Transforming Neuroscience Research through AI-Driven Data Integration and Analysis

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Abstract
Integrating multimodal data is essential to advance neuroscience as a discipline. Large-scale collaborative efforts, exemplified by the BRAIN Initiative, together with advances in single-cell and spatial omics, multimodal imaging, human genetics, behavioral phenotyping, protein structure prediction, and related technologies, are transforming neuroscience into a data-rich, highly interconnected science. As experimental resources, scientific literature, and collaborative networks continue to expand, integrating discoveries across modalities, species, and levels of biological organization has become the field's central bottleneck. This Perspective argues that artificial intelligence (AI) is becoming essential for overcoming these constraints. Using six evolving approaches we outline strategies to accelerate AI adoption through community building and pilot projects that drive AI transformation at both the organizational and technological levels. Organizational transformation cultivates broad AI awareness and a pro-AI culture while technological transformation improves the efficiency, scalability, and quality of data analysis, allowing researchers to generate insights more rapidly and effectively. Together, these advances position AI as a key driver for advancing neuroscience at scale.
Funding Information
  • HHS | NIH | National Institute on Aging (R01AG067025)
  • HHS | NIH | BRAIN Initiative (RF1MH128695)
  • HHS | NIH | BRAIN Initiative (U24MH130988)
  • HHS | NIH | BRAIN Initiative (R01MH142410)
  • HHS | NIH | National Center for Advancing Translational Sciences (1UM1TR004906-01)
  • NSF | BIO | Division of Biological Infrastructure (2144475)
  • Klingenstein-Simons Fellowship
  • McDonnell Fellow In Neuroscience Program
  • HHS | NIH | National Institute of Mental Health (T32MH065214)
  • Cancer Prevention and Research Institute of Texas (RP210045)
  • Cancer Prevention and Research Institute of Texas (RP170668)

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