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.

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).

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.

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.

Predicting and preventing relapse presents a crucial opportunity and first step to improve outcomes and reduce the care gap for persons living with schizophrenia. Using commercially available smartphones and smartwatches, technology now affords opportunities to capture real-time and longitudinal profiles of patients’ symptoms, cognition, physiology and social patterns. This novel data makes it possible to explore relationships between behaviours, physiology and symptoms, which may yield personalised relapse signals.

Smartphone Health Assessment for Relapse Prevention (SHARP), an international mental health research study supported by the Wellcome Trust, will inform the development of a scalable and sharable digital health solution to monitor personal risk of relapse. The resulting technology will be studied toward predicting and preventing relapse among individuals diagnosed with serious mental illness.

SHARP is a two-phase study with research sites in Boston, Massachusetts, and Bangalore and Bhopal, India. During phase 1, focus groups will be conducted at each study site to collect feedback on the design and features available on mindLAMP, a digital health platform. Individuals with serious mental illness will use mindLAMP for the duration of a year during phase 2.

The results of the research outlined in this protocol will guide the development of technology and digital tools to help address pervasive challenges in global mental health.

The digital tools developed as a result of this study, and participants’ experiences using them, may offer insight into opportunities to expand digital mental health resources and optimize their utilisation around the world.