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Genetic sculpture of fine-grained human cortical regionalization

Yue Cui1, Bing Liu1, Yuan Zhou2 and Tianzi Jiang1*
  • 1 Institute of Automation, Chinese Academy of Sciences, Brainnetome Center, China
  • 2 Institute of Psychology, Chinese Academy of Sciences, Key Laboratory of Behavioral Science, China

Introduction Mapping fine-grained, anatomically distinct, and functionally specialized cortical subregions is fundamental for understanding brain function. Various phenotypic features such as cytoarchitecture, topographic mapping, gyral/sulcal anatomy, and anatomical and functional connectivity have been used in human brain parcellation. Evidence has suggested that these phenotypes are under genetic control (Chiang et al. 2011; Blokland et al. 2012). However, whether genetic information can feasibly be used to identify fine-grained cortical subregions and reveal the genetic basis of cortical regionalization is unknown. Given that genetic factors play an important role in the process of brain development and cortical patterning, we hypothesized that the genetic mechanisms that underlie cortical segregation may be reflected by genetic correlations. These could account for the magnitude of the genetic covariation in brain anatomy at various cortical locations and be used to delineate functional boundaries in the cortex. To test our hypothesis, we used classical twin analysis to detect genetic correlations between various locations of the cortical surface area in a noninvasive manner. Materials and Methods Participants Participants included a total of 222 healthy young Chinese same-sex twins from the Beijing Twin Study (BeTwiSt) of the Institute of Psychology, Chinese Academy of Sciences. The exclusion of an individual with incomplete scanning, an individual with excessive head motion, and their co-twins, resulted in 218 participants comprising 124 MZ (monozygotic) and 94 DZ (dizygotic) individuals (mean age, 19.0 years; range, 17-23 years; 62 male MZ, 62 female MZ, 48 male DZ, and 46 female DZ; all twins were complete twins). This study was approved by the Institutional Review Board of the Institute of Psychology of the Chinese Academy of Sciences and the Institutional Review Board of the Beijing MRI Centre for Brain Research. Image acquisition and processing Images were acquired with a 3.0 T Siemens TrioTim scanner. The cortical surface reconstruction used the publicly available FreeSurfer software package, version 5.3.0 (http://surfer.nmr.mgh.harvard.edu/). The details of the processing techniques have been described elsewhere (Dale et al. 1999; Fischl et al. 1999). Vertex-wise estimates of the surface area were calculated by assigning one-third of the area of each triangle to each of its vertices. We used 2819-iteration nearest-neighbour averaging to smooth the vertex-wise maps as previously investigated by Chen and colleagues (Chen et al. 2012). Twin analysis We used a bivariate model which can explain the sources of genetic and environmental covariance. Specifically, in addition to examining the genetic and environmental influences on the surface area at each vertex in the seed region, the bivariate correlated-factors model allows for estimates of the genetic (rg) and environmental (re) correlations between the surface areas at every pair of vertices. The analyses were performed using the OpenMx package. Before the model fitting, the vertex-wise surface area data were adjusted for age, sex and global effects. Genetic correlation-based parcellation A genetic correlation map was generated by pairwise correlations between the vertices within the seed regions. After obtaining a genetic correlation map, which consisted of the rg between the surface area measures of each pair of vertices, a spectral clustering algorithm was used for automatic clustering. Spectral clustering is an unsupervised machine learning algorithm that groups vertices which share similar genetic correlation profiles. Results Parcellation of the SMFC, FP, IFG and M1 Our genetically-based parcellation was performed on representative cortical regions with evolutionary and functional diversity: the superior medial frontal cortex (SMFC), frontal pole (FP), inferior frontal gyrus (IFG) and primary motor cortex (M1). In agreement with existing structural- and functional-based parcellations, we found that the genetic clustering of the SMFC showed anterior and posterior clusters that correspond to the pre-supplementary motor area (pre-SMA) and the SMA (Figure 1A). The genetic architecture of the SMA and pre-SMA is in line with cytoarchitectonic (Zilles et al. 1996), anatomical (Johansen-Berg et al. 2004), and functional (Kim et al. 2010) connectivity-based parcellations. In the case of the FP, we identified three separable subregions, FPo, FPm, and FPl, from the regional maps of the bilateral FP (Figure 1B) using the genetic correlations within the FP. The left and right FP subregions presented similar patterns, which were consistent with the maximum probability maps provided by connectivity-based parcellation with diffusion tensor imaging (Liu et al. 2013). We also used a parcellation number of three in order to test the resemblance to the cytoarchitectonic division. The IFG can be divided into Brodmann’s areas (BA) 44 and 45 and the frontal operculum (FOP) in a three-cluster solution (Figure 1C). Again, this finding was largely in accordance with classical cytoarchitectonics, with a boundary that aligned with the diagonal sulcus (Nishitani et al. 2005). BA44 was subdivided into dorsal and ventral areas, 44d and 44v, and BA45 was subdivided into anterior and posterior areas, 45a and 45p, in the five-cluster solution, which corresponds to the subdivisions identified using transmitter receptor distribution data (Amunts et al. 2010). M1 was able to be subdivided into six subregions, five of which corresponded to motor representations of body parts: the face, hand and arm, trunk, hip, and leg and foot (from ventrolateral to dorsomedial). The single remaining subregion in the anterior medial part of M1 was the SMA (Figure 1D). This is in line with widely recognized topographic organization. Conclusions To the best of our knowledge, this is the first study to parcellate fine-scale, functionally distinct subregions noninvasively based on intrinsic genetic information obtained by twin analysis. Our findings suggest that genetic correlations are generally interpretable by existing phenotypic-based approaches, thereby having the potential to unravel population-based fundamental patterns of the cortex and of inter-regional connectivity. The present study is important for understanding the genetic basis of cortical regionalization and provides guidance and validation for the delineation of the next generation human brain atlas.

Figure 1
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Acknowledgements

This work was supported in part by the National Key Basic Research and Development Program (973) (Grant 2011CB707800), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDB02030300), the Natural Science Foundation of China (Grants 91132301; 61305143; 31170976; 31300843), Youth Innovation Promotion Association, Chinese Academy of Science.

References

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Keywords: cortical regionalization, genetic correlation, Genetics, Surface area, Twins

Conference: Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015.

Presentation Type: Poster, to be considered for oral presentation

Topic: Genomics and genetics

Citation: Cui Y, Liu B, Zhou Y and Jiang T (2015). Genetic sculpture of fine-grained human cortical regionalization. Front. Neurosci. Conference Abstract: Neuroinformatics 2015. doi: 10.3389/conf.fnins.2015.91.00003

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Received: 14 May 2015; Published Online: 05 Aug 2015.

* Correspondence: Prof. Tianzi Jiang, Institute of Automation, Chinese Academy of Sciences, Brainnetome Center, Beijing, China, jiangtz@nlpr.ia.ac.cn