Structural and Functional Brain Connectivity in Middle-Aged Carriers of Risk Alleles for Alzheimer\u27s Disease

Single nucleotide polymorphisms (SNPs) in APOE, COMT, BDNF, and KIBRA have been associated with age-related memory performance and executive functioning as well as risk for Alzheimer’s disease (AD). The purpose of the present investigation was to characterize differences in brain functional and structural integrity associated with these SNPs as potential endophenotypes of age-related cognitive decline. I focused my investigation on healthy, cognitively normal middle-aged adults, as disentangling the early effects of healthy versus pathological aging in this group may aid early detection and prevention of AD. The aims of the study were 1) to characterize SNP-related differences in functional connectivity within two resting state networks (RSNs; default mode network [DMN] and executive control network [ECN]) associated with memory and executive functioning, respectively; 2) to identify differences in the white matter (WM) microstructural integrity of tracts underlying these RSNs; and 3) to characterize genotype differences in the graph properties of an integrated functional-structural network. Participants (age 40-60, N = 150) underwent resting state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), and genotyping. Independent components analysis (ICA) was used to derive RSNs, while probabilistic tractography was performed to characterize tracts connecting RSN subregions. A technique known as functional-by-structural hierarchical (FSH) mapping was used to create the integrated, whole brain functional-structural network, or resting state structural connectome (rsSC). I found that BDNF risk allele carriers had lower functional connectivity within the DMN, while KIBRA risk allele carriers had poorer WM microstructural integrity in tracts underlying the DMN and ECN. In addition to these differences in the connectivity of specific RSNs, I found significant impairments in the global and local topology of the rsSC across all evaluated SNPs. Collectively, these findings suggest that integrating multiple neuroimaging modalities and using graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment

Studying brain connectivity: a new multimodal approach for structure and function integration \u200b

Il cervello \ue8 un sistema che integra organizzazioni anatomiche e funzionali. Negli ultimi dieci anni, la comunit\ue0 neuroscientifica si \ue8 posta la domanda sulla relazione struttura-funzione. Essa pu\uf2 essere esplorata attraverso lo studio della connettivit\ue0. Nello specifico, la connettivit\ue0 strutturale pu\uf2 essere definita dal segnale di risonanza magnetica pesato in diffusione seguito dalla computazione della trattografia; mentre la correlazione funzionale del cervello pu\uf2 essere calcolata a partire da diversi segnali, come la risonanza magnetica funzionale o l\u2019elettro-/magneto-encefalografia, che consente la cattura del segnale di attivazione cerebrale a una risoluzione temporale pi\uf9 elevata. Recentemente, la relazione struttura-funzione \ue8 stata esplorata utilizzando strumenti di elaborazione del segnale sui grafi, che estendono e generalizzano le operazioni di elaborazione del segnale ai grafi. In specifico, alcuni studi utilizzano la trasformata di Fourier applicata alla connettivit\ue0 strutturale per misurare la decomposizione del segnale funzionale in porzioni che si allineano (\u201caligned\u201d) e non si allineano (\u201cliberal\u201d) con la sottostante rete di materia bianca. Il relativo allineamento funzionale con l\u2019anatomia \ue8 stato associato alla flessibilit\ue0 cognitiva, sottolineando forti allineamenti di attivit\ue0 corticali, e suggerendo che i sistemi sottocorticali contengono pi\uf9 segnali liberi rispetto alla corteccia. Queste relazioni multimodali non sono, per\uf2, ancora chiare per segnali con elevata risoluzione temporale, oltre ad essere ristretti a specifiche zone cerebrali. Oltretutto, al giorno d'oggi la ricostruzione della trattografia \ue8 ancora un argomento impegnativo, soprattutto se utilizzata per l'estrazione della connettivit\ue0 strutturale. Nel corso dell'ultimo decennio si \ue8 vista una proliferazione di nuovi modelli per ricostruire la trattografia, ma il loro conseguente effetto sullo strumento di connettivit\ue0 non \ue8 ancora chiaro. In questa tesi, ho districato i dubbi sulla variabilit\ue0 dei trattogrammi derivati da diversi metodi di trattografia, confrontandoli con un paradigma di test-retest, che consente di definire la specificit\ue0 e la sensibilit\ue0 di ciascun modello. Ho cercato di trovare un compromesso tra queste, per definire un miglior metodo trattografico. Inoltre, ho affrontato il problema dei grafi pesati confrontando alcune possibili stime, evidenziando la sufficienza della connettivit\ue0 binaria e la potenza delle propriet\ue0 microstrutturali di nuova generazione nelle applicazioni cliniche. Qui, ho sviluppato un modello di proiezione che consente l'uso dei filtri aligned e liberal per i segnali di encefalografia. Il modello estende i vincoli strutturali per considerare le connessioni indirette, che recentemente si sono dimostrate utili nella relazione struttura-funzione. I risultati preliminari del nuovo modello indicano un\u2019implicazione dinamica di momenti pi\uf9 aligned e momenti pi\uf9 liberal, evidenziando le fluttuazioni presenti nello stato di riposo. Inoltre, viene presentata una relazione specifica di periodi pi\uf9 allineati e liberali per il paradigma motorio. Questo modello apre la prospettiva alla definizione di nuovi biomarcatori. Considerando che l\u2019encefalografia \ue8 spesso usata nelle applicazioni cliniche, questa integrazione multimodale applicata su dati di Parkinson o di ictus potrebbe combinare le informazioni dei cambiamenti strutturali e funzionali nelle connessioni cerebrali, che al momento sono state dimostrate individualmente.The brain is a complex system of which anatomical and functional organization is both segregated and integrated. A longstanding question for the neuroscience community has been to elucidate the mutual influences between structure and function. To that aim, first, structural and functional connectivity need to be explored individually. Structural connectivity can be measured by the Diffusion Magnetic Resonance signal followed by successive computational steps up to virtual tractography. Functional connectivity can be established by correlation between the brain activity time courses measured by different modalities, such as functional Magnetic Resonance Imaging or Electro/Magneto Encephalography. Recently, the Graph Signal Processing (GSP) framework has provided a new way to jointly analyse structure and function. In particular, this framework extends and generalizes many classical signal-processing operations to graphs (e.g., spectral analysis, filtering, and so on). The graph here is built by the structural connectome; i.e., the anatomical backbone of the brain where nodes represent brain regions and edge weights strength of structural connectivity. The functional signals are considered as time-dependent graph signals; i.e., measures associated to the nodes of the graph. The concept of the Graph Fourier Transform then allows decomposing regional functional signals into, on one side, a portion that strongly aligned with the underlying structural network (\u201caligned"), and, on the other side, a portion that is not well aligned with structure (\u201cliberal"). The proportion of aligned-vs-liberal energy in functional signals has been associated with cognitive flexibility. However, the interpretation of these multimodal relationships is still limited and unexplored for higher temporal resolution functional signals such as M/EEG. Moreover, the construction of the structural connectome itself using tractography is still a challenging topic, for which, in the last decade, many new advanced models were proposed, but their impact on the connectome remains unclear. In the first part of this thesis, I disentangled the variability of tractograms derived from different tractography methods, comparing them with a test-retest paradigm, which allows to define specificity and sensitivity of each model. I want to find the best trade-off between specificity and sensitivity to define the best model that can be deployed for analysis of functional signals. Moreover, I addressed the issue of weighing the graph comparing few estimates, highlighting the sufficiency of binary connectivity, and the power of the latest-generation microstructural properties in clinical applications. In the second part, I developed a GSP method that allows applying the aligned and liberal filters to M/EEG signals. The model extends the structural constraints to consider indirect connections, which recently demonstrated to be powerful in the structure/function link. I then show that it is possible to identify dynamic changes in aligned-vs-liberal energy, highlighting fluctuations present motor task and resting state. This model opens the perspective of novel biomarkers. Indeed, M/EEG are often used in clinical applications; e.g., multimodal integration in data from Parkinson\u2019s disease or stroke could combine changes of both structural and functional connectivity

Blending generative models with deep learning for multidimensional phenotypic prediction from brain connectivity data

Network science as a discipline has provided us with foundational machinery to study complex relational entities such as social networks, genomics, econometrics etc. The human brain is a complex network that has recently garnered immense interest within the data science community. Connectomics or the study of the underlying connectivity patterns in the brain has become an important field of study for the characterization of various neurological disorders such as Autism, Schizophrenia etc. Such connectomic studies have provided several fundamental insights into its intrinsic organisation and implications on our behavior and health. This thesis proposes a collection of mathematical models that are capable of fusing information from functional and structural connectivity with phenotypic information. Here, functional connectivity is measured by resting state functional MRI (rs-fMRI), while anatomical connectivity is captured using Diffusion Tensor Imaging (DTI). The phenotypic information of interest could refer to continuous measures of behavior or cognition, or may capture levels of impairment in the case of neuropsychiatric disorders. We first develop a joint network optimization framework to predict clinical severity from rs-fMRI connectivity matrices. This model couples two key terms into a unified optimization framework: a generative matrix factorization and a discriminative linear regression model. We demonstrate that the proposed joint inference strategy is successful in generalizing to prediction of impairments in Autism Spectrum Disorder (ASD) when compared with several machine learning, graph theoretic and statistical baselines. At the same time, the model is capable of extracting functional brain biomarkers that are informative of individual measures of clinical severity. We then present two modeling extensions to non-parametric and neural network regression models that are coupled with the same generative framework. Building on these general principles, we extend our framework to incorporate multimodal information from Diffusion Tensor Imaging (DTI) and dynamic functional connectivity. At a high level, our generative matrix factorization now estimates a time-varying functional decomposition. At the same time, it is guided by anatomical connectivity priors in a graph-based regularization setup. This connectivity model is coupled with a deep network that predicts multidimensional clinical characterizations and models the temporal dynamics of the functional scan. This framework allows us to simultaneously explain multiple impairments, isolate stable multi-modal connectivity signatures, and study the evolution of various brain states at rest. Lastly, we shift our focus to end-to-end geometric frameworks. These are designed to characterize the complementarity between functional and structural connectivity data spaces, while using clinical information as a secondary guide. As an alternative to the previous generative framework for functional connectivity, our representation learning scheme of choice is a matrix autoencoder that is crafted to reflect the underlying data geometry. This is coupled with a manifold alignment model that maps from function to structure and a deep network that maps to phenotypic information. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive yet interpretable brain biomarkers. Finally, we also present a preliminary analytical and experimental exposition on the theoretical aspects of the matrix autoencoder representation

The bed nucleus of the Stria Terminalis:Connections, genetics, & trait associations

This thesis examines the functional and structural connections of the Bed Nucleus of the Stria Terminalis (BNST). The principal motivation in doing so stems from the documented gap in our knowledge between the prolific pre-clinical animal BNST research, and that of human BNST research (Lebow & Chen, 2016). Understanding the human BNST may prove to be clinically important, as animal models often implicate this structure as being key in processes underlying the stress-response, disorders of negative affect, and in substance misuse- particularly related to alcohol (Herman et al., 2020; Maita et al., 2021). Therefore I further set out to test BNST connectivity relationships with related psychological phenotypes and examine any genetic associations. Chapter 1 provides an overview of the relevant BNST literature and a brief summary of the methods used in this thesis. In Chapter 2 I use the Human Connectome Project young human adults sample (n = ~1000) to map the intrinsic connectivity network of the BNST. In addition, I compare this network to that of the central nucleus of the amygdala, an area anatomically and functionally associated with the BNST (Alheid, 2009). Next, I test for associations across this network with self-report traits relating to dispositional negativity and alcohol use. Finally, I examine the heritability of specific BNST- amygdala sub-region functional connectivity, and co-heritability with the selfreport traits. In Chapter 3 I use the large UK biobank sample (n = ~ 19,000) to run a genome-wide association analysis, aiming to uncover specific common genetic variants that may be linked with BNST – amygdala sub-region functional connectivity. In Chapter 4, I focus on structural connectivity and use a mixture of macaque tracttracing analysis, and human and macaque diffusion MRI probabilistic tractography to examine the evidence for a connection between the subiculum and the BNST. As well, I test for associations between measures of white-matter microstructure and self-report dispositional negativity and alcohol-use phenotypes. Finally, in the Discussion, I bring together the findings of the research, noting their implications within the wider BNST literature and making several suggestions for improving similar analysis in future