation and compensation fails at any level. Sufficient decompensation in Dimension 1
will turn into a malfunction in Dimension 2 and 3 and, in turn, substantial decompensation
in Dimension 2 and 3 will turn into malfunction in Dimension 4 (i.e. mild cognitive
impairment, clinical dementia syndrome).
Abbreviations: AD, Alzheimer’s disease.
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Figure 4. Overview of the currently available technologies and the resulting biological marker
categories used for biomarker discovery in preclinical and clinical research
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Abbreviations: CNV, copy number variations; FISH, fluorescence in situ hybridization;
GCMS, gas chromatography mass spectrometry; HPLC, high-performance liquid
chromatography; LCMS, liquid chromatography–mass spectrometry; NMR, nuclear
magnetic resonance; PCR, polymerase chain reaction; SNPs, single nucleotide
polymorphisms; SVs, structural variations. Reproduced with permission from [79].
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Figure 5. Systems neurophysiology and network neuroscience: schematic representation of how
structural levels within the nervous system integrate over multiple spatial and temporal scales
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Network neuroscience encompasses the study of very different networks encountered across
many spatial and temporal scales; however, the network ideas clearly extend down to the
level of neuronal circuits and populations, individual neurons and synapses, as well as
genetic regulatory and protein interaction networks. In network neuroscience and systems
neurophysiology in general, the overall aim is to bridge information encoded in the
relationships between genes and biomolecules to the information shared between neurons
across to the brain level while integrating the additional information provided from the time
dimension. This could eventually allow access to mechanistic understanding and models
which faithfully reproduce and possibly predict both brain structure and function.
Interestingly, above the single brain level, the social network level should still be considered
a network neuroscience domain and, albeit with different measurement techniques, can be
studied with the same paradigms with the aim to understand the larger “brain” that
interacting brains give rise to (i.e. economies and cultures).
Adapted from [112] and [609].
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Figure 6.
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Sagittal slab visualisation of a fibre tractogram obtained from WM fODFs estimated with
SSST-CSD (left) and MSMT-CSD (right) with different fODF amplitude thresholds (top,
bottom).
Abbreviations: fODF, fibre orientation distribution function; MSMT-CSD, multi-shell,
multi-tissue constrained spherical deconvolution; SSST-CSD, state-of-the-art single-shell,
single-tissue constrained spherical deconvolution; WM, white matter. Reproduced with
permission from [188].
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Figure 7. Retinal amyloid imaging: from histological examination to clinical trials
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A. Spectral analysis of Aβ plaque in AD human flatmount retina via specific curcumin
labeling. Representative image and spectra curves of retinal Aβ plaque double-labeled with
curcumin [region of interest (ROI) 1; orange line] and anti-Aβ40 antibody-Cy5 conjugate
(ROI2; purple line) and corresponding background areas (ROI3 and ROI4; dashed lines) at
excitation wavelengths of 550nm (for curcumin spectra) and 640nm (for Ab-Cy5 conjugate).
Sudan black B (SBB) was applied to quench autofluorescence. Peak emission wavelengths
captured for the same individual Aβ plaque (605nm for curcumin when bound to Aβ plaque
and 675nm for anti-Aβ Ab conjugated Cy5) are distinct, indicating specific fluorescent
signals for each fluorochrome and signifying the detection of Aβ plaque by curcumin. B.
Representative z-axis projection images of flatmount retinas from AD patients. Retinal Aβ
plaques (yellow spots) co-labeled with curcumin (green) and anti-Aβ40 monoclonal antibody
(11A50-B10; red) are detected. Analysis included definite AD (n=8), probable/possible AD
(n=5), and age-matched controls (n=5). High-magnification image (right) showing an
extracellular Aβ plaque. Images A–B are adopted from [490]. C. Representative
microscopic images from flatmount retinas of a healthy control individual (CTRL; 71 years)
and a definite AD patient (74 years) stained with anti-Aβ42 C-terminal-specific antibody
(12F4) and visualized with peroxidase-based labeling. High-magnification image showing
different Aβ42 plaques including classical morphology. Analysis included definite AD
patients (n=5) and matched controls (n=5). Images reproduced from [466] and [472]. D.
Quantitative analysis of retinal Aβ42-containing plaques (12F4-immunoreactive area) in the
superior quadrant shows a significant increase in AD patients versus matched controls. E.
Quantitative Nissl+ neuronal area in retinal cross sections indicated a significant reduction in
AD patients compared to CTRLs, which is associated with retinal neuronal loss. D–E. Data
reprinted from [485](n=23 AD patients and n=14 controls). F. Retinal flatmount illustration
demonstrating the geometric distribution of pathology in AD retina by quadrant, with more
consistent findings of nerve fiber layer thinning, neuronal degeneration and retinal Aβ
deposits mapped to peripheral regions of the superior quadrant. Adopted from [472]. G.
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Representative images of a frontal cortex section and a flatmount retina from AD patients
stained with 12F4 monoclonal antibody (brown) showing different Aβ42 plaque morphology
including classical plaques (inserts). Clusters of Aβ42-containing plaques are often
associated with blood vessels (bv; right image). H. Correlation analyses using Pearson’s
coefficient (r) test between retinal 12F4+-plaque burden in the superior-temporal (ST)
quadrant and cerebral plaque burden (Thioflavin-S staining) in a total of seven brain regions
(Brain; black) and in the primary visual cortex alone (PV Ctx.; green) in a subset of AD
patients and matched CTRLs. I–J. Illustration displaying non-invasive retinal amyloid
imaging using Longvida® curcumin and a modified scanning laser ophthalmoscope in
human trials. K–M. In vivo retinal imaging in AD patients and age-matched controls. K–L.
Increased curcumin fluorescent signal (red dots) in superior hemisphere in AD patient vs.
CTRL. Color-coded spot overlay images: red spots are above threshold and considered
curcumin-positive amyloid deposits; green spots exceed 1:1 reference but not threshold; blue
spots fall below reference. Heat map images with red spot centroids (lower panel) showing
regions of interest with more amyloid plaques in the retina. L. Automated calculation of
retinal amyloid index (RAI). Blue line is 1:1 reference; green line represents the threshold
level, determined at 500 counts and above; red spots are above the threshold. The same
automated image processing and analysis was applied on all human subjects (n=16). M. RAI
scores showing significant increase in AD patients compared to age-matched CTRLs. G–M.
Republished with permission of American Society for Clinical Investigation from [485];
permission conveyed through Copyright Clearance Center, Inc. Group means and SEMs are
shown. **p < 0.01, unpaired two-tailed Student’s t-test.
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Figure 8. Evolving spectrum of biomarkers and modalities
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A. The ideal biomarker should be minimally-invasive, unexpansive, practical, rapid and
reliable with low level of expertise required. Therefore, in the clinical-setting, biomarkers
should be assessed in a multi-stage diagnostic workout carried-out along four steps (blood
biomarkers, structural MRI, lumbar puncture, PET scans) according to the overall balance
among the following factors: cost-effectiveness, time-effectiveness, invasiveness and
accessibility. B. Biomarkers represent one strategy to tailor therapy. The idealistic markers
for ND would enable their implementation in screening, diagnosis, progression of the
disease, and monitoring of the response to therapy. Therefore, in clinical trials, biomarkers
can be used for several purposes:
1) to identify people eligible for the trial, i.e. those considered at high risk for ND (screening
biomarkers),
2) to guide clinical diagnosis (diagnostic markers),
3) to optimize treatment decisions, providing information on the likelihood of response to a
given drug (predictive biomarkers),
4) to detect and quantify the response rate to treatment (response markers).
Abbreviations: MRI, magnetic resonance imaging; PET, Positron Emission Tomography;
ND, neurodegenerative diseases.
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Table 1
The five pillars of the Alzheimer Precision Medicine Initiative (APMI)
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The mission of APMI is to transform Neurology and Neuroscience embracing Precision Medicine (or
Precision Neurology) based on complex systems theory using integrative disease modeling (IDM) to
facilitate health care solutions for brain proteinopathies, protein misfolding disorders and neurodegenerative
diseases, such as Alzheimer’s disease (AD). This is facilitated through five breakthrough theoretical
scientific advances, as follows:
Concept
Comment
(1) The emergence of the
“precision medicine”
paradigm
Discovery and development of treatments targeted to the needs of individuals on the basis of systems biology
technology using genomic biomarker, phenotypic, or psychosocial characteristics that distinguish a given
individual from others. Inherent in this definition is the goal of impacting pathophysiological progression at
early disease stages and clinical outcomes at later stages and minimizing unnecessary side effects for those less
likely to have a response to a particular treatment supported by pharmacogenomics. The convergence of
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genetics/genomics/transcriptomics, bioinformatics, neurodynamics, neuroimaging and connectomics along with
other technologies such as cell sorting, epigenetics, proteomics, lipidomics and metabolomics, is rapidly
expanding the scope of precision medicine by refining the staging and classification of disease, often with
important prognostic and treatment implications. Among these new technologies, genetics and next-generation
DNA sequencing methods are having the greatest effect.
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(2) The emergence of the
“systems biology”
paradigm
Systems biology represents an integrated and deeper investigation of interacting biomolecules within cells or
organisms. This approach has only recently become feasible as high-throughput technologies including cDNA
microarrays, mass spectrometric analyses of proteins and lipids together with rigorous bioinformatics have
evolved. High-content data point to convergent pathways among diseases, which transcend descriptive studies to
reach a more integrated understanding of neurodegenerative disease pathogenesis and, in some instances,
highlighting ‘druggable’ network nodes.
(3) The emergence of the
“systems neurophysiology
and complex network”
paradigm
This is due in large part to advances in mathematics, computer science and statistical methods applied to
neuroimaging and neurophysiology; instead of thinking of the brain as a set of modules (i.e., individual brain
regions) that perform specific cognitive functions, the network paradigm argues that cognitive functions are
performed by dynamic interactions among different brain areas - i.e., by dynamically formed complex structural
and functional networks of brain regions.
(4) the emergence of
“neural modeling”
paradigm
This paradigm is required by the complex network paradigm, since, in order to deal with the large complexity of
the dynamic interactions among multiple brain regions, one must employ advanced mathematical and
computational methods.
(5) The emergence of
“integrative disease
modeling” (IDM)
paradigm
This is an evolving knowledge-based paradigm in translational research that exploits the power of advanced
computational methods to collect, store, integrate, model, and interpret accumulated disease information across
different biological scales, i.e. from molecules to phenotypes. IDM is a new paradigm at the core of translational
research, which prepares the ground for transitioning from descriptive to mechanistic representation of disease
processes. Given the tremendous potential of IDM in supporting translation of biomarker and drug research into
clinically applicable diagnostic, preventive, prognostic, and therapeutic strategies, it is anticipated that computerreadable disease models will be an indispensable part of future efforts in the P4 medicine research area.
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Table 2
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Evolving lexicon and terminology within the Alzheimer Precision Medicine Initiative (APMI) framework.
Concept
Abbreviation
Definition
A repository of large amounts of data sets generated by data mining tools. Big Data
includes information obtained through systems theory- and, knowledge-based approaches
and clinical records.
Big Data
BMs
Biomarkers
A defined characteristic that is measured as an indicator of normal biological processes,
pathogenic process, or response to an exposure or intervention, including therapeutic
interventions. Molecular, histologic, radiographic, or physiological characteristics are
types of biomarkers. A biomarker is not an assessment of how an individual feels,
functions or survives. Categories of biomarkers include: susceptibility/risk biomarker,
diagnostic biomarker, monitoring biomarker, prognostic biomarker, predictive biomarker,
pharmacodynamics/response biomarker and safety biomarker.
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Data Science
Interdisciplinary field about processes and systems to extract knowledge from data in
different forms – either structured or unstructured – which is a continuation of some of the
data analysis fields including statistics, artificial intelligence, machine learning, data
mining, and predictive analytics.
e-Health
Term indicating healthcare practice supported by electronic processes and
communication. It can also include health applications and links on mobile phones,
referred to as mobile health (“m-health”: smart personal mobile devices, such as phones,
wearables, in-home devices and Apps, collecting health information aimed at improving
patient care).
The term can also encompass a range of services or systems that are at the edge of
medicine/healthcare and information technology, including: electronic health records
(EHRs). These indicate a systematized gathering of population electronically-stored
health information and clinical data in a digital format. These registries can be shared
across different health care settings through network systems.
EPAD
European Prevention of
Alzheimer’s Dementia
Consortium
Pan-European initiative whose objective is to establish a shared platform to design and
conduct phase 2 Proof-of-Concept (PoC) clinical trials specifically aimed at developing
novel treatments for the secondary prevention of AD.
Discipline utilizing personal genomic information (see also the definition of “Personal
Genomics”) for diagnostic characterization and the development of therapeutic plans.
Genomic Medicine
Integrative Disease Modeling
IDM
Multidisciplinary approach to standardize, manage, integrate, and interpret multiple
sources of structured and unstructured quantitative and qualitative data across biological
scales using computational models that assist decision making for translation of patientspecific molecular mechanisms into tailored clinical applications.
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“Omics” or “Omic” disciplines
High-throughput screening tools aimed at fully collecting, characterizing and quantifying
pools of biological molecules (DNA sequences, transcripts, miRNAs, proteins/peptides,
metabolites/lipids) that translate into the structure, function, and dynamics of an organism
and/or whole organisms.
“One-size-fits-all” approach
Traditional approach used for the development of early detection, intervention, and
prevention options, where biomarker candidates are being validated against the plethora of
heterogeneous clinical operationalized syndromes, rather than against genetically (risk
profile) and biologically (i.e., based on molecular mechanisms and cellular pathways)
determined entities.
Ontology
Formal naming and designation of the types, properties, and interactions of the entities
that really or fundamentally exist for a specific domain of discourse.
P4 (Predictive, Preventive,
Personalized, and
Participatory) Medicine
P4M
Translational medicine component of the Precision Medicine paradigm. It is a clinical
practice model aimed at applying knowledge, tools, and strategies of systems medicine. It
involves generation, mining, and integration of enormous amounts of data on individual
patients to produce predictive and “actionable” models of wellness and disease.
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Personal Genomics
Branch of genomics that provides support in predicting the likelihood that an individual
will be affected by a disease. It helps personalize drug selection and treatment delivery to
get the best care, thus playing a crucial role both in predictive and personalized medicine,
according to the PM paradigm.
Personalized Medicine
Component of the P4M aiming at tailoring treatment for individual patients in contrast
with “one-size-fits-all” or traditional “magic bullet drug” approach.
Precision Medicine
PM
Translational science paradigm related to both health and disease. PM is a biomarkerguided medicine on systems-levels taking into account methodological advancements and
discoveries of the comprehensive pathophysiological profiles of complex polygenic,
multi-factorial neurodegenerative diseases (proteinopathies of the brain). It aims at
optimizing the effectiveness of disease prevention and therapy, by considering
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Abbreviation
Definition
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(customized) an individual’s specific “biological make-up” (e.g. genetic, biochemical,
phenotypic, lifestyle, and psychosocial characteristics) for targeted interventions through
P4M implementation.
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Systems Biology
SB
Evolving hypothesis-free, exploratory, holistic (non-reductionistic), global, integrative,
and interdisciplinary paradigm using advances in multimodal high-throughput
technological platforms that enable the examination of networks of biological pathways
where elevated amounts of structurally and functionally different molecules are
simultaneously explored over time at a system level (i.e., at the level of cells, group of
cells, tissues, organs, apparatuses, or even whole organisms).
Systems Medicine
SM
Holistic paradigm applying systems biology-based strategies to medical research. It aims
at integrating a variety of considerable biomedical data at all levels of the cellular
organization (by employing global, integrative, and statistical/mathematical/computational
modeling) to explicate the pathophysiological mechanisms, prognosis, diagnosis, and
treatment of diseases.
Systems Neurophysiology
SN
Paradigm aimed at studying the fundamental principles of integrated neural systems
functioning by integrating and analyzing neural information recorded in multimodal
fashion through computational modeling and combining data-mining methods. This
paradigm may be used to decode the information contained in experimentally-recorded
neural activity using analysis methods that are able to integrate the recordings of
simultaneous, single-modality brain cell activity such as functional magnetic resonance
imaging or electroencephalography to generate synergistic insight and possibly infer
hidden neurophysiological variables. The ultimate goal of systems neurophysiology is to
clarify how signals are represented within neocortical networks and the specific roles
played by the multitude of different neuronal components.
Systems Pharmacology
SP
Science of advancing knowledge about drug action at the molecular, cellular, tissue,
organ, organism, and population levels” (http://www.aaps.org/Systems_Pharmacology/).
Systems Theory
ST
Translational research theory of the Precision Medicine paradigm. It is an
interdisciplinary conceptual framework allowing for the conceptualization of novel/
original models to extract and explicate all systems levels and different spatiotemporal
data types of complex polygenic diseases.
Modified from [21].
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Maya Koronyo-hamaoui