Correlations Between the Microstructural Changes of the Medial Temporal Cortex and Mild Cognitive Impairment in Patients With Cerebral Small Vascular Disease (cSVD): A Diffusion Kurtosis Imaging Study
Bibliographic record
Abstract
Object: The purpose of our study was to investigate the microstructural changes of the medial temporal cortex in mild cognitive impairment (MCI) patients with cerebral small vascular disease (cSVD) using diffusion kurtosis imaging (DKI), and to examine whether DKI parameters are correlated with MCI. Method: A total of 82 cSVD patients admitted to the Department of Neurology Beijing Chaoyang Hospital, Capital Medical University were retrospectively enrolled in this study. The Montreal cognitive assessment scale (MoCA) score was used to assess the overall cognitive function. According to the presence or absence of MCI, these patients were divided into MCI group (n=48) and non-MCI group (n=34). The general clinical data of the two groups were collected and analyzed. The regions of interests (ROIs) were selected for investigation in the medial temporal cortex. The averaged values of DKI parameters were measured in each ROI and compared between the two groups, the correlations between DKI parameters and MoCA score, between diffusion and kurtosis parameters were examined. Results: Compared to non-MCI group, MCI patients showed significantly increased mean diffusion (MD) and radial diffusion (RD), and significantly decreased mean kurtosis (MK) in left hippocampus (P=0.005, 0.006, 0.002 respectively). In left hippocampus, fractional anisotropy (FA), MK, radial kurtosis (RK), and kurtosis fractional anisotropy (KFA) showed significantly positive correlations with MoCA score (r=0.374 ,0.37,0.392,0.242 respectively, all P<0.05), while MK, and RD were negatively correlated with MoCA score (r=-0.227,-0.255 respectively, all P<0.05). In left parahippocampal, axial kurtosis (AK) and KFA were positively correlated with MoCA score (r=0.228, 0.282 respectively, all P<0.05), while RK was positively correlated with MoCA score in right parahippocampal (r=0.231, P<0.05). Except for axial diffusion (AD) and AK, significant correlations with MD and MK, RD and RK, FA and KFA were observed in the medial temporal cortex (r=-0.254, -0.395, 0.807, respectively, all P<0.05). Conclusion: DKI technique can be used to observe the microstructural changes of the medial temporal cortex in MCI patients with cSVD. The DKI-derived parameters might be feasible to evaluate patients with MCI.
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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) | 0.001 | 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".