Neural Correlates of Hemispatial Neglect: A Voxel-Based SPECT Study
Bibliographic record
Abstract
BACKGROUND: Despite many studies that investigated the neural correlates of hemispatial neglect (HN) with structural imaging, studies using voxel-wise analyses of functional imaging are not available. Furthermore, previous studies neither considered the neglect severity nor investigated whether there are differences in these neural correlates according to each neglect subtest. This study aimed to investigate the neural correlates of HN by correlating the total and the individual neglect score with hypoperfusion value on single photon emission computed tomography (SPECT) using voxel-wise analyses. METHODS: Forty-two patients with acute right hemisphere strokes underwent a neglect test battery consisting of 3 bisection tasks, 2 cancellation tasks and 2 copying tasks. The SPECT images were acquired in these patients and 10 age- and education-matched normal controls. RESULTS: Patients with HN, compared to those without HN, had hypoperfusion in the right middle temporal-occipital junction, inferior frontal gyrus and retrosplenial area. The total neglect score correlated with the hypoperfusion in the right middle temporal-occipital junction, fusiform gyrus, parahippocampal gyrus, inferior temporal gyrus, anterior part of the superior and middle temporal gyri, cuneus, lingual gyrus, angular gyrus, and the cerebellum. Across the neglect subtests similar correlation patterns were observed with minor variations. CONCLUSIONS: Unlike the results of previous studies showing that the critical neural correlates for HN are inferior parietal lobule or superior temporal gyrus, our study showed that the lesions that critically contribute to the neglect severity were located in the posterior parts of the middle temporal gyrus (temporal-occipital junction).
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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".