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The "AI Neuroscientist" represents a significant advancement in democratizing neuroimaging analysis by integrating large language models with specialized toolsets for fNIRS and fMRI data. This system replaces rigid scripted pipelines with an interactive, natural-language interface that handles quality control, modeling, and visualization, effectively lowering the barrier to entry for researchers lacking deep coding expertise. By benchmarking this agent against general-purpose LLMs, the study validates its efficacy in handling domain-specific tasks without sacrificing transparency or control.
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The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis

arXiv2 min read

arXiv:2609.25254v1 Announce Type: new Abstract: Analyzing neuroimaging data requires specialized coding and statistical expertise, which limits accessibility for researchers without computational backgrounds. We present the AI Neuroscientist, a language agent for interactive data exploration. The system integrates a large language model (LLM) with a neuroimaging toolset to perform quality control, modeling, and visualization. This allows researchers to query data quality and specify analysis parameters directly in natural language, providing a transparent and interactive alternative to conventional scripted pipelines for small-scale data exploration. We demonstrate these capabilities using functional near-infrared spectroscopy (fNIRS) data, and evaluate the agent on a custom fNIRS benchmarking suite against general-purpose LLM agents with code sandboxes. Future extensions will generalize the architecture to additional modalities, including functional magnetic resonance imaging (fMRI) data, and expand the benchmarking suite to additional fNIRS tasks.

Abstract:Analyzing neuroimaging data requires specialized coding and statistical expertise, which limits accessibility for researchers without computational backgrounds. We present the AI Neuroscientist, a language agent for interactive data exploration. The system integrates a large language model (LLM) with a neuroimaging toolset to perform quality control, modeling, and visualization. This allows researchers to query data quality and specify analysis parameters directly in natural language, providing a transparent and interactive alternative to conventional scripted pipelines for small-scale data exploration. We demonstrate these capabilities using functional near-infrared spectroscopy (fNIRS) data, and evaluate the agent on a custom fNIRS benchmarking suite against general-purpose LLM agents with code sandboxes. Future extensions will generalize the architecture to additional modalities, including functional magnetic resonance imaging (fMRI) data, and expand the benchmarking suite to additional fNIRS tasks.

Artificial Intelligence (cs.AI)

arXiv:2609.25254 [cs.AI]

(or arXiv:2609.25254v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.25254 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 223 words

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