Approaches For Capturing Time-Varying Functional Network Connectivity With Application to Normative Development and Mental Illness

Since the beginning of medical science, the human brain has remained an unsolved puzzle; an illusive organ that controls everything- from breathing to heartbeats, from emotion to anger, and more. With the power of advanced neuroimaging techniques, scientists have now started to solve this nearly impossible puzzle, piece by piece. Over the past decade, various in vivo techniques, including functional magnetic resonance imaging (fMRI), have been increasingly used to understand brain functions. fMRI is extensively being used to facilitate the identification of various neuropsychological disorders such as schizophrenia (SZ), bipolar disorder (BP) and autism spectrum disorder (ASD). These disorders are currently diagnosed based on patients’ self-reported experiences, and observed symptoms and behaviors over the course of the illnesses. Therefore, efficient identification of biological-based markers (biomarkers) can lead to early diagnosis of these mental disorders, and provide a trajectory for disease progression. By applying advanced machine learning techniques on fMRI data, significant differences in brain function among patients with mental disorders and healthy controls can be identified. Moreover, by jointly estimating information from multiple modalities, such as, functional brain data and genetic factors, we can now investigate the relationship between brain function and genes. Functional connectivity (FC) has become a very common measure to characterize brain functions, where FC is defined as the temporal covariance of neural signals between multiple spatially distinct brain regions. Recently, researchers are studying the FC among functionally specialized brain networks which can be defined as a higher level of FC, and is termed as functional network connectivity (FNC, defined as the correlation value that summarizes the overall connection between brain ‘networks’ over time). Most functional connectivity studies have made the limiting assumption that connectivity is stationary over multiple minutes, and ignore to identify the time-varying and reoccurring patterns of FNC among brain regions (known as time-varying FNC). In this dissertation, we demonstrate the use of time-varying FNC features as potential biomarkers to differentiate between patients with mental disorders and healthy subjects. The developmental characteristics of time-varying FNC in children with typically developing brain and ASD have been extensively studies in a cross-sectional framework, and age-, sex- and disease-related FNC profiles have been proposed. Also, time-varying FNC is characterized in healthy adults and patients with severe mental disorders (SZ and BP). Moreover, an efficient classification algorithm is designed to identify patients and controls at individual level. Finally, a new framework is proposed to jointly utilize information from brain’s functional network connectivity and genetic features to find the associations between them. The frameworks that we presented here can help us understand the important role played by time-varying FNC to identify potential biomarkers for the diagnosis of severe mental disorders

Cardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study

Several cardiovascular and metabolic indicators, such as cholesterol and blood pressure have been associated with altered neural and cognitive health as well as increased risk of dementia and Alzheimer\u27s disease in later life. In this cross-sectional study, we examined how an aggregate index of cardiovascular and metabolic risk factor measures was associated with correlation-based estimates of resting-state functional connectivity (FC) across a broad adult age-span (36-90+ years) from 930 volunteers in the Human Connectome Project Aging (HCP-A). Increased (i.e., worse) aggregate cardiometabolic scores were associated with reduced FC globally, with especially strong effects in insular, medial frontal, medial parietal, and superior temporal regions. Additionally, at the network-level, FC between core brain networks, such as default-mode and cingulo-opercular, as well as dorsal attention networks, showed strong effects of cardiometabolic risk. These findings highlight the lifespan impact of cardiovascular and metabolic health on whole-brain functional integrity and how these conditions may disrupt higher-order network integrity

A Closer Look into the Role of Protein Tau in the Identification of Promising Therapeutic Targets for Alzheimer’s Disease

One of the most commonly known chronic neurodegenerative disorders, Alzheimer’s disease (AD), manifests the common type of dementia in 60–80% of cases. From a clinical standpoint, a patent cognitive decline and a severe change in personality, as caused by a loss of neurons, is usually evident in AD with about 50 million people affected in 2016. The disease progression in patients is distinguished by a gradual plummet in cognitive functions, eliciting symptoms such as memory loss, and eventually requiring full-time medical care. From a histopathological standpoint, the defining characteristics are intracellular aggregations of hyper-phosphorylated tau protein, known as neurofibrillary tangles (NFT), and depositions of amyloid β-peptides (Aβ) in the brain. The abnormal phosphorylation of tau protein is attributed to a wide gamut of neurological disorders known as tauopathies. In addition to the hyperphosphorylated tau lesions, neuroinflammatory processes could occur in a sustained manner through astro-glial activation, resulting in the disease progression. Recent findings have suggested a strong interplay between the mechanism of Tau phosphorylation, disruption of microtubules, and synaptic loss and pathology of AD. The mechanisms underlying these interactions along with their respective consequences in Tau pathology are still ill-defined. Thus, in this review: (1) we highlight the interplays existing between Tau pathology and AD; and (2) take a closer look into its role while identifying some promising therapeutic advances including state of the art imaging techniques

Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study

Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis

Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study

Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis

Schizophrenia Shows Disrupted Links between Brain Volume and Dynamic Functional Connectivity

Studies featuring multimodal neuroimaging data fusion for understanding brain function and structure, or disease characterization, leverage the partial information available in each of the modalities to reveal data variations not exhibited through the independent analyses. Similar to other complex syndromes, the characteristic brain abnormalities in schizophrenia may be better understood with the help of the additional information conveyed by leveraging an advanced modeling method involving multiple modalities. In this study, we propose a novel framework to fuse feature spaces corresponding to functional magnetic resonance imaging (functional) and gray matter (structural) data from 151 schizophrenia patients and 163 healthy controls. In particular, the features for the functional and structural modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) maps and the intensities of the gray matter (GM) maps, respectively. The dFNC maps are estimated from group independent component analysis (ICA) network time-courses by first computing windowed functional correlations using a sliding window approach, and then estimating subject specific states from this windowed data using temporal ICA followed by spatio-temporal regression. For each subject, the functional data features are horizontally concatenated with the corresponding GM features to form a combined feature space that is subsequently decomposed through a symmetric multimodal fusion approach involving a combination of multiset canonical correlation analysis (mCCA) and joint ICA (jICA). Our novel combined analyses successfully linked changes in the two modalities and revealed significantly disrupted links between GM volumes and time-varying functional connectivity in schizophrenia. Consistent with prior research, we found significant group differences in GM comprising regions in the superior parietal lobule, precuneus, postcentral gyrus, medial/superior frontal gyrus, superior/middle temporal gyrus, insula and fusiform gyrus, and several significant aberrations in the inter-regional functional connectivity strength as well. Importantly, structural and dFNC measures have independently shown changes associated with schizophrenia, and in this work we begin the process of evaluating the links between the two, which could shed light on the illness beyond what we can learn from a single imaging modality. In future work, we plan to evaluate replication of the inferred structure-function relationships in independent partitions of larger multi-modal schizophrenia datasets