The AI Neuroscientist is presented, a language agent for interactive data exploration that integrates a large language model with a neuroimaging toolset to perform quality control, modeling, and visualization and allows researchers to query data quality and specify analysis parameters directly in natural language.
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.
Most knowledge of graphical perception comes from behavioral studies. Understanding from a neural perspective is much more limited due in part to neuroimaging studies'expensiveness and difficulty to conduct. In this paper, we evaluate whether Meta's Tribe V2 neural encoding model can recover neural contrasts from a vis...
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The relationship between the cerebellum and cerebrum remains poorly understood in the neuroimaging literature, despite decades of functional magnetic resonance (fMRI) research. In this opinion article, we argue that this gap reflects fundamental methodological constraints rather than merely a lack of scientific interes...
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Abstract Objectives Exploratory data analysis (EDA) is foundational to clinical research yet inaccessible to clinician-scientists without programming training. Existing large language model (LLM) tools improve accessibility but struggle with hallucinations. We developed and evaluated data reflector (DR), a hybrid agent...
Moein Sabounchi, N. Hazare, Chris Capone et al.· JAMIA Open· 0 citations
Mental images are challenging to study, given that our conscious experience is notoriously hard to access. The currently prevalent introspective methods are inherently subjective and can thus only provide limited access to their qualities. Here, we developed a neuro-computational approach that approximates and assesses...
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Xiao-Min Yang, Yue Li, Xue Zhang et al.· Frontiers in Neuroinformatic...· 0 citations
