umu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
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 universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.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.
Visa övriga samt affilieringar
2018 (Engelska)Ingår i: Acta Psychiatrica Scandinavica, ISSN 0001-690X, E-ISSN 1600-0447, Vol. 138, s. 571-580Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
John Wiley & Sons, 2018. Vol. 138, s. 571-580
Nyckelord [en]
classification, schizophrenia, structural MRI, first-episode psychosis, psychoradiology
Nationell ämneskategori
Psykiatri Datorseende och robotik (autonoma system) Annan matematik
Forskningsämne
datoriserad bildanalys; psykiatri
Identifikatorer
URN: urn:nbn:se:umu:diva-152928DOI: 10.1111/acps.12964ISI: 000449521200009OAI: oai:DiVA.org:umu-152928DiVA, id: diva2:1259491
Tillgänglig från: 2018-10-30 Skapad: 2018-10-30 Senast uppdaterad: 2018-11-27Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltext

Personposter BETA

Löfstedt, Tommy

Sök vidare i DiVA

Av författaren/redaktören
de Pierrefeu, AmicieLöfstedt, Tommy
Av organisationen
Institutionen för strålningsvetenskaper
I samma tidskrift
Acta Psychiatrica Scandinavica
PsykiatriDatorseende och robotik (autonoma system)Annan matematik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 182 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf