Topological data analysis reveals robust alterations in the whole-brain and frontal lobe functional connectomes in attention-deficit/ hyperactivity disorder

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a developmental disorder characterized by difficulty to control the own behavior. Neuroimaging studies have related ADHD with the interplay of fronto-parietal attention sys-tems with the default mode network (DMN; Castellanos and Aoki, 2016). However, some results have been in-consistent, potentially due to methodological differences in the analytical strategies when defining the brain functional network, i.e., the functional connectivity threshold and/or the brain parcellation scheme. Here, we make use of topological data analysis (TDA) to explore the brain connectome as a function of the filtration value (i.e., the connectivity threshold), instead of using a static connectivity threshold. Specifically, we characterized the transition from all nodes being isolated to being connected into a single component as a function of the filtration value. We explored the utility of such a method to identify differences between 81 children with ADHD (45 male, age: 7.26–17.61 years old) and 96 typically developing children (TDC; 59 male, age: 7.17– 17.96 years old), using a public dataset of resting state (rs)fMRI in human subjects. Results were highly con-gruent when using four different brain segmentations (atlases), and exhibited significant differences for the brain topology of children with ADHD, both at the whole-brain network and the functional subnetwork levels, particularly involving the frontal lobe and the DMN. Therefore, this is a solid approach that complements con-nectomics-related methods and may contribute to identify the neurophysio-pathology of ADHD.