Connectome Patterns Research Articles

Detecting and testing altered brain connectivity networks with k-partite network topology

Emerging brain connectivity network studies suggest that interactions betweenvarious distributed neuronal populations may be characterized by an organized complex topological structure. Many neuropsychiatric disorders are associated with altered topological patterns of brain connectivity. Therefore, a key inquiry of connectivity analysis is to detect group-level differentially expressed connectome patterns from the massive neuroimaging data. Recently, statistical methods have been developed to detect differentially expressed connectivity features at a subnetwork level, extending more commonly applied edge level analysis. However, the graph topological structures in these methods are limited to community/cliques which may not effectively uncover the underlying complex and disease-related brain circuits/subnetworks. Building on these previous subnetwork detection methods, a new statistical approach is developed to automatically identify the latent differentially expressed brain connectivity subnetworks with k-partite graph topological structures from large brain connectivity matrices. In addition, statistical inferential techniques are provided to test the detected topological structure. The new methods are evaluated via extensive simulation studies and then applied to resting state fMRI data (24 cases and 18 controls) for Parkinson’s disease research. A differentially expressed connectivity network with the k-partite graph topological structure is detected which reveals underlying neural features distinguishing Parkinson’s disease patients from healthy control subjects.

Electroconvulsive therapy "corrects" the neural architecture of visuospatial memory: Implications for typical cognitive-affective functioning.

Although electroconvulsive therapy (ECT) is a widely used and effective treatment for refractory depression, the neural underpinnings of its therapeutic effects remain poorly understood. To address this issue, here, we focused on a core cognitive deficit associated with depression, which tends to be reliably ameliorated through ECT, specifically, the ability to learn visuospatial information. Thus, we pursued three goals. First, we tested whether ECT can “normalize” the functional brain organization patterns associated with visuospatial memory and whether such corrections would predict post-ECT improvements in learning visuospatial information. Second, we investigated whether, among healthy individuals, stronger expression of the neural pattern, susceptible to adjustments through ECT, would predict reduced incidence of depression-relevant cognition and affect. Third, we sought to quantify the heritability of the ECT-correctable neural profile. Thus, in a task fMRI study with a clinical and a healthy comparison sample, we characterized two functional connectome patterns: one that typifies trait depression (i.e., differentiates patients from healthy individuals) and another that is susceptible to “normalization” through ECT. Both before and after ECT, greater expression of the trait depression neural profile was associated with more frequent repetitive thinking about past personal events (affective persistence), a hallmark of depressogenic cognition. Complementarily, post-treatment, stronger expression of the ECT-corrected neural profile was linked to improvements in visuospatial learning, a mental ability which is markedly impaired in depression. Subsequently, using data from the Human Connectome Project (HCP) (N = 333), we demonstrated that the functional brain organization of healthy participants with greater levels of subclinical depression and higher incidence of its associated cognitive deficits (affective persistence, impaired learning) shows greater similarity to the trait depression neural profile and reduced similarity to the ECT-correctable neural profile, as identified in the patient sample. These results tended to be specific to learning-relevant task contexts (working memory, perceptual relational processing). Genetic analyses based on HCP twin data (N = 128 pairs) suggested that, among healthy individuals, a functional brain organization similar to the one normalized by ECT in the patient sample is endogenous to cognitive contexts that require visuospatial processing that extends beyond the here-and-now. Broadly, the present findings supported our hypothesis that some of the therapeutic effects of ECT may be due to its correcting the expression of a naturally occurring pattern of functional brain organization that facilitates integration of internal and external cognition beyond the immediate present. Given their substantial susceptibility to both genetic and environmental effects, such mechanisms may be useful both for identifying at risk individuals and for monitoring progress of interventions targeting mood-related pathology.

Unique Mapping of Structural and Functional Connectivity on Cognition.

The unique mapping of structural brain connectivity (SC) and functional brain connectivity (FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the spatial distributions of SC and FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects (males, 269; females, 340) from the Human Connectome Project. We identified several sets of connections that each uniquely map onto cognitive function. We found a small number of distributed SCs and a larger set of corticocortical and corticosubcortical FCs that express this association. Importantly, the SC and FC each show unique and distinct patterns of variance across subjects as they relate to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.SIGNIFICANCE STATEMENT Structural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal coactivations between the activity of the brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavior patterns capture the correlations between SC and FC with a wide range of cognitive tasks, respectively. These brain-behavior patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes.

Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Resonance Imaging to unveil hidden pathological brain connectome patterns to uncover diagnostic and prognostic biomarkers. Recently, state-of-the-art deep learning techniques are outperforming traditional machine learning methods and are hailed as a milestone for artificial intelligence. However, whole brain classification that combines brain connectome with deep learning has been hindered by insufficient training samples. Inspired by the transfer learning strategy employed in computer vision, we exploited previously collected resting-state functional MRI data for healthy subjects from existing databases and transferred this knowledge for new disease classification tasks. We developed a deep transfer learning neural network (DTL-NN) framework for enhancing the classification of whole brain functional connectivity patterns. Briefly, we trained a stacked sparse autoencoder (SSAE) prototype to learn healthy functional connectivity patterns in an offline learning environment. Then, the SSAE prototype was transferred to a DTL-NN model for a new classification task. To test the validity of our framework, we collected resting-state functional MRI data from the Autism Brain Imaging Data Exchange (ABIDE) repository. Using autism spectrum disorder (ASD) classification as a target task, we compared the performance of our DTL-NN approach with a traditional deep neural network and support vector machine models across four ABIDE data sites that enrolled at least 60 subjects. As compared to traditional models, our DTL-NN approach achieved an improved performance in accuracy, sensitivity, specificity and area under receiver operating characteristic curve. These findings suggest that DTL-NN approaches could enhance disease classification for neurological conditions, where accumulating large neuroimaging datasets has been challenging.

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI

BackgroundA lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. MethodsA large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. FindingsAccuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. InterpretationThe findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the “disconnectivity” model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites.

Disrupted brain network topology in chronic insomnia disorder: A resting-state fMRI study.

This study investigated the topological characteristics of brain functional networks in chronic insomnia disorder (CID) patients. The resting-state functional magnetic resonance imaging and graph theory analysis method were applied to investigate the brain functional connectome patterns among 45 CID patients and 32 healthy controls. The brain functional connectome was constructed by thresholding partial correlation matrices of 90 brain regions from an automated anatomical labeling atlas. The topologic properties of brain functional connectomes at both global and nodal levels were tested. The CID patients had decreased number of module (p = .014) and hierarchy (p = .038), and increased assortativity (p = .035). Furthermore, some brain regions located in the default mode network, dorsal attention network, and sensory-motor network in these patients showed altered nodal centralities. Within these areas, the node betweenness of right central paracentral lobule had positive correlation with the Pittsburgh Sleep Quality Index score (R = 0.319, p = .039). The results imply that functional disruptions of CID patients may be related to disruptions in global and regional topological organization of the brain functional connectome, and provide new and important insights to understand the pathophysiological mechanisms of CID.

Brain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer.

The functional connectome derived from BOLD resting-state functional magnetic resonance imaging data represents meaningful functional organizations and a shift between distinct cognitive states. However, the body of knowledge on how the long-term career experience affects the brain’s functional plasticity is still very limited. In this study, we used a dynamic functional connectome characterization (DBFCC) model with the automatic target generation process K-Means clustering to explore the functional reorganization property of resting brain states, driven by long-term career experience. Taking sailors as an example, DBFCC generated seventeen reproducibly common atomic connectome patterns (ACP) and one reproducibly distinct ACP, i.e., ACP14. The common ACPs indicating the same functional topology of the resting brain state transitions were shared by two control groups, while the distinct ACP, which mainly represented functional plasticity and only existed in the sailors, showed close relationships with the long-term career experience of sailors. More specifically, the distinct ACP14 of the sailors was made up of four specific sub-networks, such as the auditory network, visual network, executive control network, and vestibular function-related network, which were most likely linked to sailing experience, i.e., continuously suffering auditory noise, maintaining balance, locating one’s position in three-dimensional space at sea, obeying orders, etc. Our results demonstrated DBFCC’s effectiveness in revealing the specifically functional alterations modulated by sailing experience and particularly provided the evidence that functional plasticity was beneficial in reorganizing brain’s functional topology, which could be driven by career experience.

A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity.

Functional connectivity (FC) has been widely investigated in many imaging-based neuroscience and clinical studies. Since functional Magnetic Resonance Image (MRI) signal is just an indirect reflection of brain activity, it is difficult to accurately quantify the FC strength only based on signal correlation. To address this limitation, we propose a learning-based tensor model to derive high sensitivity and specificity connectome biomarkers at the individual level from resting-state fMRI images. First, we propose a learning-based approach to estimate the intrinsic functional connectivity. In addition to the low level region-to-region signal correlation, latent module-to-module connection is also estimated and used to provide high level heuristics for measuring connectivity strength. Furthermore, sparsity constraint is employed to automatically remove the spurious connections, thus alleviating the issue of searching for optimal threshold. Second, we integrate our learning-based approach with the sliding-window technique to further reveal the dynamics of functional connectivity. Specifically, we stack the functional connectivity matrix within each sliding window and form a 3D tensor where the third dimension denotes for time. Then we obtain dynamic functional connectivity (dFC) for each individual subject by simultaneously estimating the within-sliding-window functional connectivity and characterizing the across-sliding-window temporal dynamics. Third, in order to enhance the robustness of the connectome patterns extracted from dFC, we extend the individual-based 3D tensors to a population-based 4D tensor (with the fourth dimension stands for the training subjects) and learn the statistics of connectome patterns via 4D tensor analysis. Since our 4D tensor model jointly (1) optimizes dFC for each training subject and (2) captures the principle connectome patterns, our statistical model gains more statistical power of representing new subject than current state-of-the-art methods which in contrast perform above two steps separately. We have applied our tensor statistical model to identify ASD (Autism Spectrum Disorder) by using the learned dFC patterns. Promising classification results have been achieved demonstrating high discrimination power and great potentials in computer assisted diagnosis of neuro-disorders.

Identifying and characterizing resting state networks in temporally dynamic functional connectomes.

An important application of resting state fMRI data has been to identify resting state networks (RSN). The conventional RSN studies attempted to discover consistent networks through functional connectivity analysis over the whole scan time, which implicitly assumes that RSNs are static. However, the brain undergoes dynamic functional state changes and the functional connectome patterns vary along with time, even in resting state. Hence, this study aims to characterize temporal brain dynamics in resting state. It utilizes the temporally dynamic functional connectome patterns to extract a set of resting state clusters and their corresponding RSNs based on the large-scale consistent, reproducible and predictable whole-brain reference system of dense individualized and common connectivity-based cortical landmarks (DICCCOL). Especially, an effective multi-view spectral clustering method was performed by treating each dynamic functional connectome pattern as one view, and this procedure was also applied on static multi-subject functional connectomes to obtain the static clusters for comparison. It turns out that some dynamic clusters exhibit high similarity with static clusters, suggesting the stability of those RSNs including the visual network and the default mode network. Moreover, two motor-related dynamic clusters show correspondence with one static cluster, which implies substantially more temporal variability of the motor resting network. Particularly, four dynamic clusters exhibited large differences in comparison with their corresponding static networks. Thus it is suggested that these four networks might play critically important roles in functional brain dynamics and interactions during resting state, offering novel insights into the brain function and its dynamics.

Characterization of task-free and task-performance brain states via functional connectome patterns

Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during R-fMRI/T-fMRI scans, it has been challenging to ascertain whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states during task-free/task-performance periods. This paper presents a novel computational approach to characterizing and differentiating the brain's functional status into task-free or task-performance states, by which the functional brain activities can be effectively understood and differentiated. Briefly, the brain's functional state is represented by a whole-brain quasi-stable connectome pattern (WQCP) of R-fMRI or T-fMRI data based on 358 consistent cortical landmarks across individuals, and then an effective sparse representation method was applied to learn the atomic connectome patterns (ACPs) of both task-free and task-performance states. Experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. A certain portion of ACPs from R-fMRI and T-fMRI data were overlapped, suggesting some subjects with overlapping ACPs were not in the expected task-free/task-performance brain states. Besides, potential outliers in the T-fMRI dataset were further investigated via functional activation detections in different groups, and our results revealed unexpected task-performances of some subjects. This work offers novel insights into the functional architectures of the brain.