S7. MORPHOLOGICAL PROFILING OF SCHIZOPHRENIA: CLUSTER ANALYSIS OF MRI-BASED CORTICAL THICKNESS DATA
Notice bibliographique
Résumé
The clinical diagnosis of schizophrenia is suspected to include several distinct subgroups of patients, but reliable neurobiological boundaries to differentiate the subgroups remain elusive. These unknown subgroups increase the variance of biological measures within the clinically identified patient group, deflating the group-level estimates of causal factors and treatment effects. Prior studies seeking homogeneous subgroups of schizophrenia based on brain-based measures have not found consistent solutions. A major limitation in prior studies is the assumption that healthy controls form a relatively homogeneous group, that deviates biologically from the patient subgroups. As a result, cluster solutions have been generally sought only within patient samples, without pooling the patient and control data. In the current study, we assessed whether the regional values of cortical thickness estimated from structural MRI are sufficiently sensitive to identify subgroups of patients and healthy controls. We used high resolution (3 Tesla) imaging in 179 patients with schizophrenia and 77 healthy controls, to investigate possible subtypes of schizophrenia. K-means algorithm was applied to perform clustering analysis on cortical thickness data from 68 regions in Desikan-Kiliany Atlas using Freesurfer software, and gap statistics was used to find best cluster solution. General linear models were used to compare cortical thickness, cognitive performance and symptom severity among the identified clusters. A 3-cluster solution provided the most optimal clustering, with the first cluster (C1) comprised almost entirely of patients, while the other 2 clusters (C2 and C3) including a substantial number of patients as well as control subjects. There was no significant difference in cognitive performance among the 3 schizophrenia subtypes. C1 was the most morphologically impoverished group with significantly thin cortex in multiple brain regions but had no more symptom/cognitive burden than the other subtypes. C2 was an intermediate group with significant thinning in selected brain regions, with higher burden of negative symptoms. C3 was the morphologically most intact subgroup with a cortical thickness profile like healthy controls, despite having more severe delusions. In addition, C1 also had higher duration of exposure to medication among the 3 patient groups, with longer illness duration. We report 3 major findings: 1) 3 distinct morphological profiles are observed in schizophrenia 2) A large number of patients with schizophrenia have the cortical morphological profiles of apparently normal healthy controls 3) cortical thickness profiles do not map well to cognitive and symptomatic profiles in schizophrenia. Interestingly, we observed a pattern of morphological preservation among patients with higher levels of delusion. We provide evidence for the presence of morphological subgroups of schizophrenia, the delineation of which may help stratifying patients for future prognostic studies.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 tête enseignante, 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 ».