Prevalence of child maltreatment in India and its association with gender, urbanisation and policy:a rapid review and meta-analysis protocol

Introduction India is home to 20% of the world’s children and yet, little is known on the magnitude and trends of child maltreatment nationwide. The aims of this review are to provide a prevalence of child maltreatment in India with considerations for any effects of gender; urbanisation (eg, urban vs rural) and legislation (Protection of Children from Sexual Offences (POCSO) Act 2012).Methods and analysis A rapid review will be undertaken of all quantitative peer-reviewed studies on child maltreatment in India between 2005 and 2020. Four electronic databases will be systematically searched: PubMed, EMBASE, Cochrane and PsychInfo. The primary outcomes will include all aspects of child maltreatment: physical abuse, sexual abuse, emotional abuse, emotional neglect and physical neglect. Study participants will be between 0 and 18 years and will have reported maltreatment experiences using validated, reliable tools such as the Adverse Childhood Experiences Questionnaire as well as child self-reports and clinician reports. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and the methodological appraisal of the studies will be assessed by the Newcastle-Ottawa Quality assessment scale. A narrative synthesis will be conducted for all included studies. Also, if sufficient data are available, a meta-analysis will be conducted. Effect sizes will be determined from random-effects models stratified by gender, urbanisation and the pre-2012 and post-2012 POCSO Act cut-off. I2 statistics will be used to assess heterogeneity and identify their potential sources and τ2 statistics will indicate any between-study variance.Ethics and dissemination As this is a rapid review, minimal ethical risks are expected. The protocol and level 1 self-audit checklist were submitted and approved by the Usher Research Ethics Group panel in the Usher Institute (School of Medicine and Veterinary Sciences) at the University of Edinburgh (Reference B126255). Findings from this review will be disseminated widely through peer-reviewed publications and in various media, for example, conferences, congresses or symposia.PROSPERO registration number CRD42019150403

Drinking Motives, Personality Traits, Life Stressors - Identifying Pathways to Harmful Alcohol Use in Adolescence Using a Panel Network Approach

BACKGROUND AND AIMS: Models of alcohol use risk suggest that drinking motives represent the most proximal risk factors on which more distal factors converge. However, little is known about how distinct risk factors influence each other and alcohol use on different temporal scales (within a given moment vs. over time). We aimed to estimate the dynamic associations of distal (personality and life stressors) and proximal (drinking motives) risk factors, and their relationship to alcohol use in adolescence and early adulthood using a novel graphical vector autoregressive (GVAR) panel network approach.DESIGN, SETTING, AND CASES: We estimated panel networks on data from the IMAGEN study, a longitudinal European cohort study following adolescents across three waves (ages 16, 19, 22). Our sample consisted of 1829 adolescents (51% females) who reported alcohol use on at least one assessment wave.MEASUREMENTS: Risk factors included personality traits (NEO-FFI: neuroticism, extraversion, openness, agreeableness, and conscientiousness; SURPS: impulsivity and sensation seeking), stressful life events (LEQ: sum scores of stressful life events), and drinking motives (DMQ: social, enhancement, conformity, coping anxiety, coping depression). We assessed alcohol use (AUDIT: quantity and frequency) and alcohol-related problems (AUDIT: related problems).FINDINGS: Within a given moment, social (partial correlation (pcor) =0.17) and enhancement motives (pcor=0.15) co-occurred most strongly with drinking quantity and frequency, while coping depression motives (pcor=0.13), openness (pcor=0.05), and impulsivity (pcor=0.09) were related to alcohol-related problems. The temporal network showed no predictive associations between distal risk factors and drinking motives. Social motives (beta=0.21), previous alcohol use (beta=0.11), and openness (beta=0.10) predicted alcohol-related problems over time (all p&lt;0.01).CONCLUSIONS: Heavy and frequent alcohol use, along with social drinking motives, appear to be key targets for preventing the development of alcohol-related problems throughout late adolescence. We found no evidence for personality traits and life stressors predisposing towards distinct drinking motives over time.</p

Drinking motives, personality traits and life stressors-identifying pathways to harmful alcohol use in adolescence using a panel network approach

BACKGROUND AND AIMS: Models of alcohol use risk suggest that drinking motives represent the most proximal risk factors on which more distal factors converge. However, little is known about how distinct risk factors influence each other and alcohol use on different temporal scales (within a given moment versus over time). We aimed to estimate the dynamic associations of distal (personality and life stressors) and proximal (drinking motives) risk factors, and their relationship to alcohol use in adolescence and early adulthood using a novel graphical vector autoregressive (GVAR) panel network approach.DESIGN, SETTING AND CASES: We estimated panel networks on data from the IMAGEN study, a longitudinal European cohort study following adolescents across three waves (aged 16, 19 and 22 years). Our sample consisted of 1829 adolescents (51% females) who reported alcohol use on at least one assessment wave.MEASUREMENTS: Risk factors included personality traits (NEO-FFI: neuroticism, extraversion, openness, agreeableness and conscientiousness; SURPS: impulsivity and sensation-seeking), stressful life events (LEQ: sum scores of stressful life events), and drinking motives [drinking motives questionnaire (DMQ): social, enhancement, conformity, coping anxiety and coping depression]. We assessed alcohol use [alcohol use disorders identification test (AUDIT): quantity and frequency] and alcohol-related problems (AUDIT: related problems).FINDINGS: Within a given moment, social [partial correlation (pcor) = 0.17] and enhancement motives (pcor = 0.15) co-occurred most strongly with drinking quantity and frequency, while coping depression motives (pcor = 0.13), openness (pcor = 0.05) and impulsivity (pcor = 0.09) were related to alcohol-related problems. The temporal network showed no predictive associations between distal risk factors and drinking motives. Social motives (beta = 0.21), previous alcohol use (beta = 0.11) and openness (beta = 0.10) predicted alcohol-related problems over time (all P  &lt; 0.01).CONCLUSIONS: Heavy and frequent alcohol use, along with social drinking motives, appear to be key targets for preventing the development of alcohol-related problems throughout late adolescence. We found no evidence for personality traits and life stressors predisposingtowards distinct drinking motives over time.</div

A stable and replicable neural signature of lifespan adversity in the adult brain

Environmental adversities constitute potent risk factors for psychiatric disorders. Evidence suggests the brain adapts to adversity, possibly in an adversity-type and region-specific manner. However, the long-term effects of adversity on brain structure and the association of individual neurobiological heterogeneity with behavior have yet to be elucidated. Here we estimated normative models of structural brain development based on a lifespan adversity profile in a longitudinal at-risk cohort aged 25 years (n = 169). This revealed widespread morphometric changes in the brain, with partially adversity-specific features. This pattern was replicated at the age of 33 years (n = 114) and in an independent sample at 22 years (n = 115). At the individual level, greater volume contractions relative to the model were predictive of future anxiety. We show a stable neurobiological signature of adversity that persists into adulthood and emphasize the importance of considering individual-level rather than group-level predictions to explain emerging psychopathology

A stable and replicable neural signature of lifespan adversity in the adult brain

Environmental adversities constitute potent risk factors for psychiatric disorders. Evidence suggests the brain adapts to adversity, possibly in an adversity-type and region-specific manner. However, the long-term effects of adversity on brain structure and the association of individual neurobiological heterogeneity with behavior have yet to be elucidated. Here we estimated normative models of structural brain development based on a lifespan adversity profile in a longitudinal at-risk cohort aged 25 years (n = 169). This revealed widespread morphometric changes in the brain, with partially adversity-specific features. This pattern was replicated at the age of 33 years (n = 114) and in an independent sample at 22 years (n = 115). At the individual level, greater volume contractions relative to the model were predictive of future anxiety. We show a stable neurobiological signature of adversity that persists into adulthood and emphasize the importance of considering individual-level rather than group-level predictions to explain emerging psychopathology

Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA):A developmental cohort study protocol

Background: Low and middle-income countries like India with a large youth population experience a different environment from that of high-income countries. The Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA), based in India, aims to examine environmental influences on genomic variations, neurodevelopmental trajectories and vulnerability to psychopathology, with a focus on externalizing disorders. Methods: cVEDA is a longitudinal cohort study, with planned missingness design for yearly follow-up. Participants have been recruited from multi-site tertiary care mental health settings, local communities, schools and colleges. 10,000 individuals between 6 and 23 years of age, of all genders, representing five geographically, ethnically, and socio-culturally distinct regions in India, and exposures to variations in early life adversity (psychosocial, nutritional, toxic exposures, slum-habitats, socio-political conflicts, urban/rural living, mental illness in the family) have been assessed using age-appropriate instruments to capture socio-demographic information, temperament, environmental exposures, parenting, psychiatric morbidity, and neuropsychological functioning. Blood/saliva and urine samples have been collected for genetic, epigenetic and toxicological (heavy metals, volatile organic compounds) studies. Structural (T1, T2, DTI) and functional (resting state fMRI) MRI brain scans have been performed on approximately 15% of the individuals. All data and biological samples are maintained in a databank and biobank, respectively. Discussion: The cVEDA has established the largest neurodevelopmental database in India, comparable to global datasets, with detailed environmental characterization. This should permit identification of environmental and genetic vulnerabilities to psychopathology within a developmental framework. Neuroimaging and neuropsychological data from this study are already yielding insights on brain growth and maturation patterns.</p

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder

This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN, without relying on body mass index as a predictor. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. Each data domain emerged as accurate classifiers individually, with personality distinguishing AN, BN, and their controls with AUC-ROCs ranging from 0.77 to 0.89. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. For risk prediction in the longitudinal population sample, the models exhibited moderate performance (AUC-ROCs, 0.64-0.71), highlighting the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.</p

4.2 NBS-Predict: An easy-to-use toolbox for connectome-based machine learning

https://osf.io/cfm7j/ NBS-Predict is a prediction-based extension of the Network-based Statistic (NBS, Zalesky et al., 2010) approach, which aims to alleviate the curse of dimensionality, lack of interpretability, and problem of generalizability when analyzing brain connectivity. NBS-Predict provides an easy and quick way to identify highly generalizable neuroimaging-based biomarkers by combining machine learning (ML) with NBS in a cross-validation structure. Compared with generic ML algorithms (e.g., support vector machines, elastic net, etc.), the results from NBS-Predict are more straightforward to interpret. Additionally, NBS-Predict does not require any expertise in programming as it comes with a well-organized graphical user interface (GUI) with a good selection of ML algorithms and additional functionalities. The toolbox also provides an interactive viewer to visualize the results. This chapter gives a practical overview of the NBS-Predict’s core concepts with regard to building and evaluating connectome-based predictive models with two real-world examples using publicly available neuroimaging data. We showed that, using resting-state functional connectomes, NBS-Predict: (i) predicted fluid intelligence scores with a prediction performance of r = 0.243; (ii) distinguished subjects’ biological sexes with an average accuracy of 65.9%, as well as identified large-scale brain networks associated with fluid intelligence and biological sex