Posterior cingulate cross-hemispheric functional connectivity predicts the level of consciousness in traumatic brain injury
Bibliographic record
Abstract
Previous studies have demonstrated that altered states of consciousness are related to changes in resting state activity in the default-mode network (DMN). Anatomically, the DMN can be divided into anterior and posterior regions. The anterior DMN includes the perigenual anterior cingulate cortex and other medial prefrontal cortical regions, whereas the posterior DMN includes regions such as the posterior cingulate cortex (PCC) and the temporal parietal junction (TPJ). Although differential roles have been attributed to the anterior and posterior DMN regions, their exact contributions to consciousness levels remain unclear. To investigate the specific role of the posterior DMN in consciousness levels, we investigated 20 healthy controls (7 females, mean age = 33.6 years old) and 20 traumatic brain injury (TBI) patients (5 females, mean age = 43 years old) whose brain lesions were mainly restricted to the bilateral frontal cortex but retained a well-preserved posterior DMN (e.g., the PCC and the TPJ) and who exhibited varying levels of consciousness. We investigated the intra- and cross-functional connectivity strengths (FCSs) between the right/left PCC and the right/left TPJ and their correlation with consciousness levels. Significant reductions in both the intra- and cross-hemispheric FCSs were observed in patients compared with controls. A significant correlation with consciousness levels was observed only for the cross-hemispheric PCC-TPJ FCS but not for the intra-hemispheric PCC-TPJ FCS. Taken together, our results show that the cross-hemispheric posterior DMN is related to consciousness levels in a specific group of patients without posterior structural lesions. We therefore propose that the PCC may be central in maintaining consciousness through its cross-hemispheric FC with the TPJ.
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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 | 0.002 |
| 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".