White matter disintegration along tracts measured by diffusion tensor imaging in people with MCI
Bibliographic record
Abstract
Abstract Background Gradual disintegration of white matter (WM) tracts has been observed in Alzheimer’s disease (AD) by postmortem histological analyses and in‐vivo neuroimaging. Previous diffusion tensor imaging (DTI) studies have shown changes in fractional anisotropy (FA) and radial diffusivity (RD) indicative of microstructural changes in select WM regions in AD and mild cognitive impairment (MCI). However, the precise location of microstructural damage along WM tracts at the early stages of cognitive decline is still unknown. In the current study, DTI metrics along the major WM tracts were measured and compared in healthy elderly and MCI subjects to localize microstructure changes. Method Diffusion tensor images (32 directions, b=1000 s/mm2) were acquired on a 3 Tesla Siemens Tim Trio and a 3 Tesla Siemens Prisma Fit MRI scanner in healthy elderly (N=40) and people with MCI (N=44) as part of the Gait and Brain Study in London, Ontario. FA and RD were quantified along 18 major WM tracts in each subject using TRACULA in FreeSurfer. The average FA and RD of each tract were compared between groups using a MANOVA (for FA and RD separately) adjusted for age, sex, education, comorbidity, and MoCA score. If the MANOVA was significant (p<0.05), post‐hoc tests were conducted to identify the tracts on which the DTI metrics differed between the groups. Then, the DTI metrics were examined along these identified tracts to localize observed differences. Result The MANOVA detected significant differences in both FA (p‐value=0.018) and RD (p‐value=0.019) in the 18 tracts studied between controls and MCI subjects. Post‐hoc tests showed that specifically, FA in right cingulum‐cingulate gyrus endings (rh_ccg) (Fig. 1) and in left superior longitudinal fasciculus‐parietal endings (lh_slfp), and RD in rh_ccg and left uncinate fasciculus (lh_unc) (p‐value < 0.05) differed between the groups. Comparison along the rh_ccg using t‐test (p‐value < 0.05) revealed that FA and RD differed significantly between the groups in the posterior portion of this tract (Figures 2 and 3). Conclusion Tract specific white matter disruption in the cingulum gyrus in people with MCI was localized in the posterior part of the rh_ccg tract adjacent to the posterior cingulate (Fig. 1).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".