Speaker: Jimmy Lu Topics: A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheimer’s Disease Date: 2011.05.31 Defense of WECO Lab at CSIE, FJU

1. 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 : HsingMei 梅 興 Web Computing Laboratory (WECO Lab) Computer Science and Information Engineering Department Fu Jen Catholic University

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 2011/5/30 WECO Lab http://www.weco.net 2

3. 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. 2011/5/30 WECO Lab http://www.weco.net 3

4. 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 2011/5/30 WECO Lab http://www.weco.net 4

5. Background and Related Work 2011/5/30 WECO Lab http://www.weco.net 5

6. Background and Related Work Brain informatics An emerging interdisciplinary research field Human Information Processing System (HIPS) 2011/5/30 WECO Lab http://www.weco.net 6 Web Intelligence Deep Web Intelligence Technology in web intelligence, especially in deep web intelligence, such as data mining, machine learning, and social network analysis, helps studies of brain science Cognitive Science Neuroscience Brain Informatics

8. by Connection Type

9. Anatomical connectivity

10. Functional connectivity

11. Effective connectivity

12. by Functionality

13. Thalamocortical Motifs

14. Polysynaptic Loop Structure

16. 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 2011/5/30 WECO Lab http://www.weco.net 9 modules shortest path cluster

17. Background and Related Work Current Status of Brain Simulator 2011/5/30 WECO Lab http://www.weco.net 10

18. The Brain Network Simulator 2011/5/30 WECO Lab http://www.weco.net 11

19. 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 2011/5/30 WECO Lab http://www.weco.net 12

20. 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 2011/5/30 WECO Lab http://www.weco.net 13

21. The BrainNetwork Simulator Internet vs. Brain Networks 2011/5/30 WECO Lab http://www.weco.net 14

22. 2011/5/30 WECO Lab http://www.weco.net 15 Layered Architecture of Brain Simulator Short Term Long Term Time Scale Cognitive System Aging Brain Disease Sleep Decision Making Neural Darwin Selection Brain Disease Models Resting State Application Layer (Behavior/Disease/Cognitive Functions) …… Sleep Switch Model Network Development Model Network Damage Model Reasoning Causal Layer (Overlays) …… ……… Processing Layer Polysynaptic Loops Diffuse Ascending Projection Thalamocortical Motif Brain Connectivity Layer

23. 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 2011/5/30 WECO Lab http://www.weco.net 16

24. Connections are maintained by a sparse matrix to optimize memory usage 2011/5/30 WECO Lab http://www.weco.net 17

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28. 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 2011/5/30 WECO Lab http://www.weco.net 21

29. 2011/5/30 WECO Lab http://www.weco.net 22 A Workflow Scenario of Brain Network Simulator Signal Filtering, Image Normalization, Transformation, etc. Research or experiment results Extract Required Information Data Preprocessing Instantiate Brain Components to Create Brain Anatomical Network Input Data time Apply Theoretical Model for Simulation Network Analysis 3D Brain Network Rendering

30. 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 2011/5/30 WECO Lab http://www.weco.net 23

31. 2011/5/30 WECO Lab http://www.weco.net 24 Case-based Incremental Delivery Research or Experiment Results Personalized Medical data New Cases Case Study and Analysis Existing Cases Layered Architecture Extending and Refactoring Cases Integration Brain Components Extending and Refactoring Feedback Build Theoretical Models Model Pool Evaluate Theoretical Models Evolved Brain Simulator

32. Spatiotemporal dynamics of Alzheimer’s Disease 2011/5/30 WECO Lab http://www.weco.net 25

33. Spatiotemporal dynamics of Alzheimer’s Disease Three different models Neuropathologicalstageing 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 2011/5/30 WECO Lab http://www.weco.net 26

34. 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 dotargetNodes[] ← CHOOSE-TARGET-NODES(t, r) 6 for each node n∈targetNodes[] 7 do compute the decreased number of neurons within n 8 do update s 9 for each edge e that connects to n 10 do compute the decreased number of connections 11 do re-compute the weight w of edge e 12 do update s 2011/5/30 WECO Lab http://www.weco.net 27

35. Spatiotemporal dynamics of Alzheimer’s Disease Three different models Neuropathologicalstageing 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 2011/5/30 WECO Lab http://www.weco.net 28

36. Isocortical Areas (including the belt fields and primary areas) Stage III Isocortex Association Area Stage II Basal Portion of Occipital Lobe Basal Portion of Frontal Lobe Stage I Basal Portion of Limbic Lobe Distribution Pattern of Amyloid Deposits 2011/5/30 WECO Lab http://www.weco.net 29

37. Isorcortex Stage V & VI Limbic Area (involve the entorhinal and transentorhinal layer Pre-α) Stage III & IV Transentorhinal Region Stage I & II Distribution Pattern of Neurofibrillary Tangles and Neuropil Threads 2011/5/30 WECO Lab http://www.weco.net 30

38. Spatiotemporal dynamics of Alzheimer’s Disease Three different models Neuropathologicalstageing 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 2011/5/30 WECO Lab http://www.weco.net 31

40. 2011/5/30 WECO Lab http://www.weco.net 34 Neurochemical Changes in Alzheimer’s Disease Postsynaptic Neuron Presynaptic Neuron Synapatic Cleft Nerve Impulse Acetyl-CoA Vesicles ChAT Ca2+ ACh 𝑡𝑎𝑢⇌𝑡𝑎𝑢 p APP Choline ACh Receptor ChAT – Choline Acetyltransferase ACh – Acetylcholine AChE – Acetylcholinesterase APP – Amyloid Precursor Protein AChE Inhibitor AChE

41. 2011/5/30 WECO Lab http://www.weco.net 35 Cholinergic Pathways neocortex cingulate retrosplenia thalamus visual area Ch1 Ch2 Ch4 hippocampus Ch3 olfactory bulb amygdala Ch1 – medial septum Ch2 – vertical limb nucleus Ch3 – horizontal limb nucleus Ch4 – nucleus basalis Ch5 – pedunculopontine nucleus Ch6 – lateral dorsal tegmental nucleus deep cerebellar nuclei Ch5 Ch6

42. Spatiotemporal dynamics of Alzheimer’s Disease Three different models Neuropathologicalstageing 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 2011/5/30 WECO Lab http://www.weco.net 36

43. Local View Global View 5K 3.6 4K 2.0 1.2 1K 2011/5/30 WECO Lab http://www.weco.net 37 Global and Local Views of Alzheimer’s Brain

44. 2011/5/30 WECO Lab http://www.weco.net 38 ∀ node 𝑖 in the network at time 𝑡, 𝜃𝑘𝑡𝑎𝑟𝑔𝑒𝑡−𝑘𝑖=1 𝑖𝑓 𝑘𝑖≤𝑘𝑡𝑎𝑟𝑔𝑒𝑡0 𝑖𝑓 𝑘𝑖>𝑘𝑡𝑎𝑟𝑔𝑒𝑡, 𝑙𝑒𝑡 𝑘𝑡𝑎𝑟𝑔𝑒𝑡=𝑓𝑡=𝑘𝑚𝑎𝑥−𝑡−𝑡0𝑝 Where 𝜽(𝒙) is the Heaviside step function, 𝒌𝒕𝒂𝒓𝒈𝒆𝒕represents a threshold of degree, 𝒌𝒎𝒂𝒙 is the maximum degree in a local region, 𝒌𝒊is the degree of node 𝒊, 𝒕𝟎 is the start point of the simulation, 𝒑is a period of time that controls the duration of an attack

45. 2011/5/30 WECO Lab http://www.weco.net 39 ∀ target node in the network, the total decreased number of neurons at time 𝑡𝑛 is 𝒇𝒕=𝑵𝒕𝟎−𝑵𝒕𝒏 =𝒕𝟎𝒕𝒏𝑽𝒕𝒅𝒕 =𝒕𝟎𝒕𝟏𝒗𝟏𝒅𝒕+𝒕𝟏𝒕𝟐𝒗𝟐𝒅𝒕+⋯+𝒕𝒏−𝟏𝒕𝒏𝒗𝒏𝒅𝒕 =𝒗𝟏𝒕𝟏−𝒕𝟎+𝒗𝟐𝒕𝟐−𝒕𝟏+⋯+𝒗𝒏𝒕𝒏−𝒕𝒏−𝟏 =𝒗𝒄𝒕𝟏−𝒕𝟎𝒂𝟏+𝒕𝟐−𝒕𝟏𝒂𝟐+⋯+𝒕𝒏−𝒕𝒏−𝟏𝒂𝒏 =𝒗𝒄𝒊=𝟏𝒏𝒕𝒊−𝒕𝒊−𝟏𝒂𝒊 where𝑵𝒕is the decreased number of neurons,𝑽𝒕is the speed of neuron deaths, 𝒗𝒊 is the speed of neuron deaths at time 𝒕𝒊,𝒗𝒄 is the constant speed of neuron deaths, 𝒂𝒊 is the amount of acetylcholine at time 𝒕𝒊,

46. 2011/5/30 WECO Lab http://www.weco.net 40 ∀ edge 𝒆in the network with source node 𝑺 and target node 𝑻, the weight of 𝒆 at time 𝒕𝒏is 𝑾𝒕𝒏=𝜷𝜶×𝑪(𝒕𝒏)𝟏𝟎𝟒×𝟏𝒍 where 𝜶,𝜷are coefficients to determine the ratio between 𝑪(𝒕𝒏)and 𝒍, notice that 𝟎<𝜶,𝜷<𝟏, 𝑪(𝒕𝒏)is the number of connections that compose 𝒆 at time 𝒕𝒏, 𝒍is the length of 𝒆 𝑪𝒕𝒏=𝑵𝑺𝒕𝒏×𝟏𝟎𝟒×𝒚𝒙+𝒚×𝑵𝑻(𝒕𝒏)𝒊=𝟎𝒚𝑵𝑻𝒊(𝒕𝒏) 𝒊𝒇 𝒏=𝟎𝑪𝒕𝒏−𝟏×𝟏−∆𝒏𝑺𝑵𝑺𝒕𝒏−𝟏 𝒊𝒇 ∆𝒏𝑺≥∆𝒏𝑻𝟏−∆𝒏𝑻𝑵𝑻𝒕𝒏−𝟏 𝒊𝒇 ∆𝒏𝑺<∆𝒏𝑻 𝒊𝒇 𝒏>𝟎 where 𝒙and 𝒚are the number of inlinks and outlinks respectively, ∆𝒏𝑺and ∆𝒏𝑻are the decreased number of 𝑺and 𝑻respectively from 𝒕𝒏−𝟏 to 𝒕𝒏

47. 2011/5/30 WECO Lab http://www.weco.net 41 Steps of Brain Network Simulation of Alzheimer’s Disease Assume that 𝜶,𝜷, 𝒍are all equal to 1, 𝒗𝒄 is 2 per unit time, and 𝒂 is a factor of 2, then the dynamics of weights are as follow: 5 3 2 1.875 1.125 0.375 ACh 0.33 0.33 0.13 0.625 0.375 0.25 1.66 1 0.33 3 1 3 1 1 1 0.33 0.33 0.11 t = 0 t = 1 t = 2

48. Demo 2011/5/30 WECO Lab http://www.weco.net 42

49. 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 2011/5/30 WECO Lab http://www.weco.net 43

50. 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 2011/5/30 WECO Lab http://www.weco.net 44

51. Q&A 2011/5/30 WECO Lab http://www.weco.net 45

52. Thanks For Listening! 2011/5/30 WECO Lab http://www.weco.net 46