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‎.eslintrc.json

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{
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"env": {
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"browser": true,
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"es2021": true
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},
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"ignorePatterns": ["*.css", "*.png","*.jpg"],
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"extends": [
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"plugin:react/recommended",
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"airbnb"
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],
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"overrides": [
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],
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"parserOptions": {
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"ecmaVersion": "latest",
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"sourceType": "module",
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"ecmaFeatures": {
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"jsx": true
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}
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},
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"plugins": [
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"react"
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],
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"rules": {
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"no-return-assign":"off",
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"react/no-unescaped-entities": "off",
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"max-len": "off",
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"camelcase": "off"
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"env": {
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"browser": true,
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"es2021": true
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},
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"ignorePatterns": ["*.css", "*.png", "*.jpg"],
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"extends": ["plugin:react/recommended", "airbnb"],
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"overrides": [],
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"parserOptions": {
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"ecmaVersion": "latest",
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"sourceType": "module",
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"ecmaFeatures": {
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"jsx": true
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}
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},
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"plugins": ["react"],
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"rules": {
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"no-return-assign": "off",
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"react/no-unescaped-entities": "off",
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"max-len": "off",
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"camelcase": "off",
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"linebreak-style": 0
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}
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}

‎src/Components/Main.jsx

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@@ -14,10 +14,16 @@ import CorticalSites4 from '../Pictures/Research/CorticalSites4.jpg';
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import CorticalSites5 from '../Pictures/Research/CorticalSites5.jpg';
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function Main() {
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const [index, setIndex] = useState(0);
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const [index1, setIndex1] = useState(0);
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const handleSelect = (selectedIndex) => {
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setIndex(selectedIndex);
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const handleSelect1 = (selectedIndex) => {
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setIndex1(selectedIndex);
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};
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const [index2, setIndex2] = useState(0);
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const handleSelect2 = (selectedIndex) => {
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setIndex2(selectedIndex);
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};
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return (
@@ -69,7 +75,11 @@ function Main() {
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</Button>
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</Col>
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<Col>
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<Carousel interval={null} activeIndex={index} onSelect={handleSelect}>
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<Carousel
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interval={null}
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activeIndex={index1}
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onSelect={handleSelect1}
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>
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<Carousel.Item>
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<Image fluid src={CorticalSites1} />
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</Carousel.Item>
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<Image fluid src={CorticalSites5} />
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</Carousel.Item>
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</Carousel>
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{index === 0 ? (
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{index1 === 0 && (
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<p>
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"A DES was used either intraoperatively (depicted) or in the
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epilepsy monitoring unit to identify sites critical to language
@@ -113,24 +123,119 @@ function Main() {
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interquartile range. We used these metrics to train machine
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learning classifiers to predict which nodes would be critical to
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language and speech. Example data (C–E) are provided from a single
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participant (n = 1) for each visualization. Source data are
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participant (n = 1) for each visualization. Source data are
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provided as a Source Data file."
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</p>
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) : (
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)}
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{index1 === 1 && (
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<p>
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"An example of performance of a bivariate smoothing model,
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dependently on the number of data-points included in 2D moving
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average (window size), for ERC containing 20 channels (K=20)
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recorded during naming of ambiguous objects. Top panel shows
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results in patient #8. Top-left: the difference between the ERC
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values and the values of 2D moving average. Top-middle; confidence
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interval. Top-right: the criterion for model selection. X and Y
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axes represent window size by distances from the center-point of
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the window of 2D moving average, in time-points and
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frequency-points accordingly. Colorscale (min-max) at the right.
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Bottom panel shows the criterion for model selection averaged over
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all patients (bottom-left) and their projections on time-plane
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(bottom-middle), and on frequency-plane (bottom-right)."
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"PC participation coefficient, S strength, CC clustering
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coefficient, LEff local efficiency, EC eigenvector centrality. A
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Diagram illustrating coassignment. Two yellow-outlined coassigned
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nodes are found within the same community (dark blue fill); two
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blue-outlined nodes are found in two different communities
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(magenta and orange fill)—i.e., not coassigned. B Diagram
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demonstrating graph metrics. The large magenta node in the top
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panel has a high PC—it connects across all communities in this
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network. The same node has a low clustering coefficient (its
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neighbors are not themselves connected, denoted by dashed arrows)
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and low local efficiency (long path lengths between its
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neighbors). In the bottom panel, the large dark blue node has high
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strength, i.e., a high sum of connection weights. The large orange
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node has higher eigenvector centrality than the smaller orange
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node; both have the same number of connections, but the larger
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node’s connections themselves have more connections. C Intuition
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for three node types. Connector nodes connect across communities
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(high PC), while their neighbors do not connect as closely to each
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other (low CC, LEff). Global hubs connect to many nodes across the
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network (high PC, high S, likely high EC). Local hubs connect
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densely in their neighborhood (low PC, high CC/LEff)."
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</p>
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)}
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{index1 === 2 && (
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<p>
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"PC participation coefficient, S strength, CC clustering
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coefficient, LEff local efficiency, EC eigenvector centrality. A
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Composite of all participants’ electrodes colocalized on a single
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template brain. Speech arrest nodes (yellow fill) were primarily
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located in ventral premotor regions, but also in ventrolateral
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prefrontal and ventral temporal regions. Language error nodes
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(blue fill) were widely distributed in perisylvian regions. B
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Three example participant brain reconstructions. Node color
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(filled) represents community assignment, and node size is
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proportional to its participation coefficient. The outline color
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indicates critical nodes (blue—LE node, yellow—SA node). C
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Corresponding three network diagrams. The electrode position is
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spring-weighted (stronger connections draw electrodes closer
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together). Fill color indicates community, and if present, outline
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color indicates critical node type (LE vs. SA) D Corresponding
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network metrics for the three example patients. Metrics for all
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nodes (electrodes) for each of the three participants (n = 1 per
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graph) are plotted. Here, colored circles represent critical
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nodes; gray circles represent other nodes. Boxes demonstrate
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median and interquartile range, and whiskers demonstrate
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non-outlier maxima/minima. Source data are provided as a Source
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Data file."
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{' '}
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</p>
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)}
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{index1 === 3 && (
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<p>
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"PC participation coefficient, S strength, CC clustering
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coefficient, LEff local efficiency, EC eigenvector centrality. *p
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&lt; 0.05. **p &lt; 0.01. ***p &lt; 0.001 (FDR-corrected). A
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Histogram of the number of communities per participant (n = 16). B
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Coassignment percentages vs. chance. Coassignment is calculated as
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the mean % of critical, LE, or SA node pairs per participant
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sharing a community. Empiric chance was calculated based on 1000
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random shuffles of community assignment per participant, presented
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as mean coassignment% per participant with bars indicating
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standard error of mean (n = 16 for Critical, n = 15 for LE and
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SA). Critical nodes, language error nodes, and speech arrest nodes
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were significantly more likely to coassign in the same communities
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than chance (p &lt; 0.001 for all, one-tailed estimate against
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empiric chance). Language error and speech arrest nodes were not
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more likely to be found in the same community as each other
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compared to chance (35.2 vs. 30.4%, p = 0.112, one-tailed estimate
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against empiric chance). C Network metrics for critical vs. all
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other nodes (150 critical nodes, 1084 non-critical nodes).
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Critical nodes have higher PC and lower CC, LEff, and EC than
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other nodes. D Network metrics for LE, SA, and other nodes (92
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language error nodes, 52 speech arrest nodes, 1084 non-critical
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nodes). LE nodes have markedly higher PC than SA and other nodes.
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C, D Metrics were z-scored for each subject prior to pooling all
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nodes together. All nodes are plotted in light gray; mean values
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per participant in larger, bolder colors. Boxes indicate the
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median and IQR, and notch indicates the standard error of the
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median. Statistical testing is based on a two-sided two-sample
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t-test on z-scored metrics across all pooled nodes with FDR
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correction. For additional details, refer to Table 1. Source data
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are provided as a Source Data file."
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</p>
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)}
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{index1 === 4 && (
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<p>
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"For within-participant classification, participants with at least
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four nodes of the relevant class were included; for critical
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nodes, LE nodes, and SA nodes, n = 15, 10, and 8, respectively.
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For across-participant classification, participants with at least
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one node of the relevant class were included—for critical nodes,
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LE nodes, and SA nodes, n = 16, 13, and 13, respectively. A–D Each
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dot represents average classification balanced accuracy or
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sensitivity for a single participant. Box plots show median and
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IQR across participants and are derived from a single value per
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participant. Whiskers indicate a non-outlier maximum range. True
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balanced accuracy and sensitivity were compared against empirical
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chance calculated by label-shuffling. The average chance
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classification accuracy per participant is represented by the
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chance box plots for SVN and KNN (one value per participant). Data
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for SVM, KNN, and chance for SVM and KNN are presented in
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different colors as indicated by the legend. E, F ROC curves
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presented for SVM (solid lines) and KNN (dashed lines)
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classifiers, when classifying SA (orange), LE (magenta), and
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critical (dark blue) nodes separately, as indicated by the legend.
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For further details, refer to Tables 2, 3. Source data are
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provided as a Source Data file."
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</p>
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)}
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</Col>
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</Button>
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</Col>
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<Col>
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<Carousel interval={null} activeIndex={index} onSelect={handleSelect}>
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<Carousel
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interval={null}
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activeIndex={index2}
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onSelect={handleSelect2}
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>
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<Carousel.Item>
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<Image fluid src={ERC_Naming} />
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</Carousel.Item>
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<Carousel.Item>
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<Image fluid src={ERC_Naming2} />
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</Carousel.Item>
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</Carousel>
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{index === 0 ? (
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{index2 === 0 ? (
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<p>
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"Results of event-related causality (ERC) estimated with 2D moving
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average of window size 7x7 time-frequency points, averaged across

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