fMRI

该论文提出一个通过行为推导的语义维度将多模态大语言模型（MLLM）与人类相似性判断的认知结构对齐的框架，并借助 fMRI 数据的搜索light RSA 验证了表征层面的人机对齐提升。

CFBrainAug 提出一种模块度约束的反事实脑网络增强框架，将数据增强与反事实解释统一起来，在 ABIDE I 数据集上以轻量级 SVM 实现了具有竞争力的自闭症诊断精度与内在可解释性。

弗吉尼亚理工大学研究人员在 Nature Metabolism 发表研究发现，营养成分完全匹配的高度加工与最低加工餐食会引起不同的代谢和大脑奖赏区域反应，高度加工食品会导致更多胰岛素分泌并改变脑部活动。

ICLR 2026 论文提出 Brain-IT 系统，可通过 fMRI 脑活动重建人眼所见图像，采用语义与结构双分支引导扩散模型，并能以新受试者仅约1小时的数据实现迁移学习。

This paper conducts a scaling study for fMRI foundation models, revealing that performance depends on the combination of pretraining data size, model size, and training duration, not just compute.

MnemoDyn is a dynamical-systems based model trained on 40K fMRI sequences for learning resting-state dynamics, outperforming transformer-based approaches in reconstruction quality and generalizing well across diverse populations.

This paper presents a zero-shot time-series foundation model applied to prediction and causal analysis of functional MRI and synthetic signals.

This paper tracks how supervised training with different learning rules (backpropagation, feedback alignment, predictive coding, STDP) degrades alignment between neural network representations and early visual cortex fMRI data, finding that untrained networks often match or exceed trained ones in V1 alignment.

This paper proposes DSFM, a novel generative framework that uses wavelet decomposition and spectral flow matching to synthesize realistic fMRI time series for brain disorder identification, addressing data scarcity and non-stationarity challenges.

Investigates neural integral-operator-based models for fMRI encoding and decoding tasks, focusing on the role of nonlocal spatiotemporal context and showing that larger temporal windows improve performance across datasets.