title: Classification of autistic individuals and controls using cross-task characterization of fMRI activity.
abstract: Multivariate pattern analysis (MVPA) has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD) from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI), a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based) approach that we apply to two different fMRI experiments with social stimuli (faces and bodies). The method, based on Support Vector Machines (SVMs) and Recursive Feature Elimination (RFE), is first trained for each experiment independently and each output is then combined to obtain a final classification output. Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92.3%). Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations.
authors.affiliation: Swiss Center for Affective Sciences, Campus Biotech, University of Geneva, Geneva, Switzerland; Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Chanel
authors.affiliation: Swiss Center for Affective Sciences, Campus Biotech, University of Geneva, Geneva, Switzerland; Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland.
authors.forename: Swann
authors.initials: S
authors.lastname: Pichon
authors.affiliation: Laboratoire de Psychopathologie et Neuropsychologie EA 2027, Université Paris 8, France.
authors.forename: Laurence
authors.initials: L
authors.lastname: Conty
authors.affiliation: CESP, INSERM, Univ. Paris-Sud, Univ. Paris Descartes, UVSQ, Université Paris-Saclay, Paris, France; Departement de Psychiatrie de l'Institut Mutualiste Montsouris, Paris, France.
authors.forename: Sylvie
authors.initials: S
authors.lastname: Berthoz
authors.affiliation: Laboratoire de Neuroscience Cognitive, INSERM U960, Ecole Normale Supérieure, Paris, France.
authors.forename: Coralie
authors.initials: C
authors.lastname: Chevallier
authors.affiliation: Laboratoire de Neuroscience Cognitive, INSERM U960, Ecole Normale Supérieure, Paris, France; Centre de Neuroimagerie de Recherche (CENIR), Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Université Pierre et Marie Curie-Paris 6 UMRS 975, Inserm U975, CNRS UMR 7225, Institut du cerveau et de la moëlle épinière (ICM), Paris 75013, France.
authors.forename: Julie
authors.initials: J
authors.lastname: Grèzes
mesh_terms: D000328:Adult; D059445:Anhedonia; D001007:Anxiety; D001288:Attention / Q000502:physiology; D000067877:Autism Spectrum Disorder / Q000145:classification* / Q000503:physiopathology* / Q000523:psychology; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D005149:Facial Expression; D000066499:Facial Recognition / Q000502:physiology; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D012938:Social Perception; D060388:Support Vector Machine; D014796:Visual Perception / Q000502:physiology; D055815:Young Adult
keywords: Anger; Autistic spectrum disorder; Body perception; Diagnosis; Emotion; Face perception; Pattern classification; Recursive Feature Elimination; SVM; fMRI