清华大学：A Heterogeneous Accelerator Platform for Multi-subject Voxel-based Brain Network Analysis（PPT讲稿）

Department of Electronic Engineering Tsinghua University A Heterogeneous Accelerator Platform for Multi-subject Voxel-based Brain Network Analysis Yu WANG, Mo XU, Ling REN, Xiaorui ZHANG, Di WU, Yong HE, Ningyi XU, Huazhong YANG Joint work by tsinghua Univ, beijing normal University, and microsoft NIES Nano-scale Integrated Circuit and System Lab

Department of Electronic Engineering, Tsinghua University Nano-scale Integrated Circuit and System Lab. A Heterogeneous Accelerator Platform for Multi-subject Voxel-based Brain Network Analysis Yu WANG, Mo XU, Ling REN, Xiaorui ZHANG, Di WU, Yong HE, Ningyi XU, Huazhong YANG Joint work by Tsinghua Univ., Beijing Normal University, and Microsoft 1

Outline Background and motivation a What is the brain network Platform and algorithm Why and how we design accelerators Results Conclusion and future work a What we can do next

Outline ➢ Background and Motivation ❑ What is the brain network ➢ Platform and Algorithm ❑ Why and how we design accelerators ➢ Results ➢ Conclusion and future work ❑ What we can do next 2

Understanding the brain One of the greatest scientific challenges of 21st century Human Genome Project(HGP 1990-2003) a NIH Human Connectome Project http://humanconnectome.org/ThenIHHumanConnectomeProjectwowmc Human Connectome Project Connectome Mupping structural and functional conection in the hunan brain Human Connectome Mapping structural and functional connectivity in the human brain 5 years, $30 million, 2 consortiums, 4+ universities /hospitals for the basic analysis method and acquiring data Washington University in Saint Louis- Univeraity ofMinneswta

Understanding the Brain ➢ One of the greatest scientific challenges of 21st century ❑ NIH Human Connectome Project 3 http://humanconnectome.org/ Human Connectome: Mapping structural and functional connectivity in the human brain 5 years, $30 million, 2 consortiums, 4+ universities/hospitals, for the basic analysis method and acquiring data Human Genome Project (HGP 1990-2003)

What are brain networks? What is a network? Nodes and connections are two basic elements of a network A network (graph) What are the nodes and connections of brain networks and how do we define them? How many types of brain network s are there according to scale, physiology, and anatomy

What are brain networks? ◼ What is a network? ➔ Nodes and connections are two basic elements of a network. ◼ What are the nodes and connections of brain networks and how do we define them? ◼ How many types of brain network s are there according to scale, physiology, and anatomy A network (graph)

Scales and levels of brain networks Basic structure of brain networks(node and connection can be defined at different scales Microscale: neurons and Mesoscale: connections Macroscale: anatomically their synaptic connections within and between distinct brain regions and (about 1010 neurons in the minicolumns(about 2x108 inter-regional pathways cortey ) inicolumn in the cortex about 100 regions in the Voxel based Brain network Analysis Basic elements can be derived from Medical Imaging echniques Scale Neurons Columns 10K-100K Sporns et al(2005)PLoS Comput Biol

Scales and levels of brain networks ◼ Basic structure of brain networks (node and connection) can be defined at different scales. Sporns et al (2005) PLoS Comput Biol Macroscale: anatomically distinct brain regions and inter-regional pathways (about 100 regions in the cortex). Columns Regions Mesoscale: connections within and between minicolumns (about 2×108 minicolumn in the cortex ). Neurons Microscale: neurons and their synaptic connections (about 1010 neurons in the cortex). Voxel based Brain network Analysis Basic elements can be derived from Medical Imaging Techniques Scale: 10K-100K

Types from physiology and anatomy Basic types of brain networks can be described in terms of physiology and anatomy a Functional brain networks: Functional connectivity: temporal correlation between spatially remote neurophysiological events(Friston, Hum Brain Mapp 2004) Effective connectivity: causal effects of one neural system over another(Friston, Hum Brain Mapp 2004) a Structural brain networks: Structural connectivity: physical or structural (synaptic)connections linking neuronal units(sporns et al., Trends Cogn Sci 2004) I Morphometric connectivity: statistical interdependencies of morphological features between different brain regions such as the cortical thickness, gray matter volumes, density, areas and complexity(He et al., Neuroscientist, 2009) 6

Types from physiology and anatomy ➢ Basic types of brain networks can be described in terms of physiology and anatomy. ❑ Functional brain networks: • Functional connectivity: temporal correlation between spatially remote neurophysiological events (Friston, Hum Brain Mapp 2004). • Effective connectivity: causal effects of one neural system over another (Friston, Hum Brain Mapp 2004). ❑ Structural brain networks: • Structural connectivity: physical or structural (synaptic) connections linking neuronal units (Sporns et al., Trends Cogn Sci 2004). • Morphometric connectivity: statistical interdependencies of morphological features between different brain regions such as the cortical thickness, gray matter volumes, density, areas and complexity (He et al., Neuroscientist, 2009). 6

Brain Network Analysis(BNA) Non-invasive technique > Imaging techniques Graph theory Medical Imaging o functional MRi diffusion tensor mri, structural mri Reveal the properties of the brain a Small world, Scale free [Heuvel 20081 口 Efficiency a Modular structure valencia 2009 Understand the mechanism of brain diseases o Alzheimer's disease [he 2008; Supekar 2008 Lo 2010 a Schizophrenia [Bassett 2008; Zalskey 2010; Liu 2008 a Depression [Zhang 2011]

Brain Network Analysis (BNA) ➢ Imaging techniques + Graph theory ❑ functional MRI, diffusion tensor MRI, structural MRI, … ➢ Reveal the properties of the brain ❑ Small world, Scale free [Heuvel 2008] ❑ Efficiency ❑ Modular structure [Valencia 2009] ❑ … ➢ Understand the mechanism of brain diseases ❑ Alzheimer’s disease [He 2008; Supekar 2008; Lo 2010] ❑ Schizophrenia [Bassett 2008; Zalskey 2010; Liu 2008] ❑ Depression [Zhang 2011] ❑ … 7 Non-invasive technique: Medical Imaging

Challenge 1: Voxel-based BNA Utilize the high resolution of imaging techniques a Compared with region-based BNA 口2mm*2mm*2mm( each pixel)国国圆图图区 图图图国 口10k~100 k voxels 0o0Y 100 Regions 100 100K Vo×els 100K

10 20 30 40 50 60 5 10 15 20 25 30 35 40 45 50 Challenge 1: Voxel-based BNA ➢ Utilize the high resolution of imaging techniques ❑ Compared with region-based BNA ❑ 2mm * 2mm * 2mm (each pixel) ❑ 10k ~ 100k voxels 8 Regions 100 Regions 100 Voxels Voxels 100K 100K

Challenge 2: Multi/Many Subjects Huge computation, 2 days/ subject o 0(n,o(n)complexity Big-0 Complexity 口 Large n a Many subjects Low Signal-to-Noise Ratio [Benjamini 2006 a Solution: Take account networks from many subjects o But, Network construction is time-consuming 9

Challenge 2: Multi/Many Subjects ➢ Huge computation, 2 days / subject ❑ 𝑂 𝑛 2 , 𝑂 𝑛 3 complexity ❑ Large n ❑ Many subjects ➢ Low Signal-to-Noise Ratio [Benjamini 2006] ❑ Solution: Take account networks from many subjects ❑ But, Network construction is time-consuming 9

What we need Computing platforms and techniques that should be 口 Efficient Huge computation a Scalable Increasing network size a Affordable(infrastructure and power) Can be used in hospitals 10

What we need ➢ Computing platforms and techniques that should be ❑ Efficient • Huge computation ❑ Scalable • Increasing network size ❑ Affordable (infrastructure and power) • Can be used in hospitals 10