Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

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Issue Date
2014-03-19Author
Stephen, Emily P.
Lepage, Kyle Q.
Eden, Uri T.
Brunner, Peter
Schalk, Gerwin
Brumberg, Jonathan S.
Guenther, Frank H.
Kramer, Mark A.
Publisher
Frontiers
Type
Article
Article Version
Scholarly/refereed, publisher version
Metadata
Show full item recordAbstract
The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.
Description
This is the publisher's version, also available electronically from http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00031/abstract
ISSN
1662-5188Collections
Citation
Stephen EP, Lepage KQ, Eden UT, Brunner P, Schalk G, Brumberg JS, Guenther FH and Kramer MA (2014) Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses. Front. Comput. Neurosci. 8:31. http://dx.doi.org/10.3389/fncom.2014.00031
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