Cognitive impairment and vulnerability of cholinergic brain network in the Alzheimer’s continuum: free-water imaging based on diffusion tensor imaging
Bibliographic record
Abstract
Background Increased extracellular free water (FW) is considered to provide better pathophysiological information than conventional diffusion tensor imaging (DTI) metrics. The cholinergic brain network is a key hub for cognitive function, and microstructural changes detected by free water imaging in this system may be associated with cognitive impairment in Alzheimer’s disease (AD). However, the specific impact of FW changes in the cholinergic brain network on cognitive domains across the AD continuum and their diagnostic value remain unclear. Methods Here, we investigated the basal forebrain cholinergic free water alterations based on free water-corrected diffusion tensor imaging in healthy controls (n = 36), amnestic mild cognitive impairment (aMCI; n = 31), the AD group (n = 33). The cholinergic basal forebrain subregions were divided into the Broca diagonal band (Ch1-3) and the Meynert basal nucleus (Ch4). The cognitive domains performance was measured using the Montreal Cognitive Assessment (MoCA). Additionally, we evaluated the diagnostic value of free water fraction (FWf) within the cholinergic system. Results FWf in the bilateral Ch1-3 and Ch4 regions increased with age, and was significantly higher in aMCI and AD (p < 0.001). In AD, the FWf within Ch4 was correlated with total MoCA score (R = −0.42, p = 0.015), especially with visual spatial/executive (R = −0.47, p = 0.006) and orientation deficits (R = −0.38, p = 0.029). No significant correlations were found in the aMCI group. ROC curve analysis showed that FWf within the cholinergic brain network had high diagnostic efficacy for AD versus HC (AUC = 0.958, 95% CI = 0.909–1.00), and moderate diagnostic efficacy for aMCI versus HC (AUC = 0.795, 95% CI = 0.685–0.905) and aMCI versus AD (AUC = 0.719, 95% CI = 0.589–0.850). Conclusion FW imaging captures microstructural damage in the cholinergic brain network across the entire AD continuum. These changes occur early in aMCI but selectively affect domain-specific cognition in the later stages of AD, possibly through cholinergic network dysfunction. Our results highlight the potential of free water imaging as a biomarker for cognitive decline.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".