Characterisation of multimodal network organisation after focal prefrontal lesions in humans
Bibliographic record
Abstract
Abstract Lesion research in humans and non-human primates classically maps the behavioral effects of focal damage to the directly-injured brain region. However, given the interconnectedness of the brain, it has long been known that such damage can also have distant effects. Modern imaging methods provide new ways to assess those effects. Further, triangulating across these methods in a lesion model may shed light on the biological basis of structural and functional networks in the healthy brain. We characterised network organization assessed with multiple MRI imaging modalities in 13 patients with chronic focal damage affecting either superior or inferior frontal gyrus (SFG, IFG) and 18 demographically-matched healthy Controls. We first defined structural and functional network parameters for the two frontal regions-of-interest in healthy Controls, and then used voxel-based morphology (VBM) and tract-based spatial statistics (TBSS) analyses to investigate structural grey matter (GM) and white matter (WM) differences between patients and Controls. The functional and structural networks defined in healthy participants were then used to constrain interpretation of the whole brain network effects in patients. Finally, we applied dual regression to examine the differences in functional coupling to large-scale resting state networks (RSNs), focusing on the RSNs which most overlapped structurally with the lesion sites. Overall, the results show that lesions are associated with widespread within-network GM loss at sites distal from the lesion, yet leave WM and RSNs relatively preserved. Lesions to either prefrontal region had a very similar impact on structural networks, but SFG lesions had larger impact on RSNs than did IFG lesions. The findings provide evidence for causal contributions of specific prefrontal regions to structural and functional brain networks in humans, relevant to interpreting connectomic findings in studies of healthy people or those with psychiatric illnesses.
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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".