Multi-scale structural rich-club organization of the brain in full-term newborns: a combined DWI and fMRI study

Abstract

Objective. Our understanding of early brain development is limited due to rapid changes in white matter pathways after birth. In this study, we introduced a multi-scale cross-modal approach to investigate the rich club (RC) organization and topology of the structural brain networks in 40 healthy neonates using diffusion-weighted imaging and resting-state fMRI data. Approach. A group independent component analysis was first performed to identify eight resting state networks (RSNs) used as functional modules. A groupwise whole-brain functional parcellation was also performed at five scales comprising 100–900 parcels. The distribution of RC nodes was then investigated within and between the RSNs. We further assessed the distribution of short and long-range RC, feeder and local connections across different parcellation scales. Main results. Sharing the scale-free characteristic of small-worldness, the neonatal structural brain networks exhibited an RC organization at different nodal scales (NSs). The subcortical, sensory-motor and default mode networks were found to be strongly involved in the RC organization of the structural brain networks, especially in the zones where the RSNs overlapped, with an average cross-scale proportion of 45.9%, 28.5% and 10.5%, respectively. A large proportion of the connector hubs were found to be RC members for the coarsest (73%) to finest (92%) NSs. Our results revealed a prominent involvement of cortico-subcortical and cortico-cerebellar white matter pathways in the RC organization of the neonatal brain. Regardless of the NS, the majority (more than 65.2%) of the inter-RSN connections were long distance RC or feeder with an average physical connection of 105.5 and 97.4 mm, respectively. Several key RC regions were identified, including the insula and cingulate gyri, middle and superior temporal gyri, hippocampus and parahippocampus, fusiform gyrus, precuneus, superior frontal and precentral gyri, calcarine fissure and lingual gyrus. Significance. Our results emphasize the importance of the multi-scale connectivity analysis in assessing the cross-scale reproducibility of the connectivity results concerning the global and local topological properties of the brain networks. Our findings may improve our understanding of the early brain development.

Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study

“…We further performed the rich-club analysis to explore differences in the hierarchical organization of the functional and structural connectome between different age deciles. Our results revealed a rich-club organization in both the structural and functional brain networks in each age group, characterized by > 1 (p < 0.005, 10,000 permutations), with a range of k values falling within [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] ,…”

“…The sample size can critically reduce the statistical power, especially in aging studies dealing with a wide range of ages. In addition, the parcellation scale and scheme should ensure functional homogeneity of brain parcels and spatial continuity [37] as well as the reproducibility and stability of network topological properties [21] , [38] , [54] , [60] , [114] . To overcome these limitations, we used a large sample size (n = 619) uniformly distributed across different age deciles.…”

“…Therefore, it is important to use a functional parcellation scheme to delineate functionally homogenous parcels using a high-resolution parcellation scheme required to split small brain regions like the thalamus comprising many functionally heterogeneous sub-nuclei [50] or cerebellum, within which a structural parcellation based on anatomical landmarks is less efficient [24] . Moreover, a high spatial resolution is shown to be more representative of the brain and essential to ensure the reproducibility and stability of network topological properties [38] , [54] , [60] , [114] .…”

“…Finally, high-ranked rich-club AAL regions in the structural and functional brain networks were identified across different age deciles using weighted rich-club scores computed for all AAL regions in the MCALT-ADIR122. For this purpose, a normalized rich club score was first calculated for each AAL region [100] based on its overlap with all rich club regions [38] for each age group. The average rich-club score was then computed over the seven age deciles.…”

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study

“…To compare the rich-club spatial distribution of the brain networks computed for the control and patient groups, we selected the k level in a way that had 40% of each network’s nodes ranked as rich clubs. In each network, connections were also classified into rich-club connections linking rich-club nodes, feeder connections linking rich-club to non-rich-club nodes and local connections linking non-rich-club nodes [ 26 , 53 ].…”