Abstract WMP117: Regional Homogeneity Changes In Patients With Transient Ischemic Attack: A Resting-state Functional Magnetic Resonance Imaging Study
Bibliographic record
Abstract
Background: Transient ischemic attack (TIA) can increase the risk of some neurologic dysfunctions, of which the mechanism remains unclear. Resting-state functional MRI (rfMRI) is suggested to be a valuable tool to study the relation between spontaneous brain activity and behavioral performance. However, little is known about whether the local synchronization of spontaneous neural activity is altered in TIA patients. The purpose of this study is to detect differences in regional spontaneous activities throughout the whole brain between TIAs and normal controls. Methods: Twenty one TIA patients suffered an ischemic event in the right hemisphere and 21 healthy volunteers were enrolled in the study. All subjects were investigated using cognitive tests and rfMRI. The regional homogeneity (ReHo) was calculate and compared between two groups. Then a correlation analysis was performed to explore the relationship between ReHo values of brain regions showing abnormal resting-state properties and clinical variables in TIA group. Results: Compared with controls, TIA patients exhibited decreased ReHo in right dorsolateral prefrontal cortex (DLPFC), right inferior prefrontal gyrus, right ventral anterior cingulate cortex and right dorsal posterior cingular cortex. Moreover, the mean ReHo in right DLPFC and right inferior prefrontal gyrus were significantly correlated with MoCA in TIA patients. Conclusions: Neural activity in the resting state is changed in patients with TIA. The positive correlation between regional homogeneity of rfMRI and cognition suggests that ReHo may be a promising tool to better our understanding of the neurobiological consequences of TIA.
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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) | 0.002 | 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".