Cognitive impairments associated with silent brain lesions : profile and mechanisms
Bibliographic record
Abstract
Silent brain lesions (SBLs) have been increasingly recognized as one of the underlying causes of insidious cognitive decline. However, it is still unclear to what extent SBLs affect brain function and what mediate the association of SBLs with cognitive impairments. In the present study, the spectrum of SBLs-related cognitive impairments was assessed and the role of reduced white matter integrity in the development of these impairments was examined. \n \n398 otherwise healthy hypertensive elderly Chinese subjects were included within this study. Demographical and related clinical information, performance on standard neuropsychological tests and multi-sequences MRI scans were obtained from all participants. Standard compound z scores were constructed across a wide range of cognitive domains. Global cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale. Presence / load and location of silent brain infarcts (SBIs), brain microbleeds (BMBs), white matter hyperintensities (WMHs) were visually assessed using standard scales. The volume of WMHs was additionally measured automatically using open-source software. In order to determine the extent of white matter alterations, parameters derived from diffusion tensor imaging (DTI) were extracted from global and lobar normal appearing white matter (NAWM). Series of linear regression models and path analyses were used to examine the data. \n \nHigh prevalence of SBLs was identified in this study sample. Markers of SBLs (or their underlying pathology) showed varying impacts in certain cognitive function. BMBs load was significantly related to worse performance on tests of language-related function, and strictly lobar BMBs seemed to play a predominant role in this association. The presence of SBIs (predominantly the deep SBIs) was associated with cognitive deficits in executive function. Both the volume and Fazekas scores of WMHs showed significant association with worse performance on executive function, information processing speed and language-related function. The degree of periventricular white matter hyperintensities (PVHs), but not deep white matter hyperintensities (DWMHs) contributes to WMHs-related cognitive impairments. \n \nPVHs have the strongest impact on integrity of white matter microstructure. Other markers (DWMHs, deep SBIs, strictly lobar BMBs and deep BMBs) all showed additional effects on white matter integrity, though the effects were less extensive and weaker. The disruption of white matter integrity in turn predicted worse cognitive performance on specific domains. These results supported an intermediating role of white matter integrity in the correlation of SBLs with cognitive impairments. \n \nResults of path analyses further confirmed the independent role of white matter integrity as a mediator for cognitive impairments in individuals with SBLs. Furthermore, it suggested that SBLs-related cognitive declines across different cognitive domains were differentially mediated by white matter disruption / brain atrophy. However, white matter microstructure and brain volume changes cannot account for the whole cognitive declines, and other mechanisms like impaired network should be considered in further investigation. \n \nIn summary, this thesis provides a detailed profile of SBLs-related cognitive impairments, as well as evidence supporting the independent role of white matter disruption as a mediator. These findings paved the way for a more detailed analysis on the relation among SBI markers, DTI metrics and cognitive function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".