The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture

Fan, Lingzhong; Li, Hai; Zhuo, Junjie; Zhang, Yu; Wang, Jiaojian; Chen, Liangfu; Yang, Zhengyi; Chu, Congying; Xie, Shaolei; Laird, Angela R.; Fox, Peter T.; Eickhoff, Simon B.; Yu, Chunshui; Jiang, Tianzi

doi:10.1093/cercor/bhw157

Cited by 2,335 publications

(2,085 citation statements)

References 101 publications

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“…The brain volume of each subject was divided into 246 nonoverlapping anatomical regions of interest (ROIs) according to the Brainnetome Atlas [23]. Thirty regions from the most ventral part of the brain not acquired during scans were discarded and are not included in the following analysis.…”

Section: Networkmentioning

confidence: 99%

A Novel Synchronization-Based Approach for Functional Connectivity Analysis

et al. 2017

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Complex network analysis has become a gold standard to investigate functional connectivity in the human brain. Popular approaches for quantifying functional coupling between fMRI time series are linear zero-lag correlation methods; however, they might reveal only partial aspects of the functional links between brain areas. In this work, we propose a novel approach for assessing functional coupling between fMRI time series and constructing functional brain networks. A phase space framework is used to map couples of signals exploiting their cross recurrence plots (CRPs) to compare the trajectories of the interacting systems. A synchronization metric is extracted from the CRP to assess the coupling behavior of the time series. Since the functional communities of a healthy population are expected to be highly consistent for the same task, we defined functional networks of taskrelated fMRI data of a cohort of healthy subjects and applied a modularity algorithm in order to determine the community structures of the networks. The within-group similarity of communities is evaluated to verify whether such new metric is robust enough against noise. The synchronization metric is also compared with Pearson's correlation coefficient and the detected communities seem to better reflect the functional brain organization during the specific task.

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Section: Networkmentioning

confidence: 99%

A Novel Synchronization-Based Approach for Functional Connectivity Analysis

et al. 2017

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“…Fan et al. (2016) designed a connectivity‐based parcellation framework that identifies the subdivisions of the entire human brain which named CC subregions but the description was a few. Glasser et al.…”

Section: Introductionmentioning

confidence: 99%

Functional and anatomical connectivity‐based parcellation of human cingulate cortex

et al. 2018

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IntroductionHuman cingulate cortex (CC) has been implicated in many functions, which is highly suggestive of the existence of functional subregions.MethodsIn this study, we used resting‐state functional magnetic resonance imaging (rs‐fMRI) and diffusion tensor imaging (DTI) to parcellate the human cingulate cortex (CC) based on resting‐state functional connectivity (rsFC) patterns and anatomical connectivity (AC) patterns, to analyze the rsFC patterns and the AC patterns of different subregions, and to recognize whether the parcellation results obtained by the two different methods were consistent.ResultsThe CC was divided into six functional subregions, including the anterior cingulate cortex, dorsal anterior midcingulate cortex, ventral anterior midcingulate cortex, posterior midcingulate cortex, dorsal posterior cingulate cortex, and ventral posterior cingulate cortex. The CC was also divided into ten anatomical subregions, termed Subregion 1 (S1) to Subregion 10 (S10). Each subregion showed specific connectivity patterns, although the functional subregions and the anatomical subregions were internally consistent.ConclusionsUsing different model MRI images, we established a parcellation scheme, which is internally consistent for the human CC, which may provide an in vivo guide for subregion‐level studies and improve our understanding of this brain area at subregional levels.

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“…Although diffusion MRI data is quite different from resting-state fMRI data, the clustering algorithms could be implemented similarly once connectivity is defined. For example, K-means (Johansen-Berg et al, 2004; Klein et al, 2007), spectral clustering (Venkataraman et al, 2009; Fan et al, 2014, 2016; Zhang et al, 2014), hierarchical clustering (Gorbach et al, 2011; Moreno-Dominguez et al, 2014), and some other clustering algorithms (Behrens et al, 2003; Jbabdi et al, 2009) are applied to perform structural connectivity-based parcellation.…”

Section: Introductionmentioning

confidence: 99%

A Supervoxel-Based Method for Groupwise Whole Brain Parcellation with Resting-State fMRI Data

Wang

2016

Front. Hum. Neurosci.

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Node definition is a very important issue in human brain network analysis and functional connectivity studies. Typically, the atlases generated from meta-analysis, random criteria, and structural criteria are utilized as nodes in related applications. However, these atlases are not originally designed for such purposes and may not be suitable. In this study, we combined normalized cut (Ncut) and a supervoxel method called simple linear iterative clustering (SLIC) to parcellate whole brain resting-state fMRI data in order to generate appropriate brain atlases. Specifically, Ncut was employed to extract features from connectivity matrices, and then SLIC was applied on the extracted features to generate parcellations. To obtain group level parcellations, two approaches named mean SLIC and two-level SLIC were proposed. The cluster number varied in a wide range in order to generate parcellations with multiple granularities. The two SLIC approaches were compared with three state-of-the-art approaches under different evaluation metrics, which include spatial contiguity, functional homogeneity, and reproducibility. Both the group-to-group reproducibility and the group-to-subject reproducibility were evaluated in our study. The experimental results showed that the proposed approaches obtained relatively good overall clustering performances in different conditions that included different weighting functions, different sparsifying schemes, and several confounding factors. Therefore, the generated atlases are appropriate to be utilized as nodes for network analysis. The generated atlases and major source codes of this study have been made publicly available at http://www.nitrc.org/projects/slic/.

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The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture

Cited by 2,335 publications

References 101 publications

A Novel Synchronization-Based Approach for Functional Connectivity Analysis

A Novel Synchronization-Based Approach for Functional Connectivity Analysis

Functional and anatomical connectivity‐based parcellation of human cingulate cortex

A Supervoxel-Based Method for Groupwise Whole Brain Parcellation with Resting-State fMRI Data

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