Thesis
Reference
Functional imaging markers of the MCI brain in task and at rest:
detecting memory and connectivity impairments in prodromal Alzheimer's disease
KEBETS, Valeria
Abstract
Alzheimer's disease (AD) is a major neurodegenerative disease, and currently the leading cause of dementia in the world. The application of machine learning algorithms has recently brought a novel perspective to the early identification of neurological diseases such as AD, as they can advantageously exploit multivariate information in high-dimensional data to predict patient diagnosis and ultimately prognosis at the individual level. This work aimed to develop an early functional marker for AD using task-based and resting-state (RS) functional magnetic resonance imaging (fMRI), and to build a multimodal marker for predicting future conversion to AD. We found several regions of interest in which memory task-related fMRI activity could reliably discriminate between elderly controls and patients with prodromal AD, as well as functional connectivity during RS in the whole brain and within networks. Finally, we found that imaging markers were able to accurately predict conversion to AD, while clinical data was less informative.
KEBETS, Valeria. Functional imaging markers of the MCI brain in task and at rest:
detecting memory and connectivity impairments in prodromal Alzheimer's disease. Thèse de doctorat : Univ. Genève et Lausanne, 2016, no. Neur. 189
URN : urn:nbn:ch:unige-909342
DOI : 10.13097/archive-ouverte/unige:90934
Available at:
http://archive-ouverte.unige.ch/unige:90934
Disclaimer: layout of this document may differ from the published version.
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DOCTORAT EN NEUROSCIENCES des Universités de Genève
et de Lausanne
UNIVERSITÉ DE GENÈVE FACULTÉ DE PSYCHOLOGIE Docteur Assal, co-directeur de thèse
Professeur Van De Ville, co-directeur de thèse
TITRE DE LA THESE
FUNCTIONAL IMAGING MARKERS OF THE MCI BRAIN IN TASK AND AT REST: DETECTING MEMORY AND CONNECTIVITY
IMPAIRMENTS IN PRODROMAL ALZHEIMER’S DISEASE
THESE Présentée à la
Faculté de Psychologie et Sciences de l’Education de l’Université de Genève
pour obtenir le grade de Docteure en Neurosciences
par
Valeria KEBETS
de Thônex [GE], Suisse
Thèse N° 189 Genève
Editeur ou imprimeur : Université de Genève 2016
ACKNOWLEDGMENTS
I would like to sincerely thank all the people who I have worked with during these past four and a half years. Without their guidance and support, I would have not enjoyed these years as much as I have. This has been a journey, sometimes difficult, but fascinating, extremely interesting and fun. But more specifically, I am grateful to…
Dimitri, who has been such as inspiration in his guidance and supervision, who impresses both experts and beginners by his brilliance and his kindness, who always has the great idea that you wish you had had (because it is often so simple! And brilliant!), and who can put himself at anyone’s level without making them feel ignorant.
Fred, who has been very supportive during my PhD, and who gave me such independence and freedom to pursue what I wanted, who has taught me how subtle a clinical diagnosis can be.
Mitsouko, who I admire deeply, who inspires me to be not only a better scientist but also a better person, who is so competent and smart but never arrogant. She has been kind and patient with me during each moment of this thesis and I am forever grateful to have worked by her side all this time.
Jonas, who, with his amazing energy, has motivated me, pushed me, and challenged me so much, and with whom I learned so much. He has been so generous in offering me the opportunity to go to Stanford and work in a super cool lab there (and enjoy California!).
And of course, Patrik, who has been helpful and supportive when I needed his guidance.
Rachel, who has been welcoming and kind, and with whom it was a pleasure to work. Bruno, with whom scanning sessions were serious AND fun (and full of disturbing videos).
Lore and Mira, who showed me just how cool neuroscientists could be, and with whom I have shared so many memories of endless dinners, parties, concerts and trips.
Aurore, my PhD buddy, who reminded me how great hip-hop is (and not just Will Smith), and who has been the closest and most important support for me during the hardest times of this PhD. Going through this s*** together helped me beyond words. Not forgetting Irish dances, endless drinks and all the mornings after.
Elsa, who showed such a great example of how to end a PhD with style, and encouraged me to be more proactive and brave about my future.
Dimitri’s lab, full of smart AND nice people, and especially Thomas, who is one the kindest people I’ve met, always ready to help even when he is crushed with workload.
And finally, my family and all my friends for being there, for trying to understand what I was doing, for loving me and supporting me even though I was probably not making sense to them.
ABSTRACT
Alzheimer’s disease (AD) is a major neurodegenerative disease, and currently the leading cause of dementia in the world. There is an urgent need to develop effective treatments for these patients. However, an early and accurate diagnosis is essential for assessing potential therapeutic interventions. The application of machine learning algorithms have recently brought a novel perspective to the early identification of neurological diseases such as AD, as they can advantageously exploit multivariate information in high-dimensional data (such as magnetic resonance imaging [MRI]) to predict patient diagnosis and ultimately prognosis at the individual level.
In the first study, we sought to distinguish patients in the prodromal stage of AD - mild cognitive impairment (MCI) – from healthy elderly controls, using functional MRI (fMRI) activity acquired during the completion of a memory task. We found several regions of interest in which task-related activity could reliably discriminate between controls and patients. Then, we accurately separated our groups by extracting task-related activation patterns that maximally covaried with memory measures.
In the second study, we classified controls vs MCI patients using resting-state (RS) functional connectivity in either the whole brain or within networks/cerebral lobes, and assessed the impact of the atlas choice in the classification performance. We showed that functional connectivity in the whole brain, but also within networks, could reliable distinguish controls from patients. Moreover, prediction accuracies obtained with a functional atlas were higher than those obtained with both anatomical atlases.
We also studied the way alterations in both states (task and RS) covary within the same individuals across the AD clinical spectrum, and found a strong correspondence between task- related activity and RS connectivity that persisted across health and disease.
Finally, we were interested in predicting the prognosis of MCI patients using a multimodal marker based on clinical and imaging measures. We found that imaging markers (structural MRI and RS fMRI) were able to accurately predict conversion to AD, while sociodemographic and clinical data were less informative.
In conclusion, fMRI has a clear potential in a clinical setting. The combination and integration of data coming from several modalities could improve our understanding of inter-individual variability in terms of function, behavior and disease.
RESUME
La maladie d’Alzheimer (MA) est une maladie neurodégénérative, et la première cause de démence dans le monde. De ce fait, il est urgent de développer des interventions thérapeutiques efficaces pour ces patients. Cependant, un diagnostic précoce et précis serait essentiel pour évaluer des traitements potentiels. Les algorithmes d’apprentissage automatique (machine learning) ont récemment permis de faire des avancées dans le domaine de l’identification précoce de maladies neurologiques telles que la MA. En effet, ceux-ci permettent d’exploiter de manière multivariée l’information contenue dans des données à haute dimensionalité (telles que l’imagerie par résonance magnétique [IRM]), afin de prédire le diagnostic ainsi que le pronostic d’un patient au niveau individuel.
Dans notre première étude, nous avons voulu distinguer des patients avec un trouble cognitif léger (MCI, considéré comme le stage prodromique de la MA) de sujets contrôles âgés, en utilisant l’activité cérébrale fonctionnelle acquise pendant une tâche de mémoire. Nous avons mis en évidence plusieurs régions-clé dans lesquelles l’activité fonctionnelle permettait de discriminer les sujets contrôles des patients. Nous avons également pu séparer nos deux groupes grâce à des patterns d’activation fonctionnelle corrélés avec des mesures de mémoire.
Dans notre seconde étude, nous avons voulu séparer ces groupes en utilisant la connectivité fonctionnelle acquise au repos (resting state). Plus spécifiquement, nous avons évalué différents atlas de parcellisation du cerveau, et leur impact sur la performance de classification. Nous avons montré que la connectivité fonctionnelle, non seulement dans le cerveau entier, mais aussi au sein de réseaux et lobes cérébraux, pouvait distinguer de manière fiable les deux groupes. De plus, nous avons obtenu de meilleures performances de classification avec un atlas fonctionnel comparé à deux atlas anatomiques.
Nous avons également étudié la manière dont les altérations fonctionnelles en tâche et au repos covariaient au sein des mêmes individus à travers le spectre clinique de la MA. Nous avons mis en évidence une forte correspondance entre l’activité fonctionnelle liée à la tâche et la connectivité fonctionnelle au repos.
Dans notre dernière étude, notre but était de prédire le pronostic des patients MCI à l’aide d’un marqueur multimodal basé sur des mesures cliniques et d’imagerie. Nous avons trouvé que les marqueurs d’imagerie (IRM structurelle et IRM fonctionnelle au repos) permettaient de prédire de manière fiable la conversion à la MA, tandis que les données sociodémographiques et cliniques étaient moins informatives.
En conclusion, l’IRM fonctionnelle a un fort potentiel pour être utile dans un cadre clinique.
Enfin, l’intégration de données multimodales permettrait de mieux comprendre la variabilité inter-individuelle qui existe au niveau fonctionnel, comportemental et pathologique.
LIST OF ABBREVIATIONS
AAL automated anatomical labelling ACC anterior cingulate cortex AD Alzheimer’s disease APOE Apolipoprotein E AUC area under the curve
Aβ amyloid beta
BOLD blood oxygenation level dependent CCA canonical correlation analysis
CDR Clinical Dementia Rating CSF cerebrospinal fluid
DAN dorsal attentional network DMN default mode network DRS Dementia Rating Scale EC elderly controls
ECN executive control network ER encoding related
ERC entorhinal cortex EU encoding unrelated FC functional connectivity FDG 18Fluorodeoxyglucose
fMRI functional magnetic resonance imaging FWHM full-width-at-half-maximum
GLM general linear model
GM grey matter
HAD Hospital Anxiety and Depression
HIP hippocampus
HRF hemodynamic response function ICA independent component analysis IFG inferior frontal gyrus
LV latent variable
MCC middle cingulate cortex MCI mild cognitive impairment
MCIc mild cognitive impairment converters MCInc mild cognitive impairment nonconverters MKL multiple kernel learning
MMSE Mini-Mental Status Examination MRI magnetic resonance imaging MTG middle temporal gyrus MTL medial temporal lobe NFTs neurofibrillary tangles PC principal component
PCA principal component analysis PCC posterior cingulate cortex PET positron emission tomography PFC prefrontal cortex
PHC parahippocampal cortex PHG parahippocampal gyrus PiB 11C-Pittsburgh Compound B PLS partial least squares
PLSC partial least squares correlation PRC perirhinal cortex
p-tau phosphorylated tau
RF random forest
ROC receiver operating characteric ROI region of interest
RRN recognition related new RRO recognition related old RS resting state
RSC retrosplenial cortex RSNs resting state network RUN recognition unrelated new RUO recognition unrelated old SD standard deviation
SMA supplementary motor area
sMRI structural magnetic resonance imaging SN salience network
STG superior temporal gyrus SVD singular value decomposition SVM support vector machine t-tau total tau
WM white matter
TABLE OF CONTENTS
1. INTRODUCTION ………..………1
1.1 Aim of the project and its importance ………..…1
1.2 AD and its diagnosis ……….…2
1.2.1 Clinico-pathological diagnosis ………... 2
1.2.2 Interest for prodromal stage ………... 2
1.2.3 Neuropathological features ……… 3
1.2.4 The advent of biomarkers ……….. 4
1.2.5 Cerebrospinal fluid biomarkers ………... 5
1.2.6 Neuroimaging markers ……….. 5
1.2.6.1 PET ……….. 6
1.2.6.2 MRI ……….. 6
1.2.6.2.1 Structural MRI ……….……… 7
1.2.6.2.2 Functional MRI ……… 7
1.2.6.2.3 Arterial spin labeling………..………….. 8
1.2.7 AD neuropathological cascade ………..…9
1.3 Memory ……….. 10
1.3.1 Different forms of memory ………...11
1.3.2 Recollection and familiarity ………. 11
1.3.3 Associative memory ………... ..12
1.3.4 Neuropsychology of memory in MCI/AD ………... 12
1.4 Neuroimaging by fMRI ……….. 14
1.4.1 Medial temporal lobe system ………. ..14
1.4.2 Memory networks ……… 15
1.4.3 Resting state and default mode network ………...17
1.4.4 Functional connectivity and other resting state networks ……….19
1.4.5 fMRI analysis methods ……… 21
1.4.5.1 Univariate methods..………... 21
1.4.5.2 Multivariate methods ….……….... 22
1.4.5.2.1 PLS ………. 22
1.4.5.2.2 Pattern recognition ………. 23
1.5 Neuroimaging studies in MCI/AD ………. 25
1.5.1 Task fMRI studies in MCI/AD ……….... 25
1.5.2 Rest fMRI studies in MCI/AD ………... 27
1.5.3 Pattern recognition studies in MCI/AD ………... 29
1.5.3.1 Classifying EC vs MCI/AD ………... 29
1.5.3.1.1 Task fMRI ………... 29
1.5.3.1.2 RS fMRI ………. 30
1.5.3.2 Classifying MCIc vs MCInc……….………... 30
1.5.3.2.1 Single modalities ……… 31
1.5.3.2.2 Joint modalities ……….. 33
1.6 Main research questions and hypotheses ………... 35
2. MATERIAL AND METHODS………...…37
2.1 General methods………...…37
2.1.1 Participants………...……….…37
2.1.2 Experimental pipeline………...……37
2.1.3 Neuropsychological assessment………...…38
2.1.4 Data acquisition……….…....39
2.1.5 MRI acquisition parameters………...….39
2.1.6 Experimental paradigm……….……40
2.1.7 MRI analysis……….……41
2.1.7.1 sMRI preprocessing……….……...…41
2.1.7.2 Task fMRI preprocessing and modeling……….…...….41
2.1.7.3 RS fMRI preprocessing and modeling………..…….….42
2.1.8 Behavioral analyses………..…...43
2.1.9 Classifiers………...43
2.1.9.1 Support vector machine………..……....44
2.1.9.2 Random forest………..……..….44
2.2 Specific methods………..…….…….….45
2.2.1 Classifying EC vs MCI using task-based fMRI……….……..….45
2.2.1.1 Activity in atlas-based ROIs………...………45
2.2.1.1.1 Participants……….…...46
2.2.1.1.2 GLM model………...…...46
2.2.1.1.3 ROIs………46
2.2.1.1.4 Classification……….……..46
2.2.1.2 Activity in memory-related networks……….…....47
2.2.1.2.1 Participants………...….47
2.2.1.2.2 GLM model……….47
2.2.1.2.3 Memory scores………..…..48
2.2.1.2.4 PLS………...…..48
2.2.1.2.5 Classification……….……..51
2.2.2 Classifying EC vs MCI using RS fMRI……….…...51
2.2.2.1 Participants………...52
2.2.2.2 RS modeling………..…..52
2.2.2.3 Classification………..…….52
2.2.3 Classifying MCIc vs MCInc using single and joint modalities …………....…..…….52
2.2.3.1 Participants………..………53
2.2.3.2 Features………..….…53
2.2.3.3 Classification………...……53
2.2.3.3.1 Single modalities……….…54
2.2.3.3.2 Joint modalities………...…55
2.2.4 Fusing task and RS fMRI………..…55
2.2.4.1 Participants………..…56
2.2.4.2 Neuropsychological data analysis………..……….56
2.2.4.3 Task performance analysis……….……….56
2.2.4.4 Task fMRI modeling………..….57
2.2.4.5 RS fMRI modeling……….….….…..57
2.2.4.6 PLS………..….…..57
2.2.4.7 Canonical correlation analysis………...………59
3. RESULTS………...….61
3.1 Classifying EC vs MCI using task-based fMRI………...……..61
3.1.1 Activity in atlas-based ROIs……….….…...61
3.1.1.1 Neuropsychological and task performance……….………..….…...62
3.1.1.2 Classification performance…………..……….….…...63
3.1.2 Activity in memory-related networks……….….….66
3.1.2.1 Neuropsychological and task performance……….…..66
3.1.2.2 Behavior PLS results………...………..68
3.1.2.2.1 Task accuracy as behavior……….…….…...68
3.1.2.2.2 Memory scores as behavior……….…...68
3.1.2.3 Classification performance……….…….…..71
3.1.2.3.1 Task accuracy as behavior……….………....71
3.1.2.3.2 Memory scores as behavior……….…...72
3.2 Classifying EC vs MCI using RS fMRI………...………...75
3.2.1 Sociodemographical and neuropsychological profile………...……….………...75
3.2.2 Classification performance using whole-brain FC…….………...77
3.2.3 Classification performance using within-network/lobe FC….………..……...77
3.3 Classifying MCIc vs MCInc using single and joint modalities….……….79
3.3.1 Sociodemographic and neuropsychological profile………...79
3.3.2 Classification performance using single modalities………...81
3.3.3 Classification performance using joint modalities….………...81
3.3.3.1 Weight of each modality in classification performance………82
3.4 Fusing task and RS fMRI………....………83
3.4.1 Sociodemographic and neuropsychological profile………..83
3.4.2 PLS….………...86
3.4.3 Canonical correlation analysis……….….95
4. DISCUSSION………...………..…...……….97
4.1 The early diagnosis of AD using task-based fMRI……….….…..….97
4.1.1 Using an associative memory task in the early diagnosis of AD……….…….…97
4.1.1.1 From a behavior perspective……….……..………..97
4.1.1.2 From an imaging perspective………..………..……98
4.1.1.2.1 ROI-based classification………....….98
4.1.1.2.2 PLS-based classification……….………..100
4.1.2 Limitations………....………..102
4.1.3 The potential of an early task-based functional marker for AD……….….103
4.2 The early diagnosis of AD using RS fMRI………..……….104
4.2.1 The choice of brain parcellation….……….105
4.2.2 Limitations………..………106
4.2.3 A network disruption………..106
4.2.4 The potential of RS fMRI in the early diagnosis of AD………...…108
4.2.5 The links between RS FC and biomarkers………..…....………..……..110
4.3 A multimodal marker of conversion to AD……….…….…111
4.3.1 The predictive value of imaging indices………..………..….112
4.3.2 Limitations………..………..…..113
4.3.3 Which modalities should we use in the future?...113
4.3.4 Other future perspectives………...………..….. 114
4.4 Relationship between task and RS fMRI in health and disease………..………..115
4.4.1 The relationship between task and RS fMRI………...………..…….115
4.4.2 DMN as the link between task and RS fMRI in AD……….……….……….116
4.4.3 Interactions between the DMN and other RSNs……….…...…….117
4.4.4 The benefits of multimodal studies……….………..……..119
4.4.5 Limitations and future perspectives………..……..120
4.5 General conclusions………..121
5. REFERENCES………..………..…………123
APPENDIX………..………..………....153
Appendix A. AAL atlas : list of brain regions and lobes to which they belong……….153
Appendix B. Hammers atlas : list of brain regions and lobes to which they belong………..155
Appendix C. Shirer atlas : list of brain regions and networks to which they belong………..157
LIST OF FIGURES
Figure 1. Hypothetical model of AD pathological cascade.
Figure 2. Anatomical organization of MTL structures.
Figure 3. Main connections between brain regions underlying episodic memory.
Figure 4. RS patterns identified using probabilistic ICA.
Figure 5. Pattern recognition pipeline.
Figure 6. Timeline of subjects’ participation in the study.
Figure 7. Associative memory task.
Figure 8. Behavior PLS analysis embedded in a cross-validation scheme.
Figure 9. Brain coverage of the three atlases that we compared.
Figure 10. Feature extraction with SVD.
Figure 11. Analysis pipeline.
Figure 12. LV1 for the ER condition, and LV1 for the EU condition.
Figure 13. LV1 for the RRN, RRO and RUN condition.
Figure 14. Classification performance using whole-brain RS FC of 3 different atlases as features.
Figure 15. Classification performance using within-network RS FC as features (Shirer atlas).
Figure 16. Classification performance using within-lobe RS FC as features (AAL atlas).
Figure 17. Classification performance using within-lobe RS FC as features (Hammers atlas).
Figure 18. Classification performance of the RF and SVM classifiers using single modalities.
Figure 19. Classification performance of combined modalities.
Figure 20. Weight of each modality in the classification performance of combined modalities.
Figure 21. Task accuracy depending on the distractor familiarity in EC and MCI.
Figure 22. Representation of LV1 in the task activity space and RS FC space.
Figure 23. Representation of LV2 in the task activity space and RS FC space.
Figure 24. Representation of LV7 in the task activity space and RS FC space.
Figure 25. Representation of LV8 in the task activity space and RS FC space.
Figure 26. Representation of LV9 in the task activity space and RS FC space.
Figure 27. Representation of LVs 3,4,5,6,10 in the task activity space and RS FC space.
Figure 28. Canonical correlations between LV scores and memory measures.
LIST OF TABLES
Table 1. Pattern recognition studies using sMRI to predict MCI conversion to AD.
Table 2. Pattern recognition studies using different combinations of modalities to predict MCI conversion to AD.
Table 3. Sociodemographic and neuropsychological data.
Table 4. Associative memory task performance.
Table 5. Classification performance using the SVM classifier.
Table 6. Classification performance using the RF classifier.
Table 7. Sociodemographic and neuropsychological data.
Table 8. Associative memory task performance.
Table 9. Correlations between meanbetas and memory tests for each group and condition.
Table 10. Prediction accuracies using brain scores of LV1, LV2 or both LVs as features.
Table 11. Prediction accuracies using brain scores of all LVs as features.
Table 12. Prediction accuracies using brain scores of LV1-8.
Table 13. Sociodemographic and neuropsychological data.
Table 14. Sociodemographic and neuropsychological data.
Table 15. Sociodemographic and neuropsychological data.
Table 16. P-values corresponding to the significance each LV after permutation testing.
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1. INTRODUCTION
1.1 Aim of the project and its importance
Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive, functional, neuropsychiatric and behavioral deficits, and the first cause of dementia in the world.
According to the World Alzheimer Report 2015 (http://www.alz.co.uk/research/world-report- 2015), 9.9 million new cases of dementia are diagnosed each year. 46.8 million individuals worldwide were living with dementia in 2015, and this number is estimated to double every 20 years. One of the main contributors to this rise in prevalence is increasing life expectancy.
Moreover, the incidence of dementia augments exponentially with increasing age, reaching 0.4%
of the population between age 60-64 and 10.5% at age 90+. The global cost of dementia in the US has attained 818 billion US dollars in 2015, and is predicted to rise to 2 trillion US dollars by 2030.
These figures highlight the urgency of developing effective treatments for patients with AD.
However, an early and accurate diagnosis is essential for assessing potential therapeutic interventions. For almost a century, AD was characterized by a specific clinical syndrome and associated neuropathological features that could only be examined postmortem. In the past two decades however, technical advances have enabled the detection and quantification of AD neuropathological changes in vivo, with the use of biomarkers. Nonetheless, several of these markers are invasive, and while their clinical relevance is undoubted, their utilization is less suitable for research purposes. For the latter, the potential of noninvasive biomarkers such as functional magnetic resonance imaging (fMRI) is promising, as it can provide unique information about the functional integrity of neural processes, likely before the macrostructure of the brain is affected. A great number of studies have used fMRI to investigate AD patients, and have reported functional alterations in both task-related and resting state (RS) paradigms.
In parallel, the application and further development of machine learning algorithms have recently brought a novel perspective to the early identification of neurological diseases such as AD, as they can advantageously exploit multivariate information in high-dimensional data to predict patient diagnosis and ultimately prognosis at the individual level. Furthermore, the combination of several modalities now appears to be the most promising method to predict progression to AD.
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This work thus aimed to (1) develop an early functional marker for AD using task-based and RS fMRI, and (2) to build a multimodal marker for predicting future conversion to AD.
1.2 AD and its diagnosis
1.2.1 Clinico-pathological diagnosis
In 1907, Alois Alzheimer first described the case of a 51-year old woman that developed rapidly progressive memory impairment, as well as spatial and temporal disorientation. A post-mortem examination revealed cerebral atrophy, neurofibrillary pathology and the presence of unusual deposits in the cortex (Small and Cappai 2006). His report thus already described the hallmarks of the disease named after him, namely amyloid plaques and neurofibrillary tangles.
In 1984, the National Institute of Neurologic and Communicative Disorders and Stroke- Alzheimer’s Disease and Related Disorders Association developed diagnostic criteria for AD.
Core clinical criteria include interference with functioning in daily life activities, insidious onset, decline from previous level of functioning and performing, prominent cognitive deficits, and absence of evidence of other neurological diseases. Cognitive manifestations encompass memory impairment, language and visuospatial deficits. The diagnosis of AD could either be definite (with histological confirmation), probable (typical clinical features without histological confirmation) or possible (atypical clinical features with an absence of alternative diagnosis, and no histological confirmation) (McKhann, Drachman et al. 1984). Typically, AD begins with memory impairment, then spreads to executive functioning (impaired abstract thinking, working memory, verbal fluency), language (naming or comprehension), praxis (impaired imitiation/production/recognition of gestures), complex visual processing and gnosis (impaired object/face recognition). Several atypical AD phenotypes have also been described, including focal cortical symptoms, such as posterior cortical atrophy (with prominent visual/visuospatial impairment), primary progressive non-fluent aphasia and logopenic aphasia (with prominent language impairment), and frontal variant AD (with prominent behavioral symptomatology).
1.2.2 Interest for prodromal stage
As clinicopathological studies showed that substantial pathophysiological burden was already present at the moment of the clinical diagnosis, it became clear that there was a long clinically silent phase that demanded further attention. Moreover, this shift was motivated by hope that
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disease-modifying treatments could gain efficiency if used before neuronal damage had become irreversible. The construct of mild cognitive impairment (MCI) was then proposed by Petersen and collaborators (Petersen, Smith et al. 1999), and a great deal of emphasis was put on detecting individuals in the early stages of the disease, as individuals at this crucial period could potentially benefit from clinical trials designed to delay the onset of dementia.
MCI is a state in which subjects are cognitively impaired, but not sufficiently to fulfill dementia criteria. MCI criteria include both subjective and objective cognitive impairment, and spared ability to function and perform daily activities (Petersen, Smith et al. 1999). Cognitive impairment is determined in comparison to normative measures for a given sociodemographic profile (originally defined as at least 1.5 standard deviations below the norm on neuropsychological assessment of memory function), and with respect to the individual’s performance. The latter is preferably corroborated by an informant in order to evaluate the progression of cognitive changes.
However, MCI is a heterogeneous entity with various possible underlying aetiologies. Alternative outcomes (apart from AD) include reverting back to normal cognition, remaining stable, or progressing to another type of dementia (vascular dementia, dementia with Lewy bodies, frontotemporal dementia) or depression. This heterogeneity was addressed with the description of separate clinical profiles: single and multiple-domain, amnestic and non-amnestic subtypes (Petersen, Doody et al. 2001). Subsequently, large cohort studies of MCI patients and a long follow-up have shown that across the four clinical MCI subtypes, only non-amnestic MCI- multiple domain were more likely to progress to a non-AD dementia (Busse, Hensel et al. 2006).
1.2.3 Neuropathological features
Neurofibrillary tangles (NFTs) are formed by abnormally misfolded and hyperphosphorylated protein tau (Serrano-Pozo, Frosch et al. 2011), and are invariably accompanied by neuropil threads. Braak and Braak first staged their topographic pattern of progression, starting in the perirhinal (stage I), then entorhinal cortices and hippocampus (II), then spreading to limbic regions (amygdala, thalamus; III-IV), and finally to the isocortical regions/neocortex (V-VI) (Braak and Braak 1991). While NFTs in the entorhinal cortex are rather common in normal aging, their extension to neocortical regions is indicative of disease (AD or another tauopathy) (Bouras, Hof et al. 1994, Braak and Braak 1995, Delacourte, David et al. 1999). This pattern
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correlates well with the typical clinical presentation that starts with memory impairment, and expands to other cognitive domains. Moreover, several clinicopathological studies have shown that, contrary to amyloid, the amount and distribution of NFTs were correlated with severity and duration of dementia (Arriagada, Growdon et al. 1992, Bierer, Hof et al. 1995). Amyloid plaques are deposits that result from the extracellular aggregation of amyloid beta (Aβ) peptide (Delacourte, David et al. 1999). The initiating pathological event responsible for AD is assumed to be the abnormal processing of amyloid precursor protein (leading to excessive production or reduced clearance of β-amyloid in the brain), because all (autosomal dominant) genetic forms of AD involve genes that encode either amyloid precursor protein or protease subunits (PS1 and PS2) that are responsible for Aβ cleavage. However, it has been demonstrated that NFTs occurred most frequently before amyloid lesions (Price, Davis et al. 1991, Schonheit, Zarski et al.
2004), which suggests that AD is caused either by interactions between amyloid and tau, or that other factors could have a role in AD pathogenesis. The topographic pattern of progression of amyloid plaques is less stereotypical than that of NFTs: they are first found in the basal portions of the frontal, temporal and occipital lobes; then they progress to all isocortical association areas except primary sensory and motor cortices; finally, the latter areas are affected, in addition to subcortical regions and the cerebellum (Braak and Braak 1991).
1.2.4 The advent of biomarkers
The great body of work undertaken for the investigation of MCI and AD biological processes led the international Working Group for New Research Criteria for the Diagnosis of AD to propose a revision of the 1984 criteria (Dubois, Feldman et al. 2007). The consortium came up with a paradigm shift based on separating the clinical manifestation of the disease from the underlying neuropathology ; AD was no longer defined by the manifestation of dementia, as it was simply the « clinical syndrome that arises as a consequence of the AD pathophysiological process » (McKhann, Knopman et al. 2011). Thus, there was no longer a difference between prodromal AD and AD dementia, as they were on the same spectrum. This shift allowed for a broader diagnostic coverage of the whole spectrum of AD.
The criteria aims to represent the majority of patients, and therefore focus on the typical AD phenotype: prominent episodic memory impairment supported by biomarker evidence of AD pathology, which can potentially improve the pathophysiological specificity of AD diagnosis.
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When AD-associated pathological changes are observed in the absence of concomitant clinical features of AD, it is probably because the disease is at its preclinical (and thus presymptomatic) stage (Knopman, Parisi et al. 2003, Bennett, Schneider et al. 2006). Hypotheses for explaining the asymptomatology in individuals who are biomarker positive include cognitive reserve, the presence of risk factors (e.g., APOE genotype) and protective factors (e.g., diet), and comorbidities (e.g., cerebrovascular, synucleinopathy).
Several biomarkers have now been developed and validated, measuring amyloid and tau deposition, but also metabolic dysfunction and atrophy. All these biomarkers are both sensitive (and can therefore well discriminate AD from EC) but also specific to AD (distinguishing them from other types of dementia, such as frontotemporal dementia, dementia with Lewy bodies, vascular dementia), and they are increasingly used in clinical practice to support differential diagnosis.
1.2.5 Cerebrospinal fluid biomarkers
Cerebrospinal fluid (CSF) biomarkers of AD include Aβ42 (which detects amyloid deposition), phosphorylated (p-tau) and total tau (t-tau). Low concentrations of CSF Aβ42 correlate with both the clinical diagnosis of AD and postmortem Aβ pathology (Clark, Xie et al. 2003, Strozyk, Blennow et al. 2003, Schoonenboom, van der Flier et al. 2008). While amyloid deposition can be observed thoughout the whole span of the AD cognitive spectrum, its severity is not correlated to the severity or duration of dementia (Arriagada, Growdon et al. 1992, Bierer, Hof et al. 1995).
This suggests that amyloid load reaches a plateau when atrophy and clinical symtoms first appear (Ingelsson, Fukumoto et al. 2004), and implies that amyloid burden is not sufficient to cause clinical manifestations.
Both p-tau and t-tau in the CSF are increased in AD, and are thought to be a direct indicator of tau accumulation in neurons. While these measures are not specific to AD, they do correlate with clinical disease severity, and with greater cognitive impairment in normal aging, MCI and AD patients (Shaw, Vanderstichele et al. 2009).
1.2.6 Neuroimaging markers
In contrast to fluid biomarkers, imaging biomarkers can indicate the topographic progression of AD changes, and therefore possibly indicate the disease stage.
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1.2.6.1 PET
Positron emission tomography (PET) enables to measure the regional tissue concentration of positron-emitting radionuclides in vivo.
18Fluorodeoxyglucose (FDG)-PET is used to measure brain glucose metabolism, an indicator of synaptic activity. A specific topographic pattern of decreased glucose uptake in medial parietal and lateral temporo-parietal regions is a robust marker of AD (Jagust, Reed et al. 2007). Not only is it sensitive for distinguishing AD from normal aging (Patwardhan, McCrory et al. 2004), but it is also specific enough to discriminate between AD and other types of dementia (Koeppe, Gilman et al. 2005). The amount of FDG uptake also correlates with cognitive impairment along the spectrum from normal cognition to MCI to AD (Minoshima, Giordani et al. 1997), and can predict conversion to AD in MCI patients (Modrego 2006).
The most commonly used PET tracer for amyloid deposition is 11C-Pittsburgh Compound B (PiB), which binds to fibrillar Aβ peptide. High PiB binding was found in frontal, parietal and temporal regions in AD patients (Klunk, Engler et al. 2004, Kemppainen, Aalto et al. 2006).
Moreover, PiB retention was found to increase with disease progression (Beckett, Webb et al.
2012), and to correlate with low CSF Aβ42 concentrations in AD (Fagan, Mintun et al. 2006).
Recently, PET tracers for tau, such as the most widely used 18F-AV-1451 (formely called 18F- T807), were developed. Abnormally high binding at sites of tauopathy was found in MCI and AD patients, compared to elderly controls (EC) (Cho, Choi et al. 2016, Johnson, Schultz et al. 2016, Scholl, Lockhart et al. 2016). Moreover, the binding topography was strongly correlated with neuropsychological scores in memory, visuospatial function and language in patients with different AD phenotypes (Ossenkoppele, Schonhaut et al. 2016).
1.2.6.2 MRI
MRI is a noninvasive technique that uses a strong magnetic field and radio waves to measure differences in the magnetic properties of certain molecules. The human body is largely made of water molecules, which are composed of hydrogen and oxygen atoms. At the center of each 1H atom lies a proton which serves as a magnet and is sensitive to magnetic field. The water molecules are arranged randomly in our body; however, upon entering an MRI scanner, the first magnet causes these molecules to align in one direction, creating a net magnetization, then the
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second magnetic field, with a series of quick radio wave pulses, allows to excite and detect the magnetization of water to create an image.
MRI-based markers have the great advantage of being non-invasive (as do not rely on exogenous contrast agents, ionizing radiation or radiotracers), which makes them a good candidate for monitoring treatment effect (in addition to the diagnosis stage where they are commonly used today). Moreover, MRI provides many different types of sequences that can offer precious insights into several aspects of AD: structural alterations in the grey and white matter, as well as functional changes at rest and during the completion of a task, can be readily assessed.
1.2.6.2.1 Structural MRI
Structural MRI measures the distribution of water molecules: tissue contrast is based on differences in desnity and/or relaxation properties of the tissues, which is different in white matter, grey matter, and CSF. In AD, it can be used to map cerebral atrophy caused by loss of synapses and neurons (Bobinski, de Leon et al. 2000). The pattern of cerebral atrophy predominantly involves the medial temporal lobes, symmetrically (Serrano-Pozo, Frosch et al.
2011). The hippocampus and the entorhinal cortex are specifically affected by structural changes early in the course of the disease (Du, Schuff et al. 2001, Pennanen, Kivipelto et al. 2004, Tapiola, Pennanen et al. 2008). It is commonly found in AD, but also in MCI, and to a lesser extent, in normal aging (De Leon, George et al. 1997, Jack, Petersen et al. 1998). Both qualitative ratings using validated scales (Scheltens, Leys et al. 1992) and quantitative measures using tissue segmentation (voxel-based morphometry) have generated similar findings, namely that atrophy correlates with cognitive decline (Fox, Scahill et al. 1999) along the continuum from normal aging to MCI and to AD (Jack, Lowe et al. 2009). It also correlates well with Braak staging at autopsy (Silbert, Quinn et al. 2003). Moreover, it seems that atrophy is the biomarker showing the closest correlation with severity of cognitive symptoms in later AD stages (Terry, Masliah et al. 1991, Vemuri, Wiste et al. 2009).
1.2.6.2.2 Functional MRI
Functional MRI (fMRI) measures the blood oxygenation level dependent (BOLD) signal, which depends on the ratio of oxygenated to deoxygenated hemoglobin. Neural activity in a specific area causes a rise in the metabolic demand, which together with the vascular response, causes a
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rush in oxygenated hemoglobin to the area. Immediately after, there is usually an oxygen debt, and the ratio of oxygenated to deoxygenated hemoglobin falls below baseline levels. The rush of oxygenated hemoglobin causes the BOLD signal to rise rapidly. The vascular system overcompensates, with the BOLD signal rising well above baseline to a peak at about 6 seconds after neural activity. The BOLD signal then decays back to baseline over a period of 20-25 seconds (Ashby 2011). Typically in fMRI, hemodynamic signal changes are observed at 1-2 seconds after the onset of neural stimulation and reach a maximum at 4-8 seconds (Kim and Bandettini 2006). Hence, the BOLD signal essentially measures reactive dynamic hemodynamic changes during an experimental task. Importantly, Logothetis and collaborators demonstrated that the BOLD response was correlated with local field potentials (Logothetis, Pauls et al. 2001, Logothetis 2003), and concluded that “a spatially localized increase in the BOLD contrast directly (…) reflects an increase in neural activity” (Logothetis, Pauls et al. 2001).
fMRI is therefore a great tool for mapping brain activity and studying the dynamics of neural networks by tracing the BOLD response. Importantly, it is sensitive to earliest changes associated with neuropathology, and can detect aberrant (adapative or maladaptive) functional changes that precede neuronal loss (Prvulovic, Bokde et al. 2011). It could also be used to assess longitudinal changes in cognition during the disease course, and provide insights into brain plasticity and resilience. Furthermore, fMRI indices could be used in clinical trials to investigate the efficacy of potential therapeutic agents for treatment of AD-related changes. Therefore, fMRI measures of brain activity and connectivity hold significant potential to be useful in clinical assessment, not only as a diagnostic tool for AD, but also as a disease and treatment monitoring tool.
Functional alterations assessed by fMRI will be discussed in Section 5 (Neuroimaging studies in MCI/AD).
1.2.6.2.3 Arterial spin labeling
Arterial spin labeling perfusion MRI quantifies regional cerebral blood flow (CBF) by magnetically labeling arterial blood water as an endogenous diffusive tracer (Williams, Detre et al. 1992). As fMRI, it is closely related to brain function, but has lower signal-to-noise-ratio.
Hypoperfusion has been consistently shown in AD compared to EC (Wang 2014), especially in medial and lateral parietal and temporal regions (Trebeschi, Riederer et al. 2016, Verclytte, Lopes et al. 2016). Moreover, decreasing CBF was associated with more advanced AD stages,
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suggesting that arterial spin labeling can be used to monitor disease progression (Binnewijzend, Benedictus et al. 2016). In MCI, both hypo- and hyper-perfusion were reported, and could predict conversion to AD (Wang 2014).
1.2.7 AD neuropathological cascade
Biomarker studies have provided in vivo evidence that pathological changes were taking place (in an ordered manner) before the earliest clinical symptoms occurred.
In 2010, Jack and collaborators proposed an influential model of the dynamics of different biomarkers in the AD pathological cascade, which speculates on the temporal order in which brain abnormalities appear in each biomarker (Jack, Knopman et al. 2010) (Figure 1A). They suggested that the initiating event in AD pathology is amyloid deposition, starting decades before the first clinical manifestations appear. Between amyloid changes and the first signs of neurodegeneration, a lag period occurs, which is variable from patient to patient, depending on differences in Aβ processing, mechanisms of resilience or cognitive reserve, protective and risk factors, or contribution of additional pathologies. Finally, neuronal dysfunction and neurodegeneration become prominent, the latter driving cognitive impairment. The last biomarker to become abnormal is structural MRI, indicating cerebral atrophy.
In 2013, they updated their model (Jack, Knopman et al. 2013), based on new evidence (Figure 1B). First, time has replaced clinical disease stage on the horizontal axis, as the profile of each individual is unique in the disease course, and can be placed anywhere on the cognitive impairment curve. Second, the ordering of biomarkers has slightly changed, with amyloid deposition being first detected by CSF Aβ42, then on PiB-PET. Finally, FDG-PET and MRI are now both presented as the last biomarkers to show changes. Most importantly, this updated model integrates contradictory findings about whether the initiating pathological event is amyloid or tau. Both pathologies are now modelled as independent processes: tauopathy slowly begins first, without leading to AD on its own; amyloid pathology arises later and triggers acceleration of antecedent tauopathy. This view is supported by the findings that Aβ pathology reaches a plateau early in the disease process, while NFTs, synaptic loss and gliosis continue throughout the course of the disease (Ingelsson, Fukumoto et al. 2004).
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Figure 1. Hypothetical model of AD pathological cascade. A) Original model, reproduced from (Jack, Knopman et al. 2010) by permission of Elsevier. B) Updated version of the model, reproduced from (Jack, Knopman et al. 2013) by permission of Elsevier.
1.3 Memory
As memory impairment is a defining feature of AD, we will now define some basic concepts, and introduce what has been a object of contention for many years, i.e., the differentiation between recollection and familiarity-based recognition. We also define associative memory, and how different types of associations are related to recollection and familiarity. Finally, we will discuss
A
B
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neuropsychological deficits in the memory domain in MCI and AD. Memory-related structures and networks will be covered in Section 4.1 (Memory networks), while memory-related neuroimaging studies in MCI and AD patients will be addressed in Section 5.1 (Univarite task fMRI studies in MCI/AD).
1.3.1 Different forms of memory
Memory has traditionally been subdivided into different forms (Dickerson and Eichenbaum 2010). A first dissociation is usually made between declarative memory (also called explicit memory), and implicit (nonconscious) memory. The latter encompasses different types of learning that modify behavior, in terms of performance or choices. One example is procedural memory, which involves learning a sequence of movements or actions. Declarative memory encompasses short-term and long-term memory. The first one is commonly referred to as working memory, and implies the short-term maintenance of information for subsequent manipulation. Long-term declarative memory is then subdivided into episodic and semantic memory. Episodic memory incorporates memories of personal experiences, including events and their context, whereas semantic memory involves factual knowledge without the context of their memorization.
1.3.2 Recollection and familiarity
The retrieval of episodic memories can either occur via recall, i.e., remembering an item or an event, or via recognition, i.e., the ability to decide whether an item had been previously presented. Whereas recall is inherently associative, recognition can either be based on recollection or familiarity according to dual-process models (Eichenbaum, Otto et al. 1994, Aggleton and Brown 1999, Yonelinas 2002, Ranganath 2010). Recollection implies the recognition of an item based on the retrieval of specific contextual details, while familiarity- based recognition is based on a “feeling” of memory, without the retrieval of any specific details about the encoding context. However, another model, the “single process account” (Squire, Wixted et al. 2007, Slotnick 2013), speculates that instead of being two qualitatively different processes, recollection and familiarity are quantitatively different, i.e., a weaker memory trace will evoke familiarity, whereas a stronger memory trace will elicit recollection.
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1.3.3 Associative memory
Associative or relational memory involves remembering relations between items of information, such as pairs of words, or an object and its physical or spatio-temporal characteristics (Achim and Lepage 2005, Troyer, Murphy et al. 2012). It contrasts with item memory, which involves remembering individual items independent of any other information associated with them at acquisition.
Inter-items associations can be formed within the same domain/modality (e.g., sensory), or between different domains/modalities. Within-domain inter-item associations can be represented (i.e., perceived and remembered) as a single entity, if their components are bound together into a single unitized representation (e.g., eyes, nose and mouth of a face). However, this is not necessarily the case, as these associations can simply imply similar kinds of items (e.g., two faces; or a table and a chair). Nonetheless, they are likely to be represented by activity in adjacent and interconnected neurons (Mayes, Montaldi et al. 2007). Between-domain inter-item associations cannot be represented as a single entity; they can bind items coming from different sensory modalities, that are spatially or temporally related. These associations are likely to be represented by activity in distant and weakly connected neurons (Mayes, Montaldi et al. 2007).
Familiarity is thought to occur for item memories, but can also take place for within-domain inter-item associations if they are represented as a single entity. In contrast, recollection processes are necessary for between-domain inter-items associations.
1.3.4 Neuropsychology of memory in MCI/AD
Distinct memory systems are differentially affected by AD. Episodic and semantic memory are both impaired early in the course of the disease, whereas procedural learning may remain unaltered until the late stages (Kaszniak, Wilson et al. 1986, Carlesimo and Oscar-Berman 1992).
Both recall and recognition are affected, in both the verbal and nonverbal domains (Chen, Ratcliff et al. 2001). Delayed recall, in particular, is very accurate in discriminating very mild AD from cognitively normal healthy controls (Welsh, Butters et al. 1991, Tabert, Manly et al. 2006, Sarazin, Berr et al. 2007), and in predicting progression to AD in MCI patients (Devanand, Folz et al. 1997, Arnaiz, Almkvist et al. 2004, Gainotti, Quaranta et al. 2014) and in nondemented elderly individuals (Tierney, Yao et al. 2005).
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Associative memory is impaired early in the course of AD (Dudas, Clague et al. 2005, Anderson, Ebert et al. 2008, Troyer, Murphy et al. 2008, Troyer, Murphy et al. 2012). It is commonly tested using the paired associates learning task (which consists of memorizing pairs of items), which is sensitive to MCI (de Jager, Milwain et al. 2002) and predictive of future conversion to AD (Fowler, Saling et al. 2002, Blackwell, Sahakian et al. 2004, Ahmed, Mitchell et al. 2008). Many studies show that recollection processes are affected in MCI and AD, but that familiarity-based recognition is spared (Dalla Barba 1997, Westerberg, Paller et al. 2006, Anderson, Ebert et al.
2008, Serra, Bozzali et al. 2010). Other studies have shown that both processes were actually affected in MCI and AD patients (Wolk, Signoff et al. 2008, Algarabel, Escudero et al. 2009, Ally, Gold et al. 2009, Algarabel, Fuentes et al. 2012, van der Meulen, Lederrey et al. 2012, Wolk, Mancuso et al. 2013). Schoemaker and collaborators suggested that “while recollection is broadly affected through all stages of the disease process, familiarity deficits seem to be present only at more advanced stages of cognitive impairment” (Schoemaker, Gauthier et al. 2014). In agreement with this conclusion, Hudon and collaborators reported recollection deficits in both MCI and AD, but familiarity deficits in AD only (Hudon, Belleville et al. 2009).
Semantic memory is also impaired in MCI and AD (Salmon, Butters et al. 1999, Dudas, Clague et al. 2005, Vogel, Gade et al. 2005, Joubert, Felician et al. 2008, Joubert, Brambati et al. 2010) and is predictive of cognitive decline (Howieson, Carlson et al. 2008, Hantke, Nielson et al.
2013) and future progression to AD (Blackwell, Sahakian et al. 2004). Deficits in people naming were found in MCI and AD patients (Greene and Hodges 1996, Thompson, Graham et al. 2002, Estevez-Gonzalez, Garcia-Sanchez et al. 2004, Dudas, Clague et al. 2005, Vogel, Gade et al.
2005, Hodges, Erzinclioglu et al. 2006, Joubert, Felician et al. 2008). Category fluency is also impaired in MCI and AD (Petersen, Smith et al. 1994, Tounsi, Deweer et al. 1999, Vogel, Gade et al. 2005, Adlam, Bozeat et al. 2006, Hodges, Erzinclioglu et al. 2006, Price, Kinsella et al.
2012). Furthermore, it was shown that MCI patients were unable to use semantic information to improve their performance in associative memory (Atienza, Atalaia-Silva et al. 2011). It has been suggested that intentional access to semantic knowledge was impaired before automatic access in the course of AD (Perri, Carlesimo et al. 2003, Duong, Whitehead et al. 2006).
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1.4 Neuroimaging by fMRI
In this section, we will first describe the role of several regions and networks in memory-related processes, then discuss networks that are active at rest, and finally outline the different fMRI analysis methods that will be used throughout this work.
1.4.1 Medial temporal lobe system
Historically, episodic memory was believed to be subtended by the medial temporal lobe (MTL) system (Squire and Zola-Morgan 1991), which is composed of the hippocampal formation, the entorhinal, perirhinal and parahippocampal cortices. The hippocampal formation (HIP) is a complex circuit (Figure 2) that includes the entorhinal cortex (ERC), the “HIP proper” composed of the dentate gyrus and cornu ammonis subfields, and the subiculum. In its vicinity, the parahippocampal gyrus (PHG) can be divided into an anterior part, called the perirhinal cortex (PRC), and a posterior part, called the parahippocampal cortex (PHC).
The role of different MTL structures in memory processes has given rise to many hypothetical models inspired by animal connectivity studies. In an influential model, Eichenbaum (Eichenbaum 2006, Eichenbaum, Yonelinas et al. 2007) proposed that different MTL subregions are responsible for processing distinct types of information (e.g., item, context) and processes (e.g., binding), and are thus differentially involved depending on the task demands and the type of information involved. The PRC would encode and retrieve specific item information, while the PHC would process contextual information; the HIP would then bind the two information, using a pattern separation algorithm, which creates distinct memory representations of the inputs. This algorithm is particularly suited to enable pattern completion, as the recollection of encoding- context details can act as cues for the retrieval of the binded items. This view is backed up by anatomical studies in rodents and monkeys, which have shown that the PRC receives most of its inputs from unimodal visual association areas (ventral stream, conveying detailed item information), while the PHC receives its strongest inputs from visuospatial areas (dorsal stream, conveying integrative context information).
These studies have also highlighted that MTL regions are heavily interconnected. The PRC and PHC both project to the ERC, but not to the exact same area : while the PRC projects primarily to lateral ERC areas, the PHC projects mostly to medial ERC regions. The two PHG subregions are
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also interconnected. Finally, the ERC mainly projects to the HIP, which in turn projects back to the PRC, PHC and ERC.
Figure 2. Anatomical organization of MTL structures (y≈ -18). Modified from (Kahn, Andrews-Hanna et al. 2008), by permission of the American Physiological Society. PRc/ERc=perirhinal/entorhinal cortex;
bHipp=body of the hippocampus
1.4.2 Memory networks
Importantly, the existence of two parallel pathways arising from the PRC and PHC, and converging in the HIP has been highlighted (Witter, Naber et al. 2000, Kahn, Andrews-Hanna et al. 2008): the first one involves the body of the HIP, the posterior PHC, lateral and medial parietal areas (posterior cingulate cortex [PCC], retrosplenial cortex [RSC]) as well as ventral medial prefrontal (PFC) regions ; and the second one implicates the anterior HIP, the PRC and ERC, the PFC, lateral temporal cortex and temporal pole. These structures are illustrated in the figure below (Figure 3).
The first network is central to episodic memory processes, and is active when individuals are engaged in internally-focused tasks such as autobiographical memory retrieval, future planning, and conceiving the perpectives of others (Buckner, Andrews-Hanna et al. 2008). With other regions such as the mammillary bodies and anterior thalamic nuclei, it has also been implicated in recollection-based processes (Shannon and Buckner 2004, Daselaar, Fleck et al. 2006, Aggleton 2012, Ranganath and Ritchey 2012), and is similar to the circuit described by Papez (Papez 1937). On the other hand, the second network, along with the mediodorsal nucleus of the thalamus, is thought to subtend familiarity-based recognition (Aggleton 2012).
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Figure 3. Main connections between brain regions underlying episodic memory. Modified from (Aggleton and Brown 2006), by permission of Elsevier. ATN= anterior dorsal thalamic nucleus, HPC=hippocampus
A set of overlapping and unique regions has been described in encoding and retrieval paradigms.
The MTL is consistently activated during encoding of subsequently remembered items. In associative memory studies, increased activity in the HIP is typically described during successful encoding (Zeineh, Engel et al. 2003, Chua, Schacter et al. 2007). The PFC is also typically activated during encoding (Sperling, Bates et al. 2001, Weis, Klaver et al. 2004), more often lateralized to the left hemisphere (Wagner, Schacter et al. 1998, Prince, Daselaar et al. 2005).
Activations are also often observed in occipito-temporal areas (Wagner, Schacter et al. 1998, Sperling, Bates et al. 2001) during encoding.
Whether or not the activity of the HIP is necessary to retrieval has been an issue of dispute.
According to the “reinstatement or cortical reactivation hypothesis”, the successful retrieval of episodic memory should require the reactivation of the same regions that were activated at encoding (Wheeler, Petersen et al. 2000). Some studies have found the HIP to be activated during retrieval, but possibly a different subregion than during encoding. Indeed, an anterior-posterior gradient has been proposed, but in both directions: some have suggested that an anterior portion of the HIP is activated at encoding and a posterior portion HIP at retrieval (Pihlajamaki, Tanila et
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al. 2003, Zeineh, Engel et al. 2003, Prince, Daselaar et al. 2005), while others have reported the opposite (Schacter and Wagner 1999, Rombouts, Barkhof et al. 2001). However, other studies have been unable to corroborate this finding, which may be explained by the role of the MTL system in memory being only temporary. While the MTL is necessary for encoding a new memory, as time passes, the memory stored gradually becomes independent of these structures (Squire and Zola-Morgan 1991) and is stored in association areas, such as inferior temporal and parietal cortex.
In addition to the HIP, successful retrieval has implicated a network of PFC, lateral temporal and parietal regions (Konishi, Wheeler et al. 2000, Shannon and Buckner 2004, Prince, Daselaar et al.
2005, Wagner, Shannon et al. 2005). Interestingly, in some of these regions, an opposing relationship between their activity during encoding and retrieval has been demonstrated. While the HIP and PFC show increased activation during both encoding and retrieval, a group of parietal regions (precuneus, PCC, RSC, inferior and superior parietal lobule) show decreased activation during encoding and increased activation during successful retrieval (Daselaar, Prince et al. 2004, Wheeler and Buckner 2004, Shrager, Kirwan et al. 2008, Daselaar, Prince et al. 2009, Huijbers, Pennartz et al. 2011, Vannini, O'Brien et al. 2011). Importantly, increased activation of the HIP (Huijbers, Pennartz et al. 2011) and increased deactivation of posteromedial regions (Miller, Celone et al. 2008, Pihlajamaki, K et al. 2010) are both correlated with memory performance.
1.4.3 Resting state and default mode network
Task fMRI studies have aimed to detect task-induced increases in brain activity, by comparing task-evoked states to a baseline rest state, which would typically consist of blocks of visual fixation or inter-trial intervals. However, it was soon noticed that this baseline was associated with significant cognitive activity, sometimes more substantial than during certain tasks, and was thus far from being an optimal control state for cognitive task studies (Stark and Squire 2001).
When directly comparing RS to nine different cognitive tasks acquired using FDG-PET, Mazoyer and collaborators (Mazoyer, Zago et al. 2001) found a consistent set of brain regions that were active at rest; these regions included the angular gyrus, PCC/precuneus, middle and superior frontal gyrus, and anterior cingulate cortex (ACC). These low frequency fluctuations (0.01-0.1 Hz) were observed during different resting states, such as with eyes closed, visual fixation,
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passive viewing of simple visual stimuli (Shulman, Fiez et al. 1997, Greicius, Krasnow et al.
2003), and other tasks with low cognitive demand (Greicius, Srivastava et al. 2004). Raichle and collaborators (Raichle, MacLeod et al. 2001) were the first to name this network of regions as a
“default mode of brain function”, and to recognize that “in contrast to the transient nature of typical activations, the presence of this functional activity in the baseline implies the presence of sustained information processing” (Gusnard, Raichle et al. 2001).
The default mode network (DMN) has since received a tremendous amount of attention, and fed the imagination of numerous neuroscientists in regard to its supposedly adaptive function. The fact that it is altered in several neurological and psychiatric disorders has only intensified the interest that it generates in the field. Various functions have been assigned to the DMN, including internal mentation, autobiographical and prospective memory, theory of mind, self-referential and affective decision making (Mazoyer, Zago et al. 2001, Buckner, Andrews-Hanna et al. 2008).
Congruently with the diversity of functions that have been associated with the DMN, it has been shown that the DMN can be subdivided into multiple interacting subcomponents. Andrews- Hanna and collaborators (Andrews-Hanna, Reidler et al. 2010) described an MTL subsystem (composed of the ventral medial PFC, posterior inferior parietal lobule, RSC, PHC and HIP) and a dorsal medial PFC subsystem (comprising the dorsomedial PFC, temporoparietal junction, lateral temporal cortex and temporal pole). These subsystems are thought to be differentially modulated by task variations. Furthermore, both are strongly correlated with a midline core, that encompasses the anterior medial PFC and PCC.
Crucially, the set of regions composing the DMN was very similar to some regions described by Shulman and collaborators as being consistently deactivated during various active tasks (Shulman, Fiez et al. 1997). They had suggested that these task-independent decreases could reflect “ongoing processes, such as unconstrained verbally mediated thoughts and monitoring of the external environment, body, and emotional state”. This led the DMN to be designated as a
« task-negative » network (Fox, Snyder et al. 2005). Moreover, the degree of DMN deactivation was found to be positively correlated with task demands (McKiernan, Kaufman et al. 2003), and predictive of successful task performance (Daselaar, Prince et al. 2004, Grady, Springer et al.
2006). Yet, DMN regions are commonly activated during certain tasks that require internally
