Correlation Between Deep Brain White Matter Ischemia and MR Diffusion Tensor Imaging of Mild Cognitive Impairment
Bibliographic record
Abstract
Purpose To investigate the correlation between the brain white matter changes of MR diffusion tensor imaging(DTI) and cognitive function in the patients with mild cognitive impairment. Materials and Methods The patients(40 cases) were classifed into two groups: group A(20 patients with ischemic foci in the deep white matter)and group B(20 patients without ischemic foci in the deep white matter), and 20 normal controls was enrolled. Conventional MRI, DTI, mini-mental state examination(MMSE)and montreal cognitive assessment(MoCA) were applied, then fractional anisotropy(FA) value and apparent diffusion coeffcient(ADC) value were compared among three groups. The scores of MoCA was analyzed between the patient groups. Results The decreased FA value, increased ADC value and decreased MoCA scores was demonstrate in group A, and showed signifcant difference compared with group B(t=-4.229,-3.251,-7.533,-2.702,-2.660; P0.05). The increased ADC value and decreased FA value in the frontal and hippocampus region were detected in group B compared with normal controls(t=- 7.790,- 2.785,- 4.415,- 5.164; P0.05). Conclusion The early and special structural changes can be detected using DTI compared with conventional MRI.The severe white matter lesions can be demonstrated in the patients with ischemic foci in the deep white matter, who is prone to dementia.
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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 | 0.001 | 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) | 0.002 | 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".