AI驱动的神经代理模型用于认知-情感神经调控靶点的硅内设计

中文摘要 AI 辅助

在神经精神病学中，主要目标往往不仅是解码大脑活动，而是改变它，例如减轻负面情感偏差或过度突出的记忆。受控制理论启发，我们开发了一个AI驱动的神经代理框架，该框架从刺激诱发的fMRI活动快照中提出候选表征变化并测试其预测的知觉效应，无需物理刺激。该框架结合了fMRI解码、深度生成建模和约束潜空间引导。效价和可记忆性仅作为工作示例。利用来自四名深度采样的自然场景数据集参与者的超过36,000个图像-fMRI观测，受试者特定模型从视觉响应皮层恢复了粗略的生成结构（双向识别，0.79-0.88；随机水平，0.5）。分级扰动被重建为图像，并通过自动评分器和来自18名参与者的7,200次试验的人类评分进行评估。在主要VDVAE模型中，效价从-0.61 SD移至+1.03 SD，可记忆性从-1.34 SD移至+1.45 SD；后来的Versatile Diffusion细化减少或改变了这些效应。在五个扰动水平下，在线性时间校正模型下，人类效价评分沿预测方向移动（平均斜率，每单位alpha 0.038 SD；95%置信区间，0.003-0.074；18名参与者中16名为正）。感知可记忆性未可靠变化。与自动评估器的基线一致性对效价具有提示性（r = 0.30），对可记忆性较弱（r = 0.10）。极端扰动偏离原始刺激，因此预期变化必须与保真度损失相权衡。这些发现为精神病学中未来神经调控的候选表征靶点的设计和行为测试提供了一种可证伪的上游方法，同时标明了当前静态近似的局限性。

英文摘要

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.

发表机构

• University of Marburg（马尔堡大学）
• University of Münster（明斯特大学）
• University of British Columbia（不列颠哥伦比亚大学）

机构由 AI 辅助整理，请以论文原文为准。