Artificial intelligence (AI) and computational modeling are transforming the landscape of neuroscience, offering unprecedented opportunities to detect, analyze, and interpret complex neural patterns. Building on recent advances in deep learning, multimodal data integration, and neuroimaging analytics, AI-driven neuroanalytics now enables more precise identification of neural signatures associated with cognitive function, brain connectivity, and neurological or psychiatric disorders. These developments are accelerating the discovery of biomarkers, improving diagnostic accuracy, and paving the way for personalized treatment strategies.

Despite this rapid progress, major challenges remain in developing scalable, interpretable, and clinically applicable AI models. The diversity and complexity of neural data, from electrophysiology and neuroimaging to behavioral and genetic information, demand innovative approaches that bridge neuroscience, data science, and biomedical engineering.

This Research Topic aims to highlight emerging approaches and real-world applications in neural pattern recognition and brain disorder diagnosis, fostering interdisciplinary research that advances both theoretical understanding and clinical translation.

We invite contributions addressing, but not limited to, the following themes:

• AI and machine learning models for detecting, classifying, and interpreting neural patterns.

• Multimodal and multi-scale data fusion techniques for comprehensive analysis of brain function and connectivity

• Deep learning and explainable AI for transparent and interpretable neural data analysis

• Computational biomarkers and predictive models for early diagnosis of neurological and psychiatric disorders

• Novel segmentation and feature extraction methods in neuroimaging and electrophysiological data

• Translational and clinical applications of AI-based neuroanalytics in disease monitoring and treatment evaluation

• Integration of neuroinformatics platforms for large-scale data management and reproducible research

• Ethical, methodological, and validation challenges in AI-assisted neuroscience

As with related collections, manuscripts emphasizing neuroimaging methodologies, data synthesis, or informatics integration should be directed to Frontiers in Neuroinformatics, while those focusing on algorithmic innovation, computational modeling, and analytical frameworks are more suited to Frontiers in Computational Neuroscience.

This Research Topic welcomes original research, reviews, methods, perspectives, and translational studies that reflect the latest progress in AI-driven neuroscience. By combining advanced analytics with practical applications, this collection aims to accelerate the development of intelligent, reliable, and clinically impactful neuroanalytic tools for understanding and diagnosing brain disorders.

Are you interested in contributing a manuscript to this Research Topic?

Integrating AI and Computational Neuroscience for mental health: Neuroanalytic approaches to pattern detection and brain disorder diagnosis