{
  "abstract": "Visual decoding from fMRI is typically siloed by participant and experiment, obscuring whether heterogeneous neural measurements can be organized within a common computational geometry. Here we introduce BRAID-fMRI (Brain Representation Alignment across Individuals and Datasets), a shared CLIP-supervised decoding framework. BRAID-fMRI uses a single ROI-wise Transformer with optional participant conditioning across eight visual-fMRI datasets comprising 93 dataset-specific participant entries, 430,007 single-trial responses and 162,839 unique stimuli. Regional brain activity is aligned with 512-dimensional CLIP ViT-B/32 representations using a multi-positive contrastive objective that treats repeated stimuli across participants and datasets as positives. BRAID-fMRI supports retrieval across seven evaluation datasets. On eight matched participant entries, it achieves 35.0 +/- 11.1% Top-10 accuracy, exceeding the observed mean accuracy of the two evaluated baselines - the MindEye-style pooled-CLIP decoder (27.1 +/- 5.1%) and ridge regression (21.1 +/- 9.1%) - and attaining the highest observed accuracy for seven of eight entries. In separately trained participant-agnostic models, expanding the source pool increased target-dataset holdout accuracy by up to 92.1% relative to the initial source-training condition. The learned space preserves graded semantic structure, while ablations and saliency highlight ventral and early visual cortex and category-specific motion and attentional systems. These results support scalable cross-dataset decoding into a common CLIP-aligned space, with model sensitivity concentrated in ventral and early visual inputs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.09352",
  "author_total": 6,
  "authors": [
    {
      "name": "Moein Khajehnejad"
    },
    {
      "name": "Michelangelo Tronti"
    },
    {
      "name": "Forough Habibollahi"
    },
    {
      "name": "Tommaso Boccato"
    },
    {
      "name": "Matteo Ferrante"
    },
    {
      "name": "Nicola Toschi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4kvzmn1r3cj4d.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.09352",
  "fulltext_url": null,
  "id": "4kvzmn1r3cj4d",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.09352",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.HC"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "2805|Visual perception and processing mechanisms"
    ]
  ],
  "openalex": "W7220951281",
  "pdf_url": "https://arxiv.org/pdf/2610.09352",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4kvzmn1r3cj4d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:43Z",
      "native_id": "2610.09352",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.09352"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Many Brains, One Geometry: A Shared Visual-Semantic Space for Cross-Dataset fMRI Decoding",
  "title_text": "Many Brains, One Geometry: A Shared Visual-Semantic Space for Cross-Dataset fMRI Decoding",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:43Z",
  "url": "https://research.draconitic.com/paper/4kvzmn1r3cj4d",
  "venue": "arXiv",
  "versions": []
}