Multimodal gradients of basal forebrain connectivity across the neocortex
Bibliographic record
Abstract
Cortical cholinergic projections originate from subregions of the basal forebrain (BF). To examine its organization in humans, we computed multimodal gradients of BF connectivity by combining 7 T diffusion and resting state functional MRI. Moving from anteromedial to posterolateral BF, we observe reduced tethering between structural and functional connectivity gradients, with the lowest tethering in the nucleus basalis of Meynert. In the neocortex, this gradient is expressed by progressively reduced tethering from unimodal sensory to transmodal cortex, with the lowest tethering in the midcingulo-insular network, and is also spatially correlated with the molecular concentration of VAChT, measured by [18F]fluoroethoxy-benzovesamicol (FEOBV) PET. In mice, viral tracing of BF cholinergic projections and [18F]FEOBV PET confirm a gradient of axonal arborization. Altogether, our findings reveal that BF cholinergic neurons vary in their branch complexity, with certain subpopulations exhibiting greater modularity and others greater diffusivity in the functional integration with their cortical targets. The basal forebrain innervates the cortex with cholinergic projections via long-range branching projections. Using MRI and PET, the authors show that these projections exhibit gradients of increasing branch complexity traversing unimodal to transmodal cortex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".