title: A novel data-driven approach to preoperative mapping of functional cortex using resting-state functional magnetic resonance imaging.
abstract: BACKGROUND
  Recent findings associated with resting-state cortical networks have provided insight into the brain's organizational structure. In addition to their neuroscientific implications, the networks identified by resting-state functional magnetic resonance imaging (rs-fMRI) may prove useful for clinical brain mapping.
  
  
  OBJECTIVE
  To demonstrate that a data-driven approach to analyze resting-state networks (RSNs) is useful in identifying regions classically understood to be eloquent cortex as well as other functional networks.
  
  
  METHODS
  This study included 6 patients undergoing surgical treatment for intractable epilepsy and 7 patients undergoing tumor resection. rs-fMRI data were obtained before surgery and 7 canonical RSNs were identified by an artificial neural network algorithm. Of these 7, the motor and language networks were then compared with electrocortical stimulation (ECS) as the gold standard in the epilepsy patients. The sensitivity and specificity for identifying these eloquent sites were calculated at varying thresholds, which yielded receiver-operating characteristic (ROC) curves and their associated area under the curve (AUC). RSNs were plotted in the tumor patients to observe RSN distortions in altered anatomy.
  
  
  RESULTS
  The algorithm robustly identified all networks in all patients, including those with distorted anatomy. When all ECS-positive sites were considered for motor and language, rs-fMRI had AUCs of 0.80 and 0.64, respectively. When the ECS-positive sites were analyzed pairwise, rs-fMRI had AUCs of 0.89 and 0.76 for motor and language, respectively.
  
  
  CONCLUSIONS
  A data-driven approach to rs-fMRI may be a new and efficient method for preoperative localization of numerous functional brain regions.
authors.affiliation: Departments of *Neurological Surgery, ‡Neurology, §Biomedical Engineering, and ¶Mechanical Engineering and Material Sciences, ‖Mallinckrodt Institute of Radiology, #Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, Missouri.
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Mitchell
authors.forename: Carl D
authors.initials: CD
authors.lastname: Hacker
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Breshears
authors.forename: Nick P
authors.initials: NP
authors.lastname: Szrama
authors.forename: Mohit
authors.initials: M
authors.lastname: Sharma
authors.forename: David T
authors.initials: DT
authors.lastname: Bundy
authors.forename: Mrinal
authors.initials: M
authors.lastname: Pahwa
authors.forename: Maurizio
authors.initials: M
authors.lastname: Corbetta
authors.forename: Abraham Z
authors.initials: AZ
authors.lastname: Snyder
authors.forename: Joshua S
authors.initials: JS
authors.lastname: Shimony
authors.forename: Eric C
authors.initials: EC
authors.lastname: Leuthardt
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000502:physiology*; D012372:ROC Curve; D012146:Rest / Q000502:physiology