In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia ( N = 81) as well as age- and sex-matched healthy controls ( N = 93). We present an ensemble model -- EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples ( N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.

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Background:A sensation of vibration is experienced during audible ‘OM’ chanting. This has the potential for vagus nerve stimulation through its auricular branches and the effects on the brain thereof. The neurohemodynamic correlates of ‘OM’ chanting are yet to be explored.Materials and Methods:Using functional Magnetic Resonance Imaging (fMRI), the neurohemodynamic correlates of audible ‘OM’ chanting were examined in right-handed healthy volunteers (n=12; nine men). The ‘OM’ chanting condition was compared with pronunciation of “ssss” as well as a rest state. fMRI analysis was done using Statistical Parametric Mapping 5 (SPM5).Results:In this study, significant deactivation was observed bilaterally during ‘OM’ chanting in comparison to the resting brain state in bilateral orbitofrontal, anterior cingulate, parahippocampal gyri, thalami and hippocampi. The right amygdala too demonstrated significant deactivation. No significant activation was observed during ‘OM’ chanting. In contrast, neither activation nor deactivation occurred in these brain regions during the comparative task – namely the ‘ssss’ pronunciation condition.Conclusion:The neurohemodynamic correlates of ‘OM’ chanting indicate limbic deactivation. As similar observations have been recorded with vagus nerve stimulation treatment used in depression and epilepsy, the study findings argue for a potential role of this ‘OM’ chanting in clinical practice.