T34. DECREASED STRUCTURAL CONNECTIVITY IN FIRST-EPISODE PSYCHOSIS IN A VERBAL MEMORY NETWORK DERIVED FROM PARTIAL LEAST SQUARES REGRESSION
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».