Understanding how large-scale functional brain networks reorganize during cognitive decline remains a central challenge in neuroimaging. While recent self-supervised models have shown promise for learning representations from resting-state fMRI, their internal mechanisms are difficult to interpret, limiting mechanistic insight. We propose BrainInterNet, a network-aware self-supervised framework based on masked reconstruction with cross-attention that explicitly models inter-network dependencies in rs-fMRI. By selectively masking predefined functional networks and reconstructing them from remaining context, our approach enables direct quantification of network predictability and interpretable analysis of cross-network interactions. We train BrainInterNet on multi-cohort fMRI data (from the ABCD, HCP Development, HCP Young Adults, and HCP Aging datasets) and evaluate on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, in total comprising 5,582 recordings. Our method reveals systematic alterations in the brain's network interactions under AD, including in the default mode, limbic, and attention networks. In parallel, the learned representations support accurate Alzheimer's-spectrum classification and yield a compact summary marker that tracks disease severity longitudinally. Together, these results demonstrate that network-guided masked modeling with cross-attention provides an interpretable and effective framework for characterizing functional reorganization in neurodegeneration.
Figures & tables
Figure 1: Overview of the proposed framework for modeling inter-network interactions in resting-state fMRI. Resting-state BOLD signals are parcellated using the DiFuMo atlas and divided into non-overlapping temporal-spatial patches. Patches are grouped by Yeo functional networks [ 24 ] , and those belonging to a selected target network are masked. The encoder processes unmasked tokens to produce contextualized embeddings, while a cross-attention decoder reconstructs the masked network using the context and position embeddings. Learned embeddings reveal separation by disease status, offering a simple metric for mapping neurodegeneration trajectories (bottom left). Meanwhile, the cross-attention decoder provides interpretability into how predictions are made, offering a proxy for inter-network brain dependencies (bottom right).
Figure 2: Longitudinal charting of Alzheimer’s disease progression using encoder embeddings. Left: Representative longitudinal trajectories of the encoder embedding norm ( ℓ2 ) for individual subjects, including stable CN, stable MCI, stable AD, and converters who transition from MCI to AD across sessions. Faint background points show embedding norms for all subjects and sessions. Right: Group-level comparison of embedding norms for stable CN, MCI, and AD subjects, showing progressive separation with disease severity (mean ± s.e.m.). *** indicates p<0.001 (two-sided Welch’s t-test). Together, these analyses illustrate that the learned encoder representations capture both group-level differences and subject-specific trajectories associated with cognitive decline.
Figure 3: Inter-network predictability and disease-related changes in functional coupling . Control: Baseline inter-network predictability in cognitively normal (CN) subjects. Chord diagrams depict cross-network attention-based contributions during reconstruction, where edge thickness indicates the relative influence of source networks (left) on target network prediction (right). Bar plots report network-wise predictability measured by Pearson correlation between reconstructed and true signals. MCI - Control: Changes in inter-network contributions from CN to MCI. Edges and bars represent differences in attention-derived source dependencies relative to CN. AD - Control: Analogous changes in inter-network contributions from CN to AD. Red indicates increased reliance on a given source network for reconstruction, while blue indicates decreased reliance. Together, these panels illustrate progressive reorganization of functional dependencies from cognitive impairment and Alzheimer’s disease.
Method
Mask
CrossAttn
BAcc (%)
F1 (%)
AUC
Baselines
BrainLM [ 7 ]
Rand
✗
68.78
68.70
0.776
Brain-JEPA [ 13 ]
Rand
✗
51.85
51.10
0.620
BrainGNN [ 19 ]
NA
✗
40.89
40.22
0.553
Random
NA
✗
33.65
31.50
0.503
Ablations
MAE [ 18 ]
Rand
✗
69.31
71.86
0.849
Ours - Net
Rand
✓
48.26
43.23
0.520
Table 1: Performance comparison on 3-way Alzheimer’s disease spectrum classification on ADNI. Reported metrics including balanced accuracy (BAcc), F1-score, and AUC-ROC. We test four baselines, and three ablations: a vanilla MAE, replacing cross-attention with self-attention, as well as replacing the network-based masking (denoted Net in the table) with random masking (denoted Rand). All models are finetuned.
School of Medical Science and Technology · Department of Computer Science and Engineering at the Indian Institute of Technology Gandhinagar · Dr B C Roy Multi Speciality Medical Research Centre, Indian Institute of Technology Kharagpur, India
Department of Radiology, Weill Cornell Medicine, New York, NY, USA. · School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA.