Interpretable Brain Age Prediction Using White Matter Fiber Orientation Distributions with Rotation-Equivariant Deep Learning

Accurate brain-age estimation from structural MRI provides a valuable biomarker for studying healthy aging and neurodegenerative disease. We propose BrainRotViT (Brain ResNet over trained Vision Transformer), a two-stage, slice-based (2.5D) framework that approximates volumetric context while combining the strengths of CNNs and transformers. A Vision Transformer is pretrained on an auxiliary age-sex composite classification task and subsequently frozen. Its per-slice embeddings are stacked into a 160 x 768 feature matrix and processed as a pseudo-image by a lightweight 2D residual CNN regressor, with subject sex incorporated at the final prediction layer. Trained and validated on 5,108 samples from 11 multi-site datasets and tested on four independent held-out cohorts comprising 948 samples, BrainRotViT achieves a validation mean absolute error (MAE) of 3.43 years (Pearson r = 0.98, $R^2$ = 0.96, Spearman $ρ$ = 0.97) and cross-cohort MAEs of 4.79-5.35 years, outperforming retrained 3D-ResNet, SFCN, 3D-ViT, Global-Local Transformer, Triamese-ViT, and TSAN baselines. After age-bias correction and covariate adjustment, brain-age gap is significantly associated with Alzheimer's disease, mild cognitive impairment, and autism spectrum disorder. An interpretability pipeline combining ViT attention with guided backpropagation, saliency sanity checks, and AAL3-based anatomical localization identifies aging-related regions including the occipital lobe, cerebellum, precentral and postcentral gyri, temporal lobes, and medial superior frontal gyrus. BrainRotViT thus provides an efficient and interpretable bridge between CNN and transformer paradigms, demonstrating cross-cohort generalization while yielding biologically meaningful insights into brain aging.

The white matter of the brain is organised into axonal bundles that support long-range neural communication. Although diffusion MRI (dMRI) enables detailed mapping of these pathways through tractography, how white matter pathways directly facilitate large-scale neural synchronisation remains poorly understood. We developed a data-driven framework that integrates dMRI and functional MRI (fMRI) to model the dynamic coupling supported by white matter tracks. Specifically, we employed track dynamic functional connectivity (Track-DFC) to characterise functional coupling of remote grey matter connected by individual white matter tracks. Using independent component analysis followed by k-medoids clustering, we derived functionally-coherent clusters of white matter tracks from the Human Connectome Project young adult cohort. When applied to the HCP ageing cohort, these clusters exhibited widespread age-related declines in both functional coupling strength and temporal variability. Importantly, specific clusters encompassing pathways linking control, default mode, attention, and visual systems significantly mediated the relationship between age and cognitive performance. Together, these findings depict the functional organisation of white matter tracks and provide a powerful tool to study brain ageing and cognitive decline.

BrainAGE is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI, overlooking functional vascular changes that may precede tissue damage and cognitive decline. DeepCBV maps, synthesized from non-contrast MRI, offer a scalable alternative to contrast-enhanced perfusion imaging by capturing vascular information relevant to early neurodegeneration. We developed a multimodal BrainAGE framework that combines predictions from two separate three-dimensional convolutional neural networks: one trained only on structural MRI scans and another trained only on DeepCBV maps. Each model was trained and validated on 2851 scans from 13 open-source datasets and was evaluated for concordance with MCI and AD. The combined model achieved the most accurate brain age gap for CN controls, with a mean absolute error of 3.95 years, outperforming models trained on MRI or DeepCBV alone. Saliency maps revealed complementary modality contributions: MRI emphasized white matter and cortical atrophy, while DeepCBV highlighted vascular-rich and periventricular regions implicated in hypoperfusion and early cerebrovascular dysfunction, consistent with known patterns of normal ageing. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN < MCI < AD) and correlated with cognitive impairment. DeepCBV-based BrainAGE showed a particularly strong separation between stable versus progressive MCI, suggesting sensitivity to prodromal vascular changes that precede overt atrophy. Integrating structural MRI with deep learning-derived vascular measures substantially enhances BrainAGE estimation and improves sensitivity to MCI and AD progression, supporting its potential role in risk stratification, early detection and monitoring of therapeutic response.