T94. CORTICAL THICKNESS TRAJECTORIES IN RELATION TO CHANGES IN EXECUTIVE FUNCTION AMONG FIRST EPISODE PSYCHOSIS PATIENTS
Bibliographic record
Abstract
Disorganized thinking and executive function impairments are often present in patients with psychosis. Several studies have found associations between cognitive abilities and cortical thickness in schizophrenia; however, only a few studies have investigated cortical thickness relative to executive function (EF) in patients with a first episode of psychosis (FEP) compared with healthy controls (HC), using cross-sectional study designs. Moreover, the direction of findings from these studies have been inconsistent, although results converge on the idea that EF are differentially related to brain structure in patients. The present study aims to examine longitudinal relationships between changes in cortical thickness and EF in individuals with a FEP relative to HC using structural brain imaging. Structural T1-weighted images were acquired on a 3T scanner for patients (n=21) and controls (n=28). Two to four timepoints were completed per subject, over a period of approximately 3–21 months. The Groton Maze Learning test and Set Shifting test from the CogState computerized battery were collected as measures of EF. Images were processed using the CIVET pipeline, and all cortical thickness-related analyses were performed across 81,924 vertices of the cortical surface, using the SurfStat toolbox in Matlab. A slope of CT at every vertex across the brain and a slope of EF across available timepoints per subject were independently calculated and subsequently used in the analysis. A linear model was applied to test for the main effect of change in EF to change in CT per group, and to assess the interaction between group and change in EF on cortical thickness rates of change. Models controlled for age and sex. For both sets of analyses, resultant t-statistic maps were thresholded and corrected for multiple comparisons with Random Field Theory (RFT), with a stringent cluster-threshold of p=0.005. Exploration of results was also done with a more relaxed threshold of p=0.01. A significant negative main effect of change in EF on change in CT was observed for HC in the right insula (i.e. improvement in EF was related to cortical thinning); additionally, the right cingulate gyrus was significant with a relaxed threshold of p=0.01. The main effect of change in EF on changes in CT was not significant for FEP patients. The interaction between change in EF and group was found to be significant, where negative associations between change in EF and change in CT were driven by HC. Specifically, a negative association of change in EF was found in right insula, right supramarginal gyrus and the left ventrolateral prefrontal cortex, whereas no such relationship was observed in patients. To our knowledge, longitudinal changes in cortical thickness relative to changes in EF have not been investigated before in FEP. Findings from this study suggest there are no significant associations between change in CT and change in EF among individuals early in the course of psychosis. Significant associations found in controls suggest that steeper improvements in EF is associated with cortical thinning in frontal-parietal regions. These results are consistent with a cross-sectional study in healthy adolescents suggesting that more rapid structural maturation in higher-order brain regions characterized by protracted development is associated with improvement in cognitive abilities. It is possible that in patients, these maturational patterns are altered and/or more diffuse, and thus, not as readily localized with a univariate analysis. We suggest that future studies should examine such brain-behaviour relationships in FEP with brain network approaches.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".