Diffusion Tensor Imaging Study of Olfactory Identification Deficit in Patients With Mild Cognitive Impairment
Bibliographic record
Abstract
Objective: To explore the relationship between white matter changes and olfactory ability among patients with mild cognitive impairment (MCI) and to develop a tool to predict the development of Alzheimer’s disease among patients with MCI. Methods: The Montreal Cognitive Assessment (MoCA) was used for cognitive assessments, and the 70% isopropanol test paper was used to evaluate olfactory function. Tract-based spatial statistics, based on the diffusion tensor imaging technology, were used to obtain relevant parameters, and behavioral and imaging results were compared between patients with MCI ( n = 36) and healthy older adults ( n = 32). Results: The olfactory ability of MCI patients was lower overall, which was positively correlated with the MoCA score. Fractional anisotropy (FA) changes significantly of all parameters. Lower FA regions were mainly located in the corpus callosum, the orbitofrontal gyrus, and the left occipital lobe. The olfactory score was significantly correlated with the FA value of the orbitofrontal gyrus. Fibrous connections in several brain regions, such as the entorhinal cortex, were stronger in patients with MCI. Conclusion: The olfactory ability of MCI patients in our group was positively correlated with the neuropsychological scale results. Impairment in olfactory function was superior to memory deficits for predicting cognitive decline among cognitively intact participants. The fibrous connections in several brain regions, such as the entorhinal cortex, were higher in patients with MCI, which suggested that there may be a compensatory mechanism in the olfactory pathway in MCI patients. The decline in olfactory function may be a significant and useful indicator of neuropathological changes in MCI patients and an effective marker for the development of cognitive decline and dementia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".