Structured Artificial Neural Networks for Chronological Brain Age Prediction using Functional Connectivity Matrices
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".