Human Brain Networks. Aivoaakkoset BECS-C3001"

Similar documents
Human! Brain! Networks!

Functional Connectivity and Network Methods

Hierarchical Clustering Identifies Hub Nodes in a Model of Resting-State Brain Activity

Clicker Question. Discussion Question

Discovering the Human Connectome

NCNC FAU. Modeling the Network Architecture of the Human Brain

MULTISCALE MODULARITY IN BRAIN SYSTEMS

How Brain Structure Constrains Brain Function. Olaf Sporns, PhD

A Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain

腦網路連結分析 A Course of MRI

Applications of Complex Networks

Data-Driven Network Neuroscience. Sarah Feldt Muldoon Mathematics, CDSE Program, Neuroscience Program DAAD, May 18, 2016

Networks in Biology and Neuroscience

Connectomics analysis and parcellation of the brain based on diffusion-weighted fiber tractography

Spectral Perturbation of Small-World Networks with Application to Brain Disease Detection

Network Theory in Neuroscience

6.207/14.15: Networks Lecture 7: Search on Networks: Navigation and Web Search

Effective Connectivity & Dynamic Causal Modelling

New Machine Learning Methods for Neuroimaging

Structure of Brain Functional Networks

Common Connectome Constraints: From C. elegans and Drosophila to Homo sapiens

arxiv: v1 [q-bio.nc] 19 Aug 2016

Emergent Phenomena on Complex Networks

Imaging Brain Structure and Function

Dynamic Causal Modelling for EEG/MEG: principles J. Daunizeau

Dynamic Causal Modelling for fmri

Stochastic Dynamic Causal Modelling for resting-state fmri

Complex (Biological) Networks

Graph theory for complex Networks-I

Higher Order Cartesian Tensor Representation of Orientation Distribution Functions (ODFs)

Dynamic Modeling of Brain Activity

Supplementary Material & Data. Younger vs. Older Subjects. For further analysis, subjects were split into a younger adult or older adult group.

Bioinformatics 2. Yeast two hybrid. Proteomics. Proteomics

Network scaling effects in graph analytic studies of human resting-state fmri data

NeuroImage 62 (2012) Contents lists available at SciVerse ScienceDirect. NeuroImage. journal homepage:

Spiking Neural P Systems and Modularization of Complex Networks from Cortical Neural Network to Social Networks

COGNITIVE INFORMATION DYNAMICS:

First Technical Course, European Centre for Soft Computing, Mieres, Spain. 4th July 2011

Basic MRI physics and Functional MRI

RS-fMRI analysis in healthy subjects confirms gender based differences

Neuroinformatics. Giorgio A. Ascoli. Erik De Schutter David N. Kennedy IN THIS ISSUE. Editors. Medline/Pubmed/Index Medicus Science Citation Index

An Introduction to Brain Networks

Applications To Human Brain Functional Networks

Informational Theories of Consciousness: A Review and Extension

Multilayer and dynamic networks

HIERARCHICAL GRAPHS AND OSCILLATOR DYNAMICS.

M/EEG source analysis

arxiv: v1 [cs.gr] 7 Jun 2012

Network Science (overview, part 1)

Identifying Relationships in Functional and Structural Connectome Data Using a Hypergraph Learning Method

The Physics in Psychology. Jonathan Flynn

Proteomics. Yeast two hybrid. Proteomics - PAGE techniques. Data obtained. What is it?

Theoretical Neuroanatomy: Analyzing the Structure, Dynamics, and Function of Neuronal Networks

Complex networks: an introduction

Brains and Computation

Causal modeling of fmri: temporal precedence and spatial exploration

Neuroscience Introduction

A Neurosurgeon s Perspectives of Diffusion Tensor Imaging(DTI) Diffusion Tensor MRI (DTI) Background and Relevant Physics.

Cloud-Based Computational Bio-surveillance Framework for Discovering Emergent Patterns From Big Data

Research Article A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain

An eccentric perspective on brain networks

Brain Network Analysis

Complex (Biological) Networks

Collecting Aligned Activity & Connectomic Data Example: Mouse Vibrissal Touch Barrel Cortex Exploiting Coherence to Reduce Dimensionality Example: C.

Degree Distribution: The case of Citation Networks

Network Modeling and Functional Data Methods for Brain Functional Connectivity Studies. Kehui Chen

EL-GY 6813/BE-GY 6203 Medical Imaging, Fall 2016 Final Exam

Causality and communities in neural networks

Analyzing Structural and Symmetrical Properties of C. Elegans Neural Network

Network Biology: Understanding the cell s functional organization. Albert-László Barabási Zoltán N. Oltvai

Artificial Neural Network and Fuzzy Logic

arxiv: v2 [q-bio.nc] 27 Sep 2018

Research Article Thalamus Segmentation from Diffusion Tensor Magnetic Resonance Imaging

New Directions in Computer Science

SYSTEMS BIOLOGY 1: NETWORKS

Diffusion MRI. Outline. Biology: The Neuron. Brain connectivity. Biology: Brain Organization. Brain connections and fibers

UNderstanding the large-scale connectivity of the human

Evolutionary Games on Networks: from Brain Connectivity to Social Behavior

Signal, donnée, information dans les circuits de nos cerveaux

Parcellation of the Thalamus Using Diffusion Tensor Images and a Multi-object Geometric Deformable Model

Hierarchy. Will Penny. 24th March Hierarchy. Will Penny. Linear Models. Convergence. Nonlinear Models. References

Applying network science has become a common. A Mapping Between Structural and Functional Brain Networks

Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders

Infering and Calibrating Triadic Closure in a Dynamic Network

Complex Social System, Elections. Introduction to Network Analysis 1

The Structured Controllability Radius of Symmetric (Brain) Networks

Mathematical models for computational network analysis based on graph theory have risen to the forefront of the investigation of brain connectivity as

An Algebraic Generalization for Graph and Tensor-Based Neural Networks

Integrative Methods for Functional and Structural Connectivity

Social Networks. Chapter 9

Mechanistic and topological explanations: an introduction

Diffusion Weighted MRI. Zanqi Liang & Hendrik Poernama

ROI analysis of pharmafmri data: an adaptive approach for global testing

Rich-club network topology to minimize synchronization cost due to phase difference among frequency-synchronized oscillators.

Extracting the Core Structural Connectivity Network: Guaranteeing Network Connectedness Through a Graph-Theoretical Approach

From Pixels to Brain Networks: Modeling Brain Connectivity and Its Changes in Disease. Polina Golland

Introduction ROMAIN BRETTE AND ALAIN DESTEXHE

Imaging and Image Processing for Plant Phenotyping

NeuroImage 54 (2011) Contents lists available at ScienceDirect. NeuroImage. journal homepage:

Global Layout Optimization of Olfactory Cortex and of Amygdala of Rat

Transcription:

Human Brain Networks Aivoaakkoset BECS-C3001" Enrico Glerean (MSc), Brain & Mind Lab, BECS, Aalto University" www.glerean.com @eglerean becs.aalto.fi/bml enrico.glerean@aalto.fi"

Why?" 1. WHY BRAIN NETWORKS?"

Why do we want to study brain networks?! The brain is a network with ~10^10 neurons and ~10^4 connections per neuron! As for genomics in the 20 th century, many authors are now praising the connectomics as the current revolution in neuroscience! Multi-million projects like the Human Connectome Project, the BRAIN initiative" Charting the connectome presents challenges!!!

What?" 2. WHAT IS A CONNECTOME?"

The connectome! The connectome is the complete description of the structural connectivity (the physical wiring) of an organismʼs nervous system.! Olaf Sporns (2010), Scholarpedia, 5(2):5584.!

Human genome vs Human connectome! The human genome contains over 3 billion base pairs organized into 22 paired chromosomes! The human connectome contains ~10^14 connections!

What?" 3. WHAT IS A NETWORK?"

A (complex) network, a graph! Newman, M. E. J., Networks: An introduction. Oxford University Press, Oxford, March 2010.!

Many types of networks! Physical networks" - Power grid network! - Physical layer of the internet! - Water distribution network! - Transportation networks (roads, rails)! Non-physical networks" - Social networks (Facebook, Twitter, etc.)! - Stock Market! - IP layer of the internet! - Citations in science!

What?" 4. WHAT IS BRAIN CONNECTIVITY?"

Connectivity in neuroscience! Structural connectivity (estimating actual connections)" - Invasive (tract tracing methods, 2 photon calcium imaging)! - Non invasive (Diffusion Tensor and Diffusion Spectral Imaging)! Functional connectivity (based on temporal co-variance )" - Invasive (intracranial recordings)! - Non invasive (fmri, M/EEG, simulated data) Craddock, et al. (2013). Imaging human connectomes at the macroscale. Nature Methods, 10(6), 524 539. (*)!

How?" 5. HOW DO WE ESTIMATE STRUCTURAL BRAIN NETWORKS NON INVASIVELY?"

Diffusion Magnetic Resonance Imaging! http://www.humanconnectomeproject.org/gallery/"

Diffusion MRI! For every voxel we measure the diffusion of water" We can determine the main direction(s) along which the fibre is going "

Diffusion MRI! By following the directions of diffusion we can reconstruct the tracts"

Wakana, S., et al. (2004). Fiber tractbased atlas of human white matter anatomy. Radiology, 230(1), 77 87.!

How?" 6. HOW DO WE ESTIMATE FUNCTIONAL BRAIN NETWORKS NON INVASIVELY?"

Functional magnetic resonance imaging (fmri)! We measure multiple time series at once! We can consider them independently (e.g. GLM) or we can look at mutual relationships" Blood Oxygen Level signal"

Building a functional network! At each node we measure a time series We compute their similarity" b 1 (t)! b 2 (t)!

Building a functional network! Similarity value used as weight of the edge between the two nodes. Repeat for each pair of nodes.! r 12! b 1 (t)! r 12! e.g. Pearsonʼs correlation:! r 12 = corr(b 1 (t),b 2 (t))! b 2 (t)!

The activity of the brain at rest is ideal for estimating the connectome! By looking at regions that change together in time we can estimate their connectivity" Raichle, M. E. (2010). Two views of brain function. Trends in Cognitive Sciences, 14(4)!

Network topology" NETWORK LEVEL FEATURES"

What?" 7. WHAT IS A SMALL WORLD NETWORK?"

The small world experiment Stanley Milgram (1969) Try to send a letter to Boston through a chain of people by only forward it to a friend who might know the final recipient! Six degrees of separation i.e. an average path of 6 links in the network!

Small world networks! Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440 2. doi:10.1038/30918!

Small world networks! Small world networks are present in biological system as an efficient way to keep the average path low and limit connection cost. The brain is a small world network."

Why?" 8. WHY IS THE BRAIN A SMALL WORLD NETWORK?"

The small-world configuration is the optimal to optimize communication cost and efficiency! Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature reviews. Neuroscience, 13(5), 336 49.(*) "

Network topology" NODE LEVEL FEATURES"

What?" 9. WHAT IS A HUB?"

What is a hub?! A hub is the effective center of an activity, region, or network! i.e. an important node in the network"

How?" 10. HOW CAN WE QUANTIFY A HUB?"

Microscopic (node level) measures! Node degree/strength" " How strong is a node?! Clustering"! How close is the node!! with the neighbours?" Closeness centrality" " How distant is the node?! Betweenness centrality"! How many shortest paths through the node?"

What?" 11. WHAT ARE THE HUBS IN THE BRAIN?"

Cortical hubs in the human brain! Hagmann, P., et al. (2008). Mapping the structural core of human cerebral cortex. PLoS biology, 6(7), e159.!

Cortical hubs in the human brain! Buckner, R. L., et al. (2009). Cortical hubs revealed by intrinsic functional connectivity. The Journal of neuroscience 29(6), 1860 73.!

Sub-cortical hubs in the human brain: the thalamus! Zhang et al. (2010) Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy. Neuroimage. 2010 Oct 1;52(4):1289-301.!

Why?" 12. WHY ARE THERE HUBS IN THE BRAIN?"

Small world topology implies segregation and integration! Small world topology implies high clustering: within a region we have more connections, regions are specialized (e.g. visual cortex, auditory cortex)! Small world topology implies short path: densely connected regions are joined together by longrange links! Clustering -> Segregation" Short path -> Integration! Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature reviews. Neuroscience, 13(5), 336 49.(*) "

A rich club of strong hubs is at the core of the human brain" Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature reviews. Neuroscience, 13(5), 336 49.(*) "

Where?" 13. WHERE ARE THE RICH CLUB NODES IN THE BRAIN? "

Location of the rich club" Van den Heuvel, M. P., & Sporns, O. (2013). An Anatomical Substrate for Integration among Functional Networks in Human Cortex. The Journal of neuroscience, 33(36), 14489 500.!

What?" 14. WHAT IS A MODULE? "

Quantifying modules in networks! Communities/clusters" " Finding subsets of nodes that are forming a module, i.e. they are more connected with each other than with other parts of the network! Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75 174!

What?" 15. WHAT ARE THE MODULES IN THE BRAIN?"

The networks of the human brain! We look at which regions are more connected with each other (clustering)" We identify ~6 main modules in the human cortex that corresponds to important cognitive functions! They are often called networks although they are technically sub-networks!

Zhang, D., & Raichle, M. E. (2010). Disease and the brainʼs dark energy. Nature reviews. Neurology, 6(1), 15 28."

What?" 15. WHAT IS THE OVERLAP BETWEEN MODULES AND RICH HUBS?"

Van den Heuvel & Sporns (2013). JNeurosci.!

What?" 16. WHAT IS THE RELATIONSHIP BETWEEN HUBS AND BRAIN ENERGY CONSUMPTION?"

Energy consumption in the brain! The most important (central) hubs are those with higher glycolytic index, i.e. higher metabolic cost." Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature reviews. Neuroscience, 13(5), 336 49.(*)!

What?" 17. WHAT IS THE IMPACT OF THIS RESEARCH ON SOCIETY?"

Mapping the connectome and clinical applications! The connectome will provide novel insights on the functioning of the brain" There are multiple mental diseases that are caused by dysfunctions of brain networks, for example:" Alzheimerʼs disease! Schizophrenia! Autism!

Alzheimerʼs disease! The most expensive hubs are attacked by the disease"

Schizophrenia! Unbalanced small-worldness"

The future?" 18. WHAT ARE THE FUTURE CHALLENGES?"

Evolution of brain networks in time! We know that brain networks evolve in time (e.g. during development)" Power et al (2010) Neuron!

Evolution of brain networks in time! We know that brain networks evolve in time (e.g. during development)" How can we quantify faster changes due to stimuli?" How can we quantify and predict the impact of brain diseases in time?"

"! Two important references and a book! Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature reviews. Neuroscience, 13(5), 336 49.! " Craddock, et al. (2013). Imaging human connectomes at the macroscale. Nature Methods, 10(6), 524 539.! " Networks of the Brain Sporns, O; 2010, MIT Press.!