Lateralization in intrinsic functional connectivity of the temporoparietal junction with salience- and attention-related brain networks
Bibliographic record
Abstract
Neuroimaging studies have demonstrated that the right temporoparietal junction (TPJ) is activated during detection of salient stimuli, including pain, in the sensory environment. Right TPJ damage more often produces spatial neglect than left TPJ damage. We recently reported a right lateralized system of white matter connectivity of the TPJ. However, lateralization in intrinsic TPJ functional connectivity during a task/stimuli-independent state has not been fully characterized. Here we used resting-state functional MRI in healthy humans to compare the functional connectivity of right and left TPJ with salience- and attention-related brain networks. Independent components analysis revealed that both right and left TPJ were functionally connected with a network that included the anterior insula, dorsolateral prefrontal cortex (PFC), and mid-cingulate cortex, considered to be the salience/ventral attention network. Dual regression revealed this network was more strongly connected with right TPJ than left TPJ. Seed-based functional connectivity analysis showed 1) negative connectivity the TPJ bilaterally with the "default mode network"; 2) positive connectivity of TPJ bilaterally with the salience/ventral attention network; 3) stronger connectivity between right TPJ compared with left TPJ with regions within the salience/ventral attention network and mid-insula, S2, and temporal/parietal opercula (implicated in pain); and 4) stronger connectivity of left TPJ compared with right TPJ with the "executive control network," including the dorsomedial/medial PFC, inferior frontal gyrus, and cerebellum (crus I/II). Our findings build on classic lesion and neuroimaging studies, demonstrating a complex spatial network organization of lateralization in TPJ functional connectivity in the absence of an overt stimulus.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".