The human brain is a complex, integrative and segregative network that exhibits dynamic fluctuations in activity across space and time. A canonical set of large-scale networks has been historically identified from resting-state fMRI (rs-fMRI), including the default mode, visual, somatomotor, salience, attention, and executive control. However, the methods used in identification of these networks have relied on assumptions that may inadvertently constrain their properties and consequently our understanding of the human connectome. Here we define a brain "network" as a functional component that jointly describes its spatial distribution and temporal dynamics, where neither domain suffers from unrealistic constraints. Using our recently developed BrainSync algorithm and the Nadam-Accelerated SCAlable and Robust (NASCAR) tensor decomposition, we identified twenty-three brain networks using rs-fMRI data from a large group of healthy subjects acquired by the Human Connectome Project. These networks are spatially overlapped, temporally correlated, and highly reproducible across two independent groups and sessions. We show that these networks can be clustered into six distinct functional categories and naturally form a representative functional network atlas for a healthy population. Using this atlas, we demonstrate that individuals with attention-deficit/hyperactivity disorder display disproportionate brain activity increases, relative to neurotypical subjects, in visual, auditory, and somatomotor networks concurrent with decreases in the default mode and higher-order cognitive networks. Thus, this work not only yields a highly reproducible set of spatiotemporally overlapped functional brain networks, but also provides convergent evidence that individual differences in these networks can be used to explain individual differences in neurocognitive functioning.
Neurodegenerative Diseases Target Large-Scale Human Brain Networks
