Effective connectivity of the human claustrum: Triple networks, subcortical circuits, and psychedelic modulation
Bibliographic record
Abstract
Abstract Decades of cross-species research highlight the claustrums extensive bidirectional connectivity with cortical and subcortical regions, implicating it in higher-order cognitive processes requiring synchronized brain states. Psychedelics may disrupt this synchrony by modulating claustro-cortical signaling, reflected by the dissolution of cortical network signatures. Using spectral dynamic causal modeling on resting-state fMRI data from the Human Connectome Project and PsiConnect datasets at 7T and 3T, we provide the first in vivo characterization of claustral effective connectivity with triple networks and subcortical regions in humans, both at rest and under the influence of psilocybin. Claustra displayed widespread bidirectional effective connectivity and a strong inhibitory influence on all target regions. Psilocybin enhanced claustral inhibition of cortical networks while disinhibiting subcortical areas, partially associated with psychedelic subjective effect scores. These findings are consistent with cellular and functional cross-species data, supporting the proposed mechanism of claustro-cortical inhibition in regulating network synchrony, while extending this influence to the subcortex, and revealing hierarchical and hemispheric asymmetries in claustral signaling modulation under psilocybin.
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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 | 0.000 |
| 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".