P1‐481: DEFAULT MODE NETWORK CONNECTIVITY CHANGE DETECTED BY DIFFUSION TENSOR IMAGING CONTRIBUTES TO COGNITIVE IMPAIRMENTS IN VASCULAR COGNITIVE IMPAIRMENT, NO DEMENTIA
Bibliographic record
Abstract
Vascular cognitive impairment, no dementia (VCIND) refers to cognitive deficits associated with underlying vascular causes that fall short of a dementia diagnosis. Executive dysfunction is the characteristic impairment in subcortical VCIND patients. Default Mode Network (DMN) is a functional network of brain areas and has shown involved in attention, working memory and executive function. However, DMN connectivity has never been detected in VCIND patients. Thus, this study was carried out on DMN using diffusion tensor imaging (DTI) to detect white matter microstructure change in VCIND patients in order to help physicians to get an accurate diagnosis of VCIND. We also investigated correlations between DTI measurements and cognitive dysfunction, which could help in further understanding VCIND. Twenty-two VCIND patients and 32 normal cognitive patients were recruited and underwent DTI scanning and neuropsychological assessments. Voxel-based analysis was performed on each subject for extracting FA and MD measures from DMN. In VCIND patients, as compared to normal cognitive patients, decreased FA and increased MD were observed in connectivity between posterior cingulate cortex (PCC) and medial prefrontal cortex (mPFC) areas (FA: p=0.031; MD: p=0.001), which are key hubs in default mode network. Furthermore, we also found that Mini-mental State Examination (MMSE) and Montreal-Cognitive Assessment (MoCA) scores correlated with DTI values in these areas (MMSE: p=0.014; MoCA: p=0.022). The results suggested that patients with VCIND demonstrated abnormal white matter connectivity in key hubs of Default Mode Network. To our knowledge, this study was the first study performed on Default Mode Network. In addition, we revealed the severity of damage in white matter tracts correlated with cognitive dysfunction. The combination of structural DTI and neuropsychological tests will have strong sensitivity in evaluating the white matter damage in VCIND. In conclusion, we further revealed that the DMN connectivity change along white matter damage is significantly impaired in VCIND. The disrupted DMN connectivity could fully explain the executive dysfunction and cognition impairments in VICND. Our results provided better understanding of the cognitive profiles in VCIND patients and suggested that DMN connectivity change may become a valuable neuroimaging marker for the diagnosis of VCIND.
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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) | 0.005 | 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".