Imbalance Between Prefronto-Thalamic and Sensorimotor-Thalamic Circuitries Associated with Working Memory Deficit in Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Thalamocortical circuit imbalance characterized by prefronto-thalamic hypoconnectivity and sensorimotor-thalamic hyperconnectivity has been consistently documented at rest in schizophrenia (SCZ). However, this thalamocortical imbalance has not been studied during task engagement to date, limiting our understanding of its role in cognitive dysfunction in schizophrenia. METHODS: Both n-back working memory (WM) task-fMRI and resting-state fMRI data were collected from 172 patients with SCZ and 103 healthy control subjects (HC). A replication sample with 49 SCZ and 48 HC was independently obtained. Sixteen thalamic subdivisions were employed as seeds for the analysis. RESULTS: During both task-performance and rest, SCZ showed thalamic hyperconnectivity with sensorimotor cortices, but hypoconnectivity with prefrontal-cerebellar regions relative to controls. Higher sensorimotor-thalamic connectivity and lower prefronto-thalamic connectivity both relate to poorer WM performance (lower task accuracy and longer response time) and difficulties in discriminating target from nontarget (lower d' score) in n-back task. The prefronto-thalamic hypoconnectivity and sensorimotor-thalamic hyperconnectivity were anti-correlated both in SCZ and HCs; this anti-correlation was more pronounced with less cognitive demand (rest>0-back>2-back). These findings replicated well in the second sample. Finally, the hypo- and hyper-connectivity patterns during resting-state positively correlated with the hypo- and hyper-connectivity during 2-back task-state in SCZ respectively. CONCLUSIONS: The thalamocortical imbalance reflected by prefronto-thalamic hypoconnectivity and sensorimotor-thalamic hyperconnectivity is present both at rest and during task engagement in SCZ and relates to working memory performance. The frontal reduction, sensorimotor enhancement pattern of thalamocortical imbalance is a state-invariant feature of SCZ that affects a core cognitive function.
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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 | 0.000 | 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".