Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks

Abstract

Optimal integration and segregation of neuronal connections are necessary for efficient large-scale network communication between distributed cortical regions while allowing for modular specialization. This network dynamic is enabled at the cortical mesoscale by the organization of nodes into communities. Previous in vivo efforts to map the mesoscale architecture in humans had several limitations. Here we characterize a consensus multiscale community organization of the functional cortical network. We derive this consensus from the clustering of subject-level networks. We show that this subject-derived consensus framework yields clusters that better map to the individual, compared to the widely-used group-derived consensus approach. We applied this analysis to magnetic resonance imaging data from 1003 healthy individuals part of the Human Connectome Project. The hierarchical atlas and code will be made publicly available for future investigators.

A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants

“…The hierarchical atlas, here termed Akiki-Abdallah (AA) atlas, delineated architectures ranging from 3 to 150 modules. The cortical modules remained largely consistent with the cortex-only atlases in our previous work (Akiki and Abdallah, 2019) and in the literature , as shown in Figures 2A versus 2C. The AA architecture at 24 modules (AA-24) largely matches the cortical 22 modules architecture (see Supplemental Information and Figure S4) while adding the affiliation of subcortical and cerebellar nodes.…”

“…To define the network modules, we used the Akiki-Abdallah cortical (AAc) atlas (Akiki and Abdallah, 2019), a recently described hierarchical modular organization of the cortex based on the same parcellation atlas used in the current study. The AAc atlas provided an extensive characterization of the cortical modules at multiple scales, ranging from three communities at the first hierarchical split to 126 communities at the finest-grained level.…”

“…To test the hypothesis that the connectivity in the canonical networks can predict treatment response, we used the AAc architecture at 6 modules (AAc-6), i.e., the level that fully map the default mode (DM; i.e., the target network for hypothesis #2), and which also comprises modules for the central executive (CE), dorsal salience (DS), ventral salience (VS), somatomotor (SM), and visual (VI) networks (Akiki and Abdallah, 2019). For each network (a.k.a., module or community), the internal network-restricted strength (NRS) was calculated as the mean weight of edges within a given network (Akiki et al, 2018a, b).…”

“…However, considering the essential role of non-cortical brain structures in pathophysiology of depression (Drevets, 1998), and that the GBC findings spanned several subcortical structures, we pursued complementary NRS analyses including the whole-brain grayordinate (i.e., A424 atlas (Diedrichsen et al, 2009;Fan et al, 2016;Glasser et al, 2016)). Using the methods implemented in the AAc atlas (Akiki and Abdallah, 2019), we first determined the hierarchical architecture of the whole-brain using subject-level clustering of functional networks in 1003 healthy subjects from the HCP, here termed Akiki-Abdallah (AA) atlas. For full details of the scans preprocessing and modularity methods, please see (Akiki and Abdallah, 2019).…”

“…Briefly, the network-restricted approach used in this study is capable of comprehensively assessing all identifiable nodes in a given brain network (e.g., DM), to extract internal connectivity strength within that network and also calculate the external connectivity between networks. To ensure reproducibility in future studies, we opted to use a consensus hierarchical modularity atlas recently established in 1,003 subjects with high-quality functional magnetic resonance imaging (fMRI) data (Akiki and Abdallah, 2019). Based on the hypothesized hyperconnectivity in DM, we investigated whether reduction in internal DM connectivity would predict better response to sertraline.…”