A cross sectional study on the relationship between age, white matter lesion, brain atrophy and cognitive function
Bibliographic record
Abstract
Scope: the present study was designed to examine the relationship between structural brain changes, involving macrostructural and microstructural white matter changes, brain atrophy and cognitive function. Purpose: Cognitive deterioration is generally thought to be due to structural brain changes as part of the aging process. The present study sought to determine how age- related structural brain changes at the macrostructural and microstructural levels correlate with cognitive function among a cohort of reasonably healthy elderly. The study also explored the possible link between white matter damage and cognitive dysfunction, and examined whether this relationship was also influenced by age- associated loss of brain volume i.e. brain atrophy. Last but not least, the study examined the level of the usefulness of a global cognitive measure in comparison to specific neuropsychological subtests assessing executive function, working memory and episodic memory domains. Methodology: the volumetric measures were utilised to account for brain macrostructural changes, while the Diffusion Tensor Imaging (DTI)- based measures were used to examine microstructural properties of white matter disruptions among seventy reasonably healthy elderly recruited from Hunter Medical Research Institute (HMRI) Volunteer Register and specialised neurovascular clinic. The Montreal Cognitive Assessment (MoCA) was used to assess global cognition and twelve neuropsychological subtests were administered to measure specific deficit associated with subtle cognitive dysfunction in white matter pathology. The Pearson correlation coefficient and hierarchical multiple regression were utilised for data analyses. Results: the MoCA has captured variation associated with brain volumes and DTI- based changes, not fully accounted by specific cognitive tests administered in this study. Even after adjusting for age, FSIQ, and brain atrophy separately, the independent association between the MoCA and radial diffusivity remained stable. Interestingly, the MoCA is a brief, yet powerful tool that even accounts for global cognitive deficit for which white matter pathology and brain atrophy are not sensitive markers. White matter macrostructural and microstructural changes are associated with age- related cognitive dysfunction, characterised as subtle, varied and diffuse in nature. Brain atrophy not only linked to age, but also influenced the association between global cognition and white matter pathology on the volumetric measure; and partially explained the relationship between white matter microstructural disruption and specific deficit in visual working memory and visual episodic memory. In addition, the hierarchical regression analysis revealed that the MoCA was the strongest predictor of radial diffusivity (ℛ² = .476, 𝜌<.01) even after controlling for age (ℛ² = .392, 𝜌<.01), and cerebral atrophy was not a significant predictor of radial diffusivity. Conclusions and Implications: age- related white matter macrostructural and microstructural changes are not benign, even among reasonably healthy elderly by which their future functioning may be compromised. The so-called ‘healthy’ elderly appear to be a more vulnerable population, as the nature of the dysfunction can be very subtle and diffuse. Early detection thus, serves as a main preventative measure for this population. Since our findings strongly point to the MoCA as a beneficial screening tool for cognitive dysfunction at the early stage of white matter disease, we therefore recommend this brief, easily accessible tool for frontline clinicians to incorporate into their investigation protocol.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".