Basal Ganglia Functional Connectivity Based on a Meta-Analysis of 126 Positron Emission Tomography and Functional Magnetic Resonance Imaging Publications
Bibliographic record
Abstract
The striatum receives projections from the entire cerebral cortex. Different, but not mutually exclusive, models of corticostriatal connectivity have been proposed, including connectivity based on proximity, parallel loops, and a model of a tripartite division of the striatum into motor, associative, and limbic areas. All these models were largely based on studies of anatomic connectivity in nonhuman mammals and lesion studies in animals and humans. Functional neuroimaging has the potential to discern patterns of functional connectivity in humans in vivo. We analyzed the functional connectivity between the cortex and the striatum in a meta-analysis of 126 published functional neuroimaging studies. We mapped the peak activations listed in each publication into stereotaxic space and used standard functional imaging statistical methods to determine which cortical areas were most likely to coactivate with different parts of the striatum. The patterns of functional connectivity between the cortex and the different striatal nuclei are broadly consistent with the predictions of the parallel loop model. The rostrocaudal and dorsoventral patterns of corticostriatal functional connectivity are consistent with the tripartite division of the striatum into motor, associative, and limbic zones.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".