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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Transcranial direct current stimulation (tDCS) is an upcoming treatment modality for patients with schizophrenia. A series of recent observations have demonstrated improvement in clinical status of schizophrenia patients with tDCS. This review summarizes the research work that has examined the effects of tDCS in schizophrenia patients with respect to symptom amelioration, cognitive enhancement and neuroplasticity evaluation. tDCS is emerging as a safe, rapid and effective treatment for various aspects of schizophrenia symptoms ranging from auditory hallucinations-for which the effect is most marked, to negative symptoms and cognitive symptoms as well. An interesting line of investigation involves using tDCS for altering and examining neuroplasticity in patients and healthy subjects and is likely to lead to new insights into the neurological aberrations and pathophysiology of schizophrenia. The mechanistic aspects of the technique are discussed in brief. Future work should focus on establishing the clinical efficacy of this novel technique and on evaluating this modality as an adjunct to cognitive enhancement protocols. Understanding the mechanism of action of tDCS as well as the determinants and neurobiological correlates of clinical response to tDCS remains an important goal, which will help us expand the clinical applications of tDCS for the treatment of patients with schizophrenia.