title: Computer-automated focus lateralization of temporal lobe epilepsy using fMRI.
abstract: OBJECTIVE
  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).
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Statistics, Rice University, Houston, Texas, USA.
authors.forename: Sharon
authors.initials: S
authors.lastname: Chiang
authors.affiliation: Department of Physical Medicine, Baylor College of Medicine, Houston, Texas, USA.|Michael E. DeBakey VA Medical Center, Houston, Texas, USA.
authors.forename: Harvey S
authors.initials: HS
authors.lastname: Levin
authors.affiliation: Department of Neurology, Baylor College of Medicine, Houston, Texas, USA.|Neurology Care Line, VA Medical Center, Houston, Texas, USA.
authors.forename: Zulfi
authors.initials: Z
authors.lastname: Haneef
mesh_terms: D004569:Electroencephalography; D004833:Epilepsy, Temporal Lobe / Q000473:pathology*; D007839:Functional Laterality; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated; D012680:Sensitivity and Specificity
keywords: automated pattern recognition; functional connectivity; functional magnetic resonance imaging; graph theory; lateralization; temporal lobe epilepsy