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Potential Biomarkers of Schizophrenia from MEG Resting-State Functional Connectivity Networks: Preliminary Data

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DOI: 10.4236/jbbs.2015.51001    3,678 Downloads   4,479 Views   Citations

ABSTRACT

Previous studies examining coherence and connectivity deviations in schizophrenia patients relied on standard coherence measures between recording sites (at the sensor level). A coherence source imaging (CSI) methodology where coherence is assessed within imaged brain structures (at the source level) was developed recently by our group and applied successfully for detecting coherent areas in the cortical networks of patients with epilepsy. We applied this Magnetoencephalography (MEG)-CSI technique to measure normal and pathological patterns of brain oscillations (biomarkers) in normal subjects and patients diagnosed with schizophrenia. Twelve patients diagnosed with schizophrenia and twelve healthy control subjects were studied. A ten-minute resting state MEG brain scan was performed with eyes open. MEG-CSI analysis was performed to identify the cortical areas that interacted strongly within the 3 - 50 Hz frequency range. Statistically significant increased regions of coherence were detected in schizophrenia patients compared to controls in the right inferior frontal gyrus (BA 47—pars orbitalis), left superior frontal gyrus (BA9— dorsolateral prefrontal cortex), right middle frontal gyrus (BA 10—anterior prefrontal cortex & BA 46—dorsolateral prefrontal cortex), and right cingulate gyrus (BA 24—ventral anterior cingulate cortex). These areas are involved in language, memory, decision making, empathy, executive and, higher cognitive functioning. We conclude that MEG-CSI can detect imaging biomarkers from resting state brain activity in schizophrenia patients that deviates from normal control subjects in several behaviorally salient brain regions. Analysis with MEG-CSI can provide biomarkers of abnormalities in the resting-state. The findings and procedures described can be used to probe the pathophysiology of schizophrenia and possibly detect subtypes.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Bowyer, S. , Gjini, K. , Zhu, X. , Kim, L. , Moran, J. , Rizvi, S. , Gumenyuk, V. , Tepley, N. and Boutros, N. (2015) Potential Biomarkers of Schizophrenia from MEG Resting-State Functional Connectivity Networks: Preliminary Data. Journal of Behavioral and Brain Science, 5, 1-11. doi: 10.4236/jbbs.2015.51001.

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