Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration

Researchers introduce NIAgent, a multi-agent AI system that automates end-to-end neuroimaging analysis by enabling specialist agents to collaboratively build and optimize executable programs. The system outperforms conventional static workflows like fMRIPrep by adapting dynamically to data and incorporating hierarchical quality control, addressing a critical bottleneck in clinical biomarker development.

NIAgent represents a significant advancement in automating complex scientific workflows by moving beyond static, tool-calling AI architectures toward collaborative multi-agent systems that reason about downstream objectives. Traditional neuroimaging pipelines like fMRIPrep, while standardized, operate as fixed configurations unable to adapt to unexpected data characteristics or optimize for specific clinical goals. This limitation forces domain experts into repetitive manual tuning cycles, creating scalability constraints in clinical research. NIAgent solves this through a code-centric paradigm where specialist agents synthesize and refine executable programs dynamically, enabling closed-loop adaptation between intermediate evidence and subsequent decisions.

The system's hierarchical verification framework integrating cohort-level metrics with visual inspection represents an important pattern for autonomous quality assurance in high-stakes domains. Experiments on ADHD-200 and ADNI datasets demonstrate superior predictive performance compared to baseline workflows, validating the approach's practical utility. This work extends beyond neuroscience—the modular, multi-agent architecture and verification patterns have potential applications across biomedical imaging, materials science, and other domains requiring complex analytical workflows.

For the broader AI industry, NIAgent exemplifies how multi-agent systems can tackle domain-specific challenges requiring both technical sophistication and expert knowledge integration. The emphasis on adaptive strategy exploration and evidence-grounded remediation establishes templates for deploying autonomous systems in regulated, knowledge-intensive fields. Organizations developing clinical AI tools should monitor this approach's maturation, as it offers a framework for reducing human bottlenecks in biomarker development without sacrificing scientific rigor.

• →NIAgent enables autonomous neuroimaging analysis through collaborative multi-agent systems that dynamically adapt to data rather than relying on static configurations.
• →The system outperforms conventional workflows on ADHD-200 and ADNI datasets while demonstrating sophisticated adaptive behaviors including strategy exploration and refinement.
• →Hierarchical verification combining cohort-level metrics and visual inspection provides a replicable pattern for autonomous quality control in high-stakes scientific domains.
• →The code-centric execution paradigm over composable domain-specific primitives offers a scalable template for multi-agent collaboration in knowledge-intensive fields.
• →This approach addresses the critical bottleneck of manual parameter tuning and pipeline failure remediation that constrains clinical biomarker development scalability.