View in EDS HTML Full Text PDF Full Text

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

Saved in:
Bibliographic Details
Title: Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity.
Authors: Pierrefeu, A., Löfstedt, T., Laidi, C., Hadj‐Selem, F., Bourgin, J., Hajek, T., Spaniel, F., Kolenic, M., Ciuciu, P., Hamdani, N., Leboyer, M., Fovet, T., Jardri, R., Houenou, J., Duchesnay, E.
Source: Acta Psychiatrica Scandinavica. Dec2018, Vol. 138 Issue 6, p571-580. 10p. 2 Diagrams, 3 Charts.
Subjects: Schizophrenia, Magnetic resonance imaging, Psychoses, Machine learning, Neuroanatomy, Neurobiology
Abstract: Objective: 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. Method: 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. Results: 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). Conclusion: 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. [ABSTRACT FROM AUTHOR]
Copyright of Acta Psychiatrica Scandinavica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 132681639
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Pierrefeu%2C+A%2E%22">Pierrefeu, A.</searchLink><br /><searchLink fieldCode="AR" term="%22Löfstedt%2C+T%2E%22">Löfstedt, T.</searchLink><br /><searchLink fieldCode="AR" term="%22Laidi%2C+C%2E%22">Laidi, C.</searchLink><br /><searchLink fieldCode="AR" term="%22Hadj‐Selem%2C+F%2E%22">Hadj‐Selem, F.</searchLink><br /><searchLink fieldCode="AR" term="%22Bourgin%2C+J%2E%22">Bourgin, J.</searchLink><br /><searchLink fieldCode="AR" term="%22Hajek%2C+T%2E%22">Hajek, T.</searchLink><br /><searchLink fieldCode="AR" term="%22Spaniel%2C+F%2E%22">Spaniel, F.</searchLink><br /><searchLink fieldCode="AR" term="%22Kolenic%2C+M%2E%22">Kolenic, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Ciuciu%2C+P%2E%22">Ciuciu, P.</searchLink><br /><searchLink fieldCode="AR" term="%22Hamdani%2C+N%2E%22">Hamdani, N.</searchLink><br /><searchLink fieldCode="AR" term="%22Leboyer%2C+M%2E%22">Leboyer, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Fovet%2C+T%2E%22">Fovet, T.</searchLink><br /><searchLink fieldCode="AR" term="%22Jardri%2C+R%2E%22">Jardri, R.</searchLink><br /><searchLink fieldCode="AR" term="%22Houenou%2C+J%2E%22">Houenou, J.</searchLink><br /><searchLink fieldCode="AR" term="%22Duchesnay%2C+E%2E%22">Duchesnay, E.</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Acta+Psychiatrica+Scandinavica%22">Acta Psychiatrica Scandinavica</searchLink>. Dec2018, Vol. 138 Issue 6, p571-580. 10p. 2 Diagrams, 3 Charts.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Schizophrenia%22">Schizophrenia</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Psychoses%22">Psychoses</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Neuroanatomy%22">Neuroanatomy</searchLink><br /><searchLink fieldCode="DE" term="%22Neurobiology%22">Neurobiology</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: 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. Method: 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. Results: 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). Conclusion: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Acta Psychiatrica Scandinavica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=132681639
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/acps.12964
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 571
    Subjects:
      – SubjectFull: Schizophrenia
        Type: general
      – SubjectFull: Magnetic resonance imaging
        Type: general
      – SubjectFull: Psychoses
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Neuroanatomy
        Type: general
      – SubjectFull: Neurobiology
        Type: general
    Titles:
      – TitleFull: Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Pierrefeu, A.
      – PersonEntity:
          Name:
            NameFull: Löfstedt, T.
      – PersonEntity:
          Name:
            NameFull: Laidi, C.
      – PersonEntity:
          Name:
            NameFull: Hadj‐Selem, F.
      – PersonEntity:
          Name:
            NameFull: Bourgin, J.
      – PersonEntity:
          Name:
            NameFull: Hajek, T.
      – PersonEntity:
          Name:
            NameFull: Spaniel, F.
      – PersonEntity:
          Name:
            NameFull: Kolenic, M.
      – PersonEntity:
          Name:
            NameFull: Ciuciu, P.
      – PersonEntity:
          Name:
            NameFull: Hamdani, N.
      – PersonEntity:
          Name:
            NameFull: Leboyer, M.
      – PersonEntity:
          Name:
            NameFull: Fovet, T.
      – PersonEntity:
          Name:
            NameFull: Jardri, R.
      – PersonEntity:
          Name:
            NameFull: Houenou, J.
      – PersonEntity:
          Name:
            NameFull: Duchesnay, E.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2018
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 0001690X
          Numbering:
            – Type: volume
              Value: 138
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Acta Psychiatrica Scandinavica
              Type: main
ResultId 1