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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 signals precisely in the brain 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 responding synchroneously to the same task (functional connectivity) or linked by a causal interactions (effective connectivity). This tutorial introduces the measures and the 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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== On the hard drive == Document the file tags |
{{attachment:FlowChartGeneral.png||height="230",width="850"}} |
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Document how to extract the connect matrix How to input your own connect matrix |
== General terms/considerations for a connectivity analysis == |
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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.
General terms/considerations for a connectivity analysis
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