Functional Connectivity and Network Methods
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1 18/Sep/2013" Functional Connectivity and Network Methods with functional magnetic resonance imaging" Enrico Glerean (MSc), Brain & Mind Lab, BECS, Aalto University" becs.aalto.fi/bml
2 2 Questions" WHY DO WE STUDY BRAIN NETWORKS?" HOW DO WE STUDY BN WITH FMRI?"
3 Why do we want to study brain connectivity?! The brain is a network ~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!!!
4 How do we study brain connectivity? Outline of this lecture! Concepts of functional connectivity (FC)! Overview of methods to compute FC! Artefacts and confounds in FC! Preprocessing for FC! Quantifying results and graph theoretical measures (mini-course on network science)! Tools and resources"
5 Three important References " Network modelling methods for FMRI " " Smith S.M. et al.; Neuroimage 2011,!! doi: /j.neuroimage !! Complex brain networks: graph theoretical analysis of structural and functional systems"! Bullmore, E, Sporns, O; Nature Rev. Neuroscience 2009 doi: /nrn2575! " Networks of the Brain Sporns, O; 2010, MIT Press.!
6 Connectivity in neuroscience" CONCEPTS"
7 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)!
8 From univariate to multivariate (via bivariate)! 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"
9 Functional connectivity is multivariate! Functionally connected = there is a relationship between two or more voxel time series! Pairwise (bivariate / multivariate) = we consider two time series and compute their relationship to build a network. Repeat for all pairs and then use multivariate approaches e.g. network science." Multivariate = consider multiple voxels at once (PCA, ICA, MVPA)"
10
11 A (complex) network, a graph! Newman, M. E. J., Networks: An introduction. Oxford University Press, Oxford, March 2010.!
12 Directed and undirected graphs! Newman, M. E. J., Networks: An introduction. Oxford University Press, Oxford, March 2010.!
13 Representation of networks! Source: Jari Saramäkiʼs course slides! Adjacency list" Adjacency matrix"
14 Connectivity with fmri" DEFINITIONS AND METHODS"
15 Definitions Functional and effective connectivity! Functional connectivity = statistical dependencies among remote neurophysiological events! - Pairwise and data driven! - No direction in the estimated connections! Effective connectivity = the influence that one neural system exerts over another! - Estimates the direction of influence between nodes in the network! - Lag based methods (Granger causality)! - Model based (Bayesian methods such as Dynamic Causal Modelling! - Higher order statistics via ICA (e.g. LiNGAM)!
16 Paradigms for functional connectivity Resting state FC Looking at spontaneous BOLD activity while the subject is in the scanner Correlated with anatomy! Task related FC The subject is performing a task with multiple conditions (usually block design or naturalistic design, i.e. a block design with longer blocks)!
17 Building a functional network! At each node we measure a time series! b 1 (t)! b 2 (t)!
18 Nodes in fmri FC! A node is a voxel! - At 2mm isotropic voxels we have ~160K nodes, i.e. 12.8e9 links!! - At 6mm isotropic voxels we have ~6K nodes, i.e. 18e6 links! A node is a region of interest (ROI)! - We consider multiple voxels that are anatomically defined and derive one time series (using average or first PC) [e.g. atlas based: AAL atlas, Harvard Oxford atlas, UCLA atlas]! - We consider a seed: a sphere centred at a specific location (usual size of diameter is 1cm) [based on literature, or nodes templates e.g. Functional network organization of the human brain Power JD, et al. Neuron Nov 17; 72(4): ! - WARNING: selection of ROIs can introduce bias!
19 Building a functional network! At each node we measure a time series Letʼs compute their similarity" b 1 (t)! b 2 (t)!
20 Building a functional network! Similarity value used as weight of the edge between the two nodes! r 12! b 1 (t)! r 12! e.g. Pearsonʼs correlation:! r 12 = corr(b 1 (t),b 2 (t))! b 2 (t)!
21 Methods for similarity between time series! Pearsonʼs correlation: simple correlation" Partial correlation: choose a pair of nodes, regress out all other nodes (more towards a multivariate than bivariate)! Regularised inverse covariance: useful for short sess.! Mutual information: (non)linear share of information! Coherence: looking at cross-spectral similarity between a frequency representation of the time serience!
22 Which one is the best method?! The answer is: it depends.! If you are looking for subtle differences e.g. between groups or between conditions, some more refined measures could perform better (Smith et al. showed partial correlation, inverse covariance and Bayes-net methods as winners)! However, in most cases simple linear correlation is enough, see Hlinka, J., et al (2011). Functional connectivity in resting-state fmri: is linear correlation sufficient? NeuroImage, 54(3), doi: / j.neuroimage !
23 Artefacts and confounds" EFFECTS OF HEAD MOTION AND NOISE"
24 How does motion affect FC?! Carefully assessed by a recent paper Power, J. D et al. (2011) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. doi: / j.neuroimage " They show that motion affects FC" Propose a method to quantify motion (and quality of motion corrected data)! Propose a method to mitigate effects of motion with FC!
25 Quantify amount of motion!
26 Quantify amount of motion! Given the rigid body parameters! Estimate the Framewise Displacement at each time point i! Given a subset of voxels or ROIs! Estimate the DVARS (Derivative of VARS = variance over voxels) [Warn: handle with care with non rest data]!
27 Scrubbing! Once you identified the bad time points, you do scrubbing: i.e. you avoid considering them in your pairwise correlations (discard subjects that had less than 5 minutes left)!
28 Effects of motion on FC! In most cases scrubbing recovered long distance connections! In few cases it mitigated false positives" Short distance connections are basically untouched!
29 Sources of noise in the BOLD signal! Physiological noise but also scanner drift" Having the same noise in two time series will cause them to be similar because of the noise" Ideally you should de-noise the data, often this is not possible and data is band-pass filtered ( Hz)" Source: Glerean et al 2012, Brain Connectivity doi: /brain !
30 Preprocessing" PREPROCESSING PIPELINE FOR FUNCTIONAL CONNECTIVITY"
31 Preprocessing pipeline for FC! Standard pipeline: motion correction, coregistration to standard brain, slice time correction (optional see next)! Spatial smoothing (conseq. of spatial downsampling)! Scrubbing and/or de-noising and/or band-pass filtering ( Careful with that axe, Eugene )" Regressing out the six rigid body parameters! Regressing out signal at ventricles (!)! Regressing out average white matter signal (!!)! Regressing out global mean of all voxels (!!!)"! Koene R. A. Van Dijk et al. Intrinsic Functional Connectivity As a Tool For Human Connectomics: Theory, Properties, and Optimization, 2010, Journal of Neurophysiology doi: / jn !
32 Quantifying results" SEED BASED ANALYSIS" NETWORK ANALYSIS"
33 Seed based analysis! Connectivity from one seed to rest of the brain! Usually a sphere in a selected region based on literature" You will end up with one correlation map per subject per task, and then you look at group or task differences!!(t-test, permutation tests, etc)!
34 Network analysis! First build the network by looking at functional similarity for each pair of nodes" Obtain one network per subject or per task! Extract meaningful information from the networks with tools from Network science and graph theory"
35 Building the network! First step is to threshold the network to obtain a sparse graph! Thresholding based on statistics or percentage of links" b 1 (t)! r 12! b 2 (t)! threshold!
36 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?"
37 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?"
38 Mesoscopic (module level) measures! Communities/clusters" " Finding subset of nodes that are forming a module! Comparisons of modularity" " How different brains have different (sub)networks Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), ! Alexander-Bloch, et al. (2012). The discovery of population differences in network community structure NeuroImage, 59(4), !
39 Global measures! Clustering"! How densely connected are the nodes?! Path length" " Average distance between nodes" Small world-ness Many short distance links (high clustering) and few important long distance ones (short path)!
40 Quantifying network results: statistics! Micro/meso/macro scopic measures can be compared across groups or conditions with standard tests! Correlation between measures " Non independency of values" Permutation based tests "
41 Tools and resources"
42 Interesting toolboxes! CONN toolbox Functional Connectivity toolbox Brain connectivity toolbox " All Matlab based but check NiTime for Python stuff"
43 Interesting resources 1000 resting state subjects Human Connectome Project ADHD Alzheimerʼs Disease Neuroimaging Initiative "
Human Brain Networks. Aivoaakkoset BECS-C3001"
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