Age of Onset, Brain Controllability, and Working Memory Performance in First-Episode Schizophrenia
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: The onset-age of schizophrenia introduces considerable heterogeneity in cognitive functions such as working memory (WM) among patients. One of the key properties of the brain that varies with age-related development is the network-level controllability of brain state transitions. We tested the effect of onset-age on brain controllability to evaluate its impact on WM deficits in schizophrenia. STUDY DESIGN: We examined the average and modal controllability of the brain connectome in 85 first-episode early-onset schizophrenia (EOS), 62 younger healthy controls (yHC), 71 first-episode adult-onset schizophrenia (AOS), and 85 older healthy controls (oHC) during N-back tasks. We first detected the regions with illness and onset-age interaction in a whole-brain search, and then conducted a correlation analysis with WM performance and clinical characteristics, followed by an out-of-sample gene annotation analysis. STUDY RESULTS: We detected the illness*onset-age interaction in the sensorimotor network, auditory network, and subcortical network for average controllability and the default mode network, visual network, and salience network for modal controllability (p-fdr < 0.05). The interaction effects in the visual and subcortical networks primarily resulted from the AOS vs. oHC differences; the effects in the default mode network resulted from EOS vs. yHC differences. We observed no significant correlation between controllability with cognitive performance or clinical characteristics. The affected regions had preferential expression of genes relevant to synaptic signaling and neurodegenerative processes (p-fdr < 0.05). CONCLUSION: Onset-age introduces considerable heterogeneity in the controllability over brain state transition during WM tasks among patients with schizophrenia.
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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".