Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review

A comprehensive review examines how large-scale AI models and foundation models are transforming neuroscience research across neuroimaging, brain-computer interfaces, clinical decision support, and disease-specific applications. The paper emphasizes the reciprocal relationship between neuroscience and AI, where biological constraints inform AI architecture design, while highlighting critical implementation challenges including rigorous evaluation, domain knowledge integration, clinical validation, and ethical considerations.

This review represents a significant consolidation of how AI and neuroscience are converging to solve complex computational challenges. The intersection matters because large-scale AI models can process raw brain signals at scales previously impossible, enabling researchers to identify patterns in neuroimaging data, decode neural intent for brain-computer interfaces, and create clinical decision support systems. The reciprocal influence—where neuroscience informs AI architecture rather than AI simply being applied to neuroscience—signals maturation in the field.

The broader context reflects a decade-long trend of deep learning penetrating specialized scientific domains. As transformer architectures and foundation models demonstrated success in language and vision, neuroscientists recognized similar potential for multimodal neural data integration. This review documents that transition comprehensively, providing researchers with a structured understanding of applications from translational frameworks to psychiatric disorder modeling.

Industry impact extends beyond academia. Pharmaceutical companies developing neurological treatments can accelerate drug discovery pipelines. Brain-computer interface companies gain validation for AI-driven neural decoding approaches. Healthcare providers see potential for AI-assisted clinical decision support in neurology and psychiatry. However, the emphasis on "rigorous evaluation frameworks" and "ethical guidelines" signals investor caution—this domain requires clinical validation before commercialization, limiting near-term ROI.

Looking ahead, watch for regulatory frameworks governing AI use in clinical neuroscience settings and standardization of evaluation metrics across studies. The comprehensive dataset listing in this review likely becomes foundational infrastructure for future AI development, similar to how ImageNet shaped computer vision progress.

• →Large-scale AI models enable end-to-end learning from raw brain signals across neuroimaging, brain-computer interfaces, and clinical applications.
• →The field is becoming reciprocal—neuroscience now informs AI architecture design to create more interpretable and efficient models.
• →Multimodal neural data integration and spatiotemporal pattern interpretation represent major computational challenges AI can address.
• →Clinical validation and ethical guidelines are critical implementation barriers before widespread commercialization of neuroscience-AI applications.
• →Standardized datasets and rigorous evaluation frameworks will determine which AI approaches translate successfully to clinical practice.