diff --git a/src/Components/Main.jsx b/src/Components/Main.jsx index d24b088..8741c8d 100644 --- a/src/Components/Main.jsx +++ b/src/Components/Main.jsx @@ -1,245 +1,366 @@ -import React, { useState } from 'react'; -import { - Container, Carousel, Image, Row, Col, Button, -} from 'react-bootstrap'; -import labPhoto from '../Pictures/LabPhotos/LabPhoto_2020.png'; -import Brocas from '../Pictures/Research/BrocasPaperImage.jpg'; -import Hybrid_BCI from '../Pictures/Research/Hybrid_BCI.png'; -import ERC_Naming from '../Pictures/Research/ERC_Naming.png'; -import ERC_Naming2 from '../Pictures/Research/ERC_Naming2.jpg'; -import CorticalSites1 from '../Pictures/Research/CorticalSites1.jpg'; -import CorticalSites2 from '../Pictures/Research/CorticalSites2.jpg'; -import CorticalSites3 from '../Pictures/Research/CorticalSites3.jpg'; -import CorticalSites4 from '../Pictures/Research/CorticalSites4.jpg'; -import CorticalSites5 from '../Pictures/Research/CorticalSites5.jpg'; +import React, { useState } from 'react'; import { Container, Carousel, Image, +Row, Col, Button, } from 'react-bootstrap'; import labPhoto from +'../Pictures/LabPhotos/LabPhoto_2020.png'; import Brocas from +'../Pictures/Research/BrocasPaperImage.jpg'; import Hybrid_BCI from +'../Pictures/Research/Hybrid_BCI.png'; import ERC_Naming from +'../Pictures/Research/ERC_Naming.png'; import ERC_Naming2 from +'../Pictures/Research/ERC_Naming2.jpg'; import CorticalSites1 from +'../Pictures/Research/CorticalSites1.jpg'; import CorticalSites2 from +'../Pictures/Research/CorticalSites2.jpg'; import CorticalSites3 from +'../Pictures/Research/CorticalSites3.jpg'; import CorticalSites4 from +'../Pictures/Research/CorticalSites4.jpg'; import CorticalSites5 from +'../Pictures/Research/CorticalSites5.jpg'; function Main() { const [index1, +setIndex1] = useState(0); const handleSelect1 = (selectedIndex) => { +setIndex1(selectedIndex); }; const [index2, setIndex2] = useState(0); const +handleSelect2 = (selectedIndex) => { setIndex2(selectedIndex); }; return ( + +
+ +

+ 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. +

+
-function Main() { - const [index1, setIndex1] = useState(0); - - const handleSelect1 = (selectedIndex) => { - setIndex1(selectedIndex); - }; - - const [index2, setIndex2] = useState(0); - - const handleSelect2 = (selectedIndex) => { - setIndex2(selectedIndex); - }; - - return ( - -
- -

- 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. -

-
- -
- - -

- Cortical sites critical to language function act as connectors between language subnetworks -

-

- 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. -

- - - - - - - - - - - - - - - - - - - - - {index1 === 0 && ( +
+ + +

+ Cortical sites critical to language function act as connectors between + language subnetworks +

+

+ 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. +

+ + + + + + + + + + + + + + + + + + + + + {index1 === 0 && (

- "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."

- )} - {index1 === 1 && ( + "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."

+ )} {index1 === 1 && (

- "PC participation coefficient, S strength, CC clustering coefficient, LEff local efficiency, EC eigenvector centrality. A Diagram illustrating coassignment. Two yellow-outlined coassigned nodes are found within the same community (dark blue fill); two blue-outlined nodes are found in two different communities (magenta and orange fill)—i.e., not coassigned. B Diagram demonstrating graph metrics. The large magenta node in the top panel has a high PC—it connects across all communities in this network. The same node has a low clustering coefficient (its neighbors are not themselves connected, denoted by dashed arrows) and low local efficiency (long path lengths between its neighbors). In the bottom panel, the large dark blue node has high strength, i.e., a high sum of connection weights. The large orange node has higher eigenvector centrality than the smaller orange node; both have the same number of connections, but the larger node’s connections themselves have more connections. C Intuition for three node types. Connector nodes connect across communities (high PC), while their neighbors do not connect as closely to each other (low CC, LEff). Global hubs connect to many nodes across the network (high PC, high S, likely high EC). Local hubs connect densely in their neighborhood (low PC, high CC/LEff)."

- )} - {index1 === 2 && ( + "PC participation coefficient, S strength, CC clustering coefficient, + LEff local efficiency, EC eigenvector centrality. A Diagram illustrating + coassignment. Two yellow-outlined coassigned nodes are found within the + same community (dark blue fill); two blue-outlined nodes are found in + two different communities (magenta and orange fill)—i.e., not + coassigned. B Diagram demonstrating graph metrics. The large magenta + node in the top panel has a high PC—it connects across all communities + in this network. The same node has a low clustering coefficient (its + neighbors are not themselves connected, denoted by dashed arrows) and + low local efficiency (long path lengths between its neighbors). In the + bottom panel, the large dark blue node has high strength, i.e., a high + sum of connection weights. The large orange node has higher eigenvector + centrality than the smaller orange node; both have the same number of + connections, but the larger node’s connections themselves have more + connections. C Intuition for three node types. Connector nodes connect + across communities (high PC), while their neighbors do not connect as + closely to each other (low CC, LEff). Global hubs connect to many nodes + across the network (high PC, high S, likely high EC). Local hubs connect + densely in their neighborhood (low PC, high CC/LEff)."

+ )} {index1 === 2 && (

- "PC participation coefficient, S strength, CC clustering coefficient, LEff local efficiency, EC eigenvector centrality. A Composite of all participants’ electrodes colocalized on a single template brain. Speech arrest nodes (yellow fill) were primarily located in ventral premotor regions, but also in ventrolateral prefrontal and ventral temporal regions. Language error nodes (blue fill) were widely distributed in perisylvian regions. B Three example participant brain reconstructions. Node color (filled) represents community assignment, and node size is proportional to its participation coefficient. The outline color indicates critical nodes (blue—LE node, yellow—SA node). C Corresponding three network diagrams. The electrode position is spring-weighted (stronger connections draw electrodes closer together). Fill color indicates community, and if present, outline color indicates critical node type (LE vs. SA) D Corresponding network metrics for the three example patients. Metrics for all nodes (electrodes) for each of the three participants (n = 1 per graph) are plotted. Here, colored circles represent critical nodes; gray circles represent other nodes. Boxes demonstrate median and interquartile range, and whiskers demonstrate non-outlier maxima/minima. Source data are provided as a Source Data file."

- )} - {index1 === 3 && ( + "PC participation coefficient, S strength, CC clustering coefficient, + LEff local efficiency, EC eigenvector centrality. A Composite of all + participants’ electrodes colocalized on a single template brain. Speech + arrest nodes (yellow fill) were primarily located in ventral premotor + regions, but also in ventrolateral prefrontal and ventral temporal + regions. Language error nodes (blue fill) were widely distributed in + perisylvian regions. B Three example participant brain reconstructions. + Node color (filled) represents community assignment, and node size is + proportional to its participation coefficient. The outline color + indicates critical nodes (blue—LE node, yellow—SA node). C Corresponding + three network diagrams. The electrode position is spring-weighted + (stronger connections draw electrodes closer together). Fill color + indicates community, and if present, outline color indicates critical + node type (LE vs. SA) D Corresponding network metrics for the three + example patients. Metrics for all nodes (electrodes) for each of the + three participants (n = 1 per graph) are plotted. Here, colored circles + represent critical nodes; gray circles represent other nodes. Boxes + demonstrate median and interquartile range, and whiskers demonstrate + non-outlier maxima/minima. Source data are provided as a Source Data + file." +

+ )} {index1 === 3 && (

- "PC participation coefficient, S strength, CC clustering coefficient, LEff local efficiency, EC eigenvector centrality. *p < 0.05. **p < 0.01. ***p < 0.001 (FDR-corrected). A Histogram of the number of communities per participant (n = 16). B Coassignment percentages vs. chance. Coassignment is calculated as the mean % of critical, LE, or SA node pairs per participant sharing a community. Empiric chance was calculated based on 1000 random shuffles of community assignment per participant, presented as mean coassignment% per participant with bars indicating standard error of mean (n = 16 for Critical, n = 15 for LE and SA). Critical nodes, language error nodes, and speech arrest nodes were significantly more likely to coassign in the same communities than chance (p < 0.001 for all, one-tailed estimate against empiric chance). Language error and speech arrest nodes were not more likely to be found in the same community as each other compared to chance (35.2 vs. 30.4%, p = 0.112, one-tailed estimate against empiric chance). C Network metrics for critical vs. all other nodes (150 critical nodes, 1084 non-critical nodes). Critical nodes have higher PC and lower CC, LEff, and EC than other nodes. D Network metrics for LE, SA, and other nodes (92 language error nodes, 52 speech arrest nodes, 1084 non-critical nodes). LE nodes have markedly higher PC than SA and other nodes. C, D Metrics were z-scored for each subject prior to pooling all nodes together. All nodes are plotted in light gray; mean values per participant in larger, bolder colors. Boxes indicate the median and IQR, and notch indicates the standard error of the median. Statistical testing is based on a two-sided two-sample t-test on z-scored metrics across all pooled nodes with FDR correction. For additional details, refer to Table 1. Source data are provided as a Source Data file."

- )} - {index1 === 4 && ( + "PC participation coefficient, S strength, CC clustering coefficient, + LEff local efficiency, EC eigenvector centrality. *p < 0.05. + **p < 0.01. ***p < 0.001 (FDR-corrected). A Histogram of the + number of communities per participant (n = 16). B Coassignment + percentages vs. chance. Coassignment is calculated as the mean % of + critical, LE, or SA node pairs per participant sharing a community. + Empiric chance was calculated based on 1000 random shuffles of community + assignment per participant, presented as mean coassignment% per + participant with bars indicating standard error of mean (n = 16 for + Critical, n = 15 for LE and SA). Critical nodes, language error nodes, + and speech arrest nodes were significantly more likely to coassign in + the same communities than chance (p < 0.001 for all, one-tailed + estimate against empiric chance). Language error and speech arrest nodes + were not more likely to be found in the same community as each other + compared to chance (35.2 vs. 30.4%, p = 0.112, one-tailed estimate + against empiric chance). C Network metrics for critical vs. all other + nodes (150 critical nodes, 1084 non-critical nodes). Critical nodes have + higher PC and lower CC, LEff, and EC than other nodes. D Network metrics + for LE, SA, and other nodes (92 language error nodes, 52 speech arrest + nodes, 1084 non-critical nodes). LE nodes have markedly higher PC than + SA and other nodes. C, D Metrics were z-scored for each subject prior to + pooling all nodes together. All nodes are plotted in light gray; mean + values per participant in larger, bolder colors. Boxes indicate the + median and IQR, and notch indicates the standard error of the median. + Statistical testing is based on a two-sided two-sample t-test on + z-scored metrics across all pooled nodes with FDR correction. For + additional details, refer to Table 1. Source data are provided as a + Source Data file."

+ )} {index1 === 4 && (

- "For within-participant classification, participants with at least four nodes of the relevant class were included; for critical nodes, LE nodes, and SA nodes, n = 15, 10, and 8, respectively. For across-participant classification, participants with at least one node of the relevant class were included—for critical nodes, LE nodes, and SA nodes, n = 16, 13, and 13, respectively. A–D Each dot represents average classification balanced accuracy or sensitivity for a single participant. Box plots show median and IQR across participants and are derived from a single value per participant. Whiskers indicate a non-outlier maximum range. True balanced accuracy and sensitivity were compared against empirical chance calculated by label-shuffling. The average chance classification accuracy per participant is represented by the chance box plots for SVN and KNN (one value per participant). Data for SVM, KNN, and chance for SVM and KNN are presented in different colors as indicated by the legend. E, F ROC curves presented for SVM (solid lines) and KNN (dashed lines) classifiers, when classifying SA (orange), LE (magenta), and critical (dark blue) nodes separately, as indicated by the legend. For further details, refer to Tables 2, 3. Source data are provided as a Source Data file."

- )} - - - -
- - -

- Significance of event related causality (ERC) in eloquent neural - networks -

-

- 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. -

- - - - - - - - - - - - {index2 === 0 ? ( -

- "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." -

- ) : ( -

- "An example of performance of a bivariate smoothing model, dependently on the number of data-points included in 2D moving average (window size), for ERC containing 20 channels (K=20) recorded during naming of ambiguous objects. Top panel shows results in patient #8. Top-left: the difference between the ERC values and the values of 2D moving average. Top-middle; confidence interval. Top-right: the criterion for model selection. X and Y axes represent window size by distances from the center-point of the window of 2D moving average, in time-points and frequency-points accordingly. Colorscale (min-max) at the right. Bottom panel shows the criterion for model selection averaged over all patients (bottom-left) and their projections on time-plane (bottom-middle), and on frequency-plane (bottom-right)." -

- )} - - + "For within-participant classification, participants with at least four + nodes of the relevant class were included; for critical nodes, LE nodes, + and SA nodes, n = 15, 10, and 8, respectively. For across-participant + classification, participants with at least one node of the relevant + class were included—for critical nodes, LE nodes, and SA nodes, n = 16, + 13, and 13, respectively. A–D Each dot represents average classification + balanced accuracy or sensitivity for a single participant. Box plots + show median and IQR across participants and are derived from a single + value per participant. Whiskers indicate a non-outlier maximum range. + True balanced accuracy and sensitivity were compared against empirical + chance calculated by label-shuffling. The average chance classification + accuracy per participant is represented by the chance box plots for SVN + and KNN (one value per participant). Data for SVM, KNN, and chance for + SVM and KNN are presented in different colors as indicated by the + legend. E, F ROC curves presented for SVM (solid lines) and KNN (dashed + lines) classifiers, when classifying SA (orange), LE (magenta), and + critical (dark blue) nodes separately, as indicated by the legend. For + further details, refer to Tables 2, 3. Source data are provided as a + Source Data file."

+ )} + + -
- - -

- Semi-Autonomous iEEG Brain-Machine Interfaces -

-

- 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. - {' '} -

- - - - - - - -
+
+ + +

+ Significance of event related causality (ERC) in eloquent neural + networks +

+

+ 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. +

+ + + + + + + + + + + + {index2 === 0 ? ( +

+ "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." +

+ ) : ( +

+ "An example of performance of a bivariate smoothing model, dependently + on the number of data-points included in 2D moving average (window + size), for ERC containing 20 channels (K=20) recorded during naming of + ambiguous objects. Top panel shows results in patient #8. Top-left: the + difference between the ERC values and the values of 2D moving average. + Top-middle; confidence interval. Top-right: the criterion for model + selection. X and Y axes represent window size by distances from the + center-point of the window of 2D moving average, in time-points and + frequency-points accordingly. Colorscale (min-max) at the right. Bottom + panel shows the criterion for model selection averaged over all patients + (bottom-left) and their projections on time-plane (bottom-middle), and + on frequency-plane (bottom-right)." +

+ )} + + - - -

Redefining Broca's Area

-

- 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. -

- - - - - - - - ); -} +
+ + +

+ Semi-Autonomous iEEG Brain-Machine Interfaces +

+

+ 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. {' '} +

+ + + + + + + +
-export default Main; + + +

Redefining Broca's Area

+

+ 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. +

+ + + + + + + +); } export default Main;