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main page update
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@RohitGanji
RohitGanji committed Sep 16, 2024
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Showing 1 changed file with 90 additions and 183 deletions.
273 changes: 90 additions & 183 deletions src/Components/Main.jsx
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Expand Up @@ -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 (
Expand Down Expand Up @@ -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>
Expand All @@ -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>

Expand Down Expand Up @@ -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"
Expand All @@ -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
Expand Down Expand Up @@ -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>
);
}
Expand Down

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