TLDR: This paper provides a comprehensive review of how large-scale AI models, including foundation models and large language models, are transforming neuroscience. It explores their impact across five key domains: neuroimaging and data processing, brain-computer interfaces and neural decoding, molecular neuroscience and genomic modeling, clinical assistance and translational frameworks, and disease-specific applications. The review highlights the models’ capabilities in multimodal data integration, spatiotemporal pattern interpretation, and clinical translation, while also addressing significant challenges such as generalization, interpretability, data quality, and ethical considerations. It concludes by outlining future opportunities and the critical need for interdisciplinary collaboration.
The field of neuroscience is undergoing a significant transformation with the emergence of large-scale artificial intelligence (AI) models, including foundation models (FMs) and large language models (LLMs). These advanced AI systems are moving beyond traditional computational methods, enabling end-to-end learning directly from raw brain signals and neural data. This shift is creating a new era of intelligence at the intersection of AI and brain science.
These powerful AI models excel at learning rich, multimodal representations from vast and varied datasets. Unlike older machine learning techniques that relied on manually engineered features, these new models can automatically capture complex spatiotemporal dependencies in neural signals. This allows them to generalize effectively across different experimental conditions, subjects, and recording methods, directly learning hierarchical representations from brain activity.
The transformative effects of large-scale AI models are being observed across five major neuroscience domains:
Neuroimaging and Data Processing
AI models are revolutionizing how we analyze brain structure and function. For functional MRI (fMRI), models like BrainLM can predict psychological measures from neural signals, while MindEye2 can reconstruct visual images from brain activity. In structural MRI, models such as BrainSegFounder are improving segmentation for tasks like brain tumor detection. For CT scans, models like DeepCNTD-Net and FM-HCT are enhancing rapid trauma assessment and disease detection in emergency settings. In electrophysiological signal processing, models like BENDR and EEGFormer are making strides in analyzing EEG data for applications ranging from sleep monitoring to brain-computer interfaces, by learning robust representations from large amounts of unlabeled data.
Brain-Computer Interfaces and Neural Decoding
The goal of establishing direct communication between the brain and external devices is becoming more attainable with large-scale AI. These models are enabling robust neural signal decoding with unprecedented accuracy. Specialized architectures, like CBraMod for EEG decoding, are designed to overcome challenges such as limited labeled data and high variability across individuals. High-resolution neural decoding models, such as POYO, process individual spike events from intracranial recordings, allowing for precise decoding of neuronal activity patterns for real-time BCI applications.
Molecular Neuroscience and Genomic Modeling
AI is also delving into the molecular and genetic underpinnings of brain function and disease. Foundation models like The Nucleotide Transformer and AlphaGenome, trained on vast genomic datasets, are predicting chromatin states, enhancer-promoter interactions, and the functional consequences of genetic variations. The scFoundation model, trained on single-cell RNA sequencing data, provides detailed maps of cell function under normal and pathological conditions, greatly enhancing our ability to understand the molecular basis of neurological diseases.
Clinical Support and Translational Frameworks
In clinical neuroscience, scalable AI systems are increasingly aiding diagnostic decision support and improving patient outcomes. LLMs are being integrated into systems like EpiSemoLLM for epilepsy management, which analyzes seizure semiology to predict epileptogenic zones. AtlasGPT and Neura are enhancing neurosurgical decision support by integrating generative reasoning with domain-specific literature. These systems provide evidence-based advice, improve diagnostic efficiency, and facilitate sophisticated clinical reasoning.
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Disease-Specific Applications
Large-scale AI models are proving invaluable in the diagnosis and treatment of specific brain disorders. For neurodegenerative conditions like Alzheimer’s disease, ADAgent and DECT are supporting diagnosis and monitoring. In acquired neurological disorders such as epilepsy, models like SETR-PKD and VSViG are improving video-based seizure detection, while EpilepsyLLM aids in seizure classification and treatment optimization. For brain tumors, multimodal FM methods are enhancing classification and surgical planning. In psychiatric disorders, MDD-LLM and VS-LLM are assisting in the diagnosis of major depressive disorder. Furthermore, models like TRUST and SocialRecNet are supporting the assessment of anxiety, trauma-related disorders, and neurodevelopmental disorders like autism.
The success of these models relies on theoretical foundations like the transformer architecture, which efficiently captures long-range dependencies in data, and self-supervised learning, which overcomes data scarcity by learning from unlabeled neural activity. The training typically involves a three-stage pipeline: pre-training for universal knowledge acquisition, adaptation for task-specific tuning, and efficient inference and deployment for real-world use.
Despite their immense promise, challenges remain. These include achieving robust generalization across diverse patient populations, effectively integrating multimodal data with varying temporal and spatial resolutions, and ensuring computational scalability. Clinical translation also requires addressing interpretability, building trust with clinicians, and navigating complex regulatory frameworks. Ethical considerations, such as data privacy and algorithmic bias, are paramount, necessitating privacy-preserving methods and robust ethical guidelines.
The future of AI in neuroscience is bright, with emerging frontiers in real-time adaptive brain-computer interfaces, brain-inspired AI systems, and synthetic neural data generation. Realizing this potential requires sustained interdisciplinary collaboration among neuroscientists, computer scientists, clinicians, and ethicists to develop innovative methods and ethical data-sharing practices. For more in-depth information, you can read the full research paper here.
