Estimation of Voxelwise Effective Connectivities: Applications to High Connectivity Sub-Regions within Hippocampal and within Corticostriatal Networks
Bibliographic record
Abstract
Abstract Standard BOLD connectivity analyses depend on aggregating the signals of individual voxel within regions of interest (ROIs). In certain cases, this aggregation implies a loss of valuable functional and anatomical information about sub-regions of voxels that drive the ROI level connectivity. We describe a data-driven statistical search method that identifies the voxels that are chiefly responsible for exchanging signals between regions of interest that are known to be effectively connected. We apply the method to high-resolution resting state functional magnetic resonance imaging (rs-fMRI) data from medial temporal lobe regions of interest of a single healthy individual measured repeated times over a year and a half. The method successfully recovered densely connected voxels within larger ROIs of entorhinal cortex and hippocampus subfields consistent with the well-known medial temporal lobe structural connectivity. To assess the performance of our method in more common scanning protocols we apply it to resting state fMRI data of corticostriatal regions of interest for 50 healthy individuals. The method recovered densely connected voxels within the caudate nucleus and the putamen in good qualitative agreement with structural connectivity measurements. We describe related methods for estimation of effective connections at the voxel level that merit investigation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".