AI 神经代理框架用 fMRI 无创预测神经调控的认知效果
AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets
一项把控制论和生成模型用于神经调控的论文，用 fMRI 快照就能预测改变效价的效果，数据量四万多条，做脑科学方向的可以看看
arXiv 论文提出一个 AI 神经代理框架，结合 fMRI 解码、深度生成建模和约束潜空间引导，在不做物理刺激的情况下预测候选表征变化对感知的影响。
研究基于 Natural Scenes Dataset 4 名被试超过 36,000 条图像-fMRI 观测， subject-specific 模型的二选一识别率达 0.79-0.
88（随机为 0.5）。
在 VDVAE 模型中，效价从 -0.61 SD 移至 +1.
03 SD，记忆度从 -1.34 SD 移至 +1.
45 SD；18 名被试共 7,200 次人类评分中，16 人的效价朝预测方向变化。
自动化评分器与人类效价评分的相关仅为 r = 0.30，且极端扰动会导致图像偏离原始刺激。
AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets
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.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include fMRI, VDVAE, Neural Surrogates, 神经调控.
Source attribution: curated from arXiv cs.AI (@Marco Rothermel, Madleen Stenger, Soroush Daftarian, Svenja Jule Francke, Bita Shariatpanahi, José C. García Alanis, Mohammad-Ali Nikouei Mahani, Stefan G. Hofmann, Tim Hahn, Hamidreza Jamalabadi).