Stable biomarker identification for predicting schizophrenia in the human connectome.
Leonardo Gutiérrez-Gómez, Jakub Vohryzek, Benjamin Chiêm, Philipp S Baumann, Philippe Conus, Kim Do Cuenod, Patric Hagmann, Jean-Charles Delvenne
Author Information
Leonardo Gutiérrez-Gómez: Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, Louvain-la-Neuve, Belgium. Electronic address: leonardo.gutierrez@uclouvain.be.
Jakub Vohryzek: Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Department of Radiology, University Hospital Centre and University of Lausanne, Lausanne, Switzerland. Electronic address: jakub.vohryzek@queens.ox.ac.uk.
Benjamin Chiêm: Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, Louvain-la-Neuve, Belgium. Electronic address: benjamin.chiem@uclouvain.be.
Philipp S Baumann: Service of General Psychiatry and Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland. Electronic address: philipp.Baumann@chuv.ch.
Philippe Conus: Service of General Psychiatry and Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland. Electronic address: philippe.Conus@chuv.ch.
Kim Do Cuenod: Service of General Psychiatry and Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland. Electronic address: Kim.Do@chuv.ch.
Patric Hagmann: Department of Radiology, University Hospital Centre and University of Lausanne, Lausanne, Switzerland. Electronic address: Patric.Hagmann@chuv.ch.
Jean-Charles Delvenne: Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, Louvain-la-Neuve, Belgium; Center for Operations Research and Econometrics (CORE), Université catholique de Louvain, Louvain-la-Neuve, Belgium. Electronic address: jean-charles.delvenne@uclouvain.be.
Schizophrenia, as a psychiatric disorder, has recognized brain alterations both at the structural and at the functional magnetic resonance imaging level. The developing field of connectomics has attracted much attention as it allows researchers to take advantage of powerful tools of network analysis in order to study structural and functional connectivity abnormalities in schizophrenia. Many methods have been proposed to identify biomarkers in schizophrenia, focusing mainly on improving the classification performance or performing statistical comparisons between groups. However, the stability of biomarkers selection has been for long overlooked in the connectomics field. In this study, we follow a machine learning approach where the identification of biomarkers is addressed as a feature selection problem for a classification task. We perform a recursive feature elimination and support vector machines (RFE-SVM) approach to identify the most meaningful biomarkers from the structural, functional, and multi-modal connectomes of healthy controls and patients. Furthermore, the stability of the retrieved biomarkers is assessed across different subsamplings of the dataset, allowing us to identify the affected core of the pathology. Considering our technique altogether, it demonstrates a principled way to achieve both accurate and stable biomarkers while highlighting the importance of multi-modal approaches to brain pathology as they tend to reveal complementary information.
