The human claustrum initiates networks for externally and internally driven task demands
Bibliographic record
Abstract
Abstract Cognitive control is believed to arise from interactions among multiple brain networks depending on task demands. Although several debilitating neuropsychiatric disorders are characterized by cognitive network dysfunction, the neural circuit mechanisms supporting task-dependent network activation are largely unknown. Because the claustrum possesses widespread connections with cortex and can synchronize distant cortical regions, we tested whether the claustrum activates task-dependent network states using fMRI during working memory ( n = 420) and autobiographical memory ( n = 35), tasks which elicit opposing responses from key cognitive control networks. In both tasks, the claustrum exhibited increased activity and excitatory influence on task-associated cognitive control network nodes, with corroborating underlying structural connectivity. The claustrum also displayed stronger excitatory effective connectivity during task performance and greater structural connectivity with task-related network nodes than regions prominently implicated in directing network states—the anterior insula and pulvinar. These findings establish a role for the claustrum in initiating network states for cognitive control. Significance Cognitive functioning is supported by large-scale networks across the brain. Yet, the neural circuit mechanisms supporting task-dependent network activation are largely unknown. Circuit analyses using human functional and structural neuroimaging in this study found that the claustrum, a subcortical nucleus, activates during multiple cognitive tasks eliciting a wide range of network states, possesses strong anatomical connections with cognitive control network nodes, and exerts excitatory influence on task-associated network regions. These results establish the claustrum as a network activator subserving cognitive control.
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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 | 0.000 | 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) | 0.001 | 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".