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Code repository for the manuscript, "Human cognition involves the dynamic integration of neural activity and neuromodulatory systems" by Shine, J.M. et al. in Nature Neuroscience (2019).

The study involves conducting a spatial principal component analysis (PCA) on multi-task data from the Human Connectome Project (HCP; http://www.humanconnectomeproject.org/) and then tracking the trajectories (i.e., eigenvalues or time series of PCs [tPCs]) of the eigenvectors over time to interrogate the low-dimensional signature of cognitive function in the human brain.

A brief overview of the analysis plan:

  1. Concatenate parcel-wise data from 100 subjects across 7 tasks
  2. Run 'pca.m' on the concatenated data in MATLAB a) top 5 eigenvectors ('eigenvec.m') and eigenvalues ('eigenval.m') are stored in the repository, along with the XYZ coordinates and network assignment of each of the cortical parcels (n = 333); b) collapse the data according to the phase of the first tPC in order to estimate the low-dimensional manifold ('make_manifold.m');
  3. Plot the eigenvalues of the eigenvectors and compare these to: a) the combined task regressor; b) neurosynth Topic Maps ('topic_maps.m'; http://neurosynth.org/analyses/topics/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/); c) time-varying network topology (github.com/macshine/integration; https://www.ncbi.nlm.nih.gov/pubmed/27693256) d) neuromodulatory receptor maps (http://neurosynth.org/genes/).
  4. Compare the results to block-resampled null data ('block_resampling.m');
  5. Calculate network controllability measures (https://www.danisbassett.com/resources.html) and compare to top eigenvectors.

Please contact mac.shine@sydney.edu.au if you have any further questions.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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state_space
Code repository for the manuscript, "Human cognition involves the dynamic integration of neural activity and neuromodulatory systems" by Shine, J.M. et al. in Nature Neuroscience (2019).
The study involves conducting a spatial principal component analysis (PCA) on multi-task data from the Human Connectome Project (HCP; http://www.humanconnectomeproject.org/) and then tracking the trajectories (i.e., eigenvalues or time series of PCs [tPCs]) of the eigenvectors over time to interrogate the low-dimensional signature of cognitive function in the human brain.
A brief overview of the analysis plan:
  1. Concatenate parcel-wise data from 100 subjects across 7 tasks
  2. Run 'pca.m' on the concatenated data in MATLAB
a) top 5 eigenvectors ('eigenvec.m') and eigenvalues ('eigenval.m') are stored in the repository, along with the XYZ coordinates and network assignment of each of the cortical parcels (n = 333);
b) collapse the data according to the phase of the first tPC in order to estimate the low-dimensional manifold ('make_manifold.m');
  3. Plot the eigenvalues of the eigenvectors and compare these to:
a) the combined task regressor;
b) neurosynth Topic Maps ('topic_maps.m'; http://neurosynth.org/analyses/topics/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/);
c) time-varying network topology (github.com/macshine/integration; https://www.ncbi.nlm.nih.gov/pubmed/27693256)
d) neuromodulatory receptor maps (http://neurosynth.org/genes/).
  4. Compare the results to block-resampled null data ('block_resampling.m');
  5. Calculate network controllability measures (https://www.danisbassett.com/resources.html) and compare to top eigenvectors.
Please contact mac.shine@sydney.edu.au if you have any further questions.
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, 'i');
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injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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state_space
Code repository for the manuscript, "Human cognition involves the dynamic integration of neural activity and neuromodulatory systems" by Shine, J.M. et al. in Nature Neuroscience (2019).
The study involves conducting a spatial principal component analysis (PCA) on multi-task data from the Human Connectome Project (HCP; http://www.humanconnectomeproject.org/) and then tracking the trajectories (i.e., eigenvalues or time series of PCs [tPCs]) of the eigenvectors over time to interrogate the low-dimensional signature of cognitive function in the human brain.
A brief overview of the analysis plan:
  1. Concatenate parcel-wise data from 100 subjects across 7 tasks
  2. Run 'pca.m' on the concatenated data in MATLAB
a) top 5 eigenvectors ('eigenvec.m') and eigenvalues ('eigenval.m') are stored in the repository, along with the XYZ coordinates and network assignment of each of the cortical parcels (n = 333);
b) collapse the data according to the phase of the first tPC in order to estimate the low-dimensional manifold ('make_manifold.m');
  3. Plot the eigenvalues of the eigenvectors and compare these to:
a) the combined task regressor;
b) neurosynth Topic Maps ('topic_maps.m'; http://neurosynth.org/analyses/topics/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/);
c) time-varying network topology (github.com/macshine/integration; https://www.ncbi.nlm.nih.gov/pubmed/27693256)
d) neuromodulatory receptor maps (http://neurosynth.org/genes/).
  4. Compare the results to block-resampled null data ('block_resampling.m');
  5. Calculate network controllability measures (https://www.danisbassett.com/resources.html) and compare to top eigenvectors.
Please contact mac.shine@sydney.edu.au if you have any further questions.
About
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Code repository for the manuscript, "Human cognition involves the dynamic integration of neural activity and neuromodulatory systems" by Shine, J.M. et al. in Nature Neuroscience (2019).
The study involves conducting a spatial principal component analysis (PCA) on multi-task data from the Human Connectome Project (HCP; http://www.humanconnectomeproject.org/) and then tracking the trajectories (i.e., eigenvalues or time series of PCs [tPCs]) of the eigenvectors over time to interrogate the low-dimensional signature of cognitive function in the human brain.
A brief overview of the analysis plan:
  1. Concatenate parcel-wise data from 100 subjects across 7 tasks
  2. Run 'pca.m' on the concatenated data in MATLAB
a) top 5 eigenvectors ('eigenvec.m') and eigenvalues ('eigenval.m') are stored in the repository, along with the XYZ coordinates and network assignment of each of the cortical parcels (n = 333);
b) collapse the data according to the phase of the first tPC in order to estimate the low-dimensional manifold ('make_manifold.m');
  3. Plot the eigenvalues of the eigenvectors and compare these to:
a) the combined task regressor;
b) neurosynth Topic Maps ('topic_maps.m'; http://neurosynth.org/analyses/topics/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/);
c) time-varying network topology (github.com/macshine/integration; https://www.ncbi.nlm.nih.gov/pubmed/27693256)
d) neuromodulatory receptor maps (http://neurosynth.org/genes/).
  4. Compare the results to block-resampled null data ('block_resampling.m');
  5. Calculate network controllability measures (https://www.danisbassett.com/resources.html) and compare to top eigenvectors.
Please contact mac.shine@sydney.edu.au if you have any further questions.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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state_space
Code repository for the manuscript, "Human cognition involves the dynamic integration of neural activity and neuromodulatory systems" by Shine, J.M. et al. in Nature Neuroscience (2019).
The study involves conducting a spatial principal component analysis (PCA) on multi-task data from the Human Connectome Project (HCP; http://www.humanconnectomeproject.org/) and then tracking the trajectories (i.e., eigenvalues or time series of PCs [tPCs]) of the eigenvectors over time to interrogate the low-dimensional signature of cognitive function in the human brain.
A brief overview of the analysis plan:
  1. Concatenate parcel-wise data from 100 subjects across 7 tasks
  2. Run 'pca.m' on the concatenated data in MATLAB
a) top 5 eigenvectors ('eigenvec.m') and eigenvalues ('eigenval.m') are stored in the repository, along with the XYZ coordinates and network assignment of each of the cortical parcels (n = 333);
b) collapse the data according to the phase of the first tPC in order to estimate the low-dimensional manifold ('make_manifold.m');
  3. Plot the eigenvalues of the eigenvectors and compare these to:
a) the combined task regressor;
b) neurosynth Topic Maps ('topic_maps.m'; http://neurosynth.org/analyses/topics/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/);
c) time-varying network topology (github.com/macshine/integration; https://www.ncbi.nlm.nih.gov/pubmed/27693256)
d) neuromodulatory receptor maps (http://neurosynth.org/genes/).
  4. Compare the results to block-resampled null data ('block_resampling.m');
  5. Calculate network controllability measures (https://www.danisbassett.com/resources.html) and compare to top eigenvectors.
Please contact mac.shine@sydney.edu.au if you have any further questions.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Code repository for the manuscript, "Human cognition involves the dynamic integration of neural activity and neuromodulatory systems" by Shine, J.M. et al. in Nature Neuroscience (2019).
The study involves conducting a spatial principal component analysis (PCA) on multi-task data from the Human Connectome Project (HCP; http://www.humanconnectomeproject.org/) and then tracking the trajectories (i.e., eigenvalues or time series of PCs [tPCs]) of the eigenvectors over time to interrogate the low-dimensional signature of cognitive function in the human brain.
A brief overview of the analysis plan:
  1. Concatenate parcel-wise data from 100 subjects across 7 tasks
  2. Run 'pca.m' on the concatenated data in MATLAB
a) top 5 eigenvectors ('eigenvec.m') and eigenvalues ('eigenval.m') are stored in the repository, along with the XYZ coordinates and network assignment of each of the cortical parcels (n = 333);
b) collapse the data according to the phase of the first tPC in order to estimate the low-dimensional manifold ('make_manifold.m');
  3. Plot the eigenvalues of the eigenvectors and compare these to:
a) the combined task regressor;
b) neurosynth Topic Maps ('topic_maps.m'; http://neurosynth.org/analyses/topics/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/);
c) time-varying network topology (github.com/macshine/integration; https://www.ncbi.nlm.nih.gov/pubmed/27693256)
d) neuromodulatory receptor maps (http://neurosynth.org/genes/).
  4. Compare the results to block-resampled null data ('block_resampling.m');
  5. Calculate network controllability measures (https://www.danisbassett.com/resources.html) and compare to top eigenvectors.
Please contact mac.shine@sydney.edu.au if you have any further questions.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Code repository for the manuscript, "Human cognition involves the dynamic integration of neural activity and neuromodulatory systems" by Shine, J.M. et al. in Nature Neuroscience (2019).
The study involves conducting a spatial principal component analysis (PCA) on multi-task data from the Human Connectome Project (HCP; http://www.humanconnectomeproject.org/) and then tracking the trajectories (i.e., eigenvalues or time series of PCs [tPCs]) of the eigenvectors over time to interrogate the low-dimensional signature of cognitive function in the human brain.
A brief overview of the analysis plan:
  1. Concatenate parcel-wise data from 100 subjects across 7 tasks
  2. Run 'pca.m' on the concatenated data in MATLAB
a) top 5 eigenvectors ('eigenvec.m') and eigenvalues ('eigenval.m') are stored in the repository, along with the XYZ coordinates and network assignment of each of the cortical parcels (n = 333);
b) collapse the data according to the phase of the first tPC in order to estimate the low-dimensional manifold ('make_manifold.m');
  3. Plot the eigenvalues of the eigenvectors and compare these to:
a) the combined task regressor;
b) neurosynth Topic Maps ('topic_maps.m'; http://neurosynth.org/analyses/topics/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/);
c) time-varying network topology (github.com/macshine/integration; https://www.ncbi.nlm.nih.gov/pubmed/27693256)
d) neuromodulatory receptor maps (http://neurosynth.org/genes/).
  4. Compare the results to block-resampled null data ('block_resampling.m');
  5. Calculate network controllability measures (https://www.danisbassett.com/resources.html) and compare to top eigenvectors.
Please contact mac.shine@sydney.edu.au if you have any further questions.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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state_space
Code repository for the manuscript, "Human cognition involves the dynamic integration of neural activity and neuromodulatory systems" by Shine, J.M. et al. in Nature Neuroscience (2019).
The study involves conducting a spatial principal component analysis (PCA) on multi-task data from the Human Connectome Project (HCP; http://www.humanconnectomeproject.org/) and then tracking the trajectories (i.e., eigenvalues or time series of PCs [tPCs]) of the eigenvectors over time to interrogate the low-dimensional signature of cognitive function in the human brain.
A brief overview of the analysis plan:
  1. Concatenate parcel-wise data from 100 subjects across 7 tasks
  2. Run 'pca.m' on the concatenated data in MATLAB
a) top 5 eigenvectors ('eigenvec.m') and eigenvalues ('eigenval.m') are stored in the repository, along with the XYZ coordinates and network assignment of each of the cortical parcels (n = 333);
b) collapse the data according to the phase of the first tPC in order to estimate the low-dimensional manifold ('make_manifold.m');
  3. Plot the eigenvalues of the eigenvectors and compare these to:
a) the combined task regressor;
b) neurosynth Topic Maps ('topic_maps.m'; http://neurosynth.org/analyses/topics/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683652/);
c) time-varying network topology (github.com/macshine/integration; https://www.ncbi.nlm.nih.gov/pubmed/27693256)
d) neuromodulatory receptor maps (http://neurosynth.org/genes/).
  4. Compare the results to block-resampled null data ('block_resampling.m');
  5. Calculate network controllability measures (https://www.danisbassett.com/resources.html) and compare to top eigenvectors.
Please contact mac.shine@sydney.edu.au if you have any further questions.
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