BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation

Yiran Huang; Amirhossein Nouranizadeh; Christine Ahrends; Mengjia Xu

cs.LGAug 9, 2025

BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation

Authors: Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends, Mengjia Xu

Organizations: New Jersey Institute of Technology · University of Oxford

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.

Figures & tables

Figure 1: Pipeline for dFC construction and adaptive graph sequence generation. (a) The original 4D fMRI BOLD signals $\mathcal{X}\in\mathbb{R}^{N\times P\times Q\times T}$ (with $N$ , $P$ , $Q$ , $T$ denoting ROIs, subjects, imaging sessions, and time points). (b) We construct dFC $G^{p,q}$ as a graph sequence of length $K$ for subject $p$ and session $q$ , all subjects’ dFC set is denoted as $\mathcal{G}$ . (c) For training the models, we designed adaptive lookback strategy as a function of the temporal novelty index (Eq. 1 ) as a heuristic to determine temporal context $\mathcal{D}^{p,q}_{k}$ dynamically for each graph snapshot at time step $k$ , see detailed computation of adaptive lookback $\ell^{p,q}_{k}$ in Section 3.3.1 .

Figure 2: Illustration of our proposed BrainATCL framework for adaptive temporal brain connectivity embedding and two downstream tasks. (a) The BrainATCL is trained by minimizing the contrastive triplet loss. It combines a GINEConv-based GNN to encode structural information with edge attributes, followed by a Mamba-2 model for temporal aggregation across the graph sequence. The output of Mamba-2 is fed to linear layers to represent each node as a multivariate Gaussian distribution. (b) After pre-training the BrainATCL, the input graph sequence is processed and its node embeddings are used for link prediction classifier and age prediction regressor. These downstream task models are trained separately using binary cross-entropy (BCE) and mean-squared error (MSE) losses.

Within-session
Model Variant	MAP $\uparrow$	MRR $\uparrow$	AUC $\uparrow$	PR-AUC $\uparrow$	F1 $\uparrow$
w/o Edge Features	0.7313 $\pm$ 0.0024	0.6061 $\pm$ 0.0015	0.9728 $\pm$ 0.0004	0.7143 $\pm$ 0.0016	0.6830 $\pm$ 0.0011
w/ Edge Features	0.7681 $\pm$ 0.0012	0.6265 $\pm$ 0.0009	0.9746 $\pm$ 0.0001	0.7468 $\pm$ 0.0013	0.7050 $\pm$ 0.0008

Table 1: Ablation study on the effect of edge features (including network and hemisphere labels of the two endpoint nodes) for temporal link prediction under within-session and cross-session settings.

Within-session
Model Variant	MAP $\uparrow$	MRR $\uparrow$	AUC $\uparrow$	PR-AUC $\uparrow$	F1 $\uparrow$
VGAE Reconstruction	0.7054 $\pm$ 0.0019	0.5982 $\pm$ 0.0021	0.9701 $\pm$ 0.0004	0.6752 $\pm$ 0.0012	0.6488 $\pm$ 0.0011
Contrastive (BrainATCL)	0.7681 $\pm$ 0.0012	0.6265 $\pm$ 0.0009	0.9746 $\pm$ 0.0001	0.7468 $\pm$ 0.0013	0.7050 $\pm$ 0.0008

Table 2: Ablation study on training objectives by replacing the proposed contrastive loss with a VGAE-style reconstruction objective for temporal link prediction under the within-session setting.

Within-session
Model	MAP $\uparrow$	MRR $\uparrow$	AUC $\uparrow$	PR-AUC $\uparrow$	F1 $\uparrow$	MAE $\downarrow$	MSE $\downarrow$
GCN	0.2838 (0.0018)	0.2226 (0.0009)	0.9101 (0.0000)	0.2679 (0.0001)	0.3557 (0.0001)	3.09 (0.16)	13.77 (1.89)
GraphSAGE	0.3245 (0.0234)	0.2576 (0.0534)	0.9279 (0.0000)	0.3017 (0.0001)	0.4054 (0.0002)	3.29 (0.58)	16.58 (5.42)
GAT	0.3601 (0.0027)	0.2045 (0.0038)	0.9112 (0.0000)	0.3408 (0.0001)	0.4233 (0.0002)	3.16 (0.49)	15.78 (2.45)
GIN	0.3564 (0.0124)	0.2308 (0.0210)	0.9178 (0.0000)	0.3326 (0.0001)	0.4150 (0.0000)	3.15 (0.07)	14.70 (1.23)
GCN-GRU	0.2991 (0.0055)	0.2351 (0.0037)	0.9172 (0.0000)	0.2785 (0.0000)	0.3622 (0.0000)	3.08 (0.54)	14.68 (0.77)

Table 3: Benchmarking BrainATCL against multiple baselines for temporal link and age prediction under within-session and cross-session settings. MAP, MRR, AUCROC, PR-AUC, and F1 are used for link prediction, while MAE and MSE are used for age prediction. Reported values are means with standard deviations shown in parentheses. All comparisons are conducted under the same experimental configuration (stride=10, window=100, embedding=128, threshold=0.5).

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 3: Temporal Edge Appearance (TEA) plots for subject 10, session 1 under different threshold values. For each time point, the blue bars indicate the number of repeated edges (i.e., edges that have appeared in any previous time point), while the orange bars represent newly appeared edges. The red line shows the novelty index at each time point, quantifying the proportion of new edges relative to total edges. The average novelty across the time series is reported in the title of each subplot.

Panel of Figure 4 — Figure 4: The Top panel shows ablation study on the effect of hyperparameters (threshold, embedding size and stride) on within-session (blue) and cross-session (red) temporal link prediction performance in terms of MAP (solid lines) and MRR (dashed lines). Solid/dashed lines denote the mean performance, while the shaded areas correspond to the standard deviation across five runs. The bottom panel shows MAP and MRR scores across different lookback strategies and stride sizes for the link prediction task. The red dashed line shows the average novelty score corresponding to each stride setting. The x-axis denotes the stride size ( $s$ ), with the number of temporal graphs per subject indicated in parentheses.

Figure 5: Distribution of cross-subject cosine distances between time-averaged ROI embeddings of the two subjects with the largest age difference (15 years), grouped by major functional networks. All subnetworks are defined based on the Schaefer parcellation Schaefer et al. (2018)

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Abstract

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