Mapping the network underpinnings of central poststroke pain and analgesic neuromodulation
Bibliographic record
Abstract
Central poststroke pain (CPSP) is a debilitating and often treatment-refractory condition that affects numerous stroke patients. The location of lesions most likely to cause pain and the identity of the functional brain networks that they impinge upon remain incompletely understood. We aimed to (1) elucidate which lesion locations are most frequently accompanied by pain; (2) explore CPSP-associated functional networks; and (3) examine how neuromodulation interacts with these networks. This multisite study investigated 17 CPSP patients who received deep brain stimulation (DBS; n = 12) or motor cortex stimulation (MCS; n = 5). Pain-causing lesions were manually segmented and normalized to standard space. To identify areas linked to high risk of pain, the locations of CPSP lesions and 220 control lesions were compared using voxelwise odds ratio mapping. The functional connectivity of pain-causing lesions was obtained using a large (n = 1000) normative resting-state functional MRI connectome and compared to that of control lesions and therapeutic DBS activation volumes. Brain regions most associated with CPSP risk (highest value = 63 times) were located along the ascending somatosensory pathways. These areas and the majority of individual CPSP lesions were functionally connected to anterior/middle cingulate cortex, insula, thalamus, and inferior parietal lobule (PBonferroni < 0.05). The extent of connectivity to the thalamus, inferior parietal lobule, and precuneus also differed between CPSP and control lesions (PBonferroni < 0.05). Posterior insula and thalamus shared connectivity with both CPSP lesions and pain-alleviating DBS activation volumes (PBonferroni < 0.05). These findings further clarify the topography and functional connectivity of pain-causing brain lesions, and provide new insights into the network-level mechanism of CPSP neuromodulation.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".