main page update

Author: RohitGanji

src/Components/Main.jsx

@@ -14,16 +14,10 @@ import CorticalSites4 from '../Pictures/Research/CorticalSites4.jpg';
 import CorticalSites5 from '../Pictures/Research/CorticalSites5.jpg';
 
 function Main() {
-  const [index1, setIndex1] = useState(0);
+  const [index, setIndex] = useState(0);
 
-  const handleSelect1 = (selectedIndex) => {
-    setIndex1(selectedIndex);
-  };
-
-  const [index2, setIndex2] = useState(0);
-
-  const handleSelect2 = (selectedIndex) => {
-    setIndex2(selectedIndex);
+  const handleSelect = (selectedIndex) => {
+    setIndex(selectedIndex);
   };
 
   return (
@@ -74,27 +68,8 @@ function Main() {
             Full Text
           </Button>
         </Col>
-        {' '}
-        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.
         <Col>
-          <Carousel
-            interval={null}
-            activeIndex={index1}
-            onSelect={handleSelect1}
-          >
+          <Carousel interval={null} activeIndex={index} onSelect={handleSelect}>
             <Carousel.Item>
               <Image fluid src={CorticalSites1} />
             </Carousel.Item>
@@ -111,156 +86,15 @@ function Main() {
               <Image fluid src={CorticalSites5} />
             </Carousel.Item>
           </Carousel>
-
-          {index1 === 0 && (
-            <p>
-              "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."
-            </p>
-          )}
-
-          {index1 === 1 && (
-            <p>
-              "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)."
-              {' '}
-            </p>
-          )}
-
-          {index1 === 2 && (
-            <p>
-              "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."
-              {' '}
-            </p>
-          )}
-
-          {index1 === 3 && (
+          {/* {index === 0 ? (
             <p>
-              "PC participation coefficient, S strength, CC clustering
-              coefficient, LEff local efficiency, EC eigenvector centrality.
-              *p &lt; 0.05. **p &lt; 0.01. ***p &lt; 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 &lt; 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."
-              {' '}
+              "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."
             </p>
-          )}
-
-          {index1 === 4 && (
+          ) : (
             <p>
-              "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."
-              {' '}
+              "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)."
             </p>
-          )}
+          )} */}
         </Col>
       </Row>
 
@@ -292,7 +126,16 @@ function Main() {
             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.
+            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.
           </p>
           <Button
             href="https://www.sciencedirect.com/science/article/pii/S0893608022000351"
@@ -302,20 +145,15 @@ function Main() {
           </Button>
         </Col>
         <Col>
-          <Carousel
-            interval={null}
-            activeIndex={index2}
-            onSelect={handleSelect2}
-          >
+          <Carousel interval={null} activeIndex={index} onSelect={handleSelect}>
             <Carousel.Item>
               <Image fluid src={ERC_Naming} />
             </Carousel.Item>
             <Carousel.Item>
               <Image fluid src={ERC_Naming2} />
             </Carousel.Item>
           </Carousel>
-
-          {index2 === 0 ? (
+          {index === 0 ? (
             <p>
               "Results of event-related causality (ERC) estimated with 2D moving
               average of window size 7x7 time-frequency points, averaged across
@@ -350,6 +188,75 @@ function Main() {
           )}
         </Col>
       </Row>
+
+      <hr className="featurette-divider" />
+      <Row>
+        <Col>
+          <h2 className="featurette-heading">
+            Semi-Autonomous iEEG Brain-Machine Interfaces
+          </h2>
+          <p>
+            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.
+            {' '}
+          </p>
+          <Button
+            href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6683036&tag=1"
+            target="_blank"
+          >
+            Full Text
+          </Button>
+          <Button
+            href="https://ieeexplore.ieee.org/document/6683036/media#media"
+            target="_blank"
+          >
+            Videos
+          </Button>
+        </Col>
+        <Col>
+          <Image fluid src={Hybrid_BCI} />
+        </Col>
+      </Row>
+      <hr className="featurette-divider" />
+
+      <Row>
+        <Col>
+          <h2 className="featurette-heading">Redefining Broca's Area</h2>
+          <p>
+            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.
+          </p>
+          <Button
+            href="http://www.pnas.org/content/112/9/2871.short"
+            target="_blank"
+          >
+            Full Text
+          </Button>
+        </Col>
+        <Col>
+          <Image fluid src={Brocas} />
+        </Col>
+      </Row>
     </Container>
   );
 }