发表机构
Southern University of Science and Technology; University of Warwick; Shenzhen University（南方科技大学; 华威大学; 深圳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出FlatClip，一种利用冻结图像编码器处理几何感知平面图序列的fMRI表征方法，在静息态和视觉解码任务中作为ROI与体素模型间的实用中间基线，验证了解剖学空间组织的有效性。
AI 中文摘要
近期fMRI基础模型在表征大脑活动的空间尺度上存在显著差异。基于ROI和连接性的模型效率高但较为粗糙，而体素级模型保留了精细的空间结构，但需要专门的3D/4D架构和昂贵的fMRI特定预训练。我们探究图像预训练编码器能多有效地重用皮层活动的空间组织。受宏观脑活动受大脑几何结构强烈约束的证据启发，我们引入了FlatClip，一种冻结编码器的表面级基线，它将皮层活动渲染为几何感知的平面图序列，并重用冻结的SigLIP2图像编码器，仅需轻量级的下游探针。在静息态基准测试中，FlatClip作为有竞争力的中间表示，在HCP和ADNI任务上优于ROI级基线，但在PPMI上表现较弱，整体上低于最强的体素级模型。在视觉fMRI解码中，将输入限制为视觉或NSD提供的任务活跃皮层可提高性能，凸显了任务相关皮层覆盖的价值。空间扰动控制在重新训练和固定读出下均降低了平面图特征的预测性能，且解剖学相关的排列在三种颜色映射中始终优于顶点排列。总之，这些结果将表面级平面图序列定位为ROI和体素模型之间的实用中间基线，并支持解剖学相关的空间组织在重用图像预训练特征中的效用。代码可在https URL获取。
英文摘要
Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity. ROI- and connectivity-based models are efficient but coarse, whereas voxel-level models preserve fine-grained spatial structure but require specialized 3D/4D architectures and costly fMRI-specific pretraining. We ask how effectively an image-pretrained encoder can reuse the spatial organization of cortical activity. Motivated by evidence that macroscale brain activity is strongly constrained by brain geometry, we introduce FlatClip, a frozen-encoder surface-level baseline that renders cortical activity as geometry-aware flatmap sequences and reuses a frozen SigLIP2 image encoder with only a lightweight downstream probe. Across resting-state benchmarks, FlatClip serves as a competitive middle-ground representation, outperforming ROI-level baselines on HCP and ADNI tasks while remaining weaker on PPMI and below the strongest voxel-level models overall. On visual-fMRI decoding, restricting the input to visual or NSD-provided task-active cortex improves performance, highlighting the value of task-relevant cortical coverage. Spatial perturbation controls reduce the predictive performance of flatmap features under both retrained and fixed readouts, and anatomy-linked arrangements consistently outperform vertex permutations across three colormaps. Together, these results position surface-level flatmap sequences as a practical middle-ground baseline between ROI and voxel models, and support the utility of anatomy-linked spatial organization for reusing image-pretrained features. Code is available at https://github.com/OneMore1/FlatClip.
CommentsNeurIPS 2026
