Towards a Neurobiologically-driven Ontology of Mental Functions: A Data-driven Summary of the Twenty Years of Neuroimaging Meta-Analyses
Bibliographic record
Abstract
ABSTRACT A persistent effort in neuroscience has been to pinpoint the neurobiological substrates that support mental processes. The Research Domain Criteria (RDoC) aims to develop a new framework based on fundamental neurobiological dimensions. However, results from several meta-analysis of task-based fMRI showed substantial spatial overlap between several mental processes including emotion and anticipatory processes, irrespectively of the valence. Consequently, there is a crucial need to better characterize the core neurobiological processes using a data-driven techniques, given that these analytic approaches can capture the core neurobiological processes across neuroimaging literature that may not be identifiable through expert-driven categories. Therefore, we sought to examine the main data-driven co-activation networks across the past 20 years of published meta-analyses on task-based fMRI studies. We manually extracted 19,822 coordinates from 1,347 identified meta-analytic experiments. A Correlation-Matrix-Based Hierarchical Clustering was conducted on spatial similarity between these meta-analytic experiments, to identify the main co-activation networks. Activation likelihood estimation was then used to identify spatially convergent brain regions across experiments in each network. Across 1,347 meta-analyses, we found 13 co-activation networks which were further characterized by various psychological terms and distinct association with receptor density maps and intrinsic functional connectivity networks. At a fMRI activation resolution, neurobiological processes seem more similar than different across various mental functions. We discussed the potential limitation of linking brain activation to psychological labels and investigated potential avenues to tackle this long-lasting research question.
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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 | 0.116 | 0.168 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".