title: Machine learning classification of mesial temporal sclerosis in epilepsy patients.
abstract: OBJECTIVE
  Novel approaches applying machine-learning methods to neuroimaging data seek to develop individualized measures that will aid in the diagnosis and treatment of brain-based disorders such as temporal lobe epilepsy (TLE). Using a large cohort of epilepsy patients with and without mesial temporal sclerosis (MTS), we sought to automatically classify MTS using measures of cortical morphology, and to further relate classification probabilities to measures of disease burden.
  
  
  METHODS
  Our sample consisted of high-resolution T1 structural scans of 169 adults with epilepsy collected across five different 1.5T and four different 3T scanners at UCLA. We applied a multiple support vector machine recursive feature elimination algorithm to morphological measures generated from FreeSurfer's automated segmentation and parcellation in order to classify Epilepsy patients with MTS (n=85) from those without MTS (N=84).
  
  
  RESULTS
  In addition to hippocampal volume, we found that alterations in cortical thickness, surface area, volume and curvature in inferior frontal and anterior and inferior temporal regions contributed to a classification accuracy of up to 81% (p=1.3×10(-17)) in identifying MTS. We also found that MTS classification probabilities were associated with a longer duration of disease for epilepsy patients both with and without MTS.
  
  
  CONCLUSIONS
  In addition to implicating extra-hippocampal involvement of MTS, these findings shed further light on the pathogenesis of TLE and may ultimately assist in the development of automated tools that incorporate multiple neuroimaging measures to assist clinicians in detecting more subtle cases of TLE and MTS.
authors.affiliation: David Geffen School of Medicine at UCLA, United States.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Rudie
authors.affiliation: David Geffen School of Medicine at UCLA, United States.
authors.forename: John B
authors.initials: JB
authors.lastname: Colby
authors.affiliation: David Geffen School of Medicine at UCLA, United States; Department of Radiology, Ronald Reagan Hospital, UCLA, United States. Electronic address: nsalamon@mednet.ucla.edu.
authors.forename: Noriko
authors.initials: N
authors.lastname: Salamon
mesh_terms: D000328:Adult; D000465:Algorithms; D004833:Epilepsy, Temporal Lobe / Q000473:pathology*; D005260:Female; D006624:Hippocampus / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009929:Organ Size; D012598:Sclerosis / Q000473:pathology; D060388:Support Vector Machine; D013702:Temporal Lobe / Q000473:pathology*; D055815:Young Adult
keywords: Cortical folding; Cortical thickness; Hippocampus; Machine learning; Mesial temporal sclerosis; Temporal lobe epilepsy