-
Notifications
You must be signed in to change notification settings - Fork 12
/
resting_state_networks.html
432 lines (394 loc) · 26.2 KB
/
resting_state_networks.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta property="og:title" content="3.3. Extracting functional brain networks: ICA and related" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://nilearn.github.io/connectivity/resting_state_networks.html" />
<meta property="og:site_name" content="Nilearn" />
<meta property="og:description" content="Page summary: This page demonstrates the use of multi-subject decompositions models to extract brain-networks from fMRI data in a data-driven way. Specifically, we will apply Independent Component ..." />
<meta property="og:image" content="../_images/sphx_glr_plot_compare_decomposition_001.png" />
<meta property="og:image:alt" content="Nilearn" />
<title>Nilearn: Statistical Analysis for NeuroImaging in Python — Machine learning for NeuroImaging</title>
<link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
<link rel="stylesheet" type="text/css" href="../_static/nature.css" />
<link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
<script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
<script src="../_static/jquery.js"></script>
<script src="../_static/underscore.js"></script>
<script src="../_static/doctools.js"></script>
<script src="../_static/clipboard.min.js"></script>
<script src="../_static/copybutton.js"></script>
<link rel="shortcut icon" href="../_static/favicon.ico"/>
<link rel="search" title="Search" href="../search.html" />
<link rel="next" title="3.4. Region Extraction for better brain parcellations" href="region_extraction.html" />
<link rel="prev" title="3.2.3.1. Group-sparse covariance estimation" href="../developers/group_sparse_covariance.html" />
<meta content="True" name="HandheldFriendly">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=0">
<meta name="keywords" content="nilearn, neuroimaging, python, neuroscience, machinelearning">
<script type="text/javascript">
function updateTopMenuPosition(height, width) {
if($(window).scrollTop() > height && $(window).outerWidth() > 1024) {
//begin to scroll
$('.related-wrapper').css("z-index", 1000);
$('.related-wrapper').css("position", "sticky");
$('.related-wrapper').css("top", 0);
$('.related-wrapper').css("width", width)
} else {
//lock it back into place
$('.related-wrapper').css("position", "relative");
$('.related-wrapper').css("top", 0)
}
}
$(function() {
var banner_height = $('#logo-banner').outerHeight();
var banner_width = $('#logo-banner').outerWidth();
var width = $('.related-wrapper').css("height", $('.related').outerHeight());
updateTopMenuPosition(banner_height, width);
$(window).scroll(function(event) {
updateTopMenuPosition(banner_height, width)
});
$(window).resize(function(event) {
var banner_width = $('#logo-banner').outerWidth();
var menu_height = $('.related').outerHeight();
$('.related').css("width", banner_width);
$('.related-wrapper').css("height", menu_height);
updateTopMenuPosition(banner_height, width)
})
});
</script>
<script type="text/javascript">
function updateSideBarPosition(top, offset, sections) {
var pos = $(window).scrollTop();
// Lock the table of content to a fixed position once we scroll enough
var topShift = 2 * offset;
if(pos > top + topShift + 1) {
// begin to scroll with sticky menu bar
var topShift = -topShift + 1;
if ($(window).outerWidth() < 1024) {
// compensate top menu that disappears
topShift -= offset + 1
}
$('.sphinxsidebarwrapper').css("position", "fixed");
$('.sphinxsidebarwrapper').css("top", topShift)
}
else {
//lock it back into place
$('.sphinxsidebarwrapper').css("position", "relative");
$('.sphinxsidebarwrapper').css("top",0)
}
// Highlight the current section
i = 0;
current_section = 0;
$('a.internal').removeClass('active');
for(i in sections) {
if(sections[i] > pos) {
break
}
if($('a.internal[href$="' + i + '"]').is(':visible')){
current_section = i
}
}
$('a.internal[href$="' + current_section + '"]').addClass('active');
$('a.internal[href$="' + current_section + '"]').parent().addClass('active')
}
$(function () {
// Lock the table of content to a fixed position once we scroll enough
var tocOffset = $('.related-wrapper').outerHeight();
var marginTop = parseFloat($('.sphinxsidebarwrapper').css('margin-top').replace(/auto/, 0));
var top = $('.sphinxsidebarwrapper').offset().top - marginTop;
sections = {};
url = document.URL.replace(/#.*$/, "");
// Grab positions of our sections
$('.headerlink').each(function(){
sections[this.href.replace(url, '')] = $(this).offset().top - 50
});
updateSideBarPosition(top, tocOffset, sections);
$(window).scroll(function(event) {
updateSideBarPosition(top, tocOffset, sections)
});
$(window).resize(function(event) {
tocOffset = $('.related-wrapper').outerHeight();
updateSideBarPosition(top, tocOffset, sections)
});
});
</script>
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-41920728-1']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
</head><body>
<div id="logo-banner">
<div class="logo">
<a href="../index.html">
<img src="../_static/nilearn-logo.png" alt="Nilearn logo" border="0" />
</a>
</div>
<!-- A tag cloud to make it easy for people to find what they are
looking for -->
<div class="tags">
<ul>
<li>
<big><a href="../auto_examples/decoding/plot_haxby_anova_svm.html">SVM</a></big>
</li>
<li>
<small><a href="parcellating.html">Ward
clustering</a></small>
</li>
<li>
<a href="../decoding/searchlight.html">Searchlight</a>
</li>
<li>
<big><a href="#">ICA</a></big>
</li>
<li>
<a href="../manipulating_images/data_preparation.html">Nifti IO</a>
</li>
<li>
<a href="../modules/reference.html#module-nilearn.datasets">Datasets</a>
</li>
</ul>
</div>
<div class="banner">
<h1>Nilearn:</h1>
<h2>Statistics for NeuroImaging in Python</h2>
</div>
<div class="search_form">
<div class="gcse-search" id="cse" style="width: 100%;"></div>
<script>
(function() {
var cx = '017289614950330089114:elrt9qoutrq';
var gcse = document.createElement('script');
gcse.type = 'text/javascript';
gcse.async = true;
gcse.src = 'https://cse.google.com/cse.js?cx=' + cx;
var s = document.getElementsByTagName('script')[0];
s.parentNode.insertBefore(gcse, s);
})();
</script>
</div>
</div>
<div class=related-wrapper>
<div class="related" role="navigation" aria-label="related navigation">
<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="../py-modindex.html" title="Python Module Index"
>modules</a></li>
<li class="right" >
<a href="region_extraction.html" title="3.4. Region Extraction for better brain parcellations"
accesskey="N">next</a> |</li>
<li class="right" >
<a href="../developers/group_sparse_covariance.html" title="3.2.3.1. Group-sparse covariance estimation"
accesskey="P">previous</a> |</li>
<li><a href="../index.html">Nilearn Home</a> | </li>
<li><a href="../user_guide.html">User Guide</a> | </li>
<li><a href="../auto_examples/index.html">Examples</a> | </li>
<li><a href="../modules/reference.html">Reference</a> | </li>
<li id="navbar-about"><a href="../authors.html">About</a>| </li>
<li><a href="../glossary.html">Glossary</a>| </li>
<li><a href="../bibliography.html">Bibliography</a>| </li>
<li id="navbar-ecosystem"><a href="http://www.nipy.org/">Nipy ecosystem</a></li>
<li class="nav-item nav-item-1"><a href="../user_guide.html" >User guide: table of contents</a> »</li>
<li class="nav-item nav-item-2"><a href="index.html" accesskey="U"><span class="section-number">3. </span>Functional connectivity and resting state</a> »</li>
<li class="nav-item nav-item-this"><a href="">Nilearn: Statistical Analysis for NeuroImaging in Python</a></li>
</ul>
</div>
</div>
<div class="stable-banner">
This is the <em>stable</em> documentation for the latest release of Nilearn,
the current development version is available <a href="https://nilearn.github.io/dev/index.html">here</a>.
</div>
<div class="document">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="section" id="extracting-functional-brain-networks-ica-and-related">
<span id="extracting-rsn"></span><h1><span class="section-number">3.3. </span>Extracting functional brain networks: ICA and related<a class="headerlink" href="#extracting-functional-brain-networks-ica-and-related" title="Permalink to this headline">¶</a></h1>
<div class="topic">
<p class="topic-title"><strong>Page summary</strong></p>
<p>This page demonstrates the use of multi-subject decompositions models
to extract brain-networks from <a class="reference internal" href="../glossary.html#term-fMRI"><span class="xref std std-term">fMRI</span></a> data in a data-driven way.
Specifically, we will apply Independent Component Analysis (<a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a>), which
implements a multivariate random effects model across subjects. We will
then compare <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> to a newer technique, based on dictionary learning.</p>
</div>
<div class="section" id="multi-subject-ica-canica">
<h2><span class="section-number">3.3.1. </span>Multi-subject ICA: CanICA<a class="headerlink" href="#multi-subject-ica-canica" title="Permalink to this headline">¶</a></h2>
<div class="topic">
<p class="topic-title"><strong>References</strong></p>
<ul class="simple">
<li><p>G. Varoquaux et al. “A group model for stable multi-subject ICA on
fMRI datasets”, <a class="reference external" href="http://www.sciencedirect.com/science/article/pii/S1053811910001618">NeuroImage Vol 51 (2010)</a>, p. 288-299</p></li>
</ul>
</div>
<div class="section" id="objective">
<h3><span class="section-number">3.3.1.1. </span>Objective<a class="headerlink" href="#objective" title="Permalink to this headline">¶</a></h3>
<p><a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> is a useful approach for finding independent sources from <a class="reference internal" href="../glossary.html#term-fMRI"><span class="xref std std-term">fMRI</span></a>
images. <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> and similar techniques can be therefore used to define
regions or networks that share similar <a class="reference internal" href="../glossary.html#term-BOLD"><span class="xref std std-term">BOLD</span></a> signal across time. The
<a class="reference internal" href="../glossary.html#term-CanICA"><span class="xref std std-term">CanICA</span></a> incorporates information both within-subjects and across subjects
to arrive at consensus components.</p>
<div class="topic">
<p class="topic-title"><strong>Nilearn data for examples</strong></p>
<p>Nilearn provides easy-to-analyze data to explore functional connectivity and resting: the
<a class="reference external" href="https://osf.io/5hju4/files/">brain development dataset</a>, which
has been preprocessed using <a class="reference external" href="https://osf.io/wjtyq/">FMRIPrep and Nilearn</a>
We use nilearn functions to fetch data from Internet and get the
filenames (<a class="reference internal" href="../manipulating_images/input_output.html#loading-data"><span class="std std-ref">more on data loading</span></a>).</p>
</div>
</div>
<div class="section" id="fitting-canica-model-with-nilearn">
<h3><span class="section-number">3.3.1.2. </span>Fitting CanICA model with nilearn<a class="headerlink" href="#fitting-canica-model-with-nilearn" title="Permalink to this headline">¶</a></h3>
<p><a class="reference internal" href="../modules/generated/nilearn.decomposition.CanICA.html#nilearn.decomposition.CanICA" title="nilearn.decomposition.CanICA"><code class="xref py py-class docutils literal notranslate"><span class="pre">CanICA</span></code></a> is a ready-to-use object that can be applied to
multi-subject Nifti data, for instance presented as filenames, and will
perform a multi-subject <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> decomposition following the <a class="reference internal" href="../glossary.html#term-CanICA"><span class="xref std std-term">CanICA</span></a> model.
As with every object in nilearn, we give its parameters at construction,
and then fit it on the data. For examples of this process, see
here: <a class="reference internal" href="../auto_examples/03_connectivity/plot_compare_decomposition.html#sphx-glr-auto-examples-03-connectivity-plot-compare-decomposition-py"><span class="std std-ref">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</span></a></p>
<p>Once an <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> object has been fit to an <a class="reference internal" href="../glossary.html#term-fMRI"><span class="xref std std-term">fMRI</span></a> dataset, the individual
components can be accessed as a 4D Nifti object using the
<code class="docutils literal notranslate"><span class="pre">components_img_</span></code> attribute.</p>
</div>
<div class="section" id="visualizing-results">
<h3><span class="section-number">3.3.1.3. </span>Visualizing results<a class="headerlink" href="#visualizing-results" title="Permalink to this headline">¶</a></h3>
<p>We can visualize each component outlined over the brain:</p>
<div class="figure align-center">
<a class="reference external image-reference" href="../auto_examples/03_connectivity/plot_compare_decomposition.html"><img alt="../_images/sphx_glr_plot_compare_decomposition_001.png" src="../_images/sphx_glr_plot_compare_decomposition_001.png" /></a>
</div>
<p>We can also plot the map for different components separately:</p>
<p class="centered">
<strong><a class="reference internal" href="../_images/sphx_glr_plot_compare_decomposition_002.png"><img alt="ic1" src="../_images/sphx_glr_plot_compare_decomposition_002.png" style="width: 23%;" /></a> <a class="reference internal" href="../_images/sphx_glr_plot_compare_decomposition_003.png"><img alt="ic2" src="../_images/sphx_glr_plot_compare_decomposition_003.png" style="width: 23%;" /></a> <a class="reference internal" href="../_images/sphx_glr_plot_compare_decomposition_004.png"><img alt="ic3" src="../_images/sphx_glr_plot_compare_decomposition_004.png" style="width: 23%;" /></a> <a class="reference internal" href="../_images/sphx_glr_plot_compare_decomposition_005.png"><img alt="ic4" src="../_images/sphx_glr_plot_compare_decomposition_005.png" style="width: 23%;" /></a></strong></p><div class="admonition seealso">
<p class="admonition-title">See also</p>
<p>The full code can be found as an example:
<a class="reference internal" href="../auto_examples/03_connectivity/plot_compare_decomposition.html#sphx-glr-auto-examples-03-connectivity-plot-compare-decomposition-py"><span class="std std-ref">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</span></a></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Note that as the <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> components are not ordered, the two components
displayed on your computer might not match those of the documentation. For
a fair representation, you should display all components and
investigate which one resemble those displayed above.</p>
</div>
</div>
<div class="section" id="interpreting-such-components">
<h3><span class="section-number">3.3.1.4. </span>Interpreting such components<a class="headerlink" href="#interpreting-such-components" title="Permalink to this headline">¶</a></h3>
<p><a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a>, and related algorithms, extract patterns that coactivate in the
signal. As a result, it finds functional networks, but also patterns of
non neural activity, ie confounding signals. Both are visible in the
plots of the components.</p>
</div>
</div>
<div class="section" id="an-alternative-to-ica-dictionary-learning">
<h2><span class="section-number">3.3.2. </span>An alternative to <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a>: <a class="reference internal" href="../glossary.html#term-Dictionary-learning"><span class="xref std std-term">Dictionary learning</span></a><a class="headerlink" href="#an-alternative-to-ica-dictionary-learning" title="Permalink to this headline">¶</a></h2>
<p>Recent work has shown that <a class="reference internal" href="../glossary.html#term-Dictionary-learning"><span class="xref std std-term">Dictionary learning</span></a> based techniques
outperform <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> in term of stability and constitutes a better first
step in a statistical analysis pipeline.
<a class="reference internal" href="../glossary.html#term-Dictionary-learning"><span class="xref std std-term">Dictionary learning</span></a> in neuro-imaging seeks to extract a few
representative temporal elements along with their sparse spatial loadings,
which constitute good extracted maps.</p>
<div class="topic">
<p class="topic-title"><strong>References</strong></p>
<ul class="simple">
<li><p>Arthur Mensch et al. <a class="reference external" href="https://hal.archives-ouvertes.fr/hal-01271033/">Compressed online dictionary learning for fast resting-state fMRI decomposition</a>,
ISBI 2016, Lecture Notes in Computer Science</p></li>
</ul>
</div>
<p><a class="reference internal" href="../modules/generated/nilearn.decomposition.DictLearning.html#nilearn.decomposition.DictLearning" title="nilearn.decomposition.DictLearning"><code class="xref py py-class docutils literal notranslate"><span class="pre">DictLearning</span></code></a> is a ready-to-use class with the same interface as
<a class="reference internal" href="../modules/generated/nilearn.decomposition.CanICA.html#nilearn.decomposition.CanICA" title="nilearn.decomposition.CanICA"><code class="xref py py-class docutils literal notranslate"><span class="pre">CanICA</span></code></a>. Sparsity of output map is controlled by a parameter alpha: using
a larger alpha yields sparser maps.</p>
<p>We can fit both estimators to compare them. 4D plotting (using
<a class="reference internal" href="../modules/generated/nilearn.plotting.plot_prob_atlas.html#nilearn.plotting.plot_prob_atlas" title="nilearn.plotting.plot_prob_atlas"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_prob_atlas</span></code></a>) offers an efficient way to
compare both resulting outputs.</p>
<div class="figure align-center">
<a class="reference external image-reference" href="../auto_examples/03_connectivity/plot_compare_decomposition.html"><img alt="../_images/sphx_glr_plot_compare_decomposition_022.png" src="../_images/sphx_glr_plot_compare_decomposition_022.png" /></a>
</div>
<div class="figure align-center">
<a class="reference external image-reference" href="../auto_examples/03_connectivity/plot_compare_decomposition.html"><img alt="../_images/sphx_glr_plot_compare_decomposition_001.png" src="../_images/sphx_glr_plot_compare_decomposition_001.png" /></a>
</div>
<p>Maps obtained with <a class="reference internal" href="../glossary.html#term-Dictionary-learning"><span class="xref std std-term">Dictionary learning</span></a> are often easier to exploit as they are
more contrasted than <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> maps, with blobs usually better defined. Typically,
<em>smoothing can be lower than when doing ICA</em>.</p>
<p class="centered">
<strong><a class="reference internal" href="../_images/sphx_glr_plot_compare_decomposition_023.png"><img alt="dl1" src="../_images/sphx_glr_plot_compare_decomposition_023.png" style="width: 23%;" /></a> <a class="reference internal" href="../_images/sphx_glr_plot_compare_decomposition_024.png"><img alt="dl2" src="../_images/sphx_glr_plot_compare_decomposition_024.png" style="width: 23%;" /></a> <a class="reference internal" href="../_images/sphx_glr_plot_compare_decomposition_025.png"><img alt="dl3" src="../_images/sphx_glr_plot_compare_decomposition_025.png" style="width: 23%;" /></a> <a class="reference internal" href="../_images/sphx_glr_plot_compare_decomposition_026.png"><img alt="dl4" src="../_images/sphx_glr_plot_compare_decomposition_026.png" style="width: 23%;" /></a></strong></p><p>While <a class="reference internal" href="../glossary.html#term-Dictionary-learning"><span class="xref std std-term">Dictionary learning</span></a> computation time is comparable to
<a class="reference internal" href="../glossary.html#term-CanICA"><span class="xref std std-term">CanICA</span></a>, obtained atlases have been shown to outperform <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a>
in a variety of classification tasks.</p>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p>The full code can be found as an example:
<a class="reference internal" href="../auto_examples/03_connectivity/plot_compare_decomposition.html#sphx-glr-auto-examples-03-connectivity-plot-compare-decomposition-py"><span class="std std-ref">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</span></a></p>
</div>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p>Learn how to extract <a class="reference internal" href="../glossary.html#term-fMRI"><span class="xref std std-term">fMRI</span></a> data from regions created with
<a class="reference internal" href="../glossary.html#term-Dictionary-learning"><span class="xref std std-term">Dictionary learning</span></a> with this example:
<a class="reference internal" href="../auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.html#sphx-glr-auto-examples-03-connectivity-plot-extract-regions-dictlearning-maps-py"><span class="std std-ref">Regions extraction using Dictionary learning and functional connectomes</span></a></p>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
</div>
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper">
<h4> Giving credit </h4>
<ul class="simple">
<li><p>Please consider <a href="../authors.html#citing">citing the
papers</a>.</p></li>
</ul>
<h3><a href="../index.html">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">3.3. Extracting functional brain networks: ICA and related</a><ul>
<li><a class="reference internal" href="#multi-subject-ica-canica">3.3.1. Multi-subject ICA: CanICA</a><ul>
<li><a class="reference internal" href="#objective">3.3.1.1. Objective</a></li>
<li><a class="reference internal" href="#fitting-canica-model-with-nilearn">3.3.1.2. Fitting CanICA model with nilearn</a></li>
<li><a class="reference internal" href="#visualizing-results">3.3.1.3. Visualizing results</a></li>
<li><a class="reference internal" href="#interpreting-such-components">3.3.1.4. Interpreting such components</a></li>
</ul>
</li>
<li><a class="reference internal" href="#an-alternative-to-ica-dictionary-learning">3.3.2. An alternative to <span class="xref std std-term">ICA</span>: <span class="xref std std-term">Dictionary learning</span></a></li>
</ul>
</li>
</ul>
<h4>Previous topic</h4>
<p class="topless"><a href="../developers/group_sparse_covariance.html"
title="previous chapter"><span class="section-number">3.2.3.1. </span>Group-sparse covariance estimation</a></p>
<h4>Next topic</h4>
<p class="topless"><a href="region_extraction.html"
title="next chapter"><span class="section-number">3.4. </span>Region Extraction for better brain parcellations</a></p>
<div id="searchbox" style="display: none" role="search">
<h3 id="searchlabel">Quick search</h3>
<div class="searchformwrapper">
<form class="search" action="../search.html" method="get">
<input type="text" name="q" aria-labelledby="searchlabel" />
<input type="submit" value="Go" />
</form>
</div>
</div>
<script>$('#searchbox').show(0);</script>
</div>
</div>
<div class="clearer"></div>
</div>
<div class="footer">
© The nilearn developers 2010-2022.
Created using <a href="http://sphinx.pocoo.org/">Sphinx</a> 4.0.2.
<span style="padding-left: 5ex;">
<a href="../_sources/connectivity/resting_state_networks.rst.txt"
rel="nofollow">Show this page source</a>
</span>
</div>
</body>
</html>