Structural Connectivity and Cortical Thickness Alterations in Transient Global Amnesia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Transient global amnesia (TGA) is a sudden onset of anterograde and retrograde amnesia. We aimed to assess differences in terms of cortical thickness and structural brain connectome between patients with TGA (at acute and delayed postrecovery stages) and matched controls. MATERIALS AND METHODS: We report on 18 consecutive patients with TGA who underwent 3T MR imaging, including DTI and MPRAGE sequences, at the acute (mean delay postonset: 44 hours) and delayed post-recovery (mean delay: 35 days) stages. Structural connectome was assessed in patients with TGA and in 18 age- and sex-matched controls by using probabilistic fiber- tracking and segmentation of 164 cortical/subcortical structures (“nodes”). Connectivity graphs were computed and global network metrics were calculated. Network-based statistical analysis (NBS) was applied to compare patients with TGA at each stage with controls. We also compared cortical thickness between patients with TGA and healthy controls. RESULTS: Global network metrics were not altered in patients with TGA. NBS-analysis showed structural connectome alterations in patients with TGA compared with controls, in core regions involving the limbic network, with 113 nodes and 114 connections (33 left intrahemispheric, 31 right intrahemispheric, and 50 interhemispheric connections) showing significantly decreased structural connectivity (P < .05 NBS corrected, t-values ranging from 3.03 to 8.73). Lower cortical thickness compared with controls was associated with these structural alterations in patients with TGA, involving the orbitofrontal, cingulate, and inferior temporal cortices. All the abnormalities were visible at both acute and delayed postrecovery stages. CONCLUSIONS: Our preliminary study suggests there are structural abnormalities of the limbic network in patients with TGA compared with controls, including decreased structural connectivity and cortical thickness.
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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) | 0.001 | 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".