Classification of autistic individuals and controls using cross-task characterization of fMRI activity
Year: 2015
Type: article
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.
Source: NeuroImage Clinical
Institutions: University of Geneva, Université Paris 8, Inserm, Université de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Institute Mutualiste Montsouris, Centre de recherche en Epidémiologie et Santé des Populations, Université Paris Descartes, École Normale Supérieure - PSL, Centre National de la Recherche Scientifique, Institut du Cerveau, Université Pierre-et-Marie-Curie
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,490
Funders: National Science Foundation, Agence Nationale de la Recherche, Institut National de la Santé et de la Recherche Médicale, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Université de Genève, Fondation Roger de Spoelberch, National Center of Competence in Research Affective Sciences - Emotions in Individual Behaviour and Social Processes, NCCR Catalysis
Awards: NCCR Affective Sciences: Emotion in Individual Behavior and Social Processes, G1547559951, How self-relevance appraisal affects the reading of emotion: a multi-disciplinary approach., G2215459813, G2543976241, G2776576769, Impact of prosocial and violent videogames on pain perception: Investigating short- and long-term emotional changes, G4043808237, G5351285113, Institute of Cognitive Studies of Ecole normale superieure, G6782275431, G7240651115, G7265990426, G7376787254, G7802492928, G805552297
OpenAlex ID: W2174740345