The genetic and neuroimaging correlates of persistent negative symptoms in first-episode psychosis
Notice bibliographique
Résumé
Background: About one third of first-episode psychosis (FEP) patients will develop persistent negative symptoms (PNS) which prevent remission from the disorder and diminish patients' functional outcomes. In addition, there is mounting evidence that negative symptoms and PNS are not unique to schizophrenia, and genome-wide association studies (GWAS) have uncovered a number of shared risk genes between schizophrenia and bipolar disorder (BPD). Although GWAS have identified an array of risk genes for psychotic disorders, information about the phenotypic effects of such genes are scant. Additionally, while imaging studies have identified neuroanatomical associations with negative symptoms and PNS, few studies have integrated both genetics and imaging. Objectives: The overall objective of this thesis research was to examine the genetic and neuroimaging correlates of PNS in a first-episode of psychosis (FEP) cohort. The following studies were performed: 1. The first of two studies sought to i) examine whether previously-identified BPD risk genes are associated with negative symptoms in schizophrenia, and ii) to examine whether such genes influence brain morphology. 2. The second study focused on PNS and aimed to i) assess whether previously-identified risk genes for schizophrenia and bipolar disorder might have a phenotypic association with PNS; and ii) examine any neuroanatomical associations of these genes. Methods: 1. Patients experiencing FES (n=133) were genotyped for a number previously-identified BPD risk genes (n=21), and a series of ANCOVAs examined the association between negative symptom severity – as measured by the Scale for the Assessment of Negative Symptoms (SANS) – and genotype. A subset of participants (n=61) underwent a structural 1.5T MRI T1 scan, analyzed for surface area changes via the CIVET pipeline and LPBA40 atlas. 2. FEP participants (affective and non-affective) were recruited from PEPP-Montreal at the Douglas Institute (N=192). Participants underwent thorough symptom evaluations and were classified as either PNS (n=33) or non-PNS (n=148), and were genotyped for 44 risk alleles associated with schizophrenia and bipolar disorder. Additionally, a subset of 90 participants underwent a T1 structural MRI scan. A stepwise regression was performed to identify SNPs predicting PNS, and significant SNPs were included in a series of MANCOVAs to examine associated surface area and cortical thickness abnormalities. Results: 1. We observed a significant association between negative symptom severity and the BPD risk gene FOXO6 (rs4660531). Individuals with the CC genotype presented significantly higher negative symptoms (Cohen's d=0.46, F=5.854, p=.017) and significantly smaller surface area within the right middle orbitofrontal gyrus (Cohen's d=0.69, F=7.289, p=.009) compared to carriers of allele A. 2. The minor alleles of SNPs within three genes were predictive of PNS: CNNM2, C9orf5, and FLJ16124, and all three SNPs were associated with surface area and/or cortical thickness reductions in regions previously associated with PNS. Conclusion: 1. Lacking the FOXO6 minor allele was associated with an increase in negative symptoms and surface area reduction in the right orbitofrontal gyrus – an area previously associated with negative symptoms – suggesting that presence of the FOXO6 minor allele confers resistance against negative symptoms and associated neuroanatomical changes in FES. 2. These results suggest that certain SNPs previously-associated with risk for schizophrenia and bipolar disorder are associated with the PNS phenotype in first-episode psychosis, and these SNPs are also associated with neuroanatomical abnormalities previously-associated with negative symptoms and PNS. Together, these two studies provide encouraging evidence that the emergence of negative symptoms may be traced to specific biomarkers, which may ultimately inform future treatment of these currently debilitating symptoms.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».