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Human Brainnetome Atlas and its Applications (BNA) in MICCAI 2019
The Human Brainnetome Atlas and its Applications (BNA) in MICCAI 2019
Abstract:
Brain atlases play a central role in neuroscientific and clinical practice, and are a prerequisite for studying brain networks across scales. Many current human brain atlases only cover specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. The human Brainnetome Atlas (BNA) was built upon a connectivity-based parcellation framework, derived from non-invasive multimodal neuroimaging techniques. It can provide a new framework for human brain research, whcih should be regarded as an important step for creating more fine-grained atlases and it opens a new avenue for understanding brain function and dysfunction. To facilitate future investigations in healthy and pathological states. The tutorial on human Brainnetome atlas and its applications is specifically designed for the MICCAI conference. It is designed to develop participants’ understanding of:
1. the history, progress and future opportunities of human Brainnetome atlas,
2. the framework for connectivity-based brain parcellation,
3. the software and interactive website for Brainnetome atlas,
4. and how to process resting state fMRI and diffusion MRI data using Brainnetome Toolkits, including Brainnetome fMRI toolkit (BRANT) and DiffusionKit together with the Brainnetome atlas in different types of applications?
5. and how to apply these tools to his/her own data to address new questions, and how to interpret the outcomes of these analyses as well as how to draw reliable conclusion?
1. ORGANIZING TEAM
Tianzi Jiang, Lingzhong Fan, Yong Liu, Nianming Zuo, Zhengyi Yang, Brainnetome Center, Institute of Automation, Chinese Academy of Sciences (CASIA)
2. TUTORIAL DESCRIPTION
The human brain contains hundreds of anatomically and functionally distinct cortical and subcortical structures, accurate definition of brain parcellations and mapping their functions and connections are so challenging [1, 2]. A reliable atlas has become essential for quantitatively defining the spatial and functional characteristics of the human brain with the accelerating pace of research in neuroscience. Such an atlas could not only offer a powerful framework to synthesize the results of different imaging studies, but also make it possible to do brain network analyses in an informed way rather than arbitrarily dividing the brain[3]. However, despite the extensive applications of the existing brain atlases in neuroscience researches, many issues still existed because of their roughness, lacking of correspondence, little sub-regional information and variable relations between functional borders and macroscopical landmarks. Hence no reliable human atlas with detailed parcellations of the entire brain is provided yet, and neither is the brain regional and/or subregional connection patterns.
Mapping the structural and functional connectivity of the human brain via non-invasive imaging technologies such as diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) offers new insights into functional brain states emerging from their underlying structural substrate. The localized areas with different functions tend to be connected differently to other brain areas. In other words, disparate brain regions maintain their own connectivity profiles. Passingham et al. have shown that each cortical area has a unique pattern of inputs and outputs (a ‘connectional fingerprint’), and argued that this is a major determinant of the function of that area[4]. And furthermore, cortical regions, which may be delineated on the basis of cellular microstructure, have also been shown to differ in their connections to other brain regions. Therefore, the basic idea of connectivity-based parcellation is to suppose that all structural elements of the human brain share similar connectivity patterns.
In 2010, the Brainnetome project (www.brainnnetome.org) was launched to investigate the hierarchy in human brain from genetics and neuronal circuits to behaviors[5]. One of the key elements of this project focused on setting up and optimizing the framework for connectivity-based parcellation, and aim to produce a comparative, organic theory of the human brain atlas, i.e. Brainnetome atlas, based on structural and connectivity features. Using non-invasive multimodal neuroimaging techniques, we have designed a connectivity-based parcellation framework to build the human Brainnetome Atlas, which identifies the subdivisions of the entire human brain, revealing the in vivo connectivity profiles[6-9]. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. The human Brainnetome atlas has the following four features: (A) It establishes a fine-grained brain parcellation scheme for 210 cortical and 36 subcortical regions with a coherent pattern of anatomical connections; (B) It supplies a detailed map of anatomical and functional connections; (C) it decodes brain functions using a meta-analytical approach; and (D) It is an open resource for researchers to use for the analysis of whole brain parcellations, connections, and functions[10].
This tutorial on human Brainnetome atlas is designed to develop participants’ understanding of:
1. the history, progress and future opportunities of human Brainnetome atlas,
2. the framework for connectivity-based brain parcellation,
3. the software and interactive website for Brainnetome atlas
4. how to process resting state fMRI and diffusion MRI data using Brainnetome Toolkits, including Brainnetome fMRI toolkit (BRANT[11], http://brant.brainnetome.org ) and Diffusion MRI toolkit (DiffusionKit[12], http://diffusion.brainnetome.org )together with the Brainnetome atlas in different types of applications?
5. how to apply these tools to his/her own data to address new questions, and how to interpret the outcomes of these analyses as well as how to draw reliable conclusion?
3. PROPOSED SPEAKERS
Tianzi Jiang
Brainnetome Center& National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)
http://www.nlpr.ia.ac.cn/jiangtz
Professor Tianzi Jiang is Director of Beijing Key Laboratory of Brainnetome and Director of the Brainnetome Center at the Institute of Automation of the Chinese Academy of Sciences (CASIA), and Chief Professor at University of the Chinese Academy of Sciences. He is also a ChangJiang Professor at University of Electronic Science and Technology of China and Professor at Queensland Brain Institute, University of Queensland, Australia. He received his BSc degree from Lanzhou University in 1984 and PhD degree from Zhejiang University in 1994. He worked as a postdoctoral research fellow (1994-1996) and an Associate Professor (1996-1999), and full professor (1999-present) at CASIA. During that time, he worked as a Vice-Chancellor's Postdoctoral Fellow at the University of New South Wales, Australia, and a visiting scientist at Max Planck Institute for Human Cognitive and Brain Sciences. His research interests include multiscale brainnetome atlas, neuroimaging, and their applications in understanding of brain functions and disorders. He was elected a Fellow of American Institute for Medical and Biological Engineering.
Lingzhong Fan
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences (CASIA)
http://www.brainnetome.org/people/faculty/LingzhongFan/
Dr. Lingzhong Fan is a professor in the Brainnetome Center, Institute of Automation, and Chinese Academy of Sciences (CASIA). He received his BSc in Clinical Medicine, from Binzhou Medical College in 2005, and M.D. from School of Medicine, Shandong University in 2010. From Feb of 2011, he did his postdoctoral work in the National Laboratory of Pattern Recognition, CASIA. Since Apr. 2013, he has worked in Brainnetome Center, CASIA. His research interests include neuroimaging, neuroanatomical variability, and image-processing methodologies for human brain atlas, and applications in brain diseases. Recently, he focused on setting up and optimizing the framework for connectivity-based parcellation, and aim to produce a new human brain atlas, i.e. Brainnetome atlas, based on structural and connectivity features.
Yong Liu
Brainnetome Center& National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)
http://www.brainnetome.org/people/faculty/YongLiu/
Dr. Yong Liu is a professor in Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, the Chinese Academy of Sciences. He obtained his MSc degree from Beijing University of Technology in 2005 and received his PhD degree from CASIA in 2008. Since June 2008, he joined CASIA as an assistant/associate/full professor. He is a visiting scholar from April 2011 to March 2012 in Brain Mapping Unit in University of Cambridge, where he worked with Professor Ed Bullmore. To date, he has authored more than 80 peer-reviewed journal articles (including Brain, Cerebral Cortex, NeuroImage, Human Brain Mapping) and has an h-index of 30. His main interests are the brain imaging and its application in cognitive disorders. Between 2008 and 2018, Yong has organized and participated in 4 tutorials and workshops at prestigious conferences in the fields of medical image processing, brain network and its application etc.
Nianming Zuo
Brainnetome Center & National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)
http://www.brainnetome.org/people/faculty/NianmingZuo/
Nianming Zuo graduated with PhD in 2007 from Institute of Automation, Chinese Academy of Sciences (CASIA). Later, he joined Eastman Kodak Medical Group Global Research Center (Shanghai) as a research scientist. From 2009, he joined CASIA as Assistant/Associate Professor. His research includes: (1) Algorithm and software development for brain network analysis, including the method how to construct the anatomical network by diffusion MRI based on the optimized scanning protocols and computing algorithms. A light (<12M) but versatile toolkit developed by his team is freely available since 2016 (J. Neurosci. Meth., 2016), http://diffusion.brainnetome.org (more than 1400 download records); (2) Investigating structural and functional measurements and network reconfiguration mechanism for intra-/inter-individuals, such as maintenance and network reconfiguration pattern, which is utilized to measure cognitive capability of individuals with different ages (e.g., development and aging) and normal/aberrant states (e.g., depressive states).
Zhengyi Yang
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences (CASIA)
Dr. Zhengyi Yang is an associate professor at the Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China. His research interests are medical image processing and analysis in general and specifically in the fields of applications of brainnetome atlas and preclinical MRI data analysis. He received his Bachelor of Engineering and Master of Science degrees in mechanical engineering from Sichuan University in 1994 and 1997, respectively. He received his PhD degree from the Mechanical Engineering Department of The University of Hong Kong in 2003. Since 2016, as a lecturer, he has participated in 5 workshops on the brainnetome atlas and its applications in different cities in China.
4. PRELIMINARY TUTORIAL SCHEDULE
i. Introduction, Tianzi Jiang (10min)
ii. Human Brainnetome Atlas and its Applications
Tianzi Jiang (45min)
Brainnetome atlas is constructed with brain connectivity profiles obtained using multimodal magnetic resonance imaging. It is in vivo, with finer-grained brain subregions, and with anatomical and functional connection profiles. In this lecture, we will summarize the advance of the human brainnetome atlas and its biological basis applications in neuroscience and brain diseases. We first present the basic ideas of the human brainnetome atlas and the procedure to construct this atlas. Then some parcellation results of the human brain areas with different types of cytoaritectures will be presented. After that, we will present the biological basis of the Brainnetome atlas from aspects of genetics, evolution, and relationships between structure and functions of the brain. We also give a brief presentation on how to use the human brainnetome atlas to address issues in neuroscience and clinical research. Finally, we will give a brief perspective on multiscale brainnetome atlas and the related neurotechniqes.
iii. Connectivity-Based Parcellation Using Multimodal Connectivity Information
Lingzhong Fan (45min)
There is a longstanding effort to parcellate brain into areas based on micro-structural, macro-structural, or connectional features, forming various brain atlases. Among them, connectivity-based parcellation gains much emphasis, especially with the considerable progress of multimodal magnetic resonance imaging in the past two decades. The framework of connectivity-based parcellation for identifying the subdivisions in the human brain with in vivo connectivity information has opened the door to neuroanatomical studies at the macro-scale brain studies. The Brainnetome Atlas is such an atlas that follows the framework of connectivity-based parcellation. We developed an integrated “Automatic Tractography-based Parcellation Pipeline (ATPP)” to realize the parcellation using automatic processing and massive parallel computing that we share with the atlas. In fact, many approaches to parcellating the brain into subregions using different connectivity features have recently become available. These include tractography-based anatomical connectivity, resting-state functional connectivity, structural covariance, and meta-analysis-based functional coactivation. Here, I will introduce connectivity-based parcellation using multimodal connectivity information, and further discuss consistency or inconsistency of the ensuing parcellations and to evaluate different brain parcellation schemes.
-------------------------------- Break (25 min) --------------------------------
iv. An Introduction on the Brainnetome Atlas Viewer
Zhengyi Yang (45 min)
The human Brainnetome Atlas is a fine-grained, cross-validated atlas consisting of 210 cortical and 36 subcortical subregions, and contains information about anatomical and functional connections, as well as a functional mapping of each subregion by using the BrainMap database. To facilitate the navigation and exploration of such an information-rich brain atlas for researchers and clinicians, we developed a Matlab based visualization software called the Brainnetome Atlas Viewer. In this part of the tutorials, we will give a brief introduction on the BAV and demonstrate its main functions, including navigating in the atlas and select subregion of interest (SOI), viewing the selected SOI and its structural and functional connectivity profiles in both 2D tri-planar view and 3D surface rendered views, display its mental process mapping results onto the behavioural domains and paradigm classes using the BrainMap database. After that, we will demonstrate how to use the Brainnetome atlas to report the results of varies studies, such as significant regions found in a group comparison and functional activity maps. Finally, we will introduce different ways to individualize the human brainnetome atlas, with a purpose of clinical applications.
v. DiffusionKit: A Light One-Stop Solution for Diffusion MRI Data Analysis,
Nianming Zuo (45min)
Diffusion magnetic resonance imaging (dMRI) techniques are receiving increasing attention due to their ability to characterize the arrangement map of white matter in vivo. However, a comprehensive and cross-platform support toolkit with a full pipeline for analyzing and visualizing diffusion MRI data is expected to substantially facilitate studies using diffusion MRI data. We developed a light, one-stop, cross-platform solution for dMRI data analysis, called DiffusionKit. It delivers a complete pipeline, including data format conversion, dMRI preprocessing, local reconstruction, white matter fiber tracking, fiber statistical analyses and various visualization schemes. Furthermore, DiffusionKit is a self-contained executable toolkit, without the need to install any other software. Additionally, it provides both a graphical user interface (GUI) and command-line functions (compatible for MS Windows and Linux), which are helpful for batch processing. The standalone installation package has a small size and cross-platform support.
vi. BRANT: A Versatile and Extendable Brainnetome fMRI Toolkit for Resting-state fMRI Data Processing and Visualization
Yong Liu (45min)
Data processing toolboxes for resting-state functional MRI (rs-fMRI) have provided us with a variety of functions and user friendly graphic user interfaces (GUIs). However, many toolboxes only cover a certain range of functions, and use exclusively designed GUIs. To facilitate data processing and alleviate the burden of manually drawing GUIs, we have developed a versatile and extendable MATLAB-based toolbox, BRANT (BRAinNetome fmri Toolkit), with a wide range of rs-fMRI data processing functions and code-generated GUIs. During the implementation, we have also empowered the toolbox with parallel computing techniques, efficient file handling methods for compressed file format, and one-line scripting. With BRANT, users can find rs-fMRI batch processing functions for preprocessing, brain spontaneous activity analysis, functional connectivity analysis, complex network analysis, statistical analysis, and results visualization, while developers can quickly publish scripts with code-generated GUIs.
vii. Closing Words, All Presenters (10min)
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