A Mixed-Effects Model for Detecting Disrupted Connectivities in Heterogeneous Data

Article Type

Research Article

Publication Title

IEEE Transactions on Medical Imaging

Abstract

The human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson's disease, Alzheimer's disease, and autism. functional magnetic resonance imaging has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose a special type of mixed-effects model together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities for developing a neural network in whole brain studies. Results are illustrated with a large data set known as autism brain imaging data exchange which includes 361 subjects from eight medical centers.

First Page

2381

Last Page

2389

DOI

10.1109/TMI.2018.2821655

Publication Date

11-1-2018

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