a prototype of brain network simulator for spatiotemporal dynamics of alzheimer’s disease
TRANSCRIPT
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheimer’s Disease
一個模擬阿茲海默症之時空動態的腦網路模擬器原型
Speaker : Jimmy Lu 盧松筠Advisor : Hsing Mei 梅 興
Web Computing Laboratory (WECO Lab)
Computer Science and Information Engineering Department
Fu Jen Catholic University
04/18/2023 WECO Lab http://www.weco.net 2
Outline
• Introduction• Motivation• Background and Related Work• The Brain Network Simulator
– Design Concepts and Development Approaches
• Alzheimer’s Disease– Three Different Models– The Proposed Spatiotemporal Model of Alzheimer’s Disease
• Implementation and Demo• Conclusion and Future Work
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Introduction
• It’s the Decade of Brain!• NIH Blueprint for Neuroscience Research
– Grand Challenges• the connectivity of the adult human brain• targeted therapy development for neurological diseases
• Collaborative Works In the Multi-disciplinary Research Field– Computer Science plays a key role
• Brain Network Simulator– Modeling structural and functional dynamics of the human brain– Apply to different cases (brain functions, diseases, cognition, behavior)– Keep evolving– education, research, diagnosis, personal health care, etc.
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Motivation
• Few studies by similar approach– Because the issue is extremely complex– But we’d loved to be the pioneer
• The start of the Human Connectome Project– Connection map will be the foundation of brain network simulator
• The human brain is a large network– In IT research field, we have experience on real network analysis– The experiences can be inspirations for study brain networks
• We believe simulation is the trend in the future of brain science studies
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Background and Related Work
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Background and Related Work
• Brain informatics– An emerging interdisciplinary
research field– Human Information Processing
System (HIPS)– Technology in web
intelligence, especially in deep web intelligence, such as data mining, machine learning, and social network analysis, helps studies of brain science Brain Informatics
Cognitive Science
Neuroscience
Web Intelligence
Deep Web Intelligence
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Background and Related Work
• The Human Connectome Project– Comprehensive map of neural connections in the human brain will be
the foundation of studies of brain science• The-state-of-art neuroimaging technology• Macroscopic connectomes
• Brain Networks– by Connection Type
• Anatomical connectivity• Functional connectivity• Effective connectivity
– by Functionality• Thalamocortical Motifs• Polysynaptic Loop Structure• Diffuse Ascending Projections
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Basic Brain Networks
(a) Thalamocortical Motif
(b) Polysynaptic Loop Structure (c) Diffuse Ascending Projections
GPe – External Global PallidusGPi – Internal Global PallidusSTN – Subthalamic NucleusSNc – Substantia Nigra CompactaSNr – Substantia Nigra Retuculata
DA – Dopamine5-HT – SerotoninAch – Acetylcholine
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Background and Related Work
• Complex Network Analysis– Graph theory– targets: real life network– including brain networks– structure-function mapping
• Alzheimer’s Disease– the most common dementia– unknown causes, incurable, degenerative, and terminal disease– four stages shows different patterns of impairments and symptoms on
cognitive functions– lasts a long period of time
shortest path cluster
modules
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Background and Related Work
• Current Status of Brain Simulator
IBM’s C2 Blue BrainProposed Brain
Simulator
PerspectiveNeuron-LevelMicroscopic
Neuron-LevelMicroscopic
Brain-LevelMacroscopic
Basic Component Neuron Neuron Nuclei, Region, Tracts
Connection Synapse SynapseCommunication
Pathway
Communication Electrical Signal Electrical Signal Protocol Data Unit
Architecture P2P Network layered Architecture layered Architecture
Focus Area Cortex Neocortical column Whole Brain
ComputationSupercomputer
Blue GeneSupercomputer
Blue GeneCloud Computing
Environment
Granularity Fine-grained Fine-grained Coarse-grained
Approach HardwareHardware and
SoftwareSoftware
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The Brain Network Simulator
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The Brain Network Simulator
• Design Concepts and Approaches– Architecture
• Comparison between brain networks and the Internet• Layered architecture inspired by the Internet
– Data Structure• Graph Structure: node and edge• Brain Components
– thalamus– hippocampus– acetylcholine
– Workflow– Development Approach
• Case-based incremental delivery
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The Brain Network Simulator
• Design Concepts and Approaches– Architecture
• Comparison between brain networks and the Internet• Layered architecture inspired by the Internet
– Data Structure• Graph Structure: node and edge• Brain Components
– thalamus– hippocampus– acetylcholine
– Workflow– Development Approach
• Case-based incremental delivery
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The Brain Network Simulator
• Internet vs. Brain NetworksInternet Human Brain
Scale Billions of unit elements 1011 unit elements
Layered structure OSI modelAnatomical structure, network
overlays, and functional outputs
Mechanisms of fault toleranceError correction, recomputation
of routing pathsDegeneracy mechanism,
replaceable functional areas
Properties of complex networksMotif, communities, hubs,
shortest pathway, etc.Motif, communities, hubs,
shortest pathway, etc.
Capability of an unit element Versatile Specific
Network topologyDynamic (by leaving or joining
of computers)Dynamic (by learning, aging, or developmental processes)
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Layered Architecture of Brain Simulator
Brain Connectivity Layer
Processing Layer
Application Layer(Behavior/Disease/Cognitive Functions)
Causal Layer(Overlays)
Time Scale
Polysynaptic Loops Diffuse Ascending ProjectionThalamocortical Motif
ShortTerm
LongTerm
Cognitive System
Decision Making
Resting State
Sleep Aging
Neural Darwin Selection
Network Development
Model
Network Damage Model
Brain Disease
Brain Disease Models
………
……
……
ReasoningSleep Switch
Model
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The Brain Network Simulator
• Design Concepts and Approaches– Architecture
• Comparison between brain networks and the Internet• Layered architecture inspired by the Internet
– Data Structure• Graph Structure: node and edge• Brain Components
– thalamus– hippocampus– acetylcholine
– Workflow– Development Approach
• Case-based incremental delivery
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Connections are maintained by a sparse matrix to optimize memory usage
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The Brain Network Simulator
• Design Concepts and Approaches– Architecture
• Comparison between brain networks and the Internet• Layered architecture inspired by the Internet
– Data Structure• Graph Structure: node and edge• Brain Components
– thalamus– hippocampus– acetylcholine
– Workflow– Development Approach
• Case-based incremental delivery
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A Workflow Scenario of Brain Network Simulator
Input Data
Instantiate Brain Components to Create Brain Anatomical Network
Signal Filtering, Image Normalization, Transformation, etc.
Data Preprocessing
Extract Required Information
3D Brain Network Rendering
time
Apply Theoretical Model for Simulation Network Analysis
Research or experiment results
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The Brain Network Simulator
• Design Concepts and Approaches– Architecture
• Comparison between brain networks and the Internet• Layered architecture inspired by the Internet
– Data Structure• Graph Structure: node and edge• Brain Components
– thalamus– hippocampus– acetylcholine
– Workflow– Development Approach
• Case-based incremental delivery
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Case-based Incremental Delivery
Case Study and Analysis
Layered Architecture Extending and Refactoring
Brain Components Extending and Refactoring
Build Theoretical Models
Evaluate Theoretical Models
Existing C
ases
Evolved Brain Simulator
Feedback
Cases Integration
Research or Experiment
Results
Personalized Medical data
New Cases
Model Pool
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Spatiotemporal dynamics of Alzheimer’s Disease
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Spatiotemporal dynamics of Alzheimer’s Disease
• Three different models– Neuropathological stageing of Alzheimer-related changes
• Describe global pattern of lesions caused by Alzheimer’s disease• Lesions: distribution of amyloid and neurofibrillary changes
– Network Damage Model• Intentional attack on the node with highest degree• Observed in the brain affected by Alzheimer’s disease• Focus on fragments after attack
– Treatment• Based on cholingeric hypothesis• Needs to find out the cholingeric pathways
• A spatiotemporal model of Alzheimer’s Disease– A combination of three with temporal parameter added in
SIMULATE-ALZHEIMER’S-DISEASE(time t, network $s)1 while time(t) < tend
2 affectedRegions[] GLOBAL-PATTERN-OF-LESIONS(t)4 for each region r affectedRegions[]5 do targetNodes[] CHOOSE-TARGET-NODES(t, r)6 for each node n targetNodes[]7 do compute the decreased number of neurons within n8 do update s9 for each edge e that connects to n10 do compute the decreased number of connections11 do re-compute the weight w of edge e12 do update s
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Spatiotemporal dynamics of Alzheimer’s Disease
• Three different models– Neuropathological stageing of Alzheimer-related changes
• Describe global pattern of lesions caused by Alzheimer’s disease• Lesions: distribution of amyloid and neurofibrillary changes
– Network Damage Model• Intentional attack on the node with highest degree• Observed in the brain affected by Alzheimer’s disease• Focus on fragments after attack
– Treatment• Based on cholingeric hypothesis• Needs to find out the cholingeric pathways
• A spatiotemporal model of Alzheimer’s Disease– A combination of three with temporal parameter added in
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Basal Portion of Frontal Lobe
Basal Portion of Limbic Lobe
Basal Portion of Occipital Lobe
Isocortex Association Area
Isocortical Areas(including the belt fields and primary areas)
Stage I
Stage II
Stage III
Distribution Pattern of Amyloid Deposits
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Transentorhinal Region
Limbic Area(involve the entorhinal and transentorhinal layer Pre-α)
Isorcortex
Stage III & IV
Stage I & II
Stage V & VI
Distribution Pattern of Neurofibrillary Tangles and Neuropil Threads
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Spatiotemporal dynamics of Alzheimer’s Disease
• Three different models– Neuropathological stageing of Alzheimer-related changes
• Describe global pattern of lesions caused by Alzheimer’s disease• Lesions: distribution of amyloid and neurofibrillary changes
– Network Damage Model• Intentional attack on the node with highest degree• Observed in the brain affected by Alzheimer’s disease• Focus on fragments after attack
– Treatment• Based on cholingeric hypothesis• Needs to find out the cholingeric pathways
• A spatiotemporal model of Alzheimer’s Disease– A combination of three with temporal parameter added in
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Hub
Remove Hubs
A Cluster Three Cluster
Network Damage Model
𝑞𝑘=𝜃 (𝑘𝑚𝑎𝑥−𝑘 )={1𝑖𝑓 𝑘≤𝑘𝑚𝑎𝑥
0 𝑖𝑓 𝑘>𝑘𝑚𝑎𝑥
Where is the probability a node will be occupied, is the is the Heaviside step function, is the degree threshold, is the degree of a node
• It has been applied to some studies of Alzheimer’s disease
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Spatiotemporal dynamics of Alzheimer’s Disease
• Three different models– Neuropathological stageing of Alzheimer-related changes
• Describe global pattern of lesions caused by Alzheimer’s disease• Lesions: distribution of amyloid and neurofibrillary changes
– Network Damage Model• Intentional attack on the node with highest degree• Observed in the brain affected by Alzheimer’s disease• Focus on fragments after attack
– Treatment• Based on cholingeric hypothesis• Needs to find out the cholingeric pathways
• A spatiotemporal model of Alzheimer’s Disease– A combination of three with temporal parameter added in
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Neurochemical Changes in Alzheimer’s Disease
𝑡𝑎𝑢⇌𝑡𝑎𝑢p APP
Postsynaptic NeuronPresynaptic Neuron Synapatic Cleft
Ca2+ACh
ChAT
Acetyl-CoA
Choline
AChE Inhibitor
Nerve Impulse
Vesicles
AChE
ChAT – Choline AcetyltransferaseACh – AcetylcholineAChE – AcetylcholinesteraseAPP – Amyloid Precursor Protein
ACh Receptor
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Cholinergic Pathways
Ch1Ch2
Ch3Ch4
Ch5 Ch6
neocortex
hippocampus
cingulate
retrosplenia
olfactory bulb
visual areathalamus
deep cerebellar nuclei
amygdala
Ch1 – medial septumCh2 – vertical limb nucleusCh3 – horizontal limb nucleusCh4 – nucleus basalisCh5 – pedunculopontine nucleusCh6 – lateral dorsal tegmental nucleus
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Spatiotemporal dynamics of Alzheimer’s Disease
• Three different models– Neuropathological stageing of Alzheimer-related changes
• Describe global pattern of lesions caused by Alzheimer’s disease• Lesions: distribution of amyloid and neurofibrillary changes
– Network Damage Model• Intentional attack on the node with highest degree• Observed in the brain affected by Alzheimer’s disease• Focus on fragments after attack
– Treatment• Based on cholingeric hypothesis• Needs to find out the cholingeric pathways
• A spatiotemporal model of Alzheimer’s Disease– A combination of three with temporal parameter added in
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5K
4K
1K
3.6
2.0
1.2
Local View
Global View
Global and Local Views of Alzheimer’s Brain
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Where is the Heaviside step function, represents a threshold of degree, is the maximum degree in a local region, is the degree of node , is the start point of the simulation, is a period of time that controls the duration of an attack
,
𝑙𝑒𝑡 𝑘𝑡𝑎𝑟𝑔𝑒𝑡= 𝑓 (𝑡 )=𝑘𝑚𝑎𝑥 − ⌈𝑡−𝑡 0
𝑝⌉
node in the network at time ,
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target node in the network, the total decreased number of neurons at time is
where is the decreased number of neurons, is the speed of neuron deaths, is the speed of neuron deaths at time , is the constant speed of neuron deaths, is the amount of acetylcholine at time ,
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edge in the network with source node and target node , the weight of at time is
𝑾 (𝒕𝒏 )= 𝜷𝜶
×𝑪 (𝒕𝒏)𝟏𝟎𝟒 ×
𝟏𝒍
where are coefficients to determine the ratio between and , notice that , is the number of connections that compose at time , is the length of
𝑪 (𝒕𝒏 )={ 𝑵𝑺 (𝒕𝒏 ) ×𝟏𝟎𝟒×𝒚
𝒙+𝒚×
𝑵𝑻 (𝒕𝒏)
∑𝒊=𝟎
𝒚
𝑵 𝑻 𝒊(𝒕𝒏)
𝒊𝒇 𝒏=𝟎
𝑪 (𝒕𝒏−𝟏) × {𝟏−∆𝒏𝑺
𝑵𝑺 (𝒕𝒏−𝟏 )𝒊𝒇 ∆𝒏𝑺≥ ∆𝒏𝑻
𝟏−∆𝒏𝑻
𝑵𝑻 (𝒕𝒏−𝟏 )𝒊𝒇 ∆𝒏𝑺<∆𝒏𝑻
𝒊𝒇 𝒏>𝟎
where and are the number of inlinks and outlinks respectively, and are the decreased number of and respectively from to
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Steps of Brain Network Simulation of Alzheimer’s Disease
5
3 1
1.875
1.66
0.33
0.6250.33
t = 0
3
3 1
1.125
1
0.33
0.3750.33
t = 1
2
1 1
0.375
0.33
0.11
0.250.13
t = 2
ACh
Assume that are all equal to 1, is 2 per unit time, and is a factor of 2, then the dynamics of weights are as follow:
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Demo
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Conclusion
• Brain simulation is the trend in the future of brain science studies
• Try to design a brain network simulator– Layered architecture inspired by network comparison– Brain components– Workflow– Development approach
• Case-based incremental delivery
• A spatiotemporal model of Alzheimer’s disease• A prototype of brain network simulator
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Future Work
• Brain network simulator development– Brain components refinement– Input data and data preprocessing– Network analysis– Distributed computing
• Evolved brain network simulator– Add more cases into the brain network simulator– Ex: research result or experiment data of sleep
• Usage– Research– Diagnosis– Personal healthcare
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Q&A
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Thanks For Listening!