A method to classify schizophrenia using inter-task spatial correlations of functional brain images

• Published in: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2008; pp. 5510 - 5513
• Main Authors: Michael, Andrew M., Calhoun, Vince D., Andreasen, Nancy C., Baum, Stefi A.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2008
• Subjects: Adult; Artificial Intelligence; Brain - physiopathology; Brain Mapping - methods; Cognition; Correlation; Evoked Potentials; Female; Functional magnetic resonance imaging; Histograms; Humans; Image Interpretation, Computer-Assisted - methods; Large Hadron Collider; Magnetic Resonance Imaging - methods; Male; Mental disorders; Probes; Reproducibility of Results; Schizophrenia; Schizophrenia - diagnosis; Schizophrenia - physiopathology; Sensitivity and Specificity; Silicon; Statistics as Topic; Task Performance and Analysis; Thumb
• ISBN: 9781424418145; 1424418143
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/IEMBS.2008.4650462

The clinical heterogeneity of schizophrenia (scz) and the overlap of self reported and observed symptoms with other mental disorders makes its diagnosis a difficult task. At present no laboratory-based or image-based diagnostic tool for scz exists and such tools are desired to support existing methods for more precise diagnosis. Functional magnetic resonance imaging (fMRI) is currently employed to identify and correlate cognitive processes related to scz and its symptoms. Fusion of multiple fMRI tasks that probe different cognitive processes may help to better understand hidden networks of this complex disorder. In this paper we utilize three different fMRI tasks and introduce an approach to classify subjects based on inter-task spatial correlations of brain activation. The technique was applied to groups of patients and controls and its validity was checked with the leave-one-out method. We show that the classification rate increases when information from multiple tasks are combined.