Brain state dynamics and working memory in patients with schizophrenia and unaffected siblings
Bibliographic record
Abstract
BACKGROUND: Working memory (WM) deficits are a key feature of schizophrenia and are also seen in unaffected siblings. These deficits might arise from disrupted transitions from one brain state to another. Using a robust algorithm called the Bayesian Switching Dynamical System (BSDS), we studied hidden brain states and their transitions during a WM task in people with schizophrenia. METHODS: We used BSDS to identify brain states based on regions of interest (ROIs) within the default mode network and the frontoparietal network in 161 patients with schizophrenia, 37 unaffected siblings, and 96 healthy controls during N-back (0, 2, and resting fixation) tasks. We estimated group differences in the properties of brain states and studied the influence of WM performance and clinical characteristics on them using General Linear Models. RESULTS: We identified 4 brain states underlying the WM task: high-demand, low-demand, fixation, and non-dominant states. Compared with controls and siblings, patients showed reduced occupancy and lifetime of high-demand state during the "2-back," reduced lifetime of low-demand state during the "0-back," but increased occupancy and lifetime of fixation state during both task periods. Aberrant high-demand state mediated the association between WM performance and negative symptoms. Compared with controls and patients, siblings showed increased occupancy of high-demand and reduced fixation state during the resting fixation condition; this putative compensatory process correlated with better WM performance. CONCLUSIONS: Latent brain states of intrinsic connectivity that represent internal mental processes affect WM performance, influencing the expression of negative symptoms in schizophrenia and cognitive resilience in unaffected siblings.
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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 | 0.002 |
| 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".