Under the direction of Dr. Nathan Crone, the JHU Cognitive Neurophysiology and BMI Lab is working to identify and validate electrophysiological signatures of human cortical processing and to use them to study the neural mechanisms of motor, sensory, and language functions. Where applicable, we are applying this understanding to the development of assistive systems for individuals with disabilities.
Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site’s pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits.
"A DES was used either intraoperatively (depicted) or in the epilepsy monitoring unit to identify sites critical to language and speech. These were subdivided into cortical regions causing language errors (LE) or speech arrest (SA). B We recorded continuous ECoG while participants engaged in a word-reading task. C We generated one static network for each participant using pairwise high-gamma correlations. Color-coded adjacency matrix shown; the color in position (m,n) reflects to the high-gamma correlation between electrode m and n. r is the Fisher-transformed Pearson correlation. Community partitions were discovered using modularity maximization. Electrodes have been re-ordered so those belonging to the same community are adjacent (boundaries shown in black lines). D Spring-loaded network plot; nodes (circles) that are more strongly connected are drawn more closely together. The size of each node is proportional to its strength. Community membership is indicated by the fill color of each node. The nodes outlined in blue are LE nodes. E Network metrics were calculated—PC (participation coefficient), strength, CC (clustering coefficient), LE (local efficiency), and EC (eigenvector centrality). Metric values for every node are plotted; large colored points represent critical nodes and small gray points are all other nodes. Boxes demonstrate the median and interquartile range. We used these metrics to train machine learning classifiers to predict which nodes would be critical to language and speech. Example data (C–E) are provided from a single participant (n = 1) for each visualization. Source data are provided as a Source Data file."
During most cognitive tasks neural activity is propagated across large-scale cortical networks on very brief time scales. Studying such transient and complex systems calls for a short time-window on the one hand, and a great extent of recording sites in the brain, on the other. These demands are not easily satisfied, as short time intervals do not provide enough data-points to model the dynamics of large-scale brain networks. This limitation can be overcome by using multiple realizations of the same process, e.g. multiple trials of a task (Ding et al., 2000), but the price to be paid is that traditional statistical methods, cannot be used to assess the significance of event-related changes in the estimated dynamics of the system. We propose event-related causality (ERC) with two-dimensional (2D) moving average, a new method for assessing statistical confidence in such cases. This approach can be applied when very few realizations, or trials, of a studied process are available, including when only single trials are available. ERC with 2D moving average ensures precise embedding of statistical significance in two-dimensional space, and can analyze much longer time series. We also propose a criterion for statistical model selection, based on both goodness of fit and width of confidence intervals. Using ERC with 2D moving average to study naming under conditions in which perceptual modality and ambiguity were contrasted, we observed new patterns of task-related neural propagation that were nevertheless consistent with expectations derived from previous studies of naming. ERC with 2D moving average is uniquely suitable to both research and clinical applications and can be used to estimate the statistical significance of neural propagation for both cognitive neuroscientific studies and functional brain mapping prior to resective surgery for epilepsy and brain tumors.
Full Text"Results of event-related causality (ERC) estimated with 2D moving average of window size 7x7 time-frequency points, averaged across all patients. Naming of unambiguous objects (top panel), ambiguous objects (middle panel), and naming to auditory description (bottom panel). The task interval starting at stimulus onset and ending at speech onset is divided in half with the first half in the left column and the second half in the right column. Both width and color (thin-yellow: weak; thick-red: strong) of arrows represent intensity of high-gamma activity propagation, using a single colorscale across all plots. Linear arrows: propagation between regions of interest (ROIs, Ghosh et al., 2010). Circular arrows: propagation within ROIs. Top 90% of propagations depicted to reduce complexity of the figure."
We developed a novel system, the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the JHU/APL Modular Prosthetic Limb MPL. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.
During the cued production of words, a temporal cascade of neural activity proceeds from sensory representations of words in the temporal cortex to their corresponding articulatory gestures in the motor cortex. Broca's area mediates this cascade through reciprocal interactions with temporal and frontal motor regions. Contrary to classNameic notions of the role of Broca's area in speech, while the motor cortex is activated during spoken responses, Broca's area is surprisingly silent. Moreover, when novel strings of articulatory gestures must be produced in response to nonword stimuli, neural activity is enhanced in Broca's area, but not in the motor cortex. These unique data provide evidence that Broca's area coordinates the transformation of information across large-scale cortical networks involved in spoken word production. In this role, Broca's area formulates an appropriate articulatory code to be implemented by the motor cortex.
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