Longitudinal grey matter changes in the healthy aging brain as measured by voxel‐based morphometry
Bibliographic record
Abstract
Abstract Background In order to understand the changes that occur in the brain over the course of neurodegenerative disorders, it is imperative to establish how the brain changes over time under healthy conditions (Mattson & Arumugam, 2018). Older adulthood is associated with normal age‐related declines in cognitive functioning (Klimova et al., 2017). However, there are inconsistencies in the literature regarding associated changes in grey matter, with some reporting atrophy across the whole brain and others reporting focal atrophy, localized in the frontal and temporal regions (Pergher et al., 2019; Squarzoni et al., 2018). The current study took a longitudinal approach to investigate changes in grey matter over four years in healthy aging. Methods 3T T1 anatomical magnetic resonance images (MRI) were obtained from 16 healthy older adults (7 male, 9 female; mean age 74.38 ± 4.52 years at baseline) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database at two time points, four years apart. Voxel‐based morphometry (VBM) analyses were carried out using FMRIB’s Software Library to examine within‐subject changes in grey matter over time. Results Figure 1 depicts regions where there was significant atrophy (p<0.05, corrected for multiple comparisons) in grey matter at four‐year follow‐up compared to baseline. Specifically, VBM results indicated atrophy in distributed areas including bilateral frontal and temporal regions as well as in the hippocampi, bilaterally. However, there were no significant changes in cognitive performance over these four years. Conclusions The current findings reveal atrophy in multiple regions, including the temporal lobes despite normal cognitive performance over the course of four years in healthy aging. Similar regions are known to be affected in Alzheimer’s disease and associated with cognitive decline (Minkova et al., 2017). Follow up work will aim to replicate the current findings in a larger sample and to examine the relationship between cognitive performance and grey matter volume. Developing an understanding of changes in brain structure and function that occur over time in healthy aging will allow for improved interpretation of changes in neurodegenerative conditions, such as Alzheimer’s disease.
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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 | 0.001 |
| 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".