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Background: India is the largest hub for bariatric and metabolic surgery in Asia. OSSI is committed to improve the quality of care and set the standards for its practice in India. Methods: The first draft of OSSI guidelines was prepared by the secretary, Dr. Praveen Raj under the guidance of current President, Dr. Arun Prasad. All executive council members were given voting privileges, and the proposed guidelines were circulated on email for approval of the executive council. Guidelines were finalized after 100% agreement from all voting members and were also circulated among all OSSI members for their suggestions. Results: OSSI upholds the BMI criteria for bariatric and metabolic surgery of 2011 IFSO-APC guidelines. In addition to this, we recognize that waist circumference of ≥ 80 cm in females and ≥ 90 cm in males along with obesity related co-morbidities may be considered for surgery. In addition to standard procedures as recommended by IFSO, OSSI acknowledges the additional procedures, and a review of literature for these procedures is presented in the discussion. Conclusion: The burden of obesity in India is one of the highest in the world and with numbers of bariatric and metabolic procedures rising rapidly; there is a need for country specific guidelines. The Indian population is unique in its phenotype, genotype and nutritional make up. This document enlists guidelines for surgeons and allied health practitioners as also multiple other stake-holders like primary health physicians, policy makers, insurance companies and the Indian government. [ABSTRACT FROM AUTHOR]

The objective of this research was to investigate the relationship between emotion recognition and lateralization of motor onset in Parkinson's disease (PD) patients using electroencephalogram (EEG) signals. The subject pool consisted of twenty PD patients [ten with predominantly left-sided (LPD) and ten with predominantly right-sided (RPD) motor symptoms] and 20 healthy controls (HC) that were matched for age and gender. Multimodal stimuli were used to evoke simple emotions, such as happiness, sadness, fear, anger, surprise, and disgust. Artifact-free emotion EEG signals were processed using the auto regressive spectral method and then subjected to repeated ANOVA measures. No group differences were observed across behavioral measures; however, a significant reduction in EEG spectral power was observed at alpha, beta and gamma frequency oscillations in LPD, compared to RPD and HC participants, suggesting that LPD patients (inferred right-hemisphere pathology) are impaired compared to RPD patients in emotional processing. We also found that PD-related emotional processing deficits may be selective to the perception of negative emotions. Previous findings have suggested a hemispheric effect on emotion processing that could be related to emotional response impairment in a subgroup of PD patients. This study may help in clinical practice to uncover potential neurophysiologic abnormalities of emotional changes with respect to PD patient's motor onset. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3-AF4, F7-F8, F3-F4, FC5-FC6, T7-T8, P7-P8, and O1-O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities. [ABSTRACT FROM AUTHOR]

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders. [ABSTRACT FROM AUTHOR]

Assessment of awareness for those with disorders of consciousness is a challenging undertaking, due to the complex presentation of the population. Debate surrounds whether behavioral assessments provide greatest accuracy in diagnosis compared to neuro-imaging methods, and despite developments in both, misdiagnosis rates remain high. Music therapy may be effective in the assessment and rehabilitation with this population due to effects of musical stimuli on arousal, attention, and emotion, irrespective of verbal or motor deficits. However, an evidence base is lacking as to which procedures are most effective. To address this, a neurophysiological and behavioral study was undertaken comparing electroencephalogram (EEG), heart rate variability, respiration, and behavioral responses of 20 healthy subjects with 21 individuals in vegetative or minimally conscious states (VS or MCS). Subjects were presented with live preferred music and improvised music entrained to respiration (procedures typically used in music therapy), recordings of disliked music, white noise, and silence. ANOVA tests indicated a range of significant responses (p ≤0.05) across healthy subjects corresponding to arousal and attention in response to preferred music including concurrent increases in respiration rate with globally enhanced EEG power spectra responses (p =0.05-0.0001) across frequency bandwidths. Whilst physiological responses were heterogeneous across patient cohorts, significant post hoc EEG amplitude increases for stimuli associated with preferred music were found for frontal midline theta in six VS and four MCS subjects, and frontal alpha in three VS and four MCS subjects (p =0.05-0.0001). Furthermore, behavioral data showed a significantly increased blink rate for preferred music (p D0.029) within the VS cohort.Two VS cases are presented with concurrent changes (p ≤0.05) across measures indicative of discriminatory responses to both music therapy procedures. A third MCS case study is presented highlighting how more sensitive selective attention may distinguish MCS from VS. The findings suggest that further investigation iswarranted to explore the use of music therapy for prognostic indicators, and its potential to support neuroplasticity in rehabilitation programs. [ABSTRACT FROM AUTHOR]