Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications

Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163

Alterazioni topologiche cerebrali in relazione a cambiamenti cognitivi nel Mild Cognitive Impairment e nella malattia di Alzheimer

openLa malattia di Alzheimer (AD) rappresenta una delle malattie neurodegenerative più comuni. Il primo sintomo clinico che generalmente porta un soggetto sotto osservazione è l'emergere di errori di memoria episodica, che però accadono quando la patologia ha già raggiunto certo grado di diffusione. In anni recenti, è stato osservato che queste alterazioni neuronali presentano una certa sovrapposizione con networks cerebrali come il Default Mode Network (DMN) e il Network Frontoparietale (FPN). Tuttavia, non è chiaro se le alterazioni funzionali possano essere rilevate anni prima dell'emergere dei sintomi clinici, nella fase prodromica della malattia, il Mild Cognitive Impairment (MCI), e il loro potere predittivo di progressione da MCI ad AD. In questo studio, abbiamo tentato di risolvere questa lacuna investigando la relazione tra le alterazioni topologiche dei due network sopracitati e l'emergere di difficoltà di memoria episodica dopo 2 anni di follow-up. Pe fare ciò, abbiamo utilizzando uno strumento analitico di recente sviluppo, la graph theory, e una batteria di test specifica che valuta tutti le fasi della memoria (codifica, recupero e ricordo) in un campione di soggetti sani e pazienti MCI. I nostri risultati suggeriscono che un aumento nella segregazione del DMN potrebbe rappresentare un biomarker precoce di peggioramento cognitivo nella codifica di informazioni. In particolare, questo studio enfatizza anche il meccanismo compensatorio funzionale nei nodi prefrontal dello stesso network, i quali potrebbero rappresentare una caratteristica importante della patologia nella sua fase prodromica. Questi risultati potrebbero essere utili nel rilevamento precoce di pazienti ad alto rischio di progressione clinica e per i quali potrebbero ancora attuati interventi di recupero.Alzheimer’s disease (AD) represents the most common neurodegenerative disease. The first clinical symptom that usually brings an individual under clinical attention is the emergence of episodic memory pitfalls, which however occur when the underlying pathology has already reached a certain degree of spreading. In recent years, these neural alterations have been observed to largely overlap with known functional networks, especially the Default Mode (DMN) and the Frontoparietal (FPN) networks. However, much debate exists regarding whether functional alterations can be detected years before symptoms offset, i.e. that is in the prodromal disease stage of Mild Cognitive Impairment (MCI), and their predictive power of MCI-to-AD progression. In this study, we tried to fill this gap by investigating the relationship between topological networks’ alteration and the emergence of episodic memory difficulties at 2 years’ follow-up. We did so using a recently developed neuroimaging analytic tool, namely graph theory, and a specific battery of tests, assessing all stages of memory encoding, retrieval and recall in a sample of MCI patients and healthy controls. Our results suggest that increased DMN segregation might represent an early biomarker of cognitive worsening in episodic memory encoding. In particular, the study emphasizes that functional compensatory mechanisms in prefrontal nodes of the DMN might be a more prominent feature of the pathology at its prodromal stages, representing an early stressor. These findings might be potentially useful in the early detection of patients at higher risk of clinical progression and for whom resilience boosting interventions might still be put in place

Clinical connectome fingerprints of cognitive decline

Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that “clinical fingerprints” can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks

Cognitive complaints in older adults at risk for Alzheimer's disease are associated with altered resting-state networks

INTRODUCTION: Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables. METHODS: Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score). RESULTS: Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMN-pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. DISCUSSION: Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization

Information flow between resting state networks

The resting brain dynamics self-organizes into a finite number of correlated patterns known as resting state networks (RSNs). It is well known that techniques like independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting state magnetic resonance imaging. After haemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of Transfer Entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k = 1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k greater than one our method calculates the k-multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension-dependent, increasing from k =1 (i.e., the average voxels activity) up to a maximum occurring at k =5 to finally decay to zero for k greater than 10. This suggests that a small number of components (close to 5) is sufficient to describe the IF pattern between RSNs. Our method - addressing differences in IF between RSNs for any generic data - can be used for group comparison in health or disease. To illustrate this, we have calculated the interRSNs IF in a dataset of Alzheimer's Disease (AD) to find that the most significant differences between AD and controls occurred for k =2, in addition to AD showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for publication in Brain Connectivity in its current for

Association between resting-state functional connectivity, glucose metabolism and task-related activity of neural networks

The brain is organized into several large-scale functional networks. Such networks are primarily characterized by intrinsic functional connectivity, i.e. temporally synchronous activity between the different brain regions of a network. The functional connectivity of networks can be identified via functional MRI during resting state, i.e. without engaging the subject in a particular task. Resting-state fMRI is thus less demanding on the subject and therefore of particular interest from a clinical point of view to detect alterations in brain function. Applied to neurodegenerative disease including Alzheimer’s disease, resting-state fMRI has shown alterations in several resting-state networks, suggesting that basic network function is altered in AD. However, the interpretation of alterations in resting-state fMRI connectivity is inherently limited since no cognitive states are explicitly expressed during fMRI. In this regard, we aimed to elucidate how resting-state fMRI connectivity relates to 1) cognition-related brain activity and 2) markers of pathologically altered brain function in AD. In order to understand at a more basic level the association between resting-state and task-related fMRI, we first examined, in a group of elderly healthy subjects, the association between functional connectivity of major networks assessed during resting-state fMRI with those acquired during memory-task related fMRI, in the same individuals. Secondly, in order to assess whether alterations in AD are associated with already well-established markers of pathological brain function in AD, we compared resting-state fMRI functional network connectivity with that in FDG-PET metabolism in AD. Project 1: We investigated the association between functional connectivity acquired during rest and the level of activation obtained during an episodic memory task that included the encoding and forced-choice recognition of face-name pairs in elderly cognitively normal subjects. Independent component analysis (ICA) was used to identify major resting-state networks in the brain. Next, we applied ICA to the task-fMRI data to determine the components (networks) that were significantly associated with the task regressors of successful vs unsuccessful learning or recognition trials. Spatial correlation analysis between the resulting extracted resting-state and task-related fMRI components showed a spatial match in several components such as medial temporal lobe centered components and posterior components. However, apart from the spatial correspondence, the level of resting state functional connectivity did not predict the level of task-related functional connectivity in spatially matching components. Together these results suggested that particular resting-state networks are activated during a memory task, however, the level of baseline connectivity does not predict to what extent a network becomes activated during a task. Future studies may assess whether pathological resting-state connectivity predicts altered task-related connectivity in the same networks in AD. Project 2: We examined the association between resting-state fMRI functional connectivity within major functional networks and FDG-PET metabolism in those networks, assessed in elderly healthy controls, subjects with prodromal AD (mild cognitive impairment and amyloid PET biomarker confirmed AD etiology) and AD dementia. We found that FDG-PET was generally reduced in all networks in the course of AD. The main finding was that lower network functional connectivity was associated with lower FDG-PET uptake in the Default mode network and fronto-parietal attention network across the whole group and specifically in prodromal AD, suggesting that both modalities are associated in higher networks affected in the course of AD. These results provide insightful comprehension of the hypometabolism patterns that are typically found in AD

Dealing with heterogeneity in the prediction of clinical diagnosis

Le diagnostic assisté par ordinateur est un domaine de recherche en émergence et se situe à l’intersection de l’imagerie médicale et de l’apprentissage machine. Les données médi- cales sont de nature très hétérogène et nécessitent une attention particulière lorsque l’on veut entraîner des modèles de prédiction. Dans cette thèse, j’ai exploré deux sources d’hétérogénéité, soit l’agrégation multisites et l’hétérogénéité des étiquettes cliniques dans le contexte de l’imagerie par résonance magnétique (IRM) pour le diagnostic de la maladie d’Alzheimer (MA). La première partie de ce travail consiste en une introduction générale sur la MA, l’IRM et les défis de l’apprentissage machine en imagerie médicale. Dans la deuxième partie de ce travail, je présente les trois articles composant la thèse. Enfin, la troisième partie porte sur une discussion des contributions et perspectives fu- tures de ce travail de recherche. Le premier article de cette thèse montre que l’agrégation des données sur plusieurs sites d’acquisition entraîne une certaine perte, comparative- ment à l’analyse sur un seul site, qui tend à diminuer plus la taille de l’échantillon aug- mente. Le deuxième article de cette thèse examine la généralisabilité des modèles de prédiction à l’aide de divers schémas de validation croisée. Les résultats montrent que la formation et les essais sur le même ensemble de sites surestiment la précision du modèle, comparativement aux essais sur des nouveaux sites. J’ai également montré que l’entraînement sur un grand nombre de sites améliore la précision sur des nouveaux sites. Le troisième et dernier article porte sur l’hétérogénéité des étiquettes cliniques et pro- pose un nouveau cadre dans lequel il est possible d’identifier un sous-groupe d’individus qui partagent une signature homogène hautement prédictive de la démence liée à la MA. Cette signature se retrouve également chez les patients présentant des symptômes mod- érés. Les résultats montrent que 90% des sujets portant la signature ont progressé vers la démence en trois ans. Les travaux de cette thèse apportent ainsi de nouvelles con- tributions à la manière dont nous approchons l’hétérogénéité en diagnostic médical et proposent des pistes de solution pour tirer profit de cette hétérogénéité.Computer assisted diagnosis has emerged as a popular area of research at the intersection of medical imaging and machine learning. Medical data are very heterogeneous in nature and therefore require careful attention when one wants to train prediction models. In this thesis, I explored two sources of heterogeneity, multisite aggregation and clinical label heterogeneity, in an application of magnetic resonance imaging to the diagnosis of Alzheimer’s disease. In the process, I learned about the feasibility of multisite data aggregation and how to leverage that heterogeneity in order to improve generalizability of prediction models. Part one of the document is a general context introduction to Alzheimer’s disease, magnetic resonance imaging, and machine learning challenges in medical imaging. In part two, I present my research through three articles (two published and one in preparation). Finally, part three provides a discussion of my contributions and hints to possible future developments. The first article shows that data aggregation across multiple acquisition sites incurs some loss, compared to single site analysis, that tends to diminish as the sample size increase. These results were obtained through semisynthetic Monte-Carlo simulations based on real data. The second article investigates the generalizability of prediction models with various cross-validation schemes. I showed that training and testing on the same batch of sites over-estimates the accuracy of the model, compared to testing on unseen sites. However, I also showed that training on a large number of sites improves the accuracy on unseen sites. The third article, on clinical label heterogeneity, proposes a new framework where we can identify a subgroup of individuals that share a homogeneous signature highly predictive of AD dementia. That signature could also be found in patients with mild symptoms, 90% of whom progressed to dementia within three years. The thesis thus makes new contributions to dealing with heterogeneity in medical diagnostic applications and proposes ways to leverage that heterogeneity to our benefit

Network-based biomarkers in Alzheimer's disease: review and future directions

By 2050 it is estimated that the number of worldwide Alzheimer?s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD

Loss of brain inter-frequency hubs in Alzheimer's disease

Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-beta deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.Comment: 27 pages, 6 figures, 3 tables, 3 supplementary figure