T34. DECREASED STRUCTURAL CONNECTIVITY IN FIRST-EPISODE PSYCHOSIS IN A VERBAL MEMORY NETWORK DERIVED FROM PARTIAL LEAST SQUARES REGRESSION
Bibliographic record
Abstract
Verbal memory is one of the most severely affected cognitive domains in schizophrenia and related psychoses and impairment in this domain is one of the strongest predictors of poor clinical and functional outcome. Verbal memory deficits in schizophrenia have been linked to alterations in various measures of brain structure (e.g., cortical thinning, decreased brain volume) and function (e.g., aberrant functional connectivity) within widespread cortical and subcortical areas, including the hippocampus, medial temporal lobes, and frontal regions. However, previous research generally employs mass-univariate analysis or multivariate techniques that relate verbal memory to networks derived from overall variance; thus, brain networks specifically optimized to explain patterns of verbal memory performance have yet to be determined. In the current study, we examined differences between first-episode psychosis (FEP) patients and healthy controls on a verbal memory-optimized structural brain network derived from partial least squares (PLS) regression, which maximizes the covariance between the dependent (cortical thickness) and independent (verbal memory) variables of interest. Participants (81 FEP patients, 115 healthy controls, matched for age, sex, and handedness) underwent magnetic resonance imaging and completed one of two cognitive test batteries (Wechsler Memory Scale or CogState Research Battery). Composite verbal memory domain scores were calculated from the respective control z-scores for each battery and combined into a single verbal memory domain. Cortical thickness values (residualized for age, sex, and test battery) were derived using CIVET version 2.1.0 and parcellated into 78 regions of interest (ROIs) using the Automated Anatomical Labeling (AAL) atlas. PLS regression was used to identify a verbal memory-optimized cortical thickness network by maximizing covariance between these variables and significant ROIs were determined using permutation tests. Group differences on the PLS component-based cortical thickness and verbal memory scores (i.e., linear combinations of cortical thickness ROIs/verbal memory which have maximum covariance with predictor/response scores, respectively) were assessed with t-tests. The FEP group showed significantly impaired performance on verbal memory relative to healthy controls, t(194) = 7.29, p < .001, 95% CI = [0.87 - 1.51]. PLS revealed a right-dominant verbal memory-related network including significant contributions from right temporal cortex (inferior/middle temporal gyrus, parahippocampal gyrus, middle/superior temporal pole), bilateral sensorimotor regions, and left middle frontal gyrus (loadings: 0.21 - 0.53; all ps < 0.05). Significant group differences were observed on component-based cortical thickness scores, t(194) = 2.53, p < 0.05, 95% CI = [0.10 - 0.82], and verbal memory component scores, t(194) = 7.29, p < .001, CI = [0.49 - 0.85], indicating that FEP patients showed a significantly decreased contribution to this network than healthy controls. Using PLS to maximize covariance between region-based cortical thickness and verbal memory performance in a large sample of FEP patients and healthy controls, we identified a right-dominant frontotemporal network on which FEP patients showed decreased scores relative to controls. This suggests that decreased structural connectivity between regions within this network may underlie impaired verbal memory performance in FEP. Future research will further investigate network characteristics to determine the nature of these group differences and whether they are observable longitudinally through different stages of psychosis.
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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 | 0.000 | 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) | 0.003 | 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".