Mapping putative hubs in human, chimpanzee and rhesus macaque connectomes via diffusion tractography - PubMed (original) (raw)

Comparative Study

Mapping putative hubs in human, chimpanzee and rhesus macaque connectomes via diffusion tractography

Longchuan Li et al. Neuroimage. 2013.

Abstract

Mapping anatomical brain networks with graph-theoretic analysis of diffusion tractography has recently gained popularity, because of its presumed value in understanding brain function. However, this approach has seldom been used to compare brain connectomes across species, which may provide insights into brain evolution. Here, we employed a data-driven approach to compare interregional brain connections across three primate species: 1) the intensively studied rhesus macaque, 2) our closest living primate relative, the chimpanzee, and 3) humans. Specifically, we first used random parcellations and surface-based probabilistic diffusion tractography to derive the brain networks of the three species under various network densities and resolutions. We then compared the characteristics of the networks using graph-theoretic measures. In rhesus macaques, our tractography-defined hubs showed reasonable overlap with hubs previously identified using anterograde and retrograde tracer data. Across all three species, hubs were largely symmetric in the two hemispheres and were consistently identified in medial parietal, insular, retrosplenial cingulate and ventrolateral prefrontal cortices, suggesting a conserved structural architecture within these regions. However, species differences were observed in the inferior parietal cortex, polar and medial prefrontal cortices. The potential significance of these interspecies differences is discussed.

Keywords: Brain networks; Chimpanzee; Evolution; Graph theory; Human; Macaque; Parietal cortex; Prefrontal cortex; Random parcellation; Tracer.

Copyright © 2013 Elsevier Inc. All rights reserved.

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Figures

Fig. 1

Fig. 1

The schematic of random parcellation of world population density map. (a) Original world population density map with well-defined boundaries between countries. (b) Random square masks were used to parcellate the map and the value in each mask is the mean of population density covered by the mask. (c) Fifty random parcellation schemes were generated and the results were averaged. It can be seen that a great deal of world population density was revealed with the random parcellation method (e.g., the countries with highest population densities could be easily identified in b, with even more details in c) even though no prior knowledge on the boundaries of the countries is available. Extremely high network resolutions were avoided in our analyses due to the large inter-subject misregistration of parcels under the high network resolutions.

Fig. 2

Fig. 2

Flowchart of deriving whole-brain anatomical brain networks. The procedures could be divided into three steps: First, a surface template was generated on a sphere space and randomly parcellated into 300 and 600 parcels with the medial wall excluded. The parcellation schemes were transformed to the template original surface (1a). Each subject’s original WM/GM boundary surface was constructed based on the T1-weighted images using the FreeSurfer (1b). Then the parcellation schemes were transformed to each subject’s surface space (1c). In the second step, local diffusion orientation distribution functions in each voxel of subject’s diffusion data were estimated using FDT toolbox (2). Then the connections between each cortical parcel pairs were reconstructed by sending millions of samples from vertices on the WM/GM interface surface and the number of the “probabilistic streamlines” sent from every vertex and reached every other vertices were counted and summed over parcels for graph-theoretic analyses (3).

Fig. 3

Fig. 3

Comparison of tracer- and tractography- derived brain networks on monkey’s inflated surfaces. (a) Centrality hubs identified using tracer-derived brain networks (adapted from Harriger et al. and mapped to our macaque surface template). The hubs were identified based on the regional scores for node betweenness centrality, closeness centrality, vulnerability and dynamical importance. Different colors indicate the number of the four centrality measures being placed in the top 10%; (b) Centrality hubs in the tractography-derived brain networks. Hubs were first identified under each network resolution (N=300,600) and threshold (10%,15%,20% 25%,30%) and then binarized and overlapped for a probability map (see methodology for details). To easily interpret the results, we did not differentiate brain regions that were ranked as hubs (i.e., at least two of four centrality measures were placed in the top 10%) and treated them equally. As a result, different colors in (b) represent how frequent the area was identified as a hub across network densities and resolutions. (c) The overlapped brain regions that have been identified as hubs across the two methodologies. It can be seen that the overlapped hubs are mainly located at the PFCvl, PFCm, PCi, PCm and the left ventrolateral premotor cortex.

Fig. 4

Fig. 4

Betweenness centrality of the brain networks from macaques, chimpanzees and humans at the network density of 10% and resolution of 300. For each species, the left two columns represent the lateral and medial views of the left hemisphere and the right two represent the right hemisphere. For all three species, the color scale was set identically.

Fig. 5

Fig. 5

The probability map of the putative hubs in macaques (upper two rows), chimpanzees (middle two rows) and humans (bottom two rows). For each species, the left two columns showed the lateral, medial, dorsal and ventral views of the left hemisphere and the right two columns showed the corresponding views for the right hemisphere. The cortical parcels with at least two out of four centrality measures placed in the top 10% were first identified under five network densities (10%, 15%,20%,25%,30%) and two resolutions (N=300,600) respectively. Then these hubs that were identified under a specific network density and resolution were binarized and averaged for a probability map. Brain regions with high intensities in the resulting map are frequently identified as hubs. Labels for humans and macaques were based on the nomenclature proposed by Kotter and Wanke (2005) and areas for chimpanzees were labeled based on their putative homologues in humans and macaques (Kotter and Wanke, 2005). Abbreviations: CCp, posterior cingulate cortex; CCr, retrosplenial cingulate cortex; Ia, anterior insular; Ip, posterior insula; M1, primary motor cortex; PCi, inferior parietal cortex; PCip, cortex of the intraparietal sulcus; PCm, medial parietal cortex; PCs, superior parietal cortex; PFCm, medial prefrontal cortex; PFCoi, intermediate orbital prefrontal cortex; PFCol, orbitolateral prefrontal cortex; PFCom, orbitomedial prefrontal cortex; PFCpol, polar prefrontal cortex; PFCvl, ventrolateral prefrontal cortex; PMCm, medial (supplementary) premotor cortex; V1, primary visual cortex; V2, secondary visual cortex;

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