Lesion-network mapping in task-dependent frequencies uncovers remote consequences of focal damage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it