Segregation of the human basal forebrain using resting state functional MRI
Bibliographic record
Abstract
The basal forebrain (BF) is poised to play an important neuromodulatory role in brain regions important to cognition due to its broad projections and complex neurochemistry. While significant in vivo work has been done to elaborate BF function in nonhuman rodents and primates, comparatively limited work has examined the in vivo function of the human BF. In the current study we used multi-echo resting state functional magnetic resonance imaging (rs-fMRI) from 100 young adults (18-34 years) to assess the potential segregation of human BF nuclei as well as their associated projections. Multi-echo processing provided significant gains in SNR throughout the brain as compared to traditional single-echo processing, with some of the largest increases observed in the BF. Bottom-up clustering of voxel-wise BF functional connectivity maps yielded adjacent functional clusters within the BF that closely aligned with the distinct, hypothesized nuclei important to cognition: the nucleus basalis of Meynert (NBM) and the medial septum/diagonal band of Broca (MS/DB). Examining their separate functional connections, the NBM and MS/DB revealed distinct projection patterns, suggesting a conservation of nuclei-specific functional connectivity with homologous regions known to be anatomically innervated by the BF. Specifically, the NBM demonstrated coupling with a widespread cortical network as well as the amygdala, whereas the MS/DB revealed coupling with a more circumscribed network, including the orbitofrontal cortex and hippocampal complex. Collectively, these in vivo rs-fMRI data demonstrate that the human BF nuclei support distinct aspects of resting-state functional networks, suggesting that the human BF may be a neuromodulatory hub important for orchestrating network dynamics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".