Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study.
Pre-surgical evaluation of patients with temporal lobe epilepsy (TLE) relies on information obtained from multiple neuroimaging modalities. The relationship between modalities and their combined power in predicting the seizure focus is currently unknown. We investigated asymmetries from three different modalities, PET (glucose metabolism), MRI (cortical thickness), and diffusion tensor imaging (DTI; white matter anisotropy) in 28 left and 30 right TLE patients (LTLE and RTLE). Stepwise logistic regression models were built from each modality separately and from all three combined, while bootstrapped methods and split-sample validation verified the robustness of predictions. Among all multimodal asymmetries, three PET asymmetries formed the best predictive model (100% success in full sample, >95% success in split-sample validation). The combinations of PET with other modalities did not perform better than PET alone. Probabilistic classifications were obtained for new clinical cases, which showed correct lateralization for 7/7 new TLE patients (100%) and for 4/5 operated patients with discordant or non-informative PET reports (80%). Metabolism showed closer relationship with white matter in LTLE and closer relationship with gray matter in RTLE. Our data suggest that metabolism is a powerful modality that can predict seizure laterality with high accuracy, and offers high value for automated predictive models. The side of epileptogenic focus can affect the relationship of metabolism with brain structure. The data and tools necessary to obtain classifications for new TLE patients are made publicly available.
Department of Neurology, University of Pennsylvania, Philadelphia, USA.
Dorian
D
Pustina
Department of Radiology, University of Pennsylvania, Philadelphia, USA.
Brian
B
Avants
Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA.
Michael
M
Sperling
Department of Radiology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA.
Richard
R
Gorniak
Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA.
Xiaosong
X
He
Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA.
Gaelle
G
Doucet
Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA.
Paul
P
Barnett
Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA.
Scott
S
Mintzer
Department of Neurosurgery, Thomas Jefferson University, Philadelphia, USA.
Ashwini
A
Sharan
Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA ; Department of Radiology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA.
Joseph
J
Tracy
D000328:Adult; D001921:Brain / Q000378:metabolism / Q000473:pathology / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis*; D005260:Female; D007839:Functional Laterality / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D064847:Multimodal Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D012044:Regression Analysis
Asymmetry; Classification; Machine learning; Metabolism; Resection