@article {3356,
	title = {Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.},
	journal = {Frontiers in Computational Neuroscience},
	volume = {8},
	year = {2014},
	month = {03/2014},
	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{\textemdash}both in the functional network edges and the corresponding aggregate measures of network topology{\textemdash}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{\textemdash}appropriate for static and dynamic network inference and different statistical measures of coupling{\textemdash}permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.},
	keywords = {canonical correlation, coherence, ECoG, EEG, functional connectivity, MEG},
	doi = {10.3389/fncom.2014.00031},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/24678295},
	author = {Stephen, Emily P and Lepage, Kyle Q and Eden, Uri T and Peter Brunner and Gerwin Schalk and Jonathan S Brumberg and Guenther, Frank H and Kramer, Mark A}
}
@article {2950,
	title = {Spatial and temporal relationships of electrocorticographic alpha and gamma activity during auditory processing.},
	journal = {NeuroImage},
	volume = {97},
	year = {2014},
	month = {08/2014},
	pages = {188-95},
	abstract = {Neuroimaging approaches have implicated multiple brain sites in musical perception, including the posterior part of the superior temporal gyrus and adjacent perisylvian areas. However, the detailed spatial and temporal relationship of neural signals that support auditory processing is largely unknown. In this study, we applied a novel inter-subject analysis approach to electrophysiological signals recorded from the surface of the brain (electrocorticography (ECoG)) in ten human subjects. This approach allowed us to reliably identify those ECoG features that were related to the processing of a complex auditory stimulus (i.e., continuous piece of music) and to investigate their spatial, temporal, and causal relationships. Our results identified stimulus-related modulations in the alpha (8-12 Hz) and high gamma (70-110 Hz) bands at neuroanatomical locations implicated in auditory processing. Specifically, we identified stimulus-related ECoG modulations in the alpha band in areas adjacent to primary auditory cortex, which are known to receive afferent auditory projections from the thalamus (80 of a total of 15,107 tested sites). In contrast, we identified stimulus-related ECoG modulations in the high gamma band not only in areas close to primary auditory cortex but also in other perisylvian areas known to be involved in higher-order auditory processing, and in superior premotor cortex (412/15,107 sites). Across all implicated areas, modulations in the high gamma band preceded those in the alpha band by 280 ms, and activity in the high gamma band causally predicted alpha activity, but not vice versa (Granger causality, p<1e(-8)). Additionally, detailed analyses using Granger causality identified causal relationships of high gamma activity between distinct locations in early auditory pathways within superior temporal gyrus (STG) and posterior STG, between posterior STG and inferior frontal cortex, and between STG and premotor cortex. Evidence suggests that these relationships reflect direct cortico-cortical connections rather than common driving input from subcortical structures such as the thalamus. In summary, our inter-subject analyses defined the spatial and temporal relationships between music-related brain activity in the alpha and high gamma bands. They provide experimental evidence supporting current theories about the putative mechanisms of alpha and gamma activity, i.e., reflections of thalamo-cortical interactions and local cortical neural activity, respectively, and the results are also in agreement with existing functional models of auditory processing.},
	keywords = {alpha and high gamma activity, auditory processing, electrocorticography (ECoG), functional connectivity, granger causality, thalamo-cortical interactions},
	doi = {10.1016/j.neuroimage.2014.04.045},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/24768933},
	author = {Potes, Cristhian and Peter Brunner and Gunduz, Aysegul and Robert T. Knight and Gerwin Schalk}
}
