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

Functional Imaging Connectome of the Human Brain and its Associations with Biological and Behavioral Characteristics

Functional connectome of the human brain explores the temporal associations of different brain regions. Functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (rfMRI) characterize the brain network at rest and studies have shown that rfMRI FC is closely related to individual subject\u27s biological and behavioral measures. In this thesis we investigate a large rfMRI dataset from the Human Connectome Project (HCP) and utilize statistical methods to facilitate the understanding of fundamental FC-behavior associations of the human brain. Our studies include reliability analysis of FC statistics, demonstration of FC spatial patterns, and predictive analysis of individual biological and behavioral measures using FC features. Covering both static and dynamic FC (sFC and dFC) characterizations, the baseline FC patterns in healthy young adults are illustrated. Predictive analyses demonstrate that individual biological and behavioral measures, such as gender, age, fluid intelligence and language scores, can be predicted using FC. While dFC by itself performs worse than sFC in prediction accuracy, if appropriate parameters and models are utilized, adding dFC features to sFC can significantly increase the predictive power. Results of this thesis contribute to the understanding of the neural underpinnings of individual biological and behavioral differences in the human brain

Typical and atypical development of the brain’s functional network architecture

The human brain is a complex organ that gives rise to many behaviors. Specialized neural regions cooperate as functional networks that form an intricate functional architecture. Development provides a unique window into how brain functioning and human thinking are affected if the necessary neural features and connections are not fully formed. Similarly, developmental disorders can shed light on atypical trajectories of neural systems that may lead to or be a consequence of symptomatic behavior. A description of the typical and atypical development of functional networks is essential to identify the features of brain organization critical for mature human thinking and to provide better diagnosis, treatment, and prognosis in neurodevelopmental disorders. Recently, resting state functional MRI has been found to illuminate functionally related regions, giving access to functional networks and the organization of brain’s functional architecture. This thesis aims to harness resting-state functional connectivity to explore how functional networks coordinate over the course of development. First, I present our work investigating the organizing principles of typical developmental patterns in functional networks (Chapter 2). Then, I apply these approaches to the atypical development of functional networks in Tourette syndrome (TS), a developmental disorder characterized by motor and vocal tics. In this work, we tested whether the patterns in functional networks that distinguish individuals with TS from controls differ between children and adults and alter the typical developmental pattern of functional networks (Chapter 3). Lastly, I present our work to identify and describe the coordination of specific functional networks that develop atypically in TS (Chapter 4)

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

Overt social interaction and resting state in young adult males with autism: core and contextual neural features

Conversation is an important and ubiquitous social behavior. Individuals with Autism Spectrum Disorder (autism) without intellectual disability often have normal structural language abilities but deficits in social aspects of communication like pragmatics, prosody, and eye contact. Previous studies of resting state activity suggest that intrinsic connections among neural circuits involved with social processing are disrupted in autism, but to date no neuroimaging study has examined neural activity during the most commonplace yet challenging social task: spontaneous conversation. Here we used functional MRI to scan autistic males (N=19) without intellectual disability and age- and IQ-matched typically developing controls (N=20) while they engaged in a total of 193 face-to-face interactions. Participants completed two kinds of tasks: Conversation, which had high social demand, and Repetition, which had low social demand. Autistic individuals showed abnormally increased task-driven inter-regional temporal correlation relative to controls, especially among social processing regions and during high social demand. Furthermore, these increased correlations were associated with parent ratings of participants’ social impairments. These results were then compared with previously-acquired resting-state data (56 Autism, 62 Control participants). While some inter-regional correlation levels varied by task or rest context, others were strikingly similar across both task and rest, namely increased correlation among the thalamus, dorsal and ventral striatum, somatomotor, temporal and prefrontal cortex in the autistic individuals, relative to the control groups. These results suggest a basic distinction. Autistic cortico-cortical interactions vary by context, tending to increase relative to controls during Task and decrease during Rest. In contrast, striato- and thalamocortical relationships with socially engaged brain regions are increased in both Task and Rest, and may be core to the condition of autism

Brain Function in Early Childhood: Individual Differences in Age and Attentive Traits

Children, like adults, are unique individuals with complex interwoven relationships between brain function, behaviour, and phenotypic traits, which further interact with rapid developmental processes. A nuanced description of variability between children will add to our knowledge of how they think and behave, and potentially advance the development of personalized early interventions. With functional magnetic resonance imaging (fMRI), we have gained insight into brain responses – however, due to practical considerations, we have been unable to render a complete understanding of brain-behaviour relationships in young children. The use of naturalistic stimuli in fMRI studies has increased the ecological validity and the retention of developmental neuroimaging data. In this dissertation, I sought to explore the relationships between age, attentive traits, and inter-individual variability of brain function in young children in naturalistic paradigms. I conducted a scoping review to synthesize the current and historical task- and naturalistic-fMRI literature on the development of visual processing in the brain, through the lens of two influential theories: the interactive specialization and maturational frameworks. I found that while there is generally a consensus of progressive development of visual brain function throughout childhood, there is not enough evidence to fully support other aspects of these theories. I also conducted two experiments, using naturalistic fMRI and an analysis technique called inter-subject correlation (ISC), which quantifies the spatiotemporal similarity of brain activity between individuals, to explore how age and attentive traits affect inter-individual variability of brain function in children aged 4-8 years. I found that children’s brain responses to movies “homogenized” with increasing age in our sample, with greater variability seen in the younger children. Further, both inattention and hyperactivity were associated with ISC in the sample, though the relationships with these traits were different in widespread regions of the brain. Together, my research advances our understanding of functional brain responses in children and underscores the importance of an individual differences approach to developmental neuroimaging

Task-phase fMRI in detection of improvements in working memory post-interventions for carotid stenosis and early Alzheimer’s disease

Working memory allows for coordination of complex goal-driven behavior. Decline of working memory is linked to severe cognitive disabilities and is an important feature of both severe carotid stenosis and Alzheimer\u27s disease. Functional Magnetic Resonance Imaging (fMRI) can help detect functional brain changes for the evaluation of the impact of standard clinical interventions for both diagnoses. This thesis used fMRI, coupled with cognitive tasks to investigate possible working memory improvements post-standard clinical interventions for both conditions. The study observed post-intervention improvements in task-phase fMRI brain activation patterns together with improvements in task performance. Meanwhile, patients demonstrated complex response patterns associated with disease expression and other individual variability, which were considered with results interpretation. This thesis showed that working memory improvements were possible following standard clinical treatments for both conditions. It also supports for tailoring interventions based on patient peculiarities to maximize treatment effectiveness

Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis

BACKGROUND: Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed. METHODS: We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split). RESULTS: In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset. LIMITATIONS: The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects. CONCLUSIONS: This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects