A diffusion tensor imaging study of white matter lesion in amnesic mild cognitive impairment
Bibliographic record
Abstract
Objective Diffusion tensor imaging (DTI) technique with voxel ⁃ based analysis was applied to analyze the differences of whole⁃brain fractional anisotropy (FA) in an attempt to find out the characteristic changes of white matter in amnesic mild cognitive impairment (aMCI) patients. Methods According to the diagnostic criteria of aMCI and individual neuropsychological tests (verbal memory, similarity, perceptive, connection A, graphics memory, etc.), 16 aMCI patients received brain DTI and voxel⁃ based analysis were compared with the normal cognitive (NC) function subjects (control group) by differences of whole ⁃ brain FA value. Results 1) The overall rating of Mini⁃ Mental State Examination (MMSE) in the control group was 28.69 ± 1.03, higher than 27.50 ± 1.65 of the aMCI group (t = 1.278, P = 0.035). 2) The overall rating of Montreal Cognitive Assessment (MoCA) in the control group was 25.85 ± 1.52, higher than 22.50 ± 1.91 of the aMCI group (t = 0.900, P = 0.000). 3) The number of Verbal Fluency in control group was 19.08 ± 4.92, which was more than 15.14 ± 4.66 of the aMCI group (t = 0.012，P = 0.043). 4) The scores of vocabulary memory, delayed vocabulary recall, word recognition, graphic recall were 5.54 ± 0.88, 5.15 ± 1.77, 9.15 ± 1.07 and 14.69 ± 2.25, respectively, higher than those of the aMCI group 3.98 ± 1.07, 2.14 ± 1.23, 7.00 ± 2.04 and 10.57 ± 2.31 (P = 0.000, for all). 5) The FA value in the left middle frontal gyrus and right middle frontal gyrus white matter of the aMCI group was significantly lower than that of the control group. The difference was statistically significant (P < 0.001, for all). Conclusion White matter lesion (WML) of frontal lobe may be involved in the early pathophysiological processes of aMCI, and may be a new evidence for the early non⁃invasive diagnosis of aMCI. DOI：10.3969/j.issn.1672-6731.2010.02.020
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".