02409nas a2200217   4500008004100000022001400041245012900055210006900184260000800253300001300261490000700274520171000281100002701991700002502018700002402043700002002067700001902087700001802106700001902124856004802143       2016                        eng d  a1932-620300aSpatio-Temporal Progression of Cortical Activity Related to Continuous Overt and Covert Speech Production in a Reading Task.0 aSpatioTemporal Progression of Cortical Activity Related to Conti  cNov  ae01668720 v113 aHow the human brain plans, executes, and monitors continuous and fluent speech has remained largely elusive. For example, previous research has defined the cortical locations most important for different aspects of speech function, but has not yet yielded a definition of the temporal progression of involvement of those locations as speech progresses either overtly or covertly. In this paper, we uncovered the spatio-temporal evolution of neuronal population-level activity related to continuous overt speech, and identified those locations that shared activity characteristics across overt and covert speech. Specifically, we asked subjects to repeat continuous sentences aloud or silently while we recorded electrical signals directly from the surface of the brain (electrocorticography (ECoG)). We then determined the relationship between cortical activity and speech output across different areas of cortex and at sub-second timescales. The results highlight a spatio-temporal progression of cortical involvement in the continuous speech process that initiates utterances in frontal-motor areas and ends with the monitoring of auditory feedback in superior temporal gyrus. Direct comparison of cortical activity related to overt versus covert conditions revealed a common network of brain regions involved in speech that may implement orthographic and phonological processing. Our results provide one of the first characterizations of the spatiotemporal electrophysiological representations of the continuous speech process, and also highlight the common neural substrate of overt and covert speech. These results thereby contribute to a refined understanding of speech functions in the human brain.1 aBrumberg, Jonathan, S.1 aKrusienski, Dean, J.1 aChakrabarti, Shreya1 aGunduz, Aysegul1 aBrunner, Peter1 aRitaccio, A L1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2787559002354nas a2200277   4500008004100000022001400041245008600055210006900141260001200210300000700222490000600229520153700235653003201772653002701804653002601831653002201857653001201879100001801891700002601909700001901935700002001954700001801974700001701992700001902009856004802028       2015                        eng d  a1662-516100aElectrocorticographic representations of segmental features in continuous speech.0 aElectrocorticographic representations of segmental features in c  c02/2015  a970 v93 aAcoustic 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.10aelectrocorticography (ECoG)10amanner of articulation10aplace of articulation10aspeech processing10avoicing1 aLotte, Fabien1 aBrumberg, Jonathan, S1 aBrunner, Peter1 aGunduz, Aysegul1 aRitaccio, A L1 aGuan, Cuntai1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2575964702627nas a2200277   4500008004100000245009500041210006900136260001200205490000600217520181900223653002602042653001402068653000902082653000802091653002802099653000802127100002202135700002002157700001702177700001902194700001902213700002602232700002302258700002002281856004802301       2014                        eng d00aAssessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.0 aAssessing dynamics spatial scale and uncertainty in taskrelated   c03/20140 v83 aThe 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.10acanonical correlation10acoherence10aECoG10aEEG10afunctional connectivity10aMEG1 aStephen, Emily, P1 aLepage, Kyle, Q1 aEden, Uri, T1 aBrunner, Peter1 aSchalk, Gerwin1 aBrumberg, Jonathan, S1 aGuenther, Frank, H1 aKramer, Mark, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/24678295