AI-Enhanced Computational Tools for Early Detection of Mental and Neurological Disorders

Mental and neurological disorders, including Alzheimer’s disease, Parkinson’s disease, depression, and schizophrenia, represent a growing global health challenge. Traditionally, diagnosis relies on clinical observation and standardized behavioral assessments, which often occur only after significant neurobiological changes have already manifested. The delay in detection limits the effectiveness of therapeutic interventions and increases the socioeconomic burden on healthcare systems. However, the rise of computational neuroscience and artificial intelligence (AI) offers a transformative opportunity. By leveraging high-dimensional data, ranging from neuroimaging and electrophysiology to digital phenotyping and speech patterns, AI-enhanced tools can identify subtle, non-linear biomarkers of pathology long before clinical symptoms emerge. This Research Topic focuses on the intersection of machine learning and neurobiology to develop the next generation of predictive diagnostic frameworks.The primary challenge in early detection is the inherent complexity and heterogeneity of the human brain. Current diagnostic models often struggle with "noisy" biological data and the lack of interpretability required for clinical adoption. There is a critical need for computational tools that not only achieve high predictive accuracy but also provide mechanistic insights into the underlying neural dysfunctions. The goal of this Research Topic is to present cutting-edge research that utilizes AI to bridge the gap between raw neurological data and early clinical intervention. We aim to address how deep learning, reinforcement learning, and graph theoretical models can be optimized to process longitudinal data for disease progression mapping. By showcasing interdisciplinary approaches, combining data science with clinical neuroscience, we hope to foster the development of scalable, non-invasive, and cost-effective tools that can be integrated into routine clinical practice for the early screening and monitoring of brain disorders.We invite researchers to submit articles that explore the application of AI in the early detection of mental and neurological conditions. Contributions should ideally emphasize the computational aspects of the neuroscience involved, moving beyond simple classification to address model robustness and biological relevance.Specific themes for this Research Topic include:- Advanced deep learning architectures (e.g., CNNs, RNNs, Transformers) for analyzing fMRI, EEG, and MEG data.- Natural Language Processing (NLP) and acoustic analysis for identifying cognitive decline or mood disorders.- Explainable AI (XAI) methods to improve the clinical interpretability of diagnostic models.- Multi-modal data fusion techniques combining genetics, neuroimaging, and digital biomarkers.- Machine learning models for tracking disease trajectories and predicting transition states (e.g., from MCI to Alzheimer’s).- AI-driven analysis of wearable sensor data for the early detection of motor and psychiatric symptoms.- Graph neural networks for mapping dysfunctional brain connectivity in early-stage disorders. Topic editors: HengJin Ke, Haocong Rao Source: https://www.frontiersin.org/research-topics/82115/ai-enhanced-computational-tools-for-early-detection-of-mental-and-neurological-disorders