title: Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.
abstract: 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.
authors.affiliation: Center for Computational Neuroscience and Neural Technology, Boston University Boston, MA, USA.
authors.forename: Emily P
authors.initials: EP
authors.lastname: Stephen
authors.affiliation: Department of Mathematics and Statistics, Boston University Boston, MA, USA.
authors.forename: Kyle Q
authors.initials: KQ
authors.lastname: Lepage
authors.affiliation: Department of Mathematics and Statistics, Boston University Boston, MA, USA.
authors.forename: Uri T
authors.initials: UT
authors.lastname: Eden
authors.affiliation: Brain-Computer Interface Research and Development Program, Wadsworth Center Albany, NY, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Brunner
authors.affiliation: Brain-Computer Interface Research and Development Program, Wadsworth Center Albany, NY, USA.
authors.forename: Gerwin
authors.initials: G
authors.lastname: Schalk
authors.affiliation: Department of Speech-Language-Hearing, University of Kansas Lawrence, KS, USA.
authors.forename: Jonathan S
authors.initials: JS
authors.lastname: Brumberg
authors.affiliation: Center for Computational Neuroscience and Neural Technology, Boston University Boston, MA, USA ; Department of Speech, Language, and Hearing Sciences, Boston University Boston, MA, USA ; Department of Biomedical Engineering, Boston University Boston, MA, USA.
authors.forename: Frank H
authors.initials: FH
authors.lastname: Guenther
authors.affiliation: Department of Mathematics and Statistics, Boston University Boston, MA, USA.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Kramer
keywords: ECoG; EEG; MEG; canonical correlation; coherence; functional connectivity