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Convergent Functional Architecture of the Superior Parietal Lobule Unraveled with Multimodal Neuroimaging Approaches

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Convergent Functional Architecture of the Superior Parietal Lobule Unraveled with Multimodal Neuroimaging Approaches r Human Brain Mapping 00:00–00 (2014) r Convergent Functional Architecture of the Superior Parietal Lobule Unraveled With Multimodal Neuroimaging Approaches Jiaojian Wang,1 Yong Yang,2,3 Lingzhong Fan,2,3 Jinping Xu,1 Changhai Li,1 Yong Liu,2,3 Peter T. Fox,4 Simon B. Eickhoff,5,6 Chunshui Yu,7 and Tianzi Jiang1,2,3,8* 1Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China 2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 3National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 4Research Imaging Institute, University of Texas Health Science Center at San Antonio, Texas, USA 5Institute of Neuroscience and Medicine (INM-1), Research Centre Julich,€ Germany 6Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Dusseldorf, Germany 7Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China 8The Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia r r Abstract: The superior parietal lobule (SPL) plays a pivotal role in many cognitive, perceptive, and motor-related processes. This implies that a mosaic of distinct functional and structural subregions may exist in this area. Recent studies have demonstrated that the ongoing spontaneous fluctuations in the brain at rest are highly structured and, like coactivation patterns, reflect the integration of cortical locations into long-distance networks. This suggests that the internal differentiation of a complex brain region may be revealed by interaction patterns that are reflected in different neuroimaging modalities. On the basis of this perspective, we aimed to identify a convergent functional organization of the SPL using multimodal neuroimaging approaches. The SPL was first parcellated based on its structural con- nections as well as on its resting-state connectivity and coactivation patterns. Then, post hoc functional Contract grant sponsor: National Basic Research Program of *Correspondence to: Prof. Tianzi Jiang, Brainnetome Center, Insti- China (973 program); Contract grant number: 2011CB707801; Con- tute of Automation, Chinese Academy of Sciences, Beijing 100190, tract grant sponsor: Strategic Priority Research Program of the China. E-mail: [email protected] Chinese Academy of Sciences; Contract grant number: Received for publication 24 April 2014; Revised 4 August 2014; XDB02030300; Contract grant sponsor: Natural Science Founda- Accepted 21 August 2014 tion of China; Contract grant number: 91132301; Contract grant DOI: 10.1002/hbm.22626 sponsor: Deutsche Forschungsgemeinschaft (to S.B.E.); Contract Published online 00 Month 2014 in Wiley Online Library grant numbers: EI 816/4-1 and LA 3071/3-1; Contract grant spon- (wileyonlinelibrary.com). sor: National Institute of Mental Health; Contract grant number: R01-MH074457; Contract grant sponsor: EU Human Brain Project VC 2014 Wiley Periodicals, Inc. r Wang et al. r characterizations and connectivity analyses were performed for each subregion. The three types of connectivity-based parcellations consistently identified five subregions in the SPL of each hemisphere. The two anterior subregions were found to be primarily involved in action processes and in visually guided visuomotor functions, whereas the three posterior subregions were primarily associated with visual perception, spatial cognition, reasoning, working memory, and attention. This parcellation scheme for the SPL was further supported by revealing distinct connectivity patterns for each subre- gion in all the used modalities. These results thus indicate a convergent functional architecture of the SPL that can be revealed based on different types of connectivity and is reflected by different functions and interactions. Hum Brain Mapp 00:000–000, 2014. VC 2014 Wiley Periodicals, Inc. Key words: parcellation; structural connectivity; functional connectivity; coactivation; behavioral domains analyses r r both structure and function [Kelly et al., 2012]. Therefore, INTRODUCTION whether a consistent topography of the SPL can be revealed The superior parietal lobule (SPL) plays a pivotal role using different connectivity patterns needs to be further in many sensory and cognitive processes, including explored and verified. somatosensory and visuomotor integration [Culham and The goal of this study was, thus, to identify a potentially Valyear, 2006; Iacoboni, 2006], motor learning [Weiss convergent functional architecture of the SPL on the basis et al., 2003; Wenderoth et al., 2004], spatial perception of its specific structural, resting state, and coactivation pat- [Weiss et al., 2003], mental rotation [Vingerhoets et al., terns and to characterize the cortical network and func- 2002; Wolbers et al., 2003], visuospatial attention [Corbetta tional organization of each subregion. The SPL was first et al., 1993; Corbetta et al., 1995], and memory [Lacquaniti parcellated into component subregions based on differen- ces in structural as well as in resting-state and task- et al., 1997; Zago and Tzourio-Mazoyer, 2002]. The func- dependent (coactivation) FC patterns. Then, the whole- tional diversity of the SPL implies that a mosaic of subre- brain connectivity patterns of each subregion were gions, which might be both functionally and structurally mapped by each modality and the revealed networks were distinct, may exist in this area. Although recent studies compared between modalities. Finally, using behavioral have parcellated the SPL into different component subre- domain and paradigm analyses, a functional characteriza- gions on the basis of receptor distribution patterns [Sche- tion was performed to determine the functions associated perjans et al., 2005a; Scheperjans et al., 2005b], regional with each subregion. cytoarchitectonic properties [Scheperjans et al., 2008a; Sche- perjans et al., 2008b], and anatomical connectivity patterns [Mars et al., 2011], the association between these regions and particular functions and networks has, however, MATERIALS AND METHODS remained a largely open question. Furthermore, although Definition of Regions of Interest various parcellation schemes have been proposed, the rela- tionships between the structural connections, resting state, Both the left and right SPL masks were defined using and task-dependent functional connectivity (FC) patterns of the automated anatomical labeling template which was the SPL or its subregions are still unknown. obtained from the brain of a single subject in Montreal Relating structural connections to functional activity is Neurological Institute (MNI) space [Tzourio-Mazoyer fundamental for understanding the mechanisms of informa- et al., 2002]. In this template, the superior parietal cortex is tion processing in the human brain. Emerging evidence divided into two subareas. The lateral superior parietal suggests that ongoing brain spontaneous fluctuations are cortex is defined as the SPL, whereas the medial superior highly structured [Damoiseaux and Greicius, 2009; Deco parietal cortex is defined as the precuneus. Based on this et al., 2013] and are sculpted by the history of task-evoked template, we extracted the lateral SPLs as the regions of coactivation [Deco and Corbetta, 2011; Lewis et al., 2009; interest in this study. Then, the seed masks in MNI space Shirer et al., 2012]. Furthermore, correspondence, as well as were transformed into each participant’s individual diffu- divergence, between structural connectivity and resting- sion space using statistical parametric mapping (SPM8) state and task-dependent FC has been demonstrated by software for fiber tracking. In addition, the SPL seed many previous studies [Eickhoff et al., 2010; Greicius et al., masks were resampled into 3 mm cubic voxels in MNI 2009; Hagmann et al., 2008; Honey et al., 2009; Jakobs et al., space for resting-state FC analyses. Finally, the SPL seed 2012; Smith et al., 2009; Toro et al., 2008]. The convergence masks were also resampled into 2 mm cubic voxels in of structural connections, intrinsic FC, and coactivation sug- MNI space for whole-brain coactivation map calculations gests that a fundamental functional architecture governs for each voxel in the SPL. r 2 r r Parcellation of Superior Parietal Lobule r Subjects ume to provide for head motion correction, (3) normaliz- ing the mean-based intensity and spatial smoothing with Twenty healthy, right-handed subjects (10 males and 10 a Gaussian kernel of 6-mm full-width at half maximum, females, mean age 5 18.5 years, standard deviation 5 0.76) (4) removing linear and quadratic trends and temporal were recruited via advertisement. None of the participants band-pass filtering (0.01–0.08Hz), (5) regressing out nui- had ever suffered from any psychiatric or neurological dis- sance signals such as those from white matter and cere- ease, and none had any contraindications for MRI scan- brospinal fluid as well as global signals and six motion ning. All the subjects signed an informed consent form parameters, and (6) resampling the functional data into approved by the local Research Ethics Committee of the MNI space using the concatenated transformations. In the University of Electronic Science and Technology of China. end, this preprocessing procedure provided a four- dimensional time series in standard MNI space for each MRI Data Acquisition participant. All the subjects
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