用频段注意力建模脑功能动态，提升精神与认知疾病预测效果
Spatiotemporal Learning of Brain Dynamics from fMRI Using Frequency-Specific Multi-Band Attention for Cognitive and Psychiatric Applications
- 基于频率分解与多频段自注意力，捕捉fMRI中的非线性动态
- 在49,673人数据上预测抑郁、多动症等疾病，准确率提升52.5%
- 揭示不同疾病特异的频段连接模式，助力精准精神医学
理解大脑复杂非线性动态如何产生认知功能，仍是神经科学的核心挑战。尽管脑功能动态具有跨时间尺度的无标度与多分形特性，传统神经影像分析仍假设线性与平稳性，难以捕捉频段特异性神经计算。本文提出首个基于Transformer的Multi-Band Brain Net（MBBN）框架，显式建模来自fMRI的频段特异性时空脑动态。MBBN结合生物可解释的频段分解与多频段自注意力机制，发现此前无法检测的频段依赖网络交互。在包含49,673名个体的三个大规模队列（UK Biobank、ABCD、ABIDE）上训练，MBBN在预测精神疾病与认知结果（抑郁、多动症、自闭症）方面达到新基准，分类任务中最高提升52.5% AUROC，同时可预测认知智力得分。频段解析分析揭示疾病特异性特征：多动症患者高频前运动-感觉皮层连接减弱，外侧感觉皮层成为动态枢纽；自闭症患者眶额-体感回路存在局灶性高频异常，且颞顶联合区与前额叶间超低频耦合增强。通过融合尺度感知神经动态与深度学习，MBBN提供更准确且可解释的生物标志物，为精准精神病学与发育神经科学开辟新路径。
原文摘要 · Abstract (English)
Understanding how the brain's complex nonlinear dynamics give rise to cognitive function remains a central challenge in neuroscience. While brain functional dynamics exhibits scale-free and multifractal properties across temporal scales, conventional neuroimaging analytics assume linearity and stationarity, failing to capture frequency-specific neural computations. Here, we introduce Multi-Band Brain Net (MBBN), the first transformer-based framework to explicitly model frequency-specific spatiotemporal brain dynamics from fMRI. MBBN integrates biologically-grounded frequency decomposition with multi-band self-attention mechanisms, enabling discovery of previously undetectable frequency-dependent network interactions. Trained on 49,673 individuals across three large-scale cohorts (UK Biobank, ABCD, ABIDE), MBBN sets a new state-of-the-art in predicting psychiatric and cognitive outcomes (depression, ADHD, ASD), showing particular strength in classification tasks with up to 52.5\% higher AUROC and provides a novel framework for predicting cognitive intelligence scores. Frequency-resolved analyses uncover disorder-specific signatures: in ADHD, high-frequency fronto-sensorimotor connectivity is attenuated and opercular somatosensory nodes emerge as dynamic hubs; in ASD, orbitofrontal-somatosensory circuits show focal high-frequency disruption together with enhanced ultra-low-frequency coupling between the temporo-parietal junction and prefrontal cortex. By integrating scale-aware neural dynamics with deep learning, MBBN delivers more accurate and interpretable biomarkers, opening avenues for precision psychiatry and developmental neuroscience.
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