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A Mixed-Effects Model for Detecting Disrupted Connectivities in Heterogeneous Data | IEEE Journals & Magazine | IEEE Xplore

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


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 di...Show More

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.
Published in: IEEE Transactions on Medical Imaging ( Volume: 37, Issue: 11, November 2018)
Page(s): 2381 - 2389
Date of Publication: 30 March 2018

ISSN Information:

PubMed ID: 29994089

References

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