NEURA: An Autonomous Agentic Workflow System for Neuroimaging

Introducing the NEURA project, an autonomous agentic system designed specifically for neuroimaging research. It aims to address problems in traditional neuroimaging analysis workflows such as high technical barriers, poor reproducibility, weak error handling, and insufficient flexibility. Through an agentic architecture, the system shifts workflow definition from a sequence of commands to intent declaration, allowing intelligent agents to handle execution planning and error handling, thereby reducing technical burden and improving research efficiency and reproducibility.

Neuroimaging involves complex analysis processes for multiple modal data (e.g., fMRI, PET) and requires connecting dozens of professional tools such as FSL and SPM. Traditional scripting methods have four main issues:

1. High technical barriers (requiring mastery of neuroscience, statistics, and programming)
2. Poor reproducibility (implicit dependencies and environmental differences)
3. Weak error handling (difficult to recover from intermediate failures)
4. Insufficient flexibility (parameter adjustments require extensive manual coding)

NEURA adopts an agentic architecture, with the core being transforming workflows from 'how to execute' to 'what goal to achieve'. The system architecture includes a user intent interface layer, planning agent, execution engine, tool ecosystem layer, and data management layer. Core components are:

1. Intent understanding and task decomposition (breaking down natural language goals into subtasks)
2. Intelligent tool adaptation (unified interface, automatic format conversion, containerized execution)
3. Adaptive error handling (classifying errors and adopting recovery strategies)
4. Native BIDS support (validation, metadata extraction, outputting compatible formats)

NEURA is suitable for multiple scenarios:

1. Multi-center research (automatically detecting data differences, adjusting preprocessing parameters, reducing site effects)
2. Exploratory data analysis (executing variants in parallel, comparing results, tracking decision history)
3. Clinical translation research (automated cross-validation, audit logs, model performance monitoring)
4. Teaching and training (explaining step principles, visualizing intermediate results, interactive parameter adjustment)

Technical highlights of NEURA include:

1. Declarative workflow definition (YAML format, highly readable, verifiable, optimizable)
2. Intelligent caching and incremental computation (based on content-based addressing, fine-grained caching, cache propagation to avoid redundant computation)
3. Distributed execution support (cluster/cloud platform scheduling, data locality optimization, elastic scaling)

Current limitations:

1. Incomplete tool coverage (niche tools require manual adaptation)
2. Domain limitations (focused on human brain MRI, limited support for other modalities/species)
3. Insufficient explanation depth (simplified explanations of complex statistical concepts) Future directions: Multimodal fusion, real-time analysis, collaboration features, AI-assisted interpretation

NEURA represents a new direction in the evolution of neuroimaging tools, shifting from passive script execution to active intelligent planning. By lowering technical barriers, improving reproducibility and error recovery capabilities, it allows researchers to focus on scientific questions. Such agentic systems are expected to promote open science and clinical translation in neuroimaging.