2289 Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia
Bibliographic record
Abstract
Objective Cognitive fluctuations are a core clinical feature of Dementia with Lewy bodies (DLB), and although common and disabling, their pathophysiology is poorly understood. This work aimed to identify novel functional network signatures of cognitive fluctuations and investigate their underlying neurobiology by relating them to neuromodulatory systems. Methods Patients with DLB and age-matched controls were assessed on both subjective and objective measures of fluctuations and attention. Resting state dynamic functional magnetic resonance imaging was used to identify the temporal and topological signatures of cognitive fluctuations. Abnormal patterns of activation were mapped onto established gene expression atlases to determine associations with specific neuromodulators. Results DLB patients displayed more stationary brain-state configurations relative controls. This signature of reduced temporal variability correlated significantly with fluctuation-related measures using a sustained attention task (response time variability and drift rate). Topologically, patients with DLB demonstrated a less integrated (more segregated) functional network architecture compared to the control group. Regions of reduced integration were observed across dorsal and ventral attention, sensorimotor, visual, cingulo-opercular and cingulo-parietal networks. Relatively segregated networks correlated positively with subjective and objective measures of fluctuations. Regions of reduced integration and unstable regional assignments were significantly related to the pattern of expression of specific classes of noradrenergic and cholinergic receptors across the cerebral cortex. Conclusions Cognitive fluctuations in DLB are related to specific dynamic functional network impairments that are linked to the noradrenergic and cholinergic systems. Such systems may be viable targets of future therapies.
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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 | 0.000 |
| 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".