发表机构
Seoul National University; Microsoft Research（首尔大学; 微软研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出FReD，利用仅在自然图像上预训练的冻结DCAE提取fMRI表征，在特质与状态预测任务上媲美或超越fMRI基础模型，证明强性能无需fMRI特异预训练。
AI 中文摘要
在大规模fMRI数据集上预训练的基础模型在下游任务中表现出色，但代价是高昂的数据和计算成本。为了探究此类性能实际上需要多少fMRI特异的预训练，我们提出了FReD，它从仅在自然图像上预训练的冻结深度压缩自编码器（DCAE）中提取fMRI表征，并将其与任务特定的读出模块配对。对于特质预测，FReD通过时间均值和对数标准差汇总逐帧表征，并应用线性探测，在两种归一化方案间进行晚期融合。对于状态预测，它将每一帧表示为一个单一令牌，并用浅层Transformer建模时间依赖关系。在涵盖六个特质预测目标的四个静息态数据集上，基于冻结DCAE特征的线性探测通常优于基于fMRI基础模型表征的线性探测，并且与完全微调的fMRI基础模型相比仍具有竞争力。在三个任务态fMRI状态预测任务上，基于DCAE特征的时间读出模块的表现与所评估的最强基础模型相当。高斯注入分析进一步表明，从冻结的DCAE特征中恢复局部信号变化比从所评估的基础模型表征中恢复得更准确。综合来看，这些结果表明，在当前的fMRI基准上取得强性能无需fMRI特异的表征预训练，这使得冻结的自然图像特征成为评估其附加价值的有用基线。
英文摘要
Foundation models pre-trained on large-scale fMRI datasets have shown strong downstream performance, but at substantial data and computation cost. To investigate how much fMRI-specific pre-training is actually needed for such performance, we introduce FReD, which derives fMRI representations from a frozen Deep Compression AutoEncoder (DCAE) pre-trained exclusively on natural images and pairs them with a task specific readout. For trait prediction, FReD summarizes frame-wise representations by their temporal mean and log-standard deviation and applies linear probing, with late fusion across two normalization schemes. For state prediction, it represents each frame as a single token and models temporal dependencies with a shallow Transformer. Across four resting-state datasets spanning six trait-prediction targets, linear probes on frozen DCAE features generally outperform those on fMRI foundation model representations and remain competitive with fully fine-tuned fMRI foundation models. On three task-fMRI state-prediction tasks, a temporal readout on DCAE features performs comparably to the strongest foundation models evaluated. A Gaussian injection analysis further shows that localized signal changes are recovered more accurately from the frozen DCAE features than from the evaluated foundation-model representations. Together, these results show that strong performance on current fMRI benchmarks is possible without fMRI-specific representation pre-training, making frozen natural-image features as a useful baseline for assessing its added value.
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