Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, University of Montreal, Montreal, Canada
Angela Tam
Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, University of Montreal, Montreal, Canada
Pierre Orban
Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, Montreal, Canada; Département de Psychiatrie et d’Addictologie, Université de Montréal, Montreal, Canada
Clara Moreau
Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, University of Montreal, Montreal, Canada; Sainte Justine Research Center, University of Montreal, Montreal, Canada; Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université Paris Cité, Paris, France
Yassine Benhajali
Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, University of Montreal, Montreal, Canada
Our understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes this neurodevelopmental disorder. Data driven clustering offers a straightforward way to decompose autism heterogeneity into subtypes of connectivity and promises an unbiased framework to investigate behavioral symptoms and causative genetic factors. Yet, the robustness and generalizability of functional connectivity subtypes is unknown. Here, we show that a simple hierarchical cluster analysis can robustly relate a given individual and brain network to a connectivity subtype, but that continuous assignments are more robust than discrete ones. We also found that functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and these associations generalize to independent replication data. We explored systematically 18 different brain networks as we expected them to associate with different behavioral profiles as well as different key regions. Contrary to this prediction, autism functional connectivity subtypes converged on a common topography across different networks, consistent with a compression of the primary gradient of functional brain organization, as previously reported in the literature. Our results support the use of data driven clustering as a reliable data dimensionality reduction technique, where any given dimension only associates moderately with clinical manifestations.
