Auditory oddball deficits in schizophrenia: an independent component analysis of the fMRI multisite function BIRN study.

Deficits in the connectivity between brain regions have been suggested to play a major role in the pathophysiology of schizophrenia. A functional magnetic resonance imaging (fMRI) analysis of schizophrenia was implemented using independent component analysis (ICA) to identify multiple temporally cohesive, spatially distributed regions of brain activity that represent functionally connected networks. We hypothesized that functional connectivity differences would be seen in auditory networks comprised of regions such as superior temporal gyrus as well as executive networks that consisted of frontal-parietal areas. Eight networks were found to be implicated in schizophrenia during the auditory oddball paradigm. These included a bilateral temporal network containing the superior and middle temporal gyrus; a default-mode network comprised of the posterior cingulate, precuneus, and middle frontal gyrus; and multiple dorsal lateral prefrontal cortex networks that constituted various levels of between-group differences. Highly task-related sensory networks were also found. These results indicate that patients with schizophrenia show functional connectivity differences in networks related to auditory processing, executive control, and baseline functional activity. Overall, these findings support the idea that the cognitive deficits associated with schizophrenia are widespread and that a functional connectivity approach can help elucidate the neural correlates of this disorder.

Supervised multimodal fusion and its application in searching joint neuromarkers of working memory deficits in schizophrenia.

Multimodal fusion is an effective approach to better understand brain disease. To date, most current fusion approaches are unsupervised; there is need for a multivariate method that can adopt prior information to guide multimodal fusion. Here we proposed a novel supervised fusion model, called "MCCAR+jICA", which enables both identification of multimodal co-alterations and linking the covarying brain regions with a specific reference signal, e.g., cognitive scores. The proposed method has been validated on both simulated and real human brain data. Features from 3 modalities (fMRI, sMRI, dMRI) obtained from 147 schizophrenia patients and 147 age-matched healthy controls were included as fusion input, who participated in the Function Biomedical Informatics Research Network (FBIRN) Phase III study. Our aim was to investigate the group co-alterations seen in three types of MRI data that are also correlated with working memory performance. One joint IC was found both significantly group-discriminating (p=7.4E-06, 0.001, 7.0E-09) and highly correlated with working memory scores(r=0.296, 0.241, 0.301) and PANSS negative scores (r=-0.229, -0.276, -0.240) for fMRI, dMRI and sMRI, respectively. Given the simulation and FBIRN results, MCCAR+jICA is shown to be an effective multivariate approach to extract accurate and stable multimodal components associated with a particular measure of interest, and promises a wide application in identifying potential neuromarkers for mental disorders.