Altered Nucleus Basalis Connectivity Predicts Treatment Response in Mild Cognitive Impairment
Bibliographic record
Abstract
Purpose To determine whether functional connectivity (FC) mapping of nucleus basalis of Meynert (NBM) cholinergic network (hereafter, NBM FC) could provide a biomarker of central cholinergic deficits with predictive potential for response to cholinesterase inhibitor (ChEI) treatment. Materials and Methods The Alzheimer’s Disease Neuroimaging Initiative (ADNI) was approved by the institutional review boards of all participating sites. All participants and their representatives gave written informed consent prior to data collection. NBM FC was examined in 33 healthy control participants, 102 patients with mild cognitive impairment (MCI), and 33 patients with AD by using resting-state functional MRI data from the ADNI database. NBM FC was compared between groups before and after 6 months of ChEI treatment in MCI. Associations between baseline NBM FC and baseline cognitive performance as well as cognitive outcomes after treatment were investigated. Results Compared with the healthy control group, NBM FC was decreased in patients with untreated MCI and increased in patients with AD treated with ChEI (corrected P ˂ .05). Global cognition (Alzheimer’s Disease Assessment Scale-Cognitive subscale score) was associated with NBM FC (r = −0.349; P ˂ .001). NBM FC was higher 6 months after ChEI compared with before ChEI in treated MCI (corrected P ˂ .05), but did not change at 6 months in patients with untreated MCI (corrected P ˂ .05). Baseline NBM FC in MCI strongly predicted cognitive outcomes 6 months after ChEI (R2 = 0.458; P = .001). Conclusion Functional dissociation of the nucleus basalis of Meynert from a cortical network may explain the cognitive deficits in dementia and allow for the selection of individuals who are more likely to respond to cholinesterase inhibitors at early disease stages. Published under a CC BY-NC-ND 4.0 license. Online supplemental material is available for this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".