Electroconvulsive therapy (ECT) has been widely used to treat the major depressive disorder (MDD), especially for treatment-resistant depression. However, the neuroanatomical basis of ECT remains an open problem. In our study, we combined the voxel-based morphology (VBM), resting-state functional connectivity (RSFC) and granger causality analysis (GCA) to identify the longitudinal changes of st...

We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a s...

Attention Deficit/Hyperactivity Disorder (ADHD) is a common psychiatric disorder of childhood that is characterized by symptoms of inattention, impulsivity/hyperactivity, or a combination of both. Intrinsic brain dysfunction in ADHD can be examined through various methods including resting state functional Magnetic Resonance Imaging (rs-fMRI), which investigates patients’ functional brain conne...

Brain decoding is a hot spot in cognitive science, which focuses on reconstructing perceptual images from brain activities. Analyzing the correlations of collected data from human brain activities and representing activity patterns are two problems in brain decoding based on functional magnetic resonance imaging (fMRI) signals. However, existing correlation analysis methods mainly focus on the ...

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method,...

We compare a variety of different anatomic connectivity measures, including several novel ones, that may help in distinguishing Alzheimer’s disease (AD) patients from controls. We studied diffusionweighted magnetic resonance imaging from 200 subjects scanned as part of the Alzheimer’s Disease Neuroimaging Initiative. We first evaluated measures derived from connectivity matrices based on whole-...

Rumination, an internal cognitive state characterized by recursive thinking of current self-distress and past negative events, has been found to correlate with the development of depressive disorders. Here, we investigated the feasibility of using connectivity for distinguishing different emotional states induced by a novel free-streaming, subject-driven experimental paradigm. Connectivity betw...

BACKGROUND In this study, we investigated the structural connectivity profile of patients with scans without evidence of dopaminergic deficit (SWEDD) compared with normal controls (NC) and patients with Parkinson's disease (PD). An accurate understanding of SWEDD is important so that appropriate therapeutic options can be presented to patients. METHODS Diffusion magnetic resonance imaging of ...

Recent studies have shown that multivariate pattern analysis (MVPA) can be useful for distinguishing brain disorders into categories. Such analyses can substantially enrich and facilitate clinical diagnoses. Using MPVA methods, whole brain functional networks, especially those derived using different frequency windows, can be applied to detect brain states. We constructed whole brain functional...

Alzheimer’s disease and other neurological diseases are often characterized by brain atrophy. It is hypothesized that such degradation directly affects connectivity as measured by whole brain tractographies and their derived connectivity networks. It is unclear, however, that current network construction methods provide either the most useful or efficient representation of the underlying connec...