Transferability of Brain decoding using Graph Convolutional Networks
Bibliographic record
Abstract
Abstract Transfer learning has been a very active research topic in natural image processing. But few studies have reported notable benefits of transfer learning on medical imaging. In this study, we sought to investigate the transferability of deep artificial neural networks (DNN) in brain decoding, i.e. inferring brain state using fMRI brain response over a short window. Instead of using pretrained models from ImageNet, we trained our base model on a large-scale neuroimaging dataset using graph convolutional networks (GCN). The transferability of learned graph representations were evaluated under different circumstances, including knowledge transfer across cognitive domains, between different groups of subjects, and among different sites using distinct scanning sequences. We observed a significant performance boost via transfer learning either from the same cognitive domain or from other task domains. But the transferability was highly impacted by the scanner site effect. Specifically, for datasets acquired from the same site using the same scanning sequences, using transferred features highly improved the decoding performance. By contrast, the transferability of representations highly decreased between different sites, with the performance boost reducing from 20% down to 7% for the Motor task and decreasing from 15% to 5% for Working-memory tasks. Our results indicate that in contrast to natural images, the scanning condition, instead of task domain, has a larger impact on feature transfer for medical imaging. With other advanced tools such as layer-wise fine-tuning, the decoding performance can be further improved through learning more site-specific high-level features while retaining the transferred low-level representations of brain dynamics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".