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Brain Behav. 2017 Aug 30;7(10):e00801. doi: 10.1002/brb3.801. eCollection 2017 Oct.

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI.

Author information

1
Department of Neurology Medical University of South Carolina Charleston SC USA.
2
Department of Radiology and Radiological Science Medical University of South Carolina Charleston SC USA.
3
Ralph H. Johnson VA Medical Center Charleston SC USA.

Abstract

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.

KEYWORDS:

Magnetic Resonance Imaging (MRI); diffusion kurtosis imaging; epilepsy; machine learning; support vector machines

PMID:
29075561
PMCID:
PMC5651385
DOI:
10.1002/brb3.801
[Indexed for MEDLINE]
Free PMC Article

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