title: Precuneus functioning differentiates first-episode psychosis patients during the fantasy movie Alice in Wonderland.
abstract: BACKGROUND
  While group-level functional alterations have been identified in many brain regions of psychotic patients, multivariate machine-learning methods provide a tool to test whether some of such alterations could be used to differentiate an individual patient. Earlier machine-learning studies have focused on data collected from chronic patients during rest or simple tasks. We set out to unravel brain activation patterns during naturalistic stimulation in first-episode psychosis (FEP).
  
  
  METHODS
  We recorded brain activity from 46 FEP patients and 32 control subjects viewing scenes from the fantasy film Alice in Wonderland. Scenes with varying degrees of fantasy were selected based on the distortion of the 'sense of reality' in psychosis. After cleaning the data with a novel maxCorr method, we used machine learning to classify patients and healthy control subjects on the basis of voxel- and time-point patterns.
  
  
  RESULTS
  Most (136/194) of the voxels that best classified the groups were clustered in a bilateral region of the precuneus. Classification accuracies were up to 79.5% (p = 5.69 × 10-8), and correct classification was more likely the higher the patient's positive-symptom score. Precuneus functioning was related to the fantasy content of the movie, and the relationship was stronger in control subjects than patients.
  
  
  CONCLUSIONS
  These findings are the first to show abnormalities in precuneus functioning during naturalistic information processing in FEP patients. Correlational findings suggest that these alterations are associated with positive psychotic symptoms and processing of fantasy. The results may provide new insights into the neuronal basis of reality distortion in psychosis.
authors: Mental Health Unit,National Institute for Health and Welfare,Helsinki,Finland.
authors: E
authors: E
authors: Rikandi
authors: Department of Neuroscience and Biomedical Engineering, andAdvanced Magnetic Imaging Centre,Aalto NeuroImaging,Aalto University School of Science,Espoo,Finland.
authors: S
authors: S
authors: Pamilo
authors: Mental Health Unit,National Institute for Health and Welfare,Helsinki,Finland.
authors: T
authors: T
authors: Mäntylä
authors: Mental Health Unit,National Institute for Health and Welfare,Helsinki,Finland.
authors: J
authors: J
authors: Suvisaari
authors: Mental Health Unit,National Institute for Health and Welfare,Helsinki,Finland.
authors: T
authors: T
authors: Kieseppä
authors: Department of Neuroscience and Biomedical Engineering, andAdvanced Magnetic Imaging Centre,Aalto NeuroImaging,Aalto University School of Science,Espoo,Finland.
authors: R
authors: R
authors: Hari
authors: Department of Neuroscience and Biomedical Engineering, andAdvanced Magnetic Imaging Centre,Aalto NeuroImaging,Aalto University School of Science,Espoo,Finland.
authors: M
authors: M
authors: Seppä
authors: Department of Neuroscience and Biomedical Engineering, andAdvanced Magnetic Imaging Centre,Aalto NeuroImaging,Aalto University School of Science,Espoo,Finland.
authors: T T
authors: TT
authors: Raij
mesh_terms: D000328:Adult; D005201:Fantasy; D005260:Female; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009040:Motion Pictures; D010296:Parietal Lobe / Q000503:physiopathology*; D011618:Psychotic Disorders / Q000503:physiopathology*; D055815:Young Adult
keywords: Classification; fMRI; machine learning; movie; naturalistic stimulus; psychosis