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@RohitGanji
RohitGanji committed Sep 16, 2024
1 parent 1ac75a7 commit 4053f60
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47 changes: 21 additions & 26 deletions .eslintrc.json
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Original file line number Diff line number Diff line change
@@ -1,29 +1,24 @@
{
"env": {
"browser": true,
"es2021": true
},
"ignorePatterns": ["*.css", "*.png","*.jpg"],
"extends": [
"plugin:react/recommended",
"airbnb"
],
"overrides": [
],
"parserOptions": {
"ecmaVersion": "latest",
"sourceType": "module",
"ecmaFeatures": {
"jsx": true
}
},
"plugins": [
"react"
],
"rules": {
"no-return-assign":"off",
"react/no-unescaped-entities": "off",
"max-len": "off",
"camelcase": "off"
"env": {
"browser": true,
"es2021": true
},
"ignorePatterns": ["*.css", "*.png", "*.jpg"],
"extends": ["plugin:react/recommended", "airbnb"],
"overrides": [],
"parserOptions": {
"ecmaVersion": "latest",
"sourceType": "module",
"ecmaFeatures": {
"jsx": true
}
},
"plugins": ["react"],
"rules": {
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"react/no-unescaped-entities": "off",
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}
}
153 changes: 131 additions & 22 deletions src/Components/Main.jsx
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Original file line number Diff line number Diff line change
Expand Up @@ -14,10 +14,16 @@ import CorticalSites4 from '../Pictures/Research/CorticalSites4.jpg';
import CorticalSites5 from '../Pictures/Research/CorticalSites5.jpg';

function Main() {
const [index, setIndex] = useState(0);
const [index1, setIndex1] = useState(0);

const handleSelect = (selectedIndex) => {
setIndex(selectedIndex);
const handleSelect1 = (selectedIndex) => {
setIndex1(selectedIndex);
};

const [index2, setIndex2] = useState(0);

const handleSelect2 = (selectedIndex) => {
setIndex2(selectedIndex);
};

return (
Expand Down Expand Up @@ -69,7 +75,11 @@ function Main() {
</Button>
</Col>
<Col>
<Carousel interval={null} activeIndex={index} onSelect={handleSelect}>
<Carousel
interval={null}
activeIndex={index1}
onSelect={handleSelect1}
>
<Carousel.Item>
<Image fluid src={CorticalSites1} />
</Carousel.Item>
Expand All @@ -86,7 +96,7 @@ function Main() {
<Image fluid src={CorticalSites5} />
</Carousel.Item>
</Carousel>
{index === 0 ? (
{index1 === 0 && (
<p>
"A DES was used either intraoperatively (depicted) or in the
epilepsy monitoring unit to identify sites critical to language
Expand All @@ -113,24 +123,119 @@ function Main() {
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
participant (n = 1) for each visualization. Source data are
provided as a Source Data file."
</p>
) : (
)}
{index1 === 1 && (
<p>
"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)."
"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 && (
<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."
</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."
</p>
)}
</Col>
Expand Down Expand Up @@ -183,15 +288,19 @@ function Main() {
</Button>
</Col>
<Col>
<Carousel interval={null} activeIndex={index} onSelect={handleSelect}>
<Carousel
interval={null}
activeIndex={index2}
onSelect={handleSelect2}
>
<Carousel.Item>
<Image fluid src={ERC_Naming} />
</Carousel.Item>
<Carousel.Item>
<Image fluid src={ERC_Naming2} />
</Carousel.Item>
</Carousel>
{index === 0 ? (
{index2 === 0 ? (
<p>
"Results of event-related causality (ERC) estimated with 2D moving
average of window size 7x7 time-frequency points, averaged across
Expand Down

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