, Volume 8, Issue 4, pp 213–229

Identification of Imaging Biomarkers in Schizophrenia: A Coefficient-constrained Independent Component Analysis of the Mind Multi-site Schizophrenia Study


    • The Mind Research Network
  • Jing Sui
    • The Mind Research Network
  • Srinivas Rachakonda
    • The Mind Research Network
  • Tonya White
    • Department of PsychiatryUniversity of Minnesota Medical Center
  • Dara S. Manoach
    • Neuroimaging Division, Department of PsychiatryMassachusetts General Hospital
  • V. P. Clark
    • The Mind Research Network
    • Department of PsychologyUniveristy of New Mexico
  • Beng-Choon Ho
    • Department of PsychiatryUniversity of Iowa College of Medicine
  • S. Charles Schulz
    • Department of PsychiatryUniversity of Minnesota Medical Center
    • The Mind Research Network
    • Department of Electrical EngineeringUniversity of New Mexico

DOI: 10.1007/s12021-010-9077-7

Cite this article as:
Kim, D.I., Sui, J., Rachakonda, S. et al. Neuroinform (2010) 8: 213. doi:10.1007/s12021-010-9077-7


A number of recent studies have combined multiple experimental paradigms and modalities to find relevant biological markers for schizophrenia. In this study, we extracted fMRI features maps from the analysis of three experimental paradigms (auditory oddball, Sternberg item recognition, sensorimotor) for a large number (n = 154) of patients with schizophrenia and matched healthy controls. We used the general linear model (GLM) and independent component analysis (ICA) to extract feature maps (i.e. ICA component maps and GLM contrast maps), which were then subjected to a coefficient-constrained independent component analysis (CCICA) to identify potential neurobiological markers. A total of 29 different feature maps were extracted for each subject. Our results show a number of optimal feature combinations that reflect a set of brain regions that significantly discriminate between patients and controls in the spatial heterogeneity and amplitude of their feature signals. Spatial heterogeneity was seen in regions such as the superior/middle temporal and frontal gyri, bilateral parietal lobules, and regions of the thalamus. Most strikingly, an ICA feature representing a bilateral frontal pole network was consistently seen in the ten highest feature results when ranked on differences found in the amplitude of their feature signals. The implication of this frontal pole network and the spatial variability which spans regions comprising of bilateral frontal/temporal lobes and parietal lobules suggests that they might play a significant role in the pathophysiology of schizophrenia.


SchizophreniaCoefficient constrained independent component analysisIndependent component analysisfMRIBiomarkersCCICAWorking memoryAuditory oddballSensorimotor

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© Springer Science+Business Media, LLC 2010