A two-level multimodality imaging Bayesian network approach for classification of partial epilepsy: preliminary data.
BACKGROUND
Quantitative neuroimaging analyses have demonstrated gray and white matter abnormalities in group comparisons of different types of non-lesional partial epilepsy. It is unknown to what degree these type-specific patterns exist in individual patients and if they could be exploited for diagnostic purposes. In this study, a two-level multi-modality imaging Bayesian network approach is proposed that uses information about individual gray matter volume loss and white matter integrity to classify non-lesional temporal lobe epilepsy with (TLE-MTS) and without (TLE-no) mesial-temporal sclerosis and frontal lobe epilepsy (FLE).


METHODS
25 controls, 19 TLE-MTS, 22 TLE-no and 14 FLE were studied on a 4T MRI and T1 weighted structural and DTI images acquired. Spatially normalized gray matter (GM) and fractional anisotropy (FA) abnormality maps (binary maps with voxels 1 SD below control mean) were calculated for each subject. At the first level, each group's abnormality maps were compared with those from all the other groups using Graphical-Model-based Morphometric Analysis (GAMMA). GAMMA uses a Bayesian network and a Markov random field based contextual clustering method to produce maps of voxels that provide the maximal distinction between two groups and calculates a probability distribution and a group assignment based on this information. The information was then combined in a second level Bayesian network and the probability of each subject to belong to one of the three epilepsy types calculated.


RESULTS
The specificities of the two level Bayesian network to distinguish between the three patient groups were 0.87 for TLE-MTS and TLE-no and 0.86 for FLE, the corresponding sensitivities were 0.84 for TLE-MTS, 0.72 for TLE-no and 0.64 for FLE.


CONCLUSIONS
The two-level multi-modality Bayesian network approach was able to distinguish between the three epilepsy types with a reasonably high accuracy even though the majority of the images were completely normal on visual inspection.
Center for Imaging of Neurodegenerative Diseases, VA Medical Center, San Francisco, USA. susanne.mueller@ucsf.edu
Susanne G
SG
Mueller
Karl
K
Young
Miriam
M
Hartig
Jerome
J
Barakos
Paul
P
Garcia
Kenneth D
KD
Laxer
D000328:Adult; D001499:Bayes Theorem; D001931:Brain Mapping; D056324:Diffusion Tensor Imaging; D004828:Epilepsies, Partial / Q000145:classification*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male