The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain.
The brain exhibits asymmetry in both macroscopic structure and microscopic cytoarchitecture. Moreover, many studies have revealed the anatomical asymmetry corresponding with functional lateralization [
These previous studies examined the structural or functional asymmetries of some specific brain regions or the anatomical connections between them. The asymmetries of the gray matter or white matter were analyzed from the local regional attributes. Recently, network model was proposed as a useful tool for investigating the structural organization and functional mechanisms of the human brain [
In this study, we first constructed the anatomical network for each subject by deterministic diffusion tensor tractography (DTT) technique, and then we applied graph theory approaches to examine the topological properties of bilateral brain regions of the network. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries.
This study included 72 healthy adult subjects (42 males; mean age
DTI was performed with a 3T Siemens Trio MR system using a standard head coil. Head motion was minimized with restraining foam pads provided by the manufacturer. Diffusion-weighted images were acquired employing a single-shot echo planar imaging (EPI) sequence in alignment with the anterior-posterior commissural plane. Integral Parallel Acquisition Technique (iPAT) was used with an acceleration factor of 2. Acquisition time and image distortion from susceptibility artifacts can be reduced by the iPAT method. The diffusion sensitizing gradients were applied along 12 nonlinear directions (
Eddy current distortions and motion artifacts in the DTI dataset were corrected by applying affine alignment of each diffusion-weighted image to the
We constructed the anatomical network for each subject based on the fiber connectivity from deterministic DTT. The main procedures are as follows: First, the brain was automatically segmented into 90 cortical and subcortical regions (45 for each hemisphere; see Table
Cortical and subcortical regions of interest defined in the study.
Index | Regions | Abbr. |
---|---|---|
(1, 2) | Precentral gyrus | PreCG |
(3, 4) | Superior frontal gyrus, dorsolateral | SFGdor |
(5, 6) | Superior frontal gyrus, orbital part | ORBsup |
(7, 8) | Middle frontal gyrus | MFG |
(9, 10) | Middle frontal gyrus, orbital part | ORBmid |
(11, 12) | Inferior frontal gyrus, opercular part | IFGoperc |
(13, 14) | Inferior frontal gyrus, triangular part | IFGtriang |
(15, 16) | Inferior frontal gyrus, orbital part | ORBinf |
(17, 18) | Rolandic operculum | ROL |
(19, 20) | Supplementary motor area | SMA |
(21, 22) | Olfactory cortex | OLF |
(23, 24) | Superior frontal gyrus, medial | SFGmed |
(25, 26) | Superior frontal gyrus, medial orbital | ORBsupmed |
(27, 28) | Gyrus rectus | REC |
(29, 30) | Insula | INS |
(31, 32) | Anterior cingulate and paracingulate gyri | ACG |
(33, 34) | Median cingulate and paracingulate gyri | DCG |
(35, 36) | Posterior cingulate gyrus | PCG |
(37, 38) | Hippocampus | HIP |
(39, 40) | Parahippocampal gyrus | PHG |
(41, 42) | Amygdala | AMYG |
(43, 44) | Calcarine fissure and surrounding cortex | CAL |
(45, 46) | Cuneus | CUN |
(47, 48) | Lingual gyrus | LING |
(49, 50) | Superior occipital gyrus | SOG |
(51, 52) | Middle occipital gyrus | MOG |
(53, 54) | Inferior occipital gyrus | IOG |
(55, 56) | Fusiform gyrus | FFG |
(57, 58) | Postcentral gyrus | PoCG |
(59, 60) | Superior parietal gyrus | SPG |
(61, 62) | Inferior parietal but supramarginal and angular gyri | IPL |
(63, 64) | Supramarginal gyrus | SMG |
(65, 66) | Angular gyrus | ANG |
(67, 68) | Precuneus | PCUN |
(69, 70) | Paracentral lobule | PCL |
(71, 72) | Caudate nucleus | CAU |
(73, 74) | Lenticular nucleus, putamen | PUT |
(75, 76) | Lenticular nucleus, pallidum | PAL |
(77, 78) | Thalamus | THA |
(79, 80) | Heschl gyrus | HES |
(81, 82) | Superior temporal gyrus | STG |
(83, 84) | Temporal pole: superior temporal gyrus | TPOsup |
(85, 86) | Middle temporal gyrus | MTG |
(87, 88) | Temporal pole: middle temporal gyrus | TPOmid |
(89, 90) | Inferior temporal gyrus | ITG |
Note: the regions are listed in terms of a prior template of an AAL atlas [
We investigated the topological properties of the anatomical network at regional (nodal) levels. Regional properties were described in terms of degree (
The degree
The mean shortest path length
Betweenness centrality is widely used to identify the most central nodes in a network, which are associated with those nodes that act as bridges between the other nodes. The betweenness
To further investigate the local asymmetries of the structural connections, we then reconstructed several major white matter tracts connecting different brain regions. Based on the anatomical knowledge of fiber projections, several studies have suggested the tracking protocols for the major white matter tracts [
To analyze hemispheric differences in topological properties for brain regions, we computed the laterality ratio
Based on the binary anatomical network constructed for each subject, we calculated the topological properties (
Cortical regions with hemispheric asymmetry in node properties. (a) Bars represent the mean values of the nodal property of brain regions with significantly hemispheric asymmetry (
For each subject, we can successfully reconstruct most of the bilateral white matter tracts (Figure
Structural asymmetries of major white matter tracts. (a) Reconstructed bilateral white matter tracts: cingulum bundles (CB), optic radiation (OR), inferior frontooccipital fasciculus (IFO), inferior longitudinal fasciculus (ILF), arcuate fasciculus (AF), and uncinate fasciculus (UF) (red: left; yellow: right). (b) Between-hemisphere differences for the structural properties of the white matter tracts (
In this study, we investigated the hemispheric asymmetries of the human brain from the perspective of the cerebral anatomical network constructed from DTI data. By comparing bilateral topological properties, we revealed some brain regions with significant leftward or rightward asymmetries, which indicated the asymmetric connection patterns of these regions. Moreover, the structural properties of some local white matter tracts also exhibit hemispheric asymmetries, which indicated the asymmetries of local connections. It suggested that the structural organizations of the human brain are asymmetric from both the global and local connection patterns. Then, the functional meanings of these structural asymmetries should be discussed.
Previous studies of hemispheric asymmetries in the human brain focused on the structures or functions of some local regions [
Different nodal properties reflect different aspects of the node in the network. In this study, we chose three topological properties, degree, normalized betweenness centrality, and shortest path length, to analyze the hemispheric asymmetries of the anatomical network. Degree means the number of direct connections to the node. Larger degree means more structural connections to other brain regions in the binary anatomical network. Betweenness centrality reflects the importance of the node, and a node with high centrality is thus crucial to efficient communication [
Since the regions with hemispheric asymmetries in nodal properties were revealed, we categorized these regions by their functions as follows.
Language and auditory function: middle and inferior temporal gyrus [ Visual function: middle and inferior temporal gyrus [ Emotion, sensation, and addiction: insula [ Association cortex: paracentral lobule, precuneus, posterior cingulate gyrus, and inferior parietal gyrus.
Spatial attention: angular and supramarginal gyrus [ Face recognition: fusiform gyrus [ Emotion and memory: hippocampus and amygdala [ Association cortex: superior and middle frontal gyrus, superior parietal gyrus, and middle temporal pole.
From the results, we can see that regions with leftward asymmetries are mainly related to language, visual processing, and sensory functions. Regions with rightward asymmetries are mainly related to the functions of spatial attention, face recognition, emotion, and memory. Some regions in the association cortex with multiple functions also exhibit leftward or rightward asymmetries in the nodal properties.
Combined with the findings of some previous studies, we speculated that the topological asymmetries in the anatomical network are likely to form the structural substrate of different functional principles of information processing in the two hemispheres. Since Paul Broca’s discovery in 1861, the notion of left hemisphere specialization for language has been established [
Another finding is the rightward asymmetries of angular and supramarginal gyrus, which located in the temporoparietal junction. The right angular and supramarginal gyrus have been widely implicated in the functions of spatial attention [
Some subcortical structures in the limbic system, such as hippocampus and amygdala, were also revealed with rightward asymmetries in the topological properties. Hippocampus plays an important role in memory and spatial navigation [
Besides the above results, we revealed some regions with hemispheric asymmetries in all three topological properties, such as leftward asymmetries in three regions of posterior medial cortex (paracentral lobule, precuneus, and posterior cingulate gyrus), inferior parietal gyrus, and insula and rightward asymmetries in middle temporal pole, superior parietal gyrus, and superior frontal gyrus. Most of these regions are located in the association cortex, which plays a central role in receiving convergent inputs from multiple cortical regions [
Of note, abnormal asymmetric patterns of brain structure or function have been implicated in some psychiatric disorders, such as schizophrenia, and the extent of altered asymmetry is related to the symptoms of the patients [
Based on the tractography results of six major white matter tracts, we analyzed the structural asymmetries of these tracts in mean FA values and fiber numbers. Previous DTI studies have identified the anatomical asymmetries of some fiber tracts, such as leftward asymmetries of the arcuate fasciculus [
As both the global (structural connectome) and local (FA) measures were investigated in the present study, some results can be cross-validated by different measures. For example, we found that both the language related regions and WM tracts exhibited significantly leftward asymmetries. However, global and local measures may represent different physiological meanings. The local measure, such as FA, reflects the white matter integrity or the consistency of fiber orientation at microstructural level, while the nodal properties of brain connectome, such as nodal efficiency, are an integrated metric of global information flow capacity and related to all of the nodal connections, which consist of a specific tract or several tracts together. Therefore, the findings from network analysis can supply more comprehensive information than the traditional regional and local investigations from a system level.
The most essential elements of a network are the nodes and edges. The definition of the nodes and edges has a great effect on the constructed network and the analysis results. Therefore, we need to address some methodological issues about how we carried out the network construction.
First, we applied the AAL template to define the nodes for each subject’s network. The AAL template was taken from a MNI single-subject brain [
Second, we employed deterministic DTT to define the edges of the anatomical network. However, the “fiber crossing” problem is a limitation of deterministic tractography algorithms, because the tracking always stops when it reaches fiber crossing regions with low factional anisotropy values [
Another issue about the choice of a binary or weighted network needs addressing. For a weighted network, a challenge is to decide on the most representative measure of structural connectivity. Several candidate measures, such as fiber numbers, mean fiber length, fiber density, and mean fraction anisotropy, can be selected as the connectivity measure [
Besides the above methodology limitations, some other important issues should be investigated in the future. First, as the sex effects on the topological organization of brain networks have been suggested [
In this study, we have analyzed the hemispheric asymmetries from the perspective of the whole-brain anatomical network and revealed the topological asymmetries of some brain regions, which indicated the asymmetric connection patterns of these regions at the global level. Moreover, we found the structural asymmetries of some local anatomical connections between regions, and the structural asymmetries of the white matter tracts are interrelated with the topological asymmetries of the brain regions. We speculated that the asymmetric connection patterns of brain regions might reflect the functional lateralization of the human brain.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported by the 973 Program (Grant no. 2013CB837300, Ni Shu), the National Natural Science Foundation of China (Grants nos. 81471732, Ni Shu; 81101038 and 30930029, Yaou Liu), the ECTRIMS-MAGNMIS Fellowship from ECTRIMS (Yaou Liu), the Beijing Natural Science Fund (Grant no. 7133244, Yaou Liu), the Beijing Nova Program (Grant no. xx2013045, Yaou Liu), the Beijing New Medical Discipline Based Group (Grant no. 100270569, Ni Shu), and the Fundamental Research Funds for the Central Universities (Grant no. 2013YB28, Ni Shu).