Background & objectives:Clozapine may be more useful in treatment-naive patients with first-episode schizophrenia for better symptoms control and improving quality of life. The current study was carried out to compare the efficacy and tolerability of clozapine versus risperidone in treatment-naive, first-episode patients of schizophrenia.Methods:This was a comparative, open-label, six months prospective study of treatment-naive, first-episode patients with schizophrenia between the age group of 18 and 40 yr diagnosed as per the International Classification of Diseases-10 (ICD-10) criteria. A total of 63 patients were recruited and randomly assigned to clozapine group or risperidone group using computer-generated random number tables. Eight patients were lost to follow up. The dosages of the respective drugs were kept in therapeutic range of 200-600 mg/day and 4-8 mg/day orally for clozapine and risperidone, respectively.Results:On general psychopathology score, after six months of intervention, clozapine led to 60.32 per cent mean reduction in Positive and Negative Syndrome Scale (PANSS) for Schizophrenia total score while risperidone led to 56.35 per cent mean reduction in PANSS total score, which meant more improvement with clozapine. Clozapine group was found to have significant improvement in quality of life (P = 0.04339). On Glasgow Antipsychotic Side-effect Scale, clozapine was superior to risperidone. The most common side effects observed in clozapine group were oversedation (78.96%) and dizziness (55.23%), and in risperidone group, common side effects were rigidity (62.36%), sedation (38.69%), tremors (65.69%) and menstrual irregularities in 80.25 per cent of female patients.Interpretation & conclusions:The findings of this preliminary study showed clozapine as a better choice than risperidone in terms of efficacy, tolerability and better quality of life in treatment-naive, first-episode schizophrenia. However, further studies need to be done on a larger group of patients to confirm the findings.

Stimulant prescriptions are routinely used to treat Attention Deficit Hyperactivity Disorder. Reports of psychiatric symptoms that have occurred include euphoria, delirium, confusion, toxic psychosis, and hallucinations. Here, authors report two cases of Attention Deficit Hyperactivity Disorder who were prescribed methylphenidate. Both children developed suicidal ideation that abated after discontinuing the drug. There were no depressive symptoms reported along with it, and the ideation could not be explained on the basis of impulsivity either.

One of the major tasks in the society is the enhancement of the cognitive functions, to improve intellectual deficiencies or psychosomatic ailments, hence, improving the quality of life. This new period of therapeutic advances uses various treatments using various sub-disciplines of biomedical engineering and psychology. Neurofeedback (NF) based operant conditioning is one of them. Up till now, many reports have focused on efficacy of NF in context of clinical and non-clinical applications. New advances in cognitive neuroscience and imaging methods have made neuro feedback training (NFT) more efficient. So, there have recently been further developments in traditional NF procedures. A comprehensive review on the recent advancements with major issues and challenges are tabulated. Even though a number of reviews have been proposed in the literature, but not any of the study has executed analysis of the recent advances.

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Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.