Brain Substrates for Distinct Spatial Processing Components Contributing to Hemineglect in Humans
Bibliographic record
Abstract
Several cortical and sub-cortical regions in the right hemisphere, particularly in parietal and frontal lobe, but also in temporal lobe and thalamus, are part of neural networks critically implicated in spatial and attentional functions. Damage to different sites within these networks can cause hemispatial neglect. The aim of this study was to identify the neural substrates of different spatial processing components that are known to contribute to neglect symptoms. First, three different spatial tasks (visual search, bisection, and visual memory) were tested in 27 patients with focal right brain-damage. Voxel-based lesion-symptom mapping was used to determine the relationships between specific sites of damage and severity of deficits in these three spatial tasks. Secondly, fMRI was used in 26 healthy controls who performed the same tasks. In the healthy group, fMRI results showed a differential activation of regions within the parietal and frontal lobes during bisection and visual search, respectively. In the patients, we confirmed a critical role of right lateral parietal cortex in bisection, but lesions in frontal and temporal lobe were more critical for visual search. These data support the existence of distinct components in spatial attentional processes that might be damaged to different degrees in neglect patients.
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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 | 0.001 |
| 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".