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Computer‐automated focus lateralization of temporal lobe epilepsy using fMRI
Author(s) -
Chiang Sharon,
Levin Harvey S.,
Haneef Zulfi
Publication year - 2015
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.24696
Subject(s) - lateralization of brain function , ictal , temporal lobe , functional magnetic resonance imaging , epilepsy , magnetic resonance imaging , psychology , computer science , artificial intelligence , neuroscience , medicine , radiology
Purpose To compare the performance of computer‐automated diagnosis using functional magnetic resonance imaging (fMRI) interictal graph theory (CADFIG) to that achieved in standard clinical practice with MRI, for lateralizing the affected hemisphere in temporal lobe epilepsy (TLE). Materials and Methods Interictal resting state fMRI and high‐resolution MRI were performed on 14 left and 10 right TLE patients. Functional topology measures were calculated from fMRI using graph theory, and used to lateralize the epileptogenic hemisphere using quadratic discriminant analysis. Leave‐one‐out cross‐validation prediction accuracy of CADFIG was compared to performance based on expert manual analysis (MA) of MRI, using video EEG as the "gold standard" for focus lateralization. Results CADFIG correctly lateralized 95.8% (23/24) of cases, compared to 66.7% (16/24) with expert MA of MRI. Combining MA with CADFIG allowed all cases (24/24) to be correctly lateralized. CADFIG correctly identified the affected hemisphere for all patients (8/8) where MRI failed to lateralize. Conclusion CADFIG based on fMRI lateralized the affected hemisphere in TLE with superior performance compared to expert MA of MRI. These results demonstrate that functional patterns in fMRI can be used with automated machine learning for diagnostic lateralization in TLE. Addition of fMRI‐based tests to existing protocols for identifying the affected hemisphere in presurgical assessment can improve diagnostic accuracy and surgical outcome in TLE. J. Magn. Reson. Imaging 2015;41:1689–1694 . © 2014 Wiley Periodicals, Inc .