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.

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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.

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Tutorials/Connectivity (last edited 2019-02-14 08:32:22 by ?Hossein Shahabi)