Reduced functional connectivity in brain networks underlying paired associates memory encoding in schizophrenia
Bibliographic record
Abstract
Patients with schizophrenia exhibit deficits in associative learning and semantic memory. The current functional magnetic resonance imaging (fMRI) study investigated the neural correlates of successful versus unsuccessful semantic associative encoding in schizophrenia compared to healthy controls. Publicly shared fMRI data from the UCLA Consortium for Neuropsychiatric Phenomics LA5C study were analyzed. Forty-four patients with schizophrenia and 78 healthy controls performed a paired-associates encoding task. Constrained principal component analysis for fMRI (fMRI-CPCA) revealed three distinct functional networks recruited during encoding: a responding (RESP) network, a linguistic processing/attention network (LANG/ATTN), and the default mode network (DMN). Relative to healthy controls, patients showed aberrant activity in all three networks; namely, hypo-activation in the LANG/ATTN network during successful encoding, lower peak activation and weaker post-activation suppression of the RESP network, and weaker suppression in the DMN during successful encoding. Independent of group effects, a pattern of stronger anticorrelating LANG/ATTN-DMN activity during successful encoding significantly predicted subsequent retrieval of paired associates. Together with previous observations of language network hypoactivation during controlled semantic associative memory processes, these results suggest that reduced activity in linguistic processing areas is a reliable biological marker associated with impaired semantic memory in 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".