The human claustrum activates across multiple cognitive tasks
Bibliographic record
Abstract
Abstract Cognitive control, the ability to manage information during purposeful actions, is crucial for everyday functioning and can become impaired in a variety of neuropsychiatric disorders. The claustrum, a subcortical brain structure, has recently been implicated in functional mechanisms underlying cognitive control. A current theory on the claustrum’s function, the Network Instantiation in Cognitive Control (NICC) model, proposes that the claustrum acts as a cortical network hub synchronizing distant parts of the brain to optimize task performance across cognitive domains. Testing this in this study, we examined the claustrum signal within a dataset (n = 55) that includes functional MRI (fMRI) of healthy participants engaged in four well-established cognitive tasks: the Stroop task, AX-continuous performance task (AX-CPT), cued task-switching, and Sternberg working memory task. Bilateral claustrum activation was observed during certain conditions and trial phases of all four tasks, particularly during active use of cognitive control, and coinciding with task-positive cortical network activations. These findings provide further support for the NICC model of claustrum function, demonstrating claustrum activation across multiple cognitive tasks, and potentially paving the way for new insights into how cognitive processes can become compromised in neuropsychiatric disorders.
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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) | 0.002 | 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".