Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms.

ABSTRACT: Schizophrenia is often associated with disrupted brain connectivity. However, identifying specific neuroimaging-based patterns pathognomonic for schizophrenia and related symptom severity remains a challenging open problem requiring large-scale data-driven analyses emphasizing not only statistical significance but also stability across multiple datasets, contexts and cohorts. Accurate prediction on previously unseen subjects, or generalization, is also essential for any useful biomarker of schizophrenia. In order to build a predictive model based on functional network feature patterns, we studied whole-brain fMRI functional networks, both at the voxel level and lower-resolution supervoxel level. Targeting Auditory Oddball task data on the FBIRN fMRI dataset (n = 95), we considered n

SUBMITTER: Gheiratmand M 

PROVIDER: S-EPMC5441570 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature