title: Single-subject classification of schizophrenia by event-related potentials during selective attention.
abstract: BACKGROUND
  Executive dysfunction has repeatedly been proposed as a robust and promising substrate of analytical approaches in the research of neurocognitive markers of schizophrenia. Here, we present a mixed model- and data-driven classification approach by applying a task that targets executive dysfunction in schizophrenia and by investigating relevant event-related potential (ERP) features with machine learning classifiers.
  
  
  METHODS
  Forty schizophrenic patients and forty matched healthy controls completed the Attention Network Test while an electroencephalogram was recorded. Target-locked N1 and P3 ERP components were constructed and submitted to different classification analyses without a priori hypotheses. Standardized source localization was applied to estimate neural sources of N1 and P3 deficits in schizophrenia.
  
  
  RESULTS
  We obtained a classification accuracy of 79% using only very few ERP components. Central P3 components following compatible and incompatible trials and right parietal N1 latencies averaged across targets and were sufficient for classification. P3 deficits were associated with anterior cingulate cortex dysfunction, while right posterior current density deficits were observed in schizophrenia during the N1 time frame.
  
  
  CONCLUSIONS
  The data exemplarily show how automated inference may be applied to classify a pathological state in single subjects without prior knowledge of their diagnoses. While classification accuracy may be optimized by application of other executive paradigms, this approach illustrates the potential of machine learning algorithms for the identification of biomarkers that are independent of clinical assessments. Conversely, data suggest a pathophysiological mechanism that includes early visual and late executive deficits during response inhibition in schizophrenia.
authors.affiliation: Department of Psychiatry and Psychotherapy, Charité University Medicine, Campus Benjamin Franklin, Berlin, Germany. andres.neuhaus@charite.de
authors.forename: Andres H
authors.initials: AH
authors.lastname: Neuhaus
authors.forename: Florin C
authors.initials: FC
authors.lastname: Popescu
authors.forename: Cristian
authors.initials: C
authors.lastname: Grozea
authors.forename: Eric
authors.initials: E
authors.lastname: Hahn
authors.forename: Constanze
authors.initials: C
authors.lastname: Hahn
authors.forename: Carolin
authors.initials: C
authors.lastname: Opgen-Rhein
authors.forename: Carsten
authors.initials: C
authors.lastname: Urbanek
authors.forename: Michael
authors.initials: M
authors.lastname: Dettling
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001288:Attention / Q000502:physiology*; D015415:Biomarkers / Q000032:analysis; D004569:Electroencephalography; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011930:Reaction Time / Q000502:physiology; D012559:Schizophrenia / Q000145:classification* / Q000473:pathology* / Q000503:physiopathology; D055815:Young Adult
chemical_list: D015415:Biomarkers