Unveiling the higher-order organization of human brain function

Brisbane Convention & Exhibition Centre 

Traditional models of brain activity typically represent interactions between regions as pairwise connections, which limits the understanding of more complex interactions involving multiple regions simultaneously. To address this limitation, we introduce a novel topologically grounded framework that reconstructs the temporal evolution of higher-order interactions (HOIs) from fMRI data, capturing dependencies among three or more regions. By analysing fMRI data from the 100 unrelated subjects Human Connectome Project, we show that higher-order models, unlike traditional pairwise approaches, significantly enhance task decoding, improve the individual identification of unimodal and transmodal functional subsystems, and strengthen the associations between brain activity and behavior. Overall, our approach sheds new light on the higher-order organization of fMRI time series, improving the characterization of dynamic group dependencies in rest and tasks, and revealing a vast space of unexplored structures within human functional brain data, which may remain hidden when using traditional pairwise approaches.