Resting-state neural networks in cognitive decline in patients with vascular encephalopathy
Bibliographic record
Abstract
We evaluated the connectivity reorganization of resting-state neural networks in patients with cognitive decline secondary to vascular encephalopathy (VE). Quantitative cognitive functions were evaluated using the Montreal Cognitive Assessment (MoCA) scale and compared with the organization of resting-state neural networks recorded using functional magnetic resonance imaging (fMRI). The aim of this work was to assess the relationship between various resting-state neural networks and cognitive function. Materials and methods. The study involved 29 people with VE, divided into two groups: without cognitive decline (≥ 26 points on the MoCA) and with cognitive impairment (24–18 points on the MoCA). Connectivity between different brain regions was evaluated in all patients using resting-state fMRI, with SPM-12 and CONN18b software applications in Matlab. Results and conclusion. Statistically significant differences in connectivity were found between groups in the dorsal attention network, visual network, and sensorimotor networks, as well as in the left parahippocampal cortex. New, negative connectivity was observed alongside cognitive decline, which, together with reduced connectivity in resting-state neural networks, can be considered an obligatory sign accompanying cognitive impairment in VE.
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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 | 0.000 |
| 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) | 0.000 | 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".