Advancing brain network models to reconcile functional neuroimaging and clinical research
Bibliographic record
Abstract
Functional magnetic resonance imaging (fMRI) captures information on brain function beyond anatomical alterations traditionally visible to neuroradiologists. However, the fMRI signal is complex and noisy, and so far fMRI has brought limited value in a clinical research context. We argue that solutions can be found in richer fMRI-based models such as statistical, biophysical and decoding models. These models extract clinically relevant information regarding biological mechanisms and features for classification and prediction (interpretability). Moreover, they are suitable to directly predict clinical variables from their parameters (predictability). We give guidelines for useful applications and pitfalls of such fMRI-based models in a clinical research context, and we look beyond currently used models. In particular, we provide arguments that clinical relevance of fMRI calls for better fMRI activity models incorporating both interpretability and predictability. We illustrate how this synergy of interpretability and predictability can be achieved by combining biophysical models with decoding models. These hybrid models entail reliable and biologically meaningful model parameters. In our view, this synergy is fundamental for the discovery of new pharmacological and interventional targets and the use of models as biomarkers.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".