Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning

Yang, Zhengyi, Choupan, Jeiran, Reutens, David, & Hocking, Julia (2015) Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning. Frontiers in Neurology, 6(184), pp. 1-9.

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Abstract

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

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ID Code: 86990
Item Type: Journal Article
Refereed: Yes
Keywords: temporal lobe epilepsy, laterality of TLE, resting-state functional connectivity, machine learning, feature selection
DOI: 10.3389/fneur.2015.00184
ISSN: 1664-2295
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > OTHER MATHEMATICAL SCIENCES (019900) > Mathematical Sciences not elsewhere classified (019999)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > CLINICAL SCIENCES (110300) > Nuclear Medicine (110313)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > NEUROSCIENCES (110900) > Central Nervous System (110903)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > NEUROSCIENCES (110900) > Neurology and Neuromuscular Diseases (110904)
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200) > Neurocognitive Patterns and Neural Networks (170205)
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Psychology & Counselling
Funding:
Copyright Statement: This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission
Deposited On: 14 Sep 2015 00:59
Last Modified: 15 Sep 2015 22:42

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