SlideShare une entreprise Scribd logo

Brain Networks

•
•
Jimmy Lu
Jimmy Lu

1. The document examines the relationship between brain anatomical networks and intelligence by analyzing structural, functional, and effective connectivity patterns. 2. It reviews concepts from graph theory and complex networks that are relevant for studying brain networks, including small-world networks and scale-free networks. 3. An experiment analyzed diffusion tensor images and other data from 79 subjects to construct and analyze anatomical brain networks and investigate their relationships with general and high intelligence.

Technologie
1  sur  27
Brian networks2010/02/06Brain Anatomical Network and Intelligence&Organization, development and function of complex brain networks Speaker : Jimmy Lu Advisor : Hsing Mei Web Computing Laboratory Computer Science and Information Engineering Department Fu Jen Catholic University
Outline Introduction Background Graph theory Complex networks Examine principles (three aspects) Structural, functional, effective connectivity Clinical terms Experiment Future work 2
Introduction Examines principles Structural properties Relationship between the structural substrate and dynamic functional and effective connectivity patterns The efficiency of brain structural organization may be an important biological basis for intelligence 3
Background – graph theory Adjacency (connection) matrix Characteristic path length Clustering coefficient Connectedness Cycle Degree Distance Distance matrix Graph Path Regular graph Random graph Scale-free graph Small-world networks
Background – complex networks Random networks Small-world networks Scale-free networks
Background – three aspects Neuroanatomical substrate (structural connectivity) Dynamics of neurons (functional connectivity) Functional segregation Functional integration Causal interaction (effective connectivity)
Background - structural Network participation indices In-degree, out-degree transmission coefficient Broadcasters, integrators cooperative Matching index Connectional fingerprint Motif analysis
Background - structural Graph theoretical analysis High clustering, short path length Functional proximity Multivariate analysis Multidimensional scaling Hierarchical cluster Evolutionary optimization Inter-cluster connection Connectedness
Background - structural
Background - structural
Background - structural
Background - structural
Background – development Local spatial growth rules Preferential attachment Global network design
Background – clinical terms Anatomical Automatic Labeling (AAL) Magnetic resonance imaging (MRI) Diffusion MRI Functional MRI (fMRI) In vivo Cerebral cortex Tractography
Background - functional There are some problems in the previous work Only binary networks Nonzero connection probability value to brain region pairs which are unlikely to be connected Etc... But they all have evidence that brain network is close to small-world
Background - experiment 79 subjects (44 males and 35 females, mean age = 23.8 years, range = 17-33 years) intelligence quotient (IQ) test (FSIQ, PIQ, VIQ) GI (general intelligence)/HI (high intelligence) group Diffusion tensor image (DTI)/T1 image/AAL co-register Normalize, inverse Construct binary and weighted network of an individual brain Anatomical network analyses Statistical analysis
Background - experiment
Background - experiment
Background - experiment
Background - experiment
Background - experiment
Background - experiment
Background - experiment
Background - experiment
Future work
Future work From the computer science perspective How about the social network? Other internet overlaid networks? A brain simulator? Etc…?
Reference Li Y, Liu Y, Li J, Qin W, Li K, et al. (2009) Brain Anatomical Network and Intelligence. PLoSComput Olaf Sporns, Dante R. Chialvo, Marcus Kaiser and Claus C. Hilgetag (2004) Organization, development and function of complex brain networks. TRENDS in Cognitive Sciences Vol.8 No.9

Recommandé

Minicourse on Network Science
Minicourse on Network ScienceMinicourse on Network Science
Minicourse on Network SciencePavel Loskot
 
Artificial Neural Networks-Supervised Learning Models
Artificial Neural Networks-Supervised Learning ModelsArtificial Neural Networks-Supervised Learning Models
Artificial Neural Networks-Supervised Learning ModelsDrBaljitSinghKhehra
 
Statistical Pattern recognition(1)
Statistical Pattern recognition(1)Statistical Pattern recognition(1)
Statistical Pattern recognition(1)Syed Atif Naseem
 
Towards a theory of semantic communication
Towards a theory of semantic communicationTowards a theory of semantic communication
Towards a theory of semantic communicationJie Bao
 
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka
 
Principal Component Analysis (PCA) and LDA PPT Slides
Principal Component Analysis (PCA) and LDA PPT SlidesPrincipal Component Analysis (PCA) and LDA PPT Slides
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
 
Artificial Neural Networks Lect3: Neural Network Learning rules
Artificial Neural Networks Lect3: Neural Network Learning rulesArtificial Neural Networks Lect3: Neural Network Learning rules
Artificial Neural Networks Lect3: Neural Network Learning rulesMohammed Bennamoun
 
Introduction to Mobile Ad hoc Networks
Introduction to Mobile Ad hoc NetworksIntroduction to Mobile Ad hoc Networks
Introduction to Mobile Ad hoc NetworksSayed Chhattan Shah
 

Recommandé

Minicourse on Network Science
Minicourse on Network ScienceMinicourse on Network Science
Minicourse on Network SciencePavel Loskot
 
Artificial Neural Networks-Supervised Learning Models
Artificial Neural Networks-Supervised Learning ModelsArtificial Neural Networks-Supervised Learning Models
Artificial Neural Networks-Supervised Learning ModelsDrBaljitSinghKhehra
 
Statistical Pattern recognition(1)
Statistical Pattern recognition(1)Statistical Pattern recognition(1)
Statistical Pattern recognition(1)Syed Atif Naseem
 
Towards a theory of semantic communication
Towards a theory of semantic communicationTowards a theory of semantic communication
Towards a theory of semantic communicationJie Bao
 
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka
 
Principal Component Analysis (PCA) and LDA PPT Slides
Principal Component Analysis (PCA) and LDA PPT SlidesPrincipal Component Analysis (PCA) and LDA PPT Slides
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
 
Artificial Neural Networks Lect3: Neural Network Learning rules
Artificial Neural Networks Lect3: Neural Network Learning rulesArtificial Neural Networks Lect3: Neural Network Learning rules
Artificial Neural Networks Lect3: Neural Network Learning rulesMohammed Bennamoun
 
Introduction to Mobile Ad hoc Networks
Introduction to Mobile Ad hoc NetworksIntroduction to Mobile Ad hoc Networks
Introduction to Mobile Ad hoc NetworksSayed Chhattan Shah
 
Chapter 09 classification advanced
Chapter 09 classification advancedChapter 09 classification advanced
Chapter 09 classification advancedHouw Liong The
 
DBSCAN (2014_11_25 06_21_12 UTC)
DBSCAN (2014_11_25 06_21_12 UTC)DBSCAN (2014_11_25 06_21_12 UTC)
DBSCAN (2014_11_25 06_21_12 UTC)Cory Cook
 
Hebbian Learning
Hebbian LearningHebbian Learning
Hebbian LearningESCOM
 
Svm vs ls svm
Svm vs ls svmSvm vs ls svm
Svm vs ls svmPulipaka Sai Ravi Teja
 
Self Organizing Feature Map(SOM), Topographic Product, Cascade 2 Algorithm
Self Organizing Feature Map(SOM), Topographic Product, Cascade 2 AlgorithmSelf Organizing Feature Map(SOM), Topographic Product, Cascade 2 Algorithm
Self Organizing Feature Map(SOM), Topographic Product, Cascade 2 AlgorithmChenghao Jin
 
Chapter 10. Cluster Analysis Basic Concepts and Methods.ppt
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptChapter 10. Cluster Analysis Basic Concepts and Methods.ppt
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptSubrata Kumer Paul
 
Missing data handling
Missing data handlingMissing data handling
Missing data handlingQuantUniversity
 
Preparing your data for Machine Learning with Feature Scaling
Preparing your data for Machine Learning with Feature ScalingPreparing your data for Machine Learning with Feature Scaling
Preparing your data for Machine Learning with Feature ScalingRahul K Chauhan
 
Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...
Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...
Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...Lviv Data Science Summer School
 
Feed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descentFeed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descentMuhammad Rasel
 
Bayesian Linear Regression.pptx
Bayesian Linear Regression.pptxBayesian Linear Regression.pptx
Bayesian Linear Regression.pptxJerminJershaTC
 
Learning Vector Quantization LVQ
Learning Vector Quantization LVQLearning Vector Quantization LVQ
Learning Vector Quantization LVQESCOM
 
Designing LoRaWAN networks for dense IoT deployments
Designing LoRaWAN networks for dense IoT deploymentsDesigning LoRaWAN networks for dense IoT deployments
Designing LoRaWAN networks for dense IoT deploymentsActility
 
DBSCAN : A Clustering Algorithm
DBSCAN : A Clustering AlgorithmDBSCAN : A Clustering Algorithm
DBSCAN : A Clustering AlgorithmPınar Yahşi
 
Adhoc Wireless Network
Adhoc Wireless Network Adhoc Wireless Network
Adhoc Wireless Network YunusKhan38
 
papr-presentation
papr-presentationpapr-presentation
papr-presentationHeshani Thathsarani
 
boosting algorithm
boosting algorithmboosting algorithm
boosting algorithmPrithvi Paneru
 
Bloom filter
Bloom filterBloom filter
Bloom filterHamid Feizabadi
 
Channel Estimation
Channel EstimationChannel Estimation
Channel EstimationPei-Che Chang
 
PRML 5.5
PRML 5.5PRML 5.5
PRML 5.5Ryuta Shitomi
 
Functional Brain Networks - Javier M. Buldù
Functional Brain Networks - Javier M. BuldùFunctional Brain Networks - Javier M. Buldù
Functional Brain Networks - Javier M. BuldùLake Como School of Advanced Studies
 
How the brain learns, formation of new neuronal networks, makiong your meory...
How the brain learns, formation of new neuronal networks, makiong your meory...How the brain learns, formation of new neuronal networks, makiong your meory...
How the brain learns, formation of new neuronal networks, makiong your meory...Babu Appat
 

Contenu connexe

Tendances

Chapter 09 classification advanced
Chapter 09 classification advancedChapter 09 classification advanced
Chapter 09 classification advancedHouw Liong The
 
DBSCAN (2014_11_25 06_21_12 UTC)
DBSCAN (2014_11_25 06_21_12 UTC)DBSCAN (2014_11_25 06_21_12 UTC)
DBSCAN (2014_11_25 06_21_12 UTC)Cory Cook
 
Hebbian Learning
Hebbian LearningHebbian Learning
Hebbian LearningESCOM
 
Self Organizing Feature Map(SOM), Topographic Product, Cascade 2 Algorithm
Self Organizing Feature Map(SOM), Topographic Product, Cascade 2 AlgorithmSelf Organizing Feature Map(SOM), Topographic Product, Cascade 2 Algorithm
Self Organizing Feature Map(SOM), Topographic Product, Cascade 2 AlgorithmChenghao Jin
 
Chapter 10. Cluster Analysis Basic Concepts and Methods.ppt
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptChapter 10. Cluster Analysis Basic Concepts and Methods.ppt
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptSubrata Kumer Paul
 
Preparing your data for Machine Learning with Feature Scaling
Preparing your data for Machine Learning with Feature ScalingPreparing your data for Machine Learning with Feature Scaling
Preparing your data for Machine Learning with Feature ScalingRahul K Chauhan
 
Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...
Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...
Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...Lviv Data Science Summer School
 
Feed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descentFeed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descentMuhammad Rasel
 
Bayesian Linear Regression.pptx
Bayesian Linear Regression.pptxBayesian Linear Regression.pptx
Bayesian Linear Regression.pptxJerminJershaTC
 
Learning Vector Quantization LVQ
Learning Vector Quantization LVQLearning Vector Quantization LVQ
Learning Vector Quantization LVQESCOM
 
Designing LoRaWAN networks for dense IoT deployments
Designing LoRaWAN networks for dense IoT deploymentsDesigning LoRaWAN networks for dense IoT deployments
Designing LoRaWAN networks for dense IoT deploymentsActility
 
DBSCAN : A Clustering Algorithm
DBSCAN : A Clustering AlgorithmDBSCAN : A Clustering Algorithm
DBSCAN : A Clustering AlgorithmPınar Yahşi
 
Adhoc Wireless Network
Adhoc Wireless Network Adhoc Wireless Network
Adhoc Wireless Network YunusKhan38
 

Tendances (20)

Chapter 09 classification advanced
Chapter 09 classification advancedChapter 09 classification advanced
Chapter 09 classification advanced
 
DBSCAN (2014_11_25 06_21_12 UTC)
DBSCAN (2014_11_25 06_21_12 UTC)DBSCAN (2014_11_25 06_21_12 UTC)
DBSCAN (2014_11_25 06_21_12 UTC)
 
Hebbian Learning
Hebbian LearningHebbian Learning
Hebbian Learning
 
Svm vs ls svm
Svm vs ls svmSvm vs ls svm
Svm vs ls svm
 
Self Organizing Feature Map(SOM), Topographic Product, Cascade 2 Algorithm
Self Organizing Feature Map(SOM), Topographic Product, Cascade 2 AlgorithmSelf Organizing Feature Map(SOM), Topographic Product, Cascade 2 Algorithm
Self Organizing Feature Map(SOM), Topographic Product, Cascade 2 Algorithm
 
Chapter 10. Cluster Analysis Basic Concepts and Methods.ppt
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptChapter 10. Cluster Analysis Basic Concepts and Methods.ppt
Chapter 10. Cluster Analysis Basic Concepts and Methods.ppt
 
Missing data handling
Missing data handlingMissing data handling
Missing data handling
 
Preparing your data for Machine Learning with Feature Scaling
Preparing your data for Machine Learning with Feature ScalingPreparing your data for Machine Learning with Feature Scaling
Preparing your data for Machine Learning with Feature Scaling
 
Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...
Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...
Master defence 2020 - Ivan Prodaiko - Person Re-identification in a Top-view ...
 
Feed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descentFeed forward ,back propagation,gradient descent
Feed forward ,back propagation,gradient descent
 
Bayesian Linear Regression.pptx
Bayesian Linear Regression.pptxBayesian Linear Regression.pptx
Bayesian Linear Regression.pptx
 
Learning Vector Quantization LVQ
Learning Vector Quantization LVQLearning Vector Quantization LVQ
Learning Vector Quantization LVQ
 
Designing LoRaWAN networks for dense IoT deployments
Designing LoRaWAN networks for dense IoT deploymentsDesigning LoRaWAN networks for dense IoT deployments
Designing LoRaWAN networks for dense IoT deployments
 
DBSCAN : A Clustering Algorithm
DBSCAN : A Clustering AlgorithmDBSCAN : A Clustering Algorithm
DBSCAN : A Clustering Algorithm
 
Adhoc Wireless Network
Adhoc Wireless Network Adhoc Wireless Network
Adhoc Wireless Network
 
papr-presentation
papr-presentationpapr-presentation
papr-presentation
 
boosting algorithm
boosting algorithmboosting algorithm
boosting algorithm
 
Bloom filter
Bloom filterBloom filter
Bloom filter
 
Channel Estimation
Channel EstimationChannel Estimation
Channel Estimation
 
PRML 5.5
PRML 5.5PRML 5.5
PRML 5.5
 

En vedette

How the brain learns, formation of new neuronal networks, makiong your meory...
How the brain learns, formation of new neuronal networks, makiong your meory...How the brain learns, formation of new neuronal networks, makiong your meory...
How the brain learns, formation of new neuronal networks, makiong your meory...Babu Appat
 
Exploring Complex Networks
Exploring Complex NetworksExploring Complex Networks
Exploring Complex NetworksJimmy Lu
 
Brain Network - Thalamocortical Motif
Brain Network - Thalamocortical MotifBrain Network - Thalamocortical Motif
Brain Network - Thalamocortical MotifJimmy Lu
 
Bradley Voytek - Cognitive Networks
Bradley Voytek - Cognitive NetworksBradley Voytek - Cognitive Networks
Bradley Voytek - Cognitive NetworksUC San Diego
 
Brain networks and the matrix and the mind
Brain networks and the matrix and the mindBrain networks and the matrix and the mind
Brain networks and the matrix and the mindAdonis Sfera, MD
 
Brain network modelling: connectivity metrics and group analysis
Brain network modelling: connectivity metrics and group analysisBrain network modelling: connectivity metrics and group analysis
Brain network modelling: connectivity metrics and group analysisGael Varoquaux
 
The Default Mode Network
The Default Mode NetworkThe Default Mode Network
The Default Mode NetworkEvelyn McKelvie
 
Anisotropic Diffusion for Medical Image Enhancement
Anisotropic Diffusion for Medical Image EnhancementAnisotropic Diffusion for Medical Image Enhancement
Anisotropic Diffusion for Medical Image EnhancementCSCJournals
 
Perfusión en TC
Perfusión en TCPerfusión en TC
Perfusión en TCGaro TM
 
Diffusion MRI, Tractography,and Connectivity: what machine learning can do?
Diffusion MRI, Tractography,and Connectivity: what machine learning can do?Diffusion MRI, Tractography,and Connectivity: what machine learning can do?
Diffusion MRI, Tractography,and Connectivity: what machine learning can do?Ting-Shuo Yo
 
Neural networks...
Neural networks...Neural networks...
Neural networks...Molly Chugh
 
PixelSpoke Lunch and Learn: Direct Response Fundamentals with Joe McVoy
PixelSpoke Lunch and Learn: Direct Response Fundamentals with Joe McVoyPixelSpoke Lunch and Learn: Direct Response Fundamentals with Joe McVoy
PixelSpoke Lunch and Learn: Direct Response Fundamentals with Joe McVoyPixelSpoke
 
Post-Processing of Prostate Perfusion MRI
Post-Processing of Prostate Perfusion MRIPost-Processing of Prostate Perfusion MRI
Post-Processing of Prostate Perfusion MRIVanya Valindria
 
Perfusion cerebral EN TC
Perfusion cerebral EN TCPerfusion cerebral EN TC
Perfusion cerebral EN TCcristiancg2005
 
Week 5. Basics and clinical uses of MR spectroscopy.
Week 5. Basics and clinical uses of MR spectroscopy.Week 5. Basics and clinical uses of MR spectroscopy.
Week 5. Basics and clinical uses of MR spectroscopy.Dr. Jakab András
 

En vedette (20)

Functional Brain Networks - Javier M. Buldù
Functional Brain Networks - Javier M. BuldùFunctional Brain Networks - Javier M. Buldù
Functional Brain Networks - Javier M. Buldù
 
How the brain learns, formation of new neuronal networks, makiong your meory...
How the brain learns, formation of new neuronal networks, makiong your meory...How the brain learns, formation of new neuronal networks, makiong your meory...
How the brain learns, formation of new neuronal networks, makiong your meory...
 
Exploring Complex Networks
Exploring Complex NetworksExploring Complex Networks
Exploring Complex Networks
 
Brain Network - Thalamocortical Motif
Brain Network - Thalamocortical MotifBrain Network - Thalamocortical Motif
Brain Network - Thalamocortical Motif
 
Bradley Voytek - Cognitive Networks
Bradley Voytek - Cognitive NetworksBradley Voytek - Cognitive Networks
Bradley Voytek - Cognitive Networks
 
Brain networks and the matrix and the mind
Brain networks and the matrix and the mindBrain networks and the matrix and the mind
Brain networks and the matrix and the mind
 
Neuronvisio Intro
Neuronvisio IntroNeuronvisio Intro
Neuronvisio Intro
 
Brain network modelling: connectivity metrics and group analysis
Brain network modelling: connectivity metrics and group analysisBrain network modelling: connectivity metrics and group analysis
Brain network modelling: connectivity metrics and group analysis
 
The Default Mode Network
The Default Mode NetworkThe Default Mode Network
The Default Mode Network
 
Anisotropic Diffusion for Medical Image Enhancement
Anisotropic Diffusion for Medical Image EnhancementAnisotropic Diffusion for Medical Image Enhancement
Anisotropic Diffusion for Medical Image Enhancement
 
Perfusión en TC
Perfusión en TCPerfusión en TC
Perfusión en TC
 
Diffusion MRI, Tractography,and Connectivity: what machine learning can do?
Diffusion MRI, Tractography,and Connectivity: what machine learning can do?Diffusion MRI, Tractography,and Connectivity: what machine learning can do?
Diffusion MRI, Tractography,and Connectivity: what machine learning can do?
 
Neural networks...
Neural networks...Neural networks...
Neural networks...
 
Connectome
ConnectomeConnectome
Connectome
 
PixelSpoke Lunch and Learn: Direct Response Fundamentals with Joe McVoy
PixelSpoke Lunch and Learn: Direct Response Fundamentals with Joe McVoyPixelSpoke Lunch and Learn: Direct Response Fundamentals with Joe McVoy
PixelSpoke Lunch and Learn: Direct Response Fundamentals with Joe McVoy
 
DTI lecture 100710
DTI lecture 100710DTI lecture 100710
DTI lecture 100710
 
Csf flowmetry
Csf flowmetryCsf flowmetry
Csf flowmetry
 
Post-Processing of Prostate Perfusion MRI
Post-Processing of Prostate Perfusion MRIPost-Processing of Prostate Perfusion MRI
Post-Processing of Prostate Perfusion MRI
 
Perfusion cerebral EN TC
Perfusion cerebral EN TCPerfusion cerebral EN TC
Perfusion cerebral EN TC
 
Week 5. Basics and clinical uses of MR spectroscopy.
Week 5. Basics and clinical uses of MR spectroscopy.Week 5. Basics and clinical uses of MR spectroscopy.
Week 5. Basics and clinical uses of MR spectroscopy.
 

Similaire à Brain Networks

Analytical Review on the Correlation between Ai and Neuroscience
Analytical Review on the Correlation between Ai and NeuroscienceAnalytical Review on the Correlation between Ai and Neuroscience
Analytical Review on the Correlation between Ai and NeuroscienceIOSR Journals
 
Pattern Formation Drosophila
Pattern Formation DrosophilaPattern Formation Drosophila
Pattern Formation Drosophilasunon77
 
Convolutional Networks
Convolutional NetworksConvolutional Networks
Convolutional NetworksNicole Savoie
 
An Overview On Neural Network And Its Application
An Overview On Neural Network And Its ApplicationAn Overview On Neural Network And Its Application
An Overview On Neural Network And Its ApplicationSherri Cost
 
Maps of sparse memory networks reveal overlapping communities in network flows
Maps of sparse memory networks reveal overlapping communities in network flowsMaps of sparse memory networks reveal overlapping communities in network flows
Maps of sparse memory networks reveal overlapping communities in network flowsUmeå University
 
Consciousness, Graph theory and brain network tsc 2017
Consciousness, Graph theory and brain network tsc 2017Consciousness, Graph theory and brain network tsc 2017
Consciousness, Graph theory and brain network tsc 2017Nir Lahav
 
An information-theoretic, all-scales approach to comparing networks
An information-theoretic, all-scales approach to comparing networksAn information-theoretic, all-scales approach to comparing networks
An information-theoretic, all-scales approach to comparing networksJim Bagrow
 
Network Biology: A paradigm for modeling biological complex systems
Network Biology: A paradigm for modeling biological complex systemsNetwork Biology: A paradigm for modeling biological complex systems
Network Biology: A paradigm for modeling biological complex systemsGanesh Bagler
 
Why Neurons have thousands of synapses? A model of sequence memory in the brain
Why Neurons have thousands of synapses? A model of sequence memory in the brainWhy Neurons have thousands of synapses? A model of sequence memory in the brain
Why Neurons have thousands of synapses? A model of sequence memory in the brainNumenta
 
Technology R&D Theme 3: Multi-scale Network Representations
Technology R&D Theme 3: Multi-scale Network RepresentationsTechnology R&D Theme 3: Multi-scale Network Representations
Technology R&D Theme 3: Multi-scale Network RepresentationsAlexander Pico
 
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...Jimmy Lu
 
Community Analysis of Deep Networks (poster)
Community Analysis of Deep Networks (poster)Community Analysis of Deep Networks (poster)
Community Analysis of Deep Networks (poster)Behrang Mehrparvar
 
Analyzing Complex Problem Solving by Dynamic Brain Networks.pdf
Analyzing Complex Problem Solving by Dynamic Brain Networks.pdfAnalyzing Complex Problem Solving by Dynamic Brain Networks.pdf
Analyzing Complex Problem Solving by Dynamic Brain Networks.pdfNancy Ideker
 

Similaire à Brain Networks (20)

Analytical Review on the Correlation between Ai and Neuroscience
Analytical Review on the Correlation between Ai and NeuroscienceAnalytical Review on the Correlation between Ai and Neuroscience
Analytical Review on the Correlation between Ai and Neuroscience
 
Network Science: Theory, Modeling and Applications
Network Science: Theory, Modeling and ApplicationsNetwork Science: Theory, Modeling and Applications
Network Science: Theory, Modeling and Applications
 
Pattern Formation Drosophila
Pattern Formation DrosophilaPattern Formation Drosophila
Pattern Formation Drosophila
 
A tutorial in Connectome Analysis (3) - Marcus Kaiser
A tutorial in Connectome Analysis (3) - Marcus KaiserA tutorial in Connectome Analysis (3) - Marcus Kaiser
A tutorial in Connectome Analysis (3) - Marcus Kaiser
 
Convolutional Networks
Convolutional NetworksConvolutional Networks
Convolutional Networks
 
An Overview On Neural Network And Its Application
An Overview On Neural Network And Its ApplicationAn Overview On Neural Network And Its Application
An Overview On Neural Network And Its Application
 
Maps of sparse memory networks reveal overlapping communities in network flows
Maps of sparse memory networks reveal overlapping communities in network flowsMaps of sparse memory networks reveal overlapping communities in network flows
Maps of sparse memory networks reveal overlapping communities in network flows
 
Consciousness, Graph theory and brain network tsc 2017
Consciousness, Graph theory and brain network tsc 2017Consciousness, Graph theory and brain network tsc 2017
Consciousness, Graph theory and brain network tsc 2017
 
Artificial Neural Networks
Artificial Neural NetworksArtificial Neural Networks
Artificial Neural Networks
 
P2-Artificial.pdf
P2-Artificial.pdfP2-Artificial.pdf
P2-Artificial.pdf
 
An information-theoretic, all-scales approach to comparing networks
An information-theoretic, all-scales approach to comparing networksAn information-theoretic, all-scales approach to comparing networks
An information-theoretic, all-scales approach to comparing networks
 
Network Biology: A paradigm for modeling biological complex systems
Network Biology: A paradigm for modeling biological complex systemsNetwork Biology: A paradigm for modeling biological complex systems
Network Biology: A paradigm for modeling biological complex systems
 
Albus
AlbusAlbus
Albus
 
Why Neurons have thousands of synapses? A model of sequence memory in the brain
Why Neurons have thousands of synapses? A model of sequence memory in the brainWhy Neurons have thousands of synapses? A model of sequence memory in the brain
Why Neurons have thousands of synapses? A model of sequence memory in the brain
 
Technology R&D Theme 3: Multi-scale Network Representations
Technology R&D Theme 3: Multi-scale Network RepresentationsTechnology R&D Theme 3: Multi-scale Network Representations
Technology R&D Theme 3: Multi-scale Network Representations
 
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
 
A tutorial in Connectome Analysis (0) - Marcus Kaiser
A tutorial in Connectome Analysis (0) - Marcus KaiserA tutorial in Connectome Analysis (0) - Marcus Kaiser
A tutorial in Connectome Analysis (0) - Marcus Kaiser
 
Community Analysis of Deep Networks (poster)
Community Analysis of Deep Networks (poster)Community Analysis of Deep Networks (poster)
Community Analysis of Deep Networks (poster)
 
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
 
Analyzing Complex Problem Solving by Dynamic Brain Networks.pdf
Analyzing Complex Problem Solving by Dynamic Brain Networks.pdfAnalyzing Complex Problem Solving by Dynamic Brain Networks.pdf
Analyzing Complex Problem Solving by Dynamic Brain Networks.pdf
 

Plus de Jimmy Lu

All the troubles you get into when setting up a production ready Kubernetes c...
All the troubles you get into when setting up a production ready Kubernetes c...All the troubles you get into when setting up a production ready Kubernetes c...
All the troubles you get into when setting up a production ready Kubernetes c...Jimmy Lu
 
A Million ways of Deploying a Kubernetes Cluster
A Million ways of Deploying a Kubernetes ClusterA Million ways of Deploying a Kubernetes Cluster
A Million ways of Deploying a Kubernetes ClusterJimmy Lu
 
Renaissance of JUnit - Introduction to JUnit 5
Renaissance of JUnit - Introduction to JUnit 5Renaissance of JUnit - Introduction to JUnit 5
Renaissance of JUnit - Introduction to JUnit 5Jimmy Lu
 
Event sourcing with reactor and spring statemachine
Event sourcing with reactor and spring statemachineEvent sourcing with reactor and spring statemachine
Event sourcing with reactor and spring statemachineJimmy Lu
 
Bootify your spring application
Bootify your spring applicationBootify your spring application
Bootify your spring applicationJimmy Lu
 
The Model of Spatiotemporal Dynamics of Alzheimer’s Disease
The Model of Spatiotemporal Dynamics of Alzheimer’s DiseaseThe Model of Spatiotemporal Dynamics of Alzheimer’s Disease
The Model of Spatiotemporal Dynamics of Alzheimer’s DiseaseJimmy Lu
 
The Models of Alzheimer's Disease Part II
The Models of Alzheimer's Disease Part IIThe Models of Alzheimer's Disease Part II
The Models of Alzheimer's Disease Part IIJimmy Lu
 
The Models of Alzheimer's Disease Part I
The Models of Alzheimer's Disease Part IThe Models of Alzheimer's Disease Part I
The Models of Alzheimer's Disease Part IJimmy Lu
 
The Models of Alzheimer's Disease Part III
The Models of Alzheimer's Disease Part IIIThe Models of Alzheimer's Disease Part III
The Models of Alzheimer's Disease Part IIIJimmy Lu
 
On the Development of a Brain Simulator
On the Development of a Brain SimulatorOn the Development of a Brain Simulator
On the Development of a Brain SimulatorJimmy Lu
 
Design the Brain Simulator
Design the Brain SimulatorDesign the Brain Simulator
Design the Brain SimulatorJimmy Lu
 
Research Proposal and Milestone
Research Proposal and MilestoneResearch Proposal and Milestone
Research Proposal and MilestoneJimmy Lu
 
On the Development of a Brain Simulator
On the Development of a Brain SimulatorOn the Development of a Brain Simulator
On the Development of a Brain SimulatorJimmy Lu
 
Mining the Parkinson's Telemonitoring Data Set
Mining the Parkinson's Telemonitoring Data SetMining the Parkinson's Telemonitoring Data Set
Mining the Parkinson's Telemonitoring Data SetJimmy Lu
 
Brian Simulator (a draft)
Brian Simulator (a draft)Brian Simulator (a draft)
Brian Simulator (a draft)Jimmy Lu
 
How To Build A Personal Portal On Google App Engine With Django
How To Build A Personal Portal On Google App Engine With DjangoHow To Build A Personal Portal On Google App Engine With Django
How To Build A Personal Portal On Google App Engine With DjangoJimmy Lu
 

Plus de Jimmy Lu (18)

All the troubles you get into when setting up a production ready Kubernetes c...
All the troubles you get into when setting up a production ready Kubernetes c...All the troubles you get into when setting up a production ready Kubernetes c...
All the troubles you get into when setting up a production ready Kubernetes c...
 
A Million ways of Deploying a Kubernetes Cluster
A Million ways of Deploying a Kubernetes ClusterA Million ways of Deploying a Kubernetes Cluster
A Million ways of Deploying a Kubernetes Cluster
 
Renaissance of JUnit - Introduction to JUnit 5
Renaissance of JUnit - Introduction to JUnit 5Renaissance of JUnit - Introduction to JUnit 5
Renaissance of JUnit - Introduction to JUnit 5
 
Event sourcing with reactor and spring statemachine
Event sourcing with reactor and spring statemachineEvent sourcing with reactor and spring statemachine
Event sourcing with reactor and spring statemachine
 
Bootify your spring application
Bootify your spring applicationBootify your spring application
Bootify your spring application
 
The Model of Spatiotemporal Dynamics of Alzheimer’s Disease
The Model of Spatiotemporal Dynamics of Alzheimer’s DiseaseThe Model of Spatiotemporal Dynamics of Alzheimer’s Disease
The Model of Spatiotemporal Dynamics of Alzheimer’s Disease
 
The Models of Alzheimer's Disease Part II
The Models of Alzheimer's Disease Part IIThe Models of Alzheimer's Disease Part II
The Models of Alzheimer's Disease Part II
 
The Models of Alzheimer's Disease Part I
The Models of Alzheimer's Disease Part IThe Models of Alzheimer's Disease Part I
The Models of Alzheimer's Disease Part I
 
The Models of Alzheimer's Disease Part III
The Models of Alzheimer's Disease Part IIIThe Models of Alzheimer's Disease Part III
The Models of Alzheimer's Disease Part III
 
On the Development of a Brain Simulator
On the Development of a Brain SimulatorOn the Development of a Brain Simulator
On the Development of a Brain Simulator
 
Design the Brain Simulator
Design the Brain SimulatorDesign the Brain Simulator
Design the Brain Simulator
 
Research Proposal and Milestone
Research Proposal and MilestoneResearch Proposal and Milestone
Research Proposal and Milestone
 
Reward
RewardReward
Reward
 
On the Development of a Brain Simulator
On the Development of a Brain SimulatorOn the Development of a Brain Simulator
On the Development of a Brain Simulator
 
Mining the Parkinson's Telemonitoring Data Set
Mining the Parkinson's Telemonitoring Data SetMining the Parkinson's Telemonitoring Data Set
Mining the Parkinson's Telemonitoring Data Set
 
Brian Simulator (a draft)
Brian Simulator (a draft)Brian Simulator (a draft)
Brian Simulator (a draft)
 
How To Build A Personal Portal On Google App Engine With Django
How To Build A Personal Portal On Google App Engine With DjangoHow To Build A Personal Portal On Google App Engine With Django
How To Build A Personal Portal On Google App Engine With Django
 
WECO Lab
WECO LabWECO Lab
WECO Lab
 

Dernier

AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024The Digital Insurer
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machinePadma Pradeep
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfThe Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfSeasiaInfotech2
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 

Dernier (20)

AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machine
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfThe Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdf
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 

Brain Networks

  • 1. Brian networks2010/02/06Brain Anatomical Network and Intelligence&Organization, development and function of complex brain networks Speaker : Jimmy Lu Advisor : Hsing Mei Web Computing Laboratory Computer Science and Information Engineering Department Fu Jen Catholic University
  • 2. Outline Introduction Background Graph theory Complex networks Examine principles (three aspects) Structural, functional, effective connectivity Clinical terms Experiment Future work 2
  • 3. Introduction Examines principles Structural properties Relationship between the structural substrate and dynamic functional and effective connectivity patterns The efficiency of brain structural organization may be an important biological basis for intelligence 3
  • 4. Background – graph theory Adjacency (connection) matrix Characteristic path length Clustering coefficient Connectedness Cycle Degree Distance Distance matrix Graph Path Regular graph Random graph Scale-free graph Small-world networks
  • 5. Background – complex networks Random networks Small-world networks Scale-free networks
  • 6. Background – three aspects Neuroanatomical substrate (structural connectivity) Dynamics of neurons (functional connectivity) Functional segregation Functional integration Causal interaction (effective connectivity)
  • 7. Background - structural Network participation indices In-degree, out-degree transmission coefficient Broadcasters, integrators cooperative Matching index Connectional fingerprint Motif analysis
  • 8. Background - structural Graph theoretical analysis High clustering, short path length Functional proximity Multivariate analysis Multidimensional scaling Hierarchical cluster Evolutionary optimization Inter-cluster connection Connectedness
  • 13. Background – development Local spatial growth rules Preferential attachment Global network design
  • 14. Background – clinical terms Anatomical Automatic Labeling (AAL) Magnetic resonance imaging (MRI) Diffusion MRI Functional MRI (fMRI) In vivo Cerebral cortex Tractography
  • 15. Background - functional There are some problems in the previous work Only binary networks Nonzero connection probability value to brain region pairs which are unlikely to be connected Etc... But they all have evidence that brain network is close to small-world
  • 16. Background - experiment 79 subjects (44 males and 35 females, mean age = 23.8 years, range = 17-33 years) intelligence quotient (IQ) test (FSIQ, PIQ, VIQ) GI (general intelligence)/HI (high intelligence) group Diffusion tensor image (DTI)/T1 image/AAL co-register Normalize, inverse Construct binary and weighted network of an individual brain Anatomical network analyses Statistical analysis
  • 26. Future work From the computer science perspective How about the social network? Other internet overlaid networks? A brain simulator? Etc…?
  • 27. Reference Li Y, Liu Y, Li J, Qin W, Li K, et al. (2009) Brain Anatomical Network and Intelligence. PLoSComput Olaf Sporns, Dante R. Chialvo, Marcus Kaiser and Claus C. Hilgetag (2004) Organization, development and function of complex brain networks. TRENDS in Cognitive Sciences Vol.8 No.9