COVID-19 pandemic & neurosciences in India- the CoINstudy: Impact of the pandemic on research related to the neurosciences.

Background & objectives We aimed to assess the impact of COVID-19-related disruptions on ongoing and future projects related to neuroscience research and young researchers in India. Methods We conducted a countrywide online survey using a structured, self-administered questionnaire involving medical trainees, post-doctoral fellows, PhD students, early career faculty members and basic neuroscience researchers. The purpose was to assess the impact of the COVID-19 pandemic on the respondents' ongoing/planned research activities and capture their concerns related to future research. Results Five hundred and four valid responses were analyzed. More than three-fourths of the respondents were in their early careers - 64.1 per cent were resident doctors, and 19.8 per cent were early career consultants. Maximum responses were received from respondents working in neurology (228; 45.2%), followed by psychiatry (192; 38.1%) and neurosurgery (49; 9.7%). More than three-fourths [83.5%, 95% confidence interval (CI): 0.8-0.867] of the respondents reported that the pandemic had affected their research. About one-third of the respondents (171; 33.9%) reported delays in completing research studies. Respondents adapted to the pandemic's circumstances by making methodological changes in their research (155; 30.8%). Most respondents (301; 59.6%) reported being diverted from their traditional work settings to COVID-19-related clinical services. Respondents conducting prospective studies and randomized controlled trials and those diverted to COVID-related services were significantly more likely to report the adverse research impact. Interpretation & conclusions In our survey, an overwhelming majority of the respondents reported that the pandemic adversely impacted their study. This trend was independent of sex, designation, and research output of individual subjects. The serious impact of the COVID-19 pandemic on neurosciences research warrants the attention and concerted efforts of the research supervisors, institutional heads, funding agencies and other stakeholders.

Bridging the mental health gap: The scope, challenges, and future of digital psychiatry in India.

The mental health burden in India is increasing at unprecedented rates. The increased demand for mental health care and the undersupply of services has widened the treatment gap. Due to several factors, such as increasing service costs and the circumstances surrounding the COVID-19 pandemic, India has witnessed an inclination toward using digital mental health solutions to overcome the treatment gap. Drawing from the collective evidence and experience in implementing mental health solutions using digital phenotyping and smartphone app-based care delivery in India, we define the scope, potential, and challenges of implementing synchronous and asynchronous digital mental health solutions that can serve as a template for improving global mental health.

Incremental predictive value of genetic risk and functional brain connectivity in determining antipsychotic response in schizophrenia.

We aimed to assess the incremental value of schizophrenia polygenic risk score (PgRS) and resting-state functional brain connectivity (rsFC) when added to clinical data in predicting the six-week response to oral risperidone (Risperdal) in schizophrenia. Fifty-seven, 54, and 43 individuals in a group of never-before-treated first-episode schizophrenia had good quality whole-genome sequencing (10x), rsFC, and both genomic and rsFC data, respectively, at baseline. Symptom severity ratings were obtained at baseline and six-weeks of oral risperidone (Risperdal) treatment. The primary outcome was the percentage change in the Positive and Negative Syndrome Scale Total scores after risperidone (Risperdal) treatment. Clinical, PgRS, and rsFC determinants of treatment response were first evaluated independently. Subsequently, three blocks of hierarchical multiple regression analyses with leave-one-out cross-validation (n = 43), were implemented to study clinical, clinical + PgRS and clinical + PgRS + rsFC determinants of treatment response. While the combined clinical variables did not show a statistically significant prediction of treatment response, adding PgRS (9 % R2 change) and rsFC (26 % R2 change) in hierarchical steps, significantly improved the overall proportion of variance explained in treatment response. This proof-of-concept investigation underscores the incremental benefits offered by genetic and neuroimaging metrics over clinical measures in determining prospectively-ascertained short-term treatment response in first-episode schizophrenia.

Digital phenotyping correlates of mobile cognitive measures in schizophrenia: A multisite global mental health feasibility trial.

Traditional cognitive assessments in schizophrenia are time-consuming and necessitate specialized training, making routine evaluation challenging. To overcome these limitations, this study investigates the feasibility and advantages of utilizing smartphone-based assessments to capture both cognitive functioning and digital phenotyping data and compare these results to gold standard measures. We conducted a secondary analysis of data from 76 individuals with schizophrenia, who were recruited across three sites (one in Boston, two in India) was conducted. The open-source mindLAMP smartphone app captured digital phenotyping data and Trails A/B assessments of attention / memory for up to 12 months. The smartphone-cognitive tasks exhibited potential for normal distribution and these scores showed small but significant correlations with the results from the Brief Assessment of Cognition in Schizophrenia, especially the digital span and symbol coding tasks (r2 = 0.21). A small but significant correlation (r2 = 0.29) between smartphone-derived cognitive scores and health-related behaviors such as sleep duration patterns was observed. Smartphone-based cognitive assessments show promise as cross-cultural tools that can capture relevant data on momentary states among individuals with schizophrenia. Cognitive results related to sleep suggest functional applications to digital phenotyping data, and the potential of this multimodal data approach in research.

Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample.

INTRODUCTION: Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone-based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method. METHODS: Participants from two different cohorts, each n = 76, one with diagnoses of depression/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1 month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood/anxiety scores as measured via smartphone surveys. RESULTS: The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ-9 and anxiety as measured for the GAD-8 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ-9 and GAD-7, these AUCs were maintained when predicting significant symptom change at least 7 days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression/anxiety and schizophrenia populations respectively. CONCLUSION: These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.

Development of a tool for infant facial emotion recognition (InFER) for postpartum mothers with mental illnesses.

Understanding deficits in recognition of infant emotions in mothers with mental illnesses is limited by the lack of validated instruments. We present the development and content validation of the infant facial emotion recognition tool (InFER) in India to examine the ability of mothers to detect the infants' emotions. A total of 164 images of infant faces in various emotional states were gathered from the parents of four infants (two male and two female: up to 12 months old). Infant emotion in each image was identified by the respective mother. Content validation was carried out by 21 experts. Images with ≥70% concordance among experts were selected. The newly developed tool, InFER, consists of a total 39 infant images representing the six basic emotions. This tool was then administered among mothers during their postpartum period-10 healthy mothers and 10 mothers who had remitted from any schizophrenia spectrum disorder, bipolar affective disorder or major depressive disorder. The mean age and mean years of education for both groups were comparable (age∼25 years, education ∼15 years). A significant difference was found between the two groups in their ability to recognize infant emotions (Mann-Whitney U = 12.5; p = 0.004). InFER is a promising tool in Indian settings for understanding maternal recognition of infant emotions.

La comprensión de los déficits en el reconocimiento de las emociones del infante en madres con enfermedades mentales está limitada por la falta de instrumentos validados. Presentamos el desarrollo y la validación de contenido de una herramienta para reconocer la emoción facial del infante (InFER) en la India. para examinar la habilidad de las madres para detectar las emociones del infante. Se recogió un total de 164 imágenes de caras de infantes en varios estados emocionales, aportadas por los progenitores de 4 infantes (dos varones y dos niñas: de hasta 12 meses de edad). La mamá respectiva identificó la emoción del infante en cada imagen. La validación del contenido la llevaron a cabo 21 expertos. Para la construcción de esta herramienta se seleccionaron las imágenes que presentaban el ≥70% de concordancia entre los expertos. La recientemente desarrollada herramienta, InFER está compuesta de un total de 39 imágenes de infantes las cuales representan las seis emociones básicas. Esta herramienta entonces se puso en práctica entre madres durante su período de postparto ­10 madres saludables y 10 madres que habían sido remitidas en relación con cualquiera de los trastornos de la gama de la esquizofrenia, el trastorno afectivo bipolar o un trastorno depresivo mayor. El promedio de edad y el promedio de años de educación en ambos grupos eran comparables (edad ∼25 años, educación ∼15years). Una diferencia significativa se encontró entre los dos grupos en cuanto a su habilidad de reconocer las emociones del infante (Mann­Whitney U = 12.5; p = 0.004). InFER es una herramienta prometedora en entornos de la India para comprender el reconocimiento materno de las emociones del infante.

La compréhension des déficits dans la reconnaissance des émotions du bébé chez les mères ayant des maladies mentales est limitée par le manque d'instruments validés. Nous présentons le développement et la validation du contenu d'un outil de reconnaissance de l'émotion faciale du nourrisson (InFER) en Inde afin d'examiner la capacité des mères à détecter les émotions du bébé. Un total de 164 images de visages de bébés dans des états émotionnels variés ont été assemblées de parents de 4 bébés (deux garçons et deux filles: jusqu'à l'âge de 12 mois) L'émotion du bébé dans chaque image a été identifiée par la mère respective. La validation du contenu a été faite par 21 experts. Des images avec ≥70% de concordance entre les experts ont été sélectionnées pour la construction de l'outil. Ce nouvel outil, InFER, consiste en un total de 39 images de bébés représentant les six émotions de base. Cet outil a ensuite été utilisé chez des mères durant leur période postpartum ­ 10 mères en bonne santé et 10 mères s'étant remises soit d'un trouble du spectre schizophrénique, soit d'un trouble affectif bipolaire ou d'un trouble dépressif majeur. La moyenne d'âge et la moyenne d'années d'éducation pour les deux groupes étaient comparables (âge∼25ans, éducation ∼15ans). Une différence importante a été trouvée entre les deux groupes dans leur capacité à reconnaître les émotions du bébé (Mann­Whitney U = 12,5; p = 0,004). InFER est un outil prometteur dans les contextes de l'Inde pour comprendre la reconnaissance maternelle des émotions du bébé.

Development, validation and clinical utility of short-term adverse-effects of electroconvulsive therapy (SAVE) checklist.

Electroconvulsive therapy (ECT) is one of the most effective treatments in psychiatry. However, it has many cognitive and non-cognitive adverse effects (AEs). There are lacunae in the literature on systematic assessment of non-cognitive AEs. There is a need for a standard, comprehensive and specific clinical tool to evaluate this. Hence, a checklist of short-term AEs of ECT (SAVE) with a 2-phase assessment was developed. Content validation was done using 15 experts' ratings and predefined content validity ratio and index (CVR and CVI) in a two-stage modified Delphi method. The checklist had a good CVR and CVI with a final tool of 39 items. The tool was sensitive and identified the non-cognitive AEs after ECT. Cardiovascular and musculoskeletal systems displayed the highest incidence. Many participants exhibited delayed recovery in orientation, gait, and stance, highlighting a necessity for meticulous monitoring. SAVE is the first standardised tool to assess short-term ECT-related AEs systematically. This checklist likely identifies clinically significant incidences of adverse effects. Its regular use may enhance the safety of ECT and patient comfort by supporting early identification and intervention for AEs. However, given the transient nature of AEs, further studies are needed to determine their predictive validity for long-term consequences.

Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment.

BACKGROUND: Several factors shape the neurodevelopmental trajectory. A key area of focus in neurodevelopmental research is to estimate the factors that have maximal influence on the brain and can tip the balance from typical to atypical development. METHODS: Utilizing a dissimilarity maximization algorithm on the dynamic mode decomposition (DMD) of the resting state functional MRI data, we classified subjects from the cVEDA neurodevelopmental cohort (n = 987, aged 6-23 years) into homogeneously patterned DMD (representing typical development in 809 subjects) and heterogeneously patterned DMD (indicative of atypical development in 178 subjects). RESULTS: Significant DMD differences were primarily identified in the default mode network (DMN) regions across these groups (p < 0.05, Bonferroni corrected). While the groups were comparable in cognitive performance, the atypical group had more frequent exposure to adversities and faced higher abuses (p < 0.05, Bonferroni corrected). Upon evaluating brain-behavior correlations, we found that correlation patterns between adversity and DMN dynamic modes exhibited age-dependent variations for atypical subjects, hinting at differential utilization of the DMN due to chronic adversities. CONCLUSION: Adversities (particularly abuse) maximally influence the DMN during neurodevelopment and lead to the failure in the development of a coherent DMN system. While DMN's integrity is preserved in typical development, the age-dependent variability in atypically developing individuals is contrasting. The flexibility of DMN might be a compensatory mechanism to protect an individual in an abusive environment. However, such adaptability might deprive the neural system of the faculties of normal functioning and may incur long-term effects on the psyche.

Assessing Adult ADHD: An Updated Review of Rating Scales for Adult Attention Deficit Hyperactivity Disorder (ADHD).

METHOD: Scholarly articles on adult ADHD between 1996 and 2022 were reviewed using the PRISMA guidelines. ADHD rating scales with multiple citations were selected and their psychometric properties and symptom coverage were analyzed. RESULTS: Ten rating scales, with sound psychometric properties, were identified. Out of those reviewed two (BADDS and BAARS-IV) load on the inattentive domain of ADHD, while the rest focus on a comprehensive assessment of ADHD. Only one scale (BARRS-IV) incorporates an assessment of functional impairment. Some scales though widely utilized have not been adequately examined for their sensitivity. CONCLUSIONS: Rating scales are reliable and valid, in the assessment of adult ADHD. We present a review of recent scales, with an expanded focus, to help clinicians make informed decisions on diagnosis, identifying targets and planning interventions accordingly.

Posterior Cerebellar Resting-State Functional Hypoconnectivity: A Neural Marker of Schizophrenia Across Different Stages of Treatment Response.

BACKGROUND: Identifying stable and consistent resting-state functional connectivity patterns across illness trajectories has the potential to be considered fundamental to the pathophysiology of schizophrenia. We aimed to identify consistent resting-state functional connectivity patterns across heterogeneous schizophrenia groups defined based on treatment response. METHODS: In phase 1, we used a cross-sectional case-control design to characterize and compare stable independent component networks from resting-state functional magnetic resonance imaging scans of antipsychotic-naïve participants with first-episode schizophrenia (n = 54) and healthy participants (n = 43); we also examined associations with symptoms, cognition, and disability. In phase 2, we examined the stability (and replicability) of our phase 1 results in 4 groups (N = 105) representing a cross-sequential gradation of schizophrenia based on treatment response: risperidone responders, clozapine responders, clozapine nonresponders, and clozapine nonresponders following electroconvulsive therapy. Hypothesis-free whole-brain within- and between-network connectivity were examined. RESULTS: Phase 1 identified posterior and anterior cerebellar hypoconnectivity and limbic hyperconnectivity in schizophrenia at a familywise error rate-corrected cluster significance threshold of p < .01. These network aberrations had unique associations with positive symptoms, cognition, and disability. During phase 2, we replicated the phase 1 results while comparing each of the 4 schizophrenia groups to the healthy participants. The participants in 2 longitudinal subdatasets did not demonstrate a significant change in these network aberrations following risperidone or electroconvulsive therapy. Posterior cerebellar hypoconnectivity (with thalamus and cingulate) emerged as the most consistent finding; it was replicated across different stages of treatment response (Cohen's d range -0.95 to -1.44), reproduced using different preprocessing techniques, and not confounded by educational attainment. CONCLUSIONS: Posterior cerebellar-thalamo-cingulate hypoconnectivity is a consistent and stable state-independent neural marker of schizophrenia.

Role of Cognitive Control in Psychotherapy: An Integrated Review.

Background: Cognitive control (CC), including shifting, updating, and inhibiting functions, may play an integral role in various aspects of psychotherapy; however, research on this is limited. This review aims to link the disparate lines of evidence on CC as they relate to psychotherapy processes, techniques, and outcomes. Methods: A systematic search of the literature on neuropsychological domains relating to psychotherapy in adults with anxiety/depression yielded 18 eligible studies. The review also uses a narrative format to explore other potential links between CC and psychotherapy that are underinvestigated, and highlights the need for research and application to evidence-based practice of psychotherapy. Results and conclusions: Findings suggest that CC may predict psychotherapy outcomes and also improve as a function of psychotherapy. Analog sample studies suggest a possible link between CC and techniques for regulation of cognition and emotion, such as reappraisal, mindfulness, and cognitive restructuring. CC may also play an integral role in the regulation of behavior. Study of CC in the context of psychotherapy may potentially explain individual differences in psychotherapy outcomes and mechanisms of action of various psychotherapy techniques and processes. Such an understanding may have possible implications for "best fit" matching clients to therapies and modifying psychological interventions to account for poorer CC abilities. CC may be enhanced through training and further research is warranted on the impact of such training in facilitating better long-term psychotherapy outcomes.

Utility of Smartphone-Based Digital Phenotyping Biomarkers in Assessing Treatment Response to Transcranial Magnetic Stimulation in Depression: Proof-of-Concept Study.

BACKGROUND: Identifying biomarkers of response to transcranial magnetic stimulation (TMS) in treatment-resistant depression is a priority for personalizing care. Clinical and neurobiological determinants of treatment response to TMS, while promising, have limited scalability. Therefore, evaluating novel, technologically driven, and potentially scalable biomarkers, such as digital phenotyping, is necessary. OBJECTIVE: This study aimed to examine the potential of smartphone-based digital phenotyping and its feasibility as a predictive biomarker of treatment response to TMS in depression. METHODS: We assessed the feasibility of digital phenotyping by examining the adherence and retention rates. We used smartphone data from passive sensors as well as active symptom surveys to determine treatment response in a naturalistic course of TMS treatment for treatment-resistant depression. We applied a scikit-learn logistic regression model (l1 ratio=0.5; 2-fold cross-validation) using both active and passive data. We analyzed related variance metrics throughout the entire treatment duration and on a weekly basis to predict responders and nonresponders to TMS, defined as ≥50% reduction in clinician-rated symptom severity from baseline. RESULTS: The adherence rate was 89.47%, and the retention rate was 73%. The area under the curve for correct classification of TMS response ranged from 0.59 (passive data alone) to 0.911 (both passive and active data) for data collected throughout the treatment course. Importantly, a model using the average of all features (passive and active) for the first week had an area under the curve of 0.7375 in predicting responder status at the end of the treatment. CONCLUSIONS: The results of our study suggest that it is feasible to use digital phenotyping data to assess response to TMS in depression. Early changes in digital phenotyping biomarkers, such as predicting response from the first week of data, as shown in our results, may also help guide the treatment course.

Towards a youth mental health paradigm: a perspective and roadmap.

Most mental disorders have a typical onset between 12 and 25 years of age, highlighting the importance of this period for the pathogenesis, diagnosis, and treatment of mental ill-health. This perspective addresses interactions between risk and protective factors and brain development as key pillars accounting for the emergence of psychopathology in youth. Moreover, we propose that novel approaches towards early diagnosis and interventions are required that reflect the evolution of emerging psychopathology, the importance of novel service models, and knowledge exchange between science and practitioners. Taken together, we propose a transformative early intervention paradigm for research and clinical care that could significantly enhance mental health in young people and initiate a shift towards the prevention of severe mental disorders.

Multivariate associations between cognition and neighborhood geospatial characteristics in schizophrenia.

Cognitive impairment contributes to functional impairment in schizophrenia. Yet, little is known about how environmental characteristics are related to cognition in schizophrenia. By examining how cognition and the environment are intertwined, it may be possible to identify modifiable risk and protective factors that can improve cognitive outcomes in schizophrenia. We aimed to identify multivariate associations between cognition and three geospatial characteristics (built-space density, habitable green spaces, and public spaces for social interaction) within one's immediate neighborhood among individuals with schizophrenia. We recruited participants with schizophrenia from three sites - an urban metropolitan and two towns in southern India. We administered standard cognitive assessments and performed a principal axis factoring to identify episodic memory, cognitive control, and social inference-making factors for use in further analyses. We estimated geospatial characteristics of an individual's neighborhood, i.e., up to 1 km2 around the residence, by sourcing data from Google Earth. We performed unconditional and conditional (to examine the effect of clinical covariates) canonical correlation analyses to understand the multivariate relationship between cognition and geospatial characteristics. We analyzed data from 208 participants; the first canonical cognitive variate (higher social inference-making and poorer cognitive control) shared 24% of the variance (r = 0.49; P < 0.001) with the first geospatial variate (lower built density and poorer access to public spaces). Years of education, age at onset, and place of residence significantly modulated this relationship. We observe differential associations of the built environment with social and non-social cognition in schizophrenia, and highlight the clinical and demographic characteristics that shape these associations.

Postoperative delayed neurocognitive recovery: Evaluation of its incidence and correlation with intraoperative cerebral desaturation - A prospective observational study.

BACKGROUND: Delayed neurocognitive recovery (DNR) is commonly seen in patients in the postoperative period. Literature has shown that monitoring cerebral desaturation intraoperatively can predict the development of DNR in elderly patients undergoing surgery in prone position. This prospective observational study was conducted in patients of all ages, with the primary objective to determine the incidence of DNR and its correlation with cerebral oximetry. The secondary objectives were to determine if intraoperative cerebral desaturation influenced the neuropsychometric variables from preoperative to postoperative period. METHODS: This study included 61 patients, aged > 18 years undergoing spinal surgery in prone position. An evening before surgery and at 48 h postoperatively, patients were subjected to neuropsychological examination; Hindi Mental State Examination, Colour Trail Test 1 (CTT 1), CTT 2, Auditory Verbal Learning (AVLT) tests conducted by principal investigator (PI). DNR was defined as a 20% change in any of the test scores from the baseline. rSO2 was recorded bilaterally every 10 min throughout surgery by an independent person. Cerebral desaturation was defined as a 20% drop in rSO2 from the control value. RESULTS: The incidence of DNR was 24.6%. The duration of anesthesia and cerebral desaturation were found to be independently predictive of DNR, with each hour of anesthesia causing a two-fold increase in the chances of development DNR (P = 0.019) and presence of cerebral desaturation causing a 6-fold increase (P = 0.039). CTT 1 and CTT 2 tests had significantly larger increase in test scores in the postoperative period, in patients with cerebral desaturation. CONCLUSION: Duration of anesthesia and cerebral desaturation were factors predictive of the development of DNR in patients undergoing spine surgery in prone position.