Lesion-network mapping in task-dependent frequencies uncovers remote consequences of focal damage
Bibliographic record
Abstract
Abstract The brain consists of a multiplicity of networks with massively interacting nodes. Disruption of a node following brain damage can result in both short- and long-distance functional abnormalities, affecting even intact brain regions remote from the site of lesion (termed ‘diaschisis’). Diaschisis has been well described previously, and structural and functional connectivity have been related to clinical findings. However, the mechanistic and neurophysiological properties of this remote loss of function, its temporal and spectral dynamics, and its impact on the whole brain remain to be elucidated. In this study, we used high-density electroencephalography (EEG) to detect and characterize function- and frequency-dependent transcallosal diaschisis in a single-case of visual agnosia who has a perceptual deficit in object and face recognition following a focal lesion in the right posterior temporal cortex. Scalp EEG activity was evoked by images of intact and parametrically increased scrambled objects. SilenceMap, an algorithm developed for the location of reduced power (i.e., regions of silence), was used to estimate the slope of shape-selective EEG responses at levels of object scrambling, with structural and functional MRI serving as the ground truth for the lesion and diaschisis. The functional deficit, manifest as a significant reduction in the slope of EEG object shape sensitivity, was observed in the lesioned right ventral cortex and right dorsal cortex across most of the frequency bands (>4 Hz). This reduction in EEG slope was accompanied by contralesional diaschisis in the homotopic left ventral and left dorsal cortex but only in the Theta band (4−8Hz). This noninvasive approach both elucidates the neural correlates of diaschisis and confirms the viability of this approach in identifying neurological abnormality, perhaps offering a path toward precision medicine.
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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".