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The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis — arXiv:2609.25254

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.25254
License (as archived): CC0-1.0
Archived: 2026-10-08T13:44:50.830Z
SHA-256 of the archived text: c9fad3dc7b29696cbd3d597bd0f34a45b02c99ead8b775e2b7fb381a21d90a68
Stable URL of this page: https://metraipolis.com/documento/dc_2c8abbe86eea

How to cite

The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis — arXiv:2609.25254 — arXiv (en), https://arxiv.org/abs/2609.25254 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2c8abbe86eea (Metraipolis archive dc_2c8abbe86eea, sha256 c9fad3dc7b29696cbd3d597bd0f34a45b02c99ead8b775e2b7fb381a21d90a68)

BibTeX

@misc{dc2c8abbe86eea,
  title = {The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis — arXiv:2609.25254},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.25254},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2c8abbe86eea; sha256 c9fad3dc7b29696cbd3d597bd0f34a45b02c99ead8b775e2b7fb381a21d90a68},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.25254. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-23 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.25254 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T13:18:38.062Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-23?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).