Global-Local Brain Network based on Functional Connectivity for Individualized Prediction
Bibliographic record
Abstract
Functional connectivity (FC) derived from fMRI reflects the interactions between brain regions of interest (ROIs). It has become one of the important features of individualized prediction. However, in studies using FC as input, some studies have extracted global features directly from the entire brain FC, lacking focus on local critical information. On the other hand, some studies have extracted local critical features from selected ROIs or connections, but lack access to global contextual information. In this paper, we propose a novel method, namely, Global-Local Brain Network (GLBN), focusing on both global contextual information and critical local information. We validate our proposed method on the largescale public dataset, the Cambridge Centre for Ageing and Neuroscience (Cam-CAN). For the prediction of age and fluid intelligence, GLBN achieves the mean absolute errors of 6.084, and 4.160, with Pearson’s correlations of 0.908, and 0.641, respectively. Our method demonstrates superior prediction accuracy compared to existing studies. Additionally, we visualize the brain ROIs that play crucial roles in the prediction tasks, affirming the biological interpretability of GLBN.
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 | 0.003 | 0.002 |
| 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) | 0.002 | 0.001 |
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".