title: Detection of cerebral reorganization induced by real-time fMRI feedback training of insula activation: a multivariate investigation.
abstract: BACKGROUND
  Studies with real-time functional magnetic resonance imaging (fMRI) demonstrate that humans volitionally regulate hemodynamic signals from circumscribed regions of the brain, leading to area-specific behavioral consequences. Methods to better determine the nature of dynamic functional interactions between different brain regions and plasticity due to self-regulation training are still in development.
  
  
  OBJECTIVE
  The authors investigated changes in brain states while training 6 healthy participants to self-regulate insular cortex by real-time fMRI feedback.
  
  
  METHODS
  The authors used multivariate pattern analysis to observe spatial pattern changes and a multivariate Granger causality model to show changes in temporal interactions in multiple brain areas over the course of 5 repeated scans per subject during positive and negative emotional imagery with feedback about the level of insular activation.
  
  
  RESULTS
  Feedback training leads to more spatially focused recruitment of areas relevant for learning and emotion. Effective connectivity analysis reveals that initial training is associated with an increase in network density; further training "prunes" presumably redundant connections and "strengthens" relevant connections.
  
  
  CONCLUSIONS
  The authors demonstrate the application of multivariate methods for assessing cerebral reorganization during the learning of volitional control of local brain activity. The findings provide insight into mechanisms of training-induced learning techniques for rehabilitation. The authors anticipate that future studies, specifically designed with this hypothesis in mind, may be able to construct a universal index of cerebral reorganization during skill learning based on multiple similar criteria across various skilled tasks. These techniques may be able to discern recovery from compensation, dose-response curves related to training, and ways to determine whether rehabilitation training is actively engaging necessary networks.
authors.affiliation: University of Tübingen, Tübingen, Germany. lee.sangkyun@gmail.com
authors.forename: Sangkyun
authors.initials: S
authors.lastname: Lee
authors.forename: Sergio
authors.initials: S
authors.lastname: Ruiz
authors.forename: Andrea
authors.initials: A
authors.lastname: Caria
authors.forename: Ralf
authors.initials: R
authors.lastname: Veit
authors.forename: Niels
authors.initials: N
authors.lastname: Birbaumer
authors.forename: Ranganatha
authors.initials: R
authors.lastname: Sitaram
mesh_terms: D032763:Behavior Control / Q000379:methods; D001676:Biofeedback, Psychology / Q000379:methods* / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009434:Neural Pathways / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology; D013663:Teaching / Q000379:methods*; D013997:Time Factors