Is cesarean section a clinical marker for psychiatric and sleep disorder in young mothers? A cross-sectional study from rural South India

Background

Gestation and postnatal period are important stages in a woman's life. The type of delivery, vaginal delivery (VD) or cesarean Section (CS), is determined by maternal and fetal factors and their mutual fit. The type of delivery has consequences on the health and well-being of the mother and the newborn. Postpartum psychiatric disorders have been found to be both positively and negatively associated with the mode of delivery and demographic and clinical variables of the postpartum mothers. In view of the foregoing, a comprehensive investigation of the demographic and clinical variables and a range of psychiatric disorders among postpartum women delivered both vaginally and by CS in a rural tertiary care hospital in South India was proposed.

Materials and Methods

All consecutive women delivered vaginally and by CS attending Maternal and Child Clinic within 42 days of delivery were approached. Following informed consent and application of inclusion and exclusion criteria, 121 women delivered vaginally and 124 women delivered by CS were assessed using Mini International Neuropsychiatric Interview (MINI) and Pittsburgh Sleep Quality Index (PSQI). The data obtained were entered into MS Excel 2010 version and further analyzed using STATA v13.

Results

Both groups were matched on most demographic and clinical variables except age and whether pregnancy was planned or not. Postpartum depression was the most frequent diagnosis in both groups, with significantly more cases following CS compared to VD. Other psychiatric disorders were also found to be more following CS. The quality of sleep (QOS) was significantly poor following CS. QOS was significantly poor in the presence of a coexisting psychiatric disorder.

Conclusion

This study has limitations in terms of being cross-sectional and disability being defined by cutoff scores on MINI and PSQI.

Gender and sexual orientation of undergraduate medical students in India: A cross-sectional study

Background

Although the potential for alternate conceptions of gender roles and sexual orientations are diverse, it is by-and-large not well tolerated. This study explores the self-reported gender-roles and sexual orientations of Indian undergraduate medical students.

Aim

To study self-reported gender role and sexual orientation of undergraduate medical students in India.

Method

One hundred twenty volunteers were included in the study consisting of 60 males and 60 females. A questionnaire comprising of a sociodemographic proforma, Bem Sex-Role Inventory (BSRI), and Epstein Sexual Orientation Inventory (ESOI) were given to each participant. The scales were scored, tabulated, and statistically analyzed.

Results

The BSRI revealed that femininity was predominant in both female and male participants, at 68.33% and 55%, respectively. The ESOI revealed that females had significantly higher opposite-sex attraction than males. Though males had higher same-sex attraction than females, the difference was not statistically significant. Females also had a significantly higher sexual orientation range and a mean sexual orientation. Sexual drive was significantly higher in males. Significantly more females supported homosexuality and bisexuality as compared to males.

Conclusion

This study helps establish that gender roles can be non-conforming. It helps ascertain that while heterosexual orientation predominates, alternate sexual orientations also exist. It paves the way for future studies and explorations to alleviate public misconceptions.

Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network

Introduction

Autism spectrum disorder (ASD) is among the most common and pervasive neurodevelopmental disorders. Yet, despite decades of research, the neurobiology of ASD is still poorly understood, as inconsistent findings preclude the identification of robust and interpretable neurobiological markers and predictors of clinical symptoms.

Objectives

Identify robust and interpretable dynamic brain markers that distinguish children with ASD from typically-developing (TD) children and predict clinical symptom severity.

Methods

We leverage multiple functional brain imaging cohorts (ABIDE, Stanford; N = 1004) and exciting recent advances in explainable artificial intelligence (xAI), to develop a novel multivariate time series deep neural network model that extracts informative brain dynamics features that accurately distinguish between ASD and TD children, and predict clinical symptom severity.

Results

Our model achieved consistently high classification accuracies in cross-validation analysis of data from the ABIDE cohort. Crucially, despite the differences in symptom profiles, age, and data acquisition protocols, our model also accurately classified data from an independent Stanford cohort without additional training. xAI analyses revealed that brain features associated with the default mode network, and the human voice/face processing and communication systems, most clearly distinguished ASD from TD children in both cohorts. Furthermore, the posterior cingulate cortex emerged as robust predictor of the severity of social and communication deficits in ASD in both cohorts.

Conclusions

Our findings, replicated across two independent cohorts, reveal robust and neurobiologically interpretable brain features that detect ASD and predict core phenotypic features of ASD, and have the potential to transform our understanding of the etiology and treatment of the disorder.

Disclosure

No significant relationships.

Psychological well-being in medical undergraduates in a rural medical college in South India

BACKGROUND

Medical colleges strive to create a learning environment best suitable for undergraduate medical education. In this process, measures taken can affect and influence the medical undergraduate psychological well-being. The demands of medical education lead to a psychological distress far beyond that experienced by the students of other specialties.

AIMS

The aim of this study is to study the levels of psychological well-being in medical under graduates.

MATERIALS AND METHODS

Using a cross-sectional design, 402 medical students were surveyed using the Ryffs's Psychological Well-being Scale.

RESULTS

Low psychological well-being is evident in most of the medical undergraduates with the presence of stressors playing a significant role on psychological well-being with academic stress taking a major role.

CONCLUSION

As depicted from the current study, academic stress plays a role in low psychological well-being of medical students.

Study of quality of life among health workers and psychosocial factors influencing it

BACKGROUND

To cope with the challenges in the health-care delivery system and to guarantee the quality of care rendered and client satisfaction on the care received, it is important to know how satisfied health-care workers are with their quality of life, job and what characteristics influence their quality of life. This study was undertaken in a tertiary care hospital to assess the same using validated questionnaires.

AIM

This study aims to study the quality of life among the health workers (doctors and nurses) of a large multispecialty tertiary care hospital and the psychosocial factors influencing it.

MATERIALS AND METHODS

A total of 200 health-care workers with their background demographic data were assessed using quality of life questionnaire and occupational stress inventory. The data compiled were analyzed with appropriate statistical methods.

RESULTS

The overall quality of life among the study population was average, and the mean prevalence of occupational stress level was of mild level. There was a correlation between domains of occupational stress and domains of quality of life of health-care workers.

CONCLUSION

Study findings revealed that overall perception of quality of life was average, overall stress level of health-care workers was moderately elevated and majority showed average coping resources.

Decoding subject-driven cognitive states with whole-brain connectivity patterns

Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.

Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings

EEG data acquired in an MRI scanner are heavily contaminated by gradient artifacts that can significantly compromise signal quality. We developed two new methods based on independent component analysis (ICA) for reducing gradient artifacts from spiral in-out and echo-planar pulse sequences at 3 T, and compared our algorithms with four other commonly used methods: average artifact subtraction (Allen, P., Josephs, O., Turner, R., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage 12, 230-239.), principal component analysis (Niazy, R., Beckmann, C., Iannetti, G., Brady, J., Smith, S., 2005. Removal of FMRI environment artifacts from EEG data using optimal basis sets. NeuroImage 28, 720-737.), Taylor series ( Wan, X., Iwata, K., Riera, J., Kitamura, M., Kawashima, R., 2006. Artifact reduction for simultaneous EEG/fMRI recording: adaptive FIR reduction of imaging artifacts. Clin. Neurophysiol. 117, 681-692.) and a conventional temporal ICA algorithm. Models of gradient artifacts were derived from simulations as well as a water phantom and performance of each method was evaluated on datasets constructed using visual event-related potentials (ERPs) as well as resting EEG. Our new methods recovered ERPs and resting EEG below the beta band (<12.5 Hz) with high signal-to-noise ratio (SNR>4). Our algorithms outperformed all of these methods on resting EEG in the theta and alpha bands (SNR>4); however, for all methods, signal recovery was modest (SNR approximately 1) in the beta band and poor (SNR<0.3) in the gamma band and above. We found that the conventional ICA algorithm performed poorly with uniformly low SNR (<0.1). Taken together, our new ICA-based methods offer a more robust technique for gradient artifact reduction when scanning at 3 T using spiral in-out and echo-planar pulse sequences. We provide new insights into the strengths and weaknesses of each method using a unified subspace framework.