Structured Artificial Neural Networks for Chronological Brain Age Prediction using Functional Connectivity Matrices

Le résumé fourni par la source

Chronological brain age prediction using functional connectivity (FC) matrices derived from functional magnetic resonance imaging (fMRI) is an emerging biomarker for assessing neurological health, with deviations from biological age norms suggestive of cognitive decline or other brain disorders. While artificial neural networks (ANNs) outperform traditional age prediction methods by learning hierarchical patterns directly from FC data, most frameworks overlook the biological distinction of within- and between-network connectivity that exhibit divergent trajectories with age. This study addresses this gap by proposing a structured ANN that explicitly models within- and between-network connectivity as separate submodels, aligning their architectural design with modular organization of the brain. Leveraging resting-state fMRI data from 357 healthy adults, FC matrices were partitioned into six brain networks. The dedicated submodels processed within- and between-network connections, with their outputs concatenated prior to age prediction. Model performance was evaluated using mean absolute error (MAE). Grad-Cam was used for interpreting the model findings. Results demonstrated robust predictive performance (MAE was comparable to values in the literature) and revealed that between-network connectivity increased in importance when predicting age in older individuals, while within-network contributions remained stable with age. This finding aligns with prior non-ANN work showing age-related increases in cross-network integration and functional de-differentiation with age. By integrating biologically informed architecture with explainable AI, this work advances personalized brain-age prediction and clarifies network-specific mechanisms. Future directions include validation using longitudinal data and integration with multimodal data to investigate structural-functional coupling in aging.

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