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

de Pierrefeu, Amicie (författare)
NeuroSpin, CEA, Gif-sur-Yvette, France
Löfstedt, Tommy (författare)
Umeå universitet,Radiofysik
Laidi, C. (författare)
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
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Hadj-Selem, Fouad (författare)
Energy Transition Institute: VeDeCoM, Versailles, France
Bourgin, Julie (författare)
Department of Psychiatry, Louis-Mourier Hospital, AP-HP, Colombes, France; INSERM U894, Centre for Psychiatry and Neurosciences, Paris, France
Hajek, Tomas (författare)
Department of Psychiatry, Dalhousie University, Halifax, NS, Canada; National Institute of Mental Health, Klecany, Czech Republic
Spaniel, Filip (författare)
National Institute of Mental Health, Klecany, Czech Republic
Kolenic, Marian (författare)
National Institute of Mental Health, Klecany, Czech Republic
Ciuciu, Philippe (författare)
NeuroSpin, CEA, Gif-sur-Yvette, France; INRIA, CEA, Parietal team, University of Paris-Saclay, France
Hamdani, Nora (författare)
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
Leboyer, Marion (författare)
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
Fovet, Thomas (författare)
Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab-PsyCHIC), CNRS UMR 9193, University of Lille; Pôle de Psychiatrie, Unité CURE, CHU Lille, Lille, France
Jardri, Renaud (författare)
INRIA, CEA, Parietal team, University of Paris-Saclay, France; Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab-PsyCHIC), CNRS UMR 9193, University of Lille; Pôle de Psychiatrie, Unité CURE, CHU Lille, Lille, France
Houenou, Josselin (författare)
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
Duchesnay, Edouard (författare)
NeuroSpin, CEA, Gif-sur-Yvette, France
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 (creator_code:org_t)
2018-09-21
2018
Engelska.
Ingår i: Acta Psychiatrica Scandinavica. - : John Wiley & Sons. - 0001-690X .- 1600-0447. ; 138, s. 571-580
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Psykiatri (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Psychiatry (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
NATURVETENSKAP  -- Matematik -- Annan matematik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Other Mathematics (hsv//eng)

Nyckelord

classification
schizophrenia
structural MRI
first-episode psychosis
psychoradiology
Computerized Image Analysis
datoriserad bildanalys
psykiatri
Psychiatry

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