Value of Magnetic Resonance Diffusion Tensor Imaging Combined with Quantitative Electroencephalogram in Diagnosis of Neurocognitive Impairment in Patients with White Matter Demyelination
Bibliographic record
Abstract
This paper aimed to explore the clinical value of combined adoption of magnetic resonance diffusion tensor imaging (DTI) and quantitative electroencephalogram (QEEG) in assessing microstructure changes and mild neurocognitive dysfunction in patients with white matter demyelination. 128 cases of white matter demyelination admitted to the hospital from October 2018 to October 2019 were rolled into the research group, and 100 healthy patients physically examined during the same period were rolled into the control (ctrl) group. QEEG and magnetic resonance DTI examinations were performed for all patients. The wave power of δ, θ, α, and β and the ratio of α/θ and (δ + θ)/(α + β) were recorded. The FA values of white matter fibers in different brain areas were measured, and the Montreal Cognitive Assessment (MoCA) and Addenbrooke Cognitive Evaluation rating (ACE-R) were adopted to assess the neurocognitive function of patients. It was found that the dominant frequency of each brain area in the research group was 8-9 Hz slow α wave. In contrast with the ctrl, the α wave and α/θ values in the research group were lower, while θ wave and δ + θ/α + β values were higher ( P < 0.05 ); the scores of ACE-R and MoCA were lower ( P < 0.01 ); the fractional anisotropy (FA) values of the right frontal lobe white matter (0.335 ± 0.068), the left temporal lobe white matter (0.391 ± 0.032), and the corpus callosum knee white matter (0.658 ± 0.053) were lower ( P < 0.05 ). The FA values of these three areas were positively correlated with attention and calculation, memory, and memory of MoCA scale, respectively ( P < 0.05 ). The FA value of the right frontal white matter was positively correlated with the attention and calculation score of the ACE-R scale ( P < 0.05 ). In conclusion, magnetic resonance DTI combined with QEEG could reflect the microstructural changes of white matter, which may be associated with mild neurocognitive impairment. The primary objective of the study was to explore the clinical value of combined adoption of magnetic resonance DTI and QEEG in assessing microstructure changes and mild neurocognitive dysfunction in patients with white matter demyelination, expected to provide a theoretical basis for the treatment of white matter demyelination.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".