Digital phenotyping and neuro-simulations: Scaling precision neuroscience via IT

Modern neuroscience has transitioned from small-scale manual observations to a data-intensive field powered by computational innovation. Traditionally focused on the biological structures and physiological functions of neural circuits, the discipline now increasingly relies on computer modeling, large-scale data analysis, and AI-driven diagnostics. This interdisciplinary shift blends biology and chemistry with computer science and engineering, transforming processes like neural circuit mapping, which once took months, into high-speed operations via deep learning.Emerging technologies, including wearable IoT-enabled neuro-sensors and immersive AR/VR environments, are converting complex, noisy raw data into actionable insights. Today, the field stands at the intersection of medical science and information technology, where optimized data streams from electronic health records (EHRs), medical imaging, and real-time sensors support a more granular understanding of behavior, cognition, and bodily functions.We are currently witnessing a global mental health crisis and an aging population increasingly prone to neurodegeneration. Medical science alone cannot scale fast enough to address these challenges; there is an urgent need for scalable, secure, and automated tools capable of monitoring, diagnosing, and treating the brain in real-time.However, the integration of advanced technology introduces its own hurdles. Deep learning models often lack interpretability, necessitating the development of Explainable AI (XAI) for clinical decision-making. Furthermore, ensuring data privacy and mitigating algorithmic bias remain critical concerns for the field. This Research Topic aims to address these barriers by providing a platform for researchers in computer engineering, data science, and AI to present novel, "hardware-light, software-heavy" methodologies. Our goal is to bridge the interdisciplinary gap between neuroscientists, engineers, and clinicians, facilitating the creation of digital phenotyping tools and neuro-simulations that can solve complex psychological and neurological mysteries at scale.The scope of this Research Topic is strictly focused on computational and algorithmic innovations. While hardware and medical devices provide the foundational data, this issue prioritizes contributions that emphasize software frameworks and high-level data utilization for precision neuroscience. We welcome original research, reviews, and perspective articles that address the following themes:- Modeling & Simulation: Development of artificial neural networks that mimic biological plasticity and simulate complex neural circuits.- Software Frameworks: Open-source pipelines and platforms designed for neuro-data democratization and collaborative research.- Predictive Analytics: Methodologies for identifying digital biomarkers for mental health and neurodegeneration through secondary data analysis.- Human-Computer Interaction (HCI): The use of AR/VR as digital laboratories for immersive cognitive assessment and therapeutic applications.- Explainable AI (XAI) and Ethics: Addressing model interpretability, data security, and the reduction of bias in neuro-diagnostic algorithms. Topic editors: Shakir Khan, Deepti Mehrotra, Shalini Bhartiya, Anchal Garg Source: https://www.frontiersin.org/research-topics/79889/digital-phenotyping-and-neuro-simulations-scaling-precision-neuroscience-via-it