arXiv：cs.LG· Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends, Mengjia Xu·· 3 小时前
BrainATCL：面向功能连接预测与年龄估计的自适应时序脑连接学习
BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
AI 导读
研究人员提出 BrainATCL，一个无监督、非参数的自适应时序脑连接学习框架，用于功能连接预测与年龄估计。该方法依据新增边速率动态调整每个快照的回看窗口，并用 GINE-Mamba2 主干编码图序列，在 Human Connectome Project 1000 名参与者的静息态 fMRI 数据上学习时空表征。
正文
Abstract:Functional Magnetic Resonance Imaging (fMRI) is an imaging technique widely used to study human brain activity. fMRI signals in areas across the brain transiently synchronise and desynchronise their activity in a highly structured manner, even when an individual is at rest. These functional connectivity dynamics may be related to behaviour and neuropsychiatric disease. To model these dynamics, temporal brain connectivity representations are essential, as they reflect evolving interactions between brain regions and provide insight into transient neural states and network reconfigurations. However, conventional graph neural networks (GNNs) often struggle to capture long-range temporal dependencies in dynamic fMRI data. To address this challenge, we propose BrainATCL, an unsupervised, nonparametric framework for adaptive temporal brain connectivity learning, enabling functional link prediction and age estimation. Our method dynamically adjusts the lookback window for each snapshot based on the rate of newly added edges. Graph sequences are subsequently encoded using a GINE-Mamba2 backbone to learn spatial-temporal representations of dynamic functional connectivity in resting-state fMRI data of 1,000 participants from the Human Connectome Project. To further improve spatial modeling, we incorporate brain structure and function-informed edge attributes, i.e., the left/right hemispheric identity and subnetwork membership of brain regions, enabling the model to capture biologically meaningful topological patterns. We evaluate our BrainATCL on two tasks: functional link prediction and age estimation. The experimental results demonstrate superior performance and strong generalization, including in cross-session prediction scenarios.
| Comments: | Camera-ready version. Published at MIDL 2026. Final version: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2508.07106 [cs.LG] |
| (or arXiv:2508.07106v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2508.07106 arXiv-issued DOI via DataCite |
|
| Journal reference: | Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, PMLR 315:3947-3970, 2026 |
Submission history
From: Yiran Huang [view email]
[v1]
Sat, 9 Aug 2025 21:18:25 UTC (2,386 KB)
[v2]
Thu, 8 Oct 2026 13:54:17 UTC (1,267 KB)
来源：arXiv：cs.LG · arxiv.org