Correlation study between the microstructural abnormalities of medial prefrontal cortex and white matter hyperintensities with mild cognitive impairment patients: A diffusion kurtosis imaging study
Bibliographic record
Abstract
• Diffusion kurtosis imaging (DKI) is more suitable than diffusion tensor imaging (DTI) to detect changes in brain tissue microstructure, especially for changes in brain gray matter. • Patients with WMHs were related to MCI. • Anterior cingulate and paracingulate gyri (ACG) cortex plays an important role in cognitive process. • DKI parameters of WMHs with MCI patients were significantly lower compared to controls and WMHs without MCI patients. • DKI technology has certain application value in evaluating whether WMHs with MCI patients have cognitive function impaired, and could be used as one of the neuroimaging biomarkers for diagnosing MCI. Our study aimed to investigate the correlation between the microstructural changes of medial prefrontal cortex (m-PFC) and white matter hyperintensities (WMHs) with mild cognitive impairment (MCI) patients by Diffusion kurtosis imaging (DKI). We retrospectively collected 68 patients, including 47 patients with WMHs and 21 age matched controls. WMHs patients were divided into with MCI ( n = 30) and without MCI group ( n = 17). The m-PFC was selected for regions of interests (ROIs). DKI parameters were measured and compared between each group. Correlations between DKI parameters and Montreal cognitive assessment (MoCA) score were also performed. 1. Compared to controls, WMHs patients have lower MoCA score; WMHs with MCI patients have significant lower axial kurtosis (AK), mean kurtosis (MK), radial kurtosis (RK) and fractional anisotropy (FA) in right anterior cingulate and paracingulate gyri (ACG) of m-PFC; and also have significant lower MK in left ACG. 2. Compared to WMHs without MCI patients, AK, FA and kurtosis fractional anisotropy (KFA) of WMHs with MCI patients were also significantly decreased. 3. AK were positively correlated with MoCA score in both ACG. Patients with WMHs were related to MCI. DKI sequence has certain application value in evaluating whether WMHs with MCI patients have cognitive function impaired.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".