M160. INVESTIGATING STRUCTURAL CONNECTIVITY CORRELATES OF VERBAL MEMORY DEFICITS AMONG FIRST-EPISODE PSYCHOSIS PATIENTS
Bibliographic record
Abstract
Abstract Background Verbal memory is one of the most affected cognitive domains in patients with schizophrenia and related psychoses. Several studies have found associations between cognitive abilities and white matter fractional anisotropy (FA) in schizophrenia; however, only a few tractography studies have investigated FA relative to verbal memory in patients with a first episode of psychosis (FEP) compared with healthy controls (HC). Although white matter tractography differences have been well established between chronic patients and HC, the direction of findings from FEP studies has been inconsistent. Thus, the present study aims to examine whole-brain white matter differences and its association with verbal memory in individuals with a FEP relative to HC using tractography. Methods Diffusion-weighted images were acquired on a 1.5T scanner for patients (n=65) and controls (n=54) at baseline. The Wechsler Memory Scale was used as a measure of verbal memory. Pre-processing was performed on a subject-by-subject basis using MRtrix. Diffusion tractography was generated using a probabilistic anatomically-constrained tractography algorithm, which constrains the reconstruction to specific biological priors. Furthermore, the spherical-deconvolution informed filtering of tractograms (SIFT) tool will be used to ensure the tractogram is biologically meaningful. This results in subject-specific connectomes defining the mean FA between two regions of interest that were defined using the Desikan- Killiany atlas. A linear model was used to test for main effect of group and main effect of verbal memory on white matter tract FA, covarying for age and sex. For both sets of analyses, results were corrected for multiple comparisons using false discovery rate (FDR). Results A significant main effect of group on whole-brain average FA was observed, with patients displaying lower average FA compared to healthy controls (Patients=0.291, controls=0.300, p<0.05). Whole-brain white matter tract FA analysis revealed that there are widespread differences between controls and individuals with a FEP. Group most strongly predicted white matter tract FA differences between left caudal anterior cingulate and left lateral orbitofrontal (patients mean FA=0.302, controls mean FA=0.342), left hippocampus and right isthmus cingulate (patient mean FA= 0.217 controls mean FA= 0.318), and finally left lingual and left rostral anterior cingulate (patients mean FA=0.162, controls mean FA= 0.249. However, none survived correction for multiple comparisons. Further, there was no significant association between verbal memory and white matter tract FA in FEP or HC. Discussion Findings from this study suggest there are some significant differences in whole-brain average FA between individuals experiencing a FEP and healthy controls. However, when analyzing whole-brain tract FA, none of the connections survived corrections for multiple comparisons. These findings might be limited by the scanner resolution included in this study, which may not capture more subtle differences. Nonetheless, these results are consistent with a cross-sectional study comparing healthy individuals to chronic and first-episode patients suggesting that modest differences are present early in the disease and increase as the disease progresses. We suggest that future studies analyze white matter tract using a longitudinal design to identify disease progression.
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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) | 0.005 | 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".