|
Size: 1910
Comment:
|
Size: 4970
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 1: | Line 1: |
| = Tutorial 26: Connectivity = | = Tutorial 28: Connectivity = |
| Line 4: | Line 4: |
| ''Authors: Francois Tadel, Esther Florin, Sergul Aydore, Syed Ashrafulla, Elizabeth Bock, Sylvain Baillet'' | ''Authors: Hossein Shahabi, Mansoureh Fahimi, Francois Tadel, Esther Florin, Sergul Aydore, Syed Ashrafulla, Elizabeth Bock, Sylvain Baillet'' |
| Line 6: | Line 6: |
| 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. |
| Line 8: | Line 9: |
| 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. |
| Line 10: | Line 11: |
| <<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. |
| Line 12: | Line 13: |
| == On the hard drive == Document the file tags |
{{attachment:FlowChartGeneral.png||height="230",width="850"}} |
| Line 15: | Line 15: |
| Document how to extract the connect matrix | == General terms/considerations for a connectivity analysis == '''Sensors vs sources: '''The connectivity analysis can be performed either on sensor data (like EEG, MEG time series) or reconstructed sources. |
| Line 17: | Line 18: |
| How to input your own connect matrix | '''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 )-- '''Output data structure:''' __''Consequently, computed connectivity matrices in this toolbox can have up to four dimensions; channels x channels x frequency bands x time. ''__ == Method selection == --(a)-- == Granger Causality: Background == Granger Causality (GC) is a method of functional connectivity, adapted by Clive Granger in the 1960s, but later refined by John Geweke in the form that is used today. Granger Causality is originally formulated in the economics but has caught the attention of the neuroscience community in recent years. Before this, neuroscience traditionally relied on stimulation or lesioning a part of the nervous system to study its effect on another part. However, Granger Causality made it possible to estimate the statistical influence without requiring direct intervention (ref: wiener-granger causality a well-established methodology). |
| Line 20: | Line 37: |
| ==== 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 ==== |
|
| Line 21: | Line 42: |
| Line 22: | Line 44: |
| * 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 |
| Line 26: | Line 55: |
| <<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, Mansoureh Fahimi, 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=0067abb0cd.fb4c958c8d1eb266c29e7c705a03b948d45ad7bd&target=FlowChartGeneral.png "[ATTACH]"){kind=small}
General terms/considerations for a connectivity analysis
Sensors vs sources: The connectivity analysis can be performed either on sensor data (like EEG, MEG time series) or reconstructed sources.
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
Output data structure:
Consequently, computed connectivity matrices in this toolbox can have up to four dimensions; channels x channels x frequency bands x time.
Method selection
a
Granger Causality: Background
Granger Causality (GC) is a method of functional connectivity, adapted by Clive Granger in the 1960s, but later refined by John Geweke in the form that is used today. Granger Causality is originally formulated in the economics but has caught the attention of the neuroscience community in recent years. Before this, neuroscience traditionally relied on stimulation or lesioning a part of the nervous system to study its effect on another part. However, Granger Causality made it possible to estimate the statistical influence without requiring direct intervention (ref: wiener-granger causality a well-established methodology).
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