%0 Journal Article %J Front Hum Neurosci %D 2015 %T Electrocorticographic representations of segmental features in continuous speech. %A Lotte, Fabien %A Jonathan S Brumberg %A Peter Brunner %A Gunduz, Aysegul %A A L Ritaccio %A Guan, Cuntai %A Gerwin Schalk %K electrocorticography (ECoG) %K manner of articulation %K place of articulation %K speech processing %K voicing %X Acoustic speech output results from coordinated articulation of dozens of muscles, bones and cartilages of the vocal mechanism. While we commonly take the fluency and speed of our speech productions for granted, the neural mechanisms facilitating the requisite muscular control are not completely understood. Previous neuroimaging and electrophysiology studies of speech sensorimotor control has typically concentrated on speech sounds (i.e., phonemes, syllables and words) in isolation; sentence-length investigations have largely been used to inform coincident linguistic processing. In this study, we examined the neural representations of segmental features (place and manner of articulation, and voicing status) in the context of fluent, continuous speech production. We used recordings from the cortical surface [electrocorticography (ECoG)] to simultaneously evaluate the spatial topography and temporal dynamics of the neural correlates of speech articulation that may mediate the generation of hypothesized gestural or articulatory scores. We found that the representation of place of articulation involved broad networks of brain regions during all phases of speech production: preparation, execution and monitoring. In contrast, manner of articulation and voicing status were dominated by auditory cortical responses after speech had been initiated. These results provide a new insight into the articulatory and auditory processes underlying speech production in terms of their motor requirements and acoustic correlates. %B Front Hum Neurosci %V 9 %P 97 %8 02/2015 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/25759647 %R 10.3389/fnhum.2015.00097 %0 Journal Article %J Frontiers in Computational Neuroscience %D 2014 %T Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses. %A Stephen, Emily P %A Lepage, Kyle Q %A Eden, Uri T %A Peter Brunner %A Gerwin Schalk %A Jonathan S Brumberg %A Guenther, Frank H %A Kramer, Mark A %K canonical correlation %K coherence %K ECoG %K EEG %K functional connectivity %K MEG %X 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. %B Frontiers in Computational Neuroscience %V 8 %8 03/2014 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/24678295 %N 31 %R 10.3389/fncom.2014.00031