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| = Tutorial 26: Connectivity = | = Tutorial 28: Connectivity = |
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| ''Authors: Francois Tadel, Esther Florin, Sergul Aydore, Syed Ashrafulla, Elizabeth Bock, Sylvain Baillet'' | ''Authors: Hossein Shahabi, Francois Tadel, Esther Florin, Sergul Aydore, Syed Ashrafulla, Elizabeth Bock, Sylvain Baillet'' |
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| During the past few years, the research focus in brain imaging moved from localizing functional regions to understanding how different regions interact together. It is now widely accepted that some of the brain functions are not supported by isolated regions but rather by a dense network of nodes interacting in various ways. In order to quantify the amount of information exchanged between regions, experts in signal processing developed metrics to compare signals recorded in these different regions. | == Introduction == During the past few years, the research focus in brain imaging moved from localizing functional regions to understanding how different regions interact together. It is now widely accepted that some of the brain functions are not supported by isolated regions but rather by a dense network of nodes interacting in various ways. |
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| These inter-regional measures can help us explore the brain dynamics by understanding if two regions are activated synchroneously during a task (functional connectivity) or linked by causal interactions (effective connectivity). This tutorial introduces the measures and display tools available in Brainstorm to explore this inter-regional connectivity. | Brain networks (connectivity) is a recently developed field of neuroscience which investigates interactions among regions of this vital organ. These networks can be identified using a wide range of connectivity measures applied on neurophysiological signals, either in time or frequency domain. The knowledge provides a comprehensive view of brain functions and mechanisms. |
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| <<TableOfContents(2,2)>> | This module of Brainstorm tries to facilitate the computation of brain networks and representation of their corresponding graphs. Figure 1 illustrates a general framework to analyze brain networks. Preprocessing and source localization tasks for a neural data are thoroughly described in previous sections of this tutorial. The connectivity module is designed to carry out remained steps, including the computation of connectivity measures, and statistical analysis and visualizations of networks. |
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| == Connectivity processes == In the Process1 and Process2 tabs, the menu "Connectivity" contains the following options: |
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| * Correlation * Coherence (Imaginary coherence and Magnitude-squared coherence) * Granger causality * Granger causality (spectral) * Phase-locking value (PLV) |
== General terms/considerations for a connectivity analysis == '''Point-based connectivity vs. full network: '''Most of connectivity functions provide you the option to either compute the connectivity between one point (channel) and the rest of the network (1 x N) or the entire network (N x N). While the later calculate the graph thoroughly, the first options enjoys a faster computation and it is more useful when you are interested in connectivity of a ROI with the other regions of the brain. |
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| Each of these metrics offer several variations: | '''Temporal resolution: '''Connectivity networks can be computed in two ways; static and dynamic. In Table1 metrics are classified based on this feature. Dynamic networks can present the time-varying property of the brain. In contrast, the static graphs illustrate a general … which is also helpful in many conditions. The user needs to decide which type of network is more informative for their study. |
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| * '''[1xN]''': Connectivity between one signal and all the other signals in the same set of signals <<BR>> (ie. one sensor and all the other sensors in the same file, one source and all the other sources, etc). The output file has the same dimensions as the input files and can be visualized in a similar way. * '''[NxN]''': Connectivity between all the possible pairs of signals in the input files (recordings, source maps or scouts). The output file is full connectivity matrix [Nsignals x Nsignals x Ntime x Nfreq]. * '''[AxB]''': The Process2 tabs offers more flexibilty. You can select different types of files in the two lists FilesA and FilesB. For example, by selecting recordings in FilesA and sources in FilesB, you can compute the connectivity between one sensor and all the sources. |
'''Time-frequency transformation: ''' |
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| {{attachment:process_list.gif||height="375",width="710"}} | --(Consider how to choose window (length and overlap) depends on frequency bands )-- |
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| == Example 1: Correlation sensor-sources == * In Process1, select |
__''Consequently, computed connectivity matrices in this toolbox can have up to four dimensions; channels x channels x frequency bands x time. ''__ |
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| == Example 2: Coherence scout-scout == == Method: Correlation == == Method: Coherence == == Method: Granger causality == == Method: Phase locking value == == On the hard drive == Document the file tags Document how to extract the connect matrix How to input your own connect matrix |
'''Sensors vs sources: '''The connectivity analysis can be performed either on sensor data (like EEG, MEG time series) or reconstructed sources. |
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| ==== Articles ==== * '''Phase transfer entropy''': Lobier M, Siebenhühner F, Palva S, Palva JM [[http://www.sciencedirect.com/science/article/pii/S1053811913009191|Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions]], NeuroImage 2014, 85:853-872 ==== Forum discussions ==== |
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| * Forum: Reading NxN PLV matrix: http://neuroimage.usc.edu/forums/showthread.php?1681-PLV-NxN-Read-matrix | * Forum: Reading NxN PLV matrix: http://neuroimage.usc.edu/forums/t/pte-how-is-the-connectivity-matrix-stored/4618/2 * Forum: Scout function and connectivity: http://neuroimage.usc.edu/forums/showthread.php?2843 * Forum: Unconstrained sources and connectivity: http://neuroimage.usc.edu/forums/t/problem-with-surfaces-vs-volumes/3261 * Forum: Digonal values: http://neuroimage.usc.edu/forums/t/choosing-scout-function-before-or-after/2454/2 |
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| <<EmbedContent("http://neuroimage.usc.edu/bst/get_prevnext.php?prev=Tutorials/Statistics&next=Tutorials/Scripting")>> | <<EmbedContent("http://neuroimage.usc.edu/bst/get_prevnext.php?prev=Tutorials/GroupAnalysis&next=Tutorials/Scripting")>> |
Tutorial 28: Connectivity
[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE]
Authors: Hossein Shahabi, Francois Tadel, Esther Florin, Sergul Aydore, Syed Ashrafulla, Elizabeth Bock, Sylvain Baillet
Introduction
During the past few years, the research focus in brain imaging moved from localizing functional regions to understanding how different regions interact together. It is now widely accepted that some of the brain functions are not supported by isolated regions but rather by a dense network of nodes interacting in various ways.
Brain networks (connectivity) is a recently developed field of neuroscience which investigates interactions among regions of this vital organ. These networks can be identified using a wide range of connectivity measures applied on neurophysiological signals, either in time or frequency domain. The knowledge provides a comprehensive view of brain functions and mechanisms.
This module of Brainstorm tries to facilitate the computation of brain networks and representation of their corresponding graphs. Figure 1 illustrates a general framework to analyze brain networks. Preprocessing and source localization tasks for a neural data are thoroughly described in previous sections of this tutorial. The connectivity module is designed to carry out remained steps, including the computation of connectivity measures, and statistical analysis and visualizations of networks.
![[ATTACH]](/brainstorm/Tutorials/Connectivity?action=AttachFile&do=upload_form&ticket=0067acf3f1.bb8a5d2b30eafd3f337ca871481cf48526d4e8a5&target=FlowChartGeneral.png "[ATTACH]"){kind=small}
General terms/considerations for a connectivity analysis
Point-based connectivity vs. full network: Most of connectivity functions provide you the option to either compute the connectivity between one point (channel) and the rest of the network (1 x N) or the entire network (N x N). While the later calculate the graph thoroughly, the first options enjoys a faster computation and it is more useful when you are interested in connectivity of a ROI with the other regions of the brain.
Temporal resolution: Connectivity networks can be computed in two ways; static and dynamic. In Table1 metrics are classified based on this feature. Dynamic networks can present the time-varying property of the brain. In contrast, the static graphs illustrate a general … which is also helpful in many conditions. The user needs to decide which type of network is more informative for their study.
Time-frequency transformation:
Consider how to choose window (length and overlap) depends on frequency bands
Consequently, computed connectivity matrices in this toolbox can have up to four dimensions; channels x channels x frequency bands x time.
Sensors vs sources: The connectivity analysis can be performed either on sensor data (like EEG, MEG time series) or reconstructed sources.
Additional documentation
Articles
Phase transfer entropy: Lobier M, Siebenhühner F, Palva S, Palva JM Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions, NeuroImage 2014, 85:853-872
Forum discussions
Forum: Connectivity matrix storage:http://neuroimage.usc.edu/forums/showthread.php?1796
Forum: Comparing coherence values: http://neuroimage.usc.edu/forums/showthread.php?1556
Forum: Reading NxN PLV matrix: http://neuroimage.usc.edu/forums/t/pte-how-is-the-connectivity-matrix-stored/4618/2
Forum: Scout function and connectivity: http://neuroimage.usc.edu/forums/showthread.php?2843
Forum: Unconstrained sources and connectivity: http://neuroimage.usc.edu/forums/t/problem-with-surfaces-vs-volumes/3261
Forum: Digonal values: http://neuroimage.usc.edu/forums/t/choosing-scout-function-before-or-after/2454/2