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Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine-learning with structured sparsity
NeuroSpin, CEA, Gif-sur-Yvette, France.ORCID iD: 0000-0002-5231-6010
Umeå University, Faculty of Medicine, Department of Radiation Sciences.ORCID iD: 0000-0001-7119-7646
NeuroSpin, CEA, Gif-sur-Yvette, France; Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Créteil, France; Pôle de Psychiatrie, Assistance Publique–Hôpitaux de Paris (AP-HP), Faculté, de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor, Créteil, France.
Energy Transition Institute: VeDeCoM, Versailles, France.
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2018 (English)In: Acta Psychiatrica Scandinavica, ISSN 0001-690X, E-ISSN 1600-0447, Vol. 138, p. 571-580Article in journal (Refereed) Published
Abstract [en]

ObjectiveStructural 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.

MethodWe 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.

ResultsMachine 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).

ConclusionThese 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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018. Vol. 138, p. 571-580
Keywords [en]
classification, schizophrenia, structural MRI, first-episode psychosis, psychoradiology
National Category
Psychiatry Computer Vision and Robotics (Autonomous Systems) Other Mathematics
Research subject
Computerized Image Analysis; Psychiatry
Identifiers
URN: urn:nbn:se:umu:diva-152928DOI: 10.1111/acps.12964ISI: 000449521200009OAI: oai:DiVA.org:umu-152928DiVA, id: diva2:1259491
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-11-27Bibliographically approved

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Löfstedt, Tommy

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