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Functional network analysis based on matrix decomposition/factorization methods including ICA and dictionary learning models have become a popular approach in fMRI study. Yet it is still a challenging issue in interpreting the result networks because of the inter-subject variability and image noises, thus in many cases, manual inspection on the obtained networks is needed. Aiming to provide a fast...
Diffusion tensor imaging allows to infer brain connectivity from white matter, which can then be investigated aiming at finding possible biomarkers of disease. The usual initial step in graph construction is to identify the nodes in the brain using a predefined atlas. However, atlases are usually not considering the white matter structure. As a result, atlas-based brain parcellation and, hence, brain...
Autism Spectrum Disorder (ASD) is a complex developmental disorder affecting 1 in 68 children in the United States. While the prevalence may be on the rise, we currently lack a firm understanding of the etiology of the disease, and diagnosis is made purely on behavioral observation and informant report. As one potential method for improving our understanding of ASD, the current study took a network-level...
An accurate registration plays a critical role in group-wise fMRI image analysis. Inspired by the observations that common functional networks can be reconstructed from fMRI image across individuals and in different brain states, we propose a novel computational framework for fMRI image registration by using these common function networks as references for correspondence between individuals. This...
Mouse models are broadly used to study the mechanisms of neuropsychiatric disorders and to test potential treatments. In these models, automation to monitor behavioural differences during social interactions is currently limited. We propose in the present study a new method to conduct automatic behavioural classification, using an original unsupervised machine learning. We applied the proposed method...
Functional and structural connectivity convey different information about the brain. The integration of these different approaches is receiving growing attention from the research community, as it can shed new light on brain functions. This manuscript proposes a constrained autoregressive model with different lag-orders generating an “effective” connectivity matrix which models the structural connectivity...
This paper introduces a comprehensive computer-aided diagnosis (CAD) system for autism diagnosis that integrates anatomical and functional information of the brain using both structural and functional magnetic resonance (MR) brain images. In order to move towards the idea of personalized medicine, analysis of the brain's Brodmann areas (BAs) is conducted to reach a diagnosis decision on the local...
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