title: Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern analysis of resting-state fMRI from schizophrenic patients and their healthy siblings.
abstract: BACKGROUND
  Recently, a growing number of neuroimaging studies have begun to investigate the brains of schizophrenic patients and their healthy siblings to identify heritable biomarkers of this complex disorder. The objective of this study was to use multiclass pattern analysis to investigate the inheritable characters of schizophrenia at the individual level, by comparing whole-brain resting-state functional connectivity of patients with schizophrenia to their healthy siblings.
  
  
  METHODS
  Twenty-four schizophrenic patients, twenty-five healthy siblings and twenty-two matched healthy controls underwent the resting-state functional Magnetic Resonance Imaging (rs-fMRI) scanning. A linear support vector machine along with principal component analysis was used to solve the multi-classification problem. By reconstructing the functional connectivities with high discriminative power, three types of functional connectivity-based signatures were identified: (i) state connectivity patterns, which characterize the nature of disruption in the brain network of patients with schizophrenia; (ii) trait connectivity patterns, reflecting shared connectivities of dysfunction in patients with schizophrenia and their healthy siblings, thereby providing a possible neuroendophenotype and revealing the genetic vulnerability to develop schizophrenia; and (iii) compensatory connectivity patterns, which underlie special brain connectivities by which healthy siblings might compensate for an increased genetic risk for developing schizophrenia.
  
  
  RESULTS
  Our multiclass pattern analysis achieved 62.0% accuracy via leave-one-out cross-validation (p < 0.001). The identified state patterns related to the default mode network, the executive control network and the cerebellum. For the trait patterns, functional connectivities between the cerebellum and the prefrontal lobe, the middle temporal gyrus, the thalamus and the middle temporal poles were identified. Connectivities among the right precuneus, the left middle temporal gyrus, the left angular and the left rectus, as well as connectivities between the cingulate cortex and the left rectus showed higher discriminative power in the compensatory patterns.
  
  
  CONCLUSIONS
  Based on our experimental results, we saw some indication of differences in functional connectivity patterns in the healthy siblings of schizophrenic patients compared to other healthy individuals who have no relations with the patients. Our preliminary investigation suggested that the use of resting-state functional connectivities as classification features to discriminate among schizophrenic patients, their healthy siblings and healthy controls is meaningful.
authors: College of Mechatronics and Automation, National University of Defense Technology, Changsha, China.
authors: Yang
authors: Y
authors: Yu
authors: Hui
authors: H
authors: Shen
authors: Huiran
authors: H
authors: Zhang
authors: Ling-Li
authors: LL
authors: Zeng
authors: Zhimin
authors: Z
authors: Xue
authors: Dewen
authors: D
authors: Hu
mesh_terms: D000293:Adolescent; D000328:Adult; D016022:Case-Control Studies; D002531:Cerebellum / Q000503:physiopathology; D016208:Databases, Factual; D005260:Female; D005625:Frontal Lobe / Q000503:physiopathology; D059907:Functional Neuroimaging / Q000379:methods; D006179:Gyrus Cinguli / Q000503:physiopathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015999:Multivariate Analysis; D010296:Parietal Lobe / Q000503:physiopathology; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D012559:Schizophrenia / Q000175:diagnosis* / Q000235:genetics / Q000503:physiopathology*; D035781:Siblings; D012984:Software; D013702:Temporal Lobe / Q000503:physiopathology; D055815:Young Adult
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