{
  "abstract": "In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or an overly salient memory. Motivated by control theory, we develop an AI-driven neural-surrogate framework that proposes candidate representational changes and tests their predicted perceptual effects from snapshots of stimulus-evoked fMRI activity, without physical stimulation. The framework combines fMRI decoding, deep generative modeling, and constrained latent-space steering. Valence and memorability are used only as worked examples. Using more than 36,000 image-fMRI observations from four deeply sampled Natural Scenes Dataset participants, subject-specific models recovered coarse generative structure from visually responsive cortex (two-way identification, 0.79-0.88; chance, 0.5). Graded perturbations were reconstructed as images and evaluated with automated scorers and human ratings from 7,200 trials by 18 participants. In the primary VDVAE model, valence shifted from -0.61 to +1.03 SD and memorability from -1.34 to +1.45 SD; a later Versatile Diffusion refinement reduced or altered these effects. Across five perturbation levels, human valence ratings moved in the predicted direction under the linear time-correction model (mean slope, 0.038 SD per unit of alpha; 95 percent CI, 0.003-0.074; positive in 16 of 18 participants). Perceived memorability did not change reliably. Baseline agreement with the automated assessor was suggestive for valence (r = 0.30) and weak for memorability (r = 0.10). Extreme perturbations drifted from the original stimulus, so intended change must be weighed against loss of fidelity. These findings provide a falsifiable upstream method for designing and behaviorally testing candidate representational targets for future neuromodulation in psychiatry, while marking the limits of the present static approximation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.27729",
  "author_total": 10,
  "authors": [
    {
      "name": "Marco Rothermel"
    },
    {
      "name": "Madleen Stenger"
    },
    {
      "name": "Soroush Daftarian"
    },
    {
      "name": "Svenja Jule Francke"
    },
    {
      "name": "Bita Shariatpanahi"
    },
    {
      "name": "José C. García Alanis"
    },
    {
      "name": "Mohammad-Ali Nikouei Mahani"
    },
    {
      "name": "Stefan G. Hofmann"
    },
    {
      "name": "Tim Hahn"
    },
    {
      "name": "Hamidreza Jamalabadi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c1ey792g64tm7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.27729",
  "fulltext_url": null,
  "id": "c1ey792g64tm7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.27729",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2808|Transcranial Magnetic Stimulation Studies"
    ]
  ],
  "openalex": "W7214199297",
  "pdf_url": "https://arxiv.org/pdf/2609.27729",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c1ey792g64tm7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:01Z",
      "native_id": "2609.27729",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.27729"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets",
  "title_text": "AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:01Z",
  "url": "https://research.draconitic.com/paper/c1ey792g64tm7",
  "venue": "arXiv",
  "versions": []
}