Titre :

Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity

Auteur(s) :

De Pierrefeu, Amicie [Auteur]
Lofstedt, T. [Auteur]
Laidi, C. [Auteur]
Hadj-Selem, F. [Auteur]
Bourgin, J. [Auteur]
Hajek, T. [Auteur]
Spaniel, F. [Auteur]
Kolenic, M. [Auteur]
Ciuciu, P. [Auteur]
Hamdani, N. [Auteur]
Leboyer, M. [Auteur]
Fovet, Thomas [Auteur]
Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 [SCALab]
Jardri, Renaud [Auteur]
Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 [SCALab]
Houenou, J. [Auteur]
Duchesnay, E. [Auteur]

Titre de la revue :

Acta psychiatrica Scandinavica

Nom court de la revue :

Acta Psychiatr Scand

Date de publication :

2018-09-21

ISSN :

1600-0447

Mot(s)-clé(s) :

first-episode psychosis
psychoradiology
structural MRI
classification
schizophrenia

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused ...
Lire la suite >Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross-sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These elements cast doubt on previous findings' reproducibility. We propose a machine learning algorithm that provides an interpretable brain signature. Using large datasets collected from 4 sites (276 schizophrenia patients, 330 controls), we assessed cross-site prediction reproducibility and associated predictive signature. For the first time, we evaluated the predictive signature regarding medication and illness duration using an independent dataset of first-episode patients. Machine learning classifiers based on neuroanatomical features yield significant intersite prediction accuracies (72%) together with an excellent predictive signature stability. This signature provides a neural score significantly correlated with symptom severity and the extent of cognitive impairments. Moreover, this signature demonstrates its efficiency on first-episode psychosis patients (73% accuracy). These results highlight the existence of a common neuroanatomical signature for schizophrenia, shared by a majority of patients even from an early stage of the disorder.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

CHU Lille
Inserm
Université de Lille

Collections :

• Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193

Date de dépôt :

2021-06-23T13:40:47Z
2023-06-06T07:16:41Z