Open AccessUsing machine learning to classify temporal lobe epilepsy based on diffusion MRI
Author(s) -
Del Gaizo John,
Mofrad Neda,
Jensen Jens H.,
Clark David,
Glenn Russell,
Helpern Joseph,
Bonilha Leonardo
Publication year - 2017
Publication title -
brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.915
H-Index - 41
ISSN - 2162-3279
DOI - 10.1002/brb3.801
Subject(s) - diffusion mri , kurtosis , voxel , fractional anisotropy , artificial intelligence , temporal lobe , support vector machine , pattern recognition (psychology) , computer science , nuclear medicine , epilepsy , magnetic resonance imaging , neuroimaging , medicine , mathematics , neuroscience , radiology , psychology , statistics
Background It is common for patients diagnosed with medial temporal lobe epilepsy ( TLE ) to have extrahippocampal damage. However, it is unclear whether microstructural extrahippocampal abnormalities are consistent enough to enable classification using diffusion MRI imaging. Therefore, we implemented a support vector machine ( SVM )‐based method to predict TLE from three different imaging modalities: mean kurtosis ( MK ), mean diffusivity ( MD ), and fractional anisotropy ( FA ). While MD and FA can be calculated from traditional diffusion tensor imaging ( DTI ), MK requires diffusion kurtosis imaging ( DKI ). Methods Thirty‐two TLE patients and 36 healthy controls underwent DKI imaging. To measure predictive capability, a fivefold cross‐validation ( CV ) was repeated for 1000 iterations. An ensemble of SVM models, each with a different regularization value, was trained with the subject images in the training set, and had performance assessed on the test set. The different regularization values were determined using a Bayesian‐based method. Results Mean kurtosis achieved higher accuracy than both FA and MD on every iteration, and had far superior average accuracy: 0.82 ( MK ), 0.68 ( FA ), and 0.51 ( MD ). Finally, the MK voxels with the highest coefficients in the predictive models were distributed within the inferior medial aspect of the temporal lobes. Conclusion These results corroborate our earlier publications which indicated that DKI shows more promise in identifying TLE ‐associated pathological features than DTI . Also, the locations of the contributory MK voxels were in areas with high fiber crossing and complex fiber anatomy. These traits result in non‐Gaussian water diffusion, and hence render DTI less likely to detect abnormalities. If the location of consistent microstructural abnormalities can be better understood, then it may be possible in the future to identify the various phenotypes of TLE . This is important since treatment outcome varies dependent on type of TLE .