-
Notifications
You must be signed in to change notification settings - Fork 12
/
searchlight.html
1035 lines (1000 loc) · 119 KB
/
searchlight.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
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!doctype html>
<html class="no-js" lang="en" data-content_root="">
<head><meta charset="utf-8"/>
<meta name="viewport" content="width=device-width,initial-scale=1"/>
<meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
<meta property="og:title" content="5.5. Searchlight : finding voxels containing information" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://nilearn.github.io/decoding/searchlight.html" />
<meta property="og:site_name" content="Nilearn" />
<meta property="og:description" content="This page overviews searchlight analyses and how they are approached in nilearn with the SearchLight estimator. Principle of the Searchlight: SearchLight analysis was introduced in [ Kriegeskorte e..." />
<meta property="og:image" content="https://nilearn.github.io/_images/sphx_glr_plot_haxby_searchlight_001.png" />
<meta property="og:image:alt" content="Nilearn" />
<meta name="description" content="This page overviews searchlight analyses and how they are approached in nilearn with the SearchLight estimator. Principle of the Searchlight: SearchLight analysis was introduced in [ Kriegeskorte e..." />
<link rel="search" title="Search" href="../search.html" /><link rel="next" title="5.6. Running scikit-learn functions for more control on the analysis" href="going_further.html" /><link rel="prev" title="5.4. SpaceNet: decoding with spatial structure for better maps" href="space_net.html" />
<link rel="shortcut icon" href="../_static/favicon.ico"/><!-- Generated with Sphinx 7.1.2 and Furo 2023.09.10 -->
<title>5.5. Searchlight : finding voxels containing information - Nilearn</title>
<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=045299b1" />
<link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
<link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
<link rel="stylesheet" type="text/css" href="../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
<link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
<link rel="stylesheet" type="text/css" href="../_static/custom.css?v=486f7390" />
<link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/all.min.css" />
<link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/fontawesome.min.css" />
<link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/solid.min.css" />
<link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/brands.min.css" />
<style>
body {
--color-code-background: #ffffff;
--color-code-foreground: black;
--admonition-font-size: 100%;
--admonition-title-font-size: 100%;
--color-announcement-background: #FBB360;
--color-announcement-text: #111418;
--color-admonition-title--note: #448aff;
--color-admonition-title-background--note: #448aff10;
}
@media not print {
body[data-theme="dark"] {
--color-code-background: #232629;
--color-code-foreground: #cccccc;
--color-announcement-background: #935610;
--color-announcement-text: #FFFFFF;
}
@media (prefers-color-scheme: dark) {
body:not([data-theme="light"]) {
--color-code-background: #232629;
--color-code-foreground: #cccccc;
--color-announcement-background: #935610;
--color-announcement-text: #FFFFFF;
}
}
}
</style></head>
<body>
<script>
document.body.dataset.theme = localStorage.getItem("theme") || "auto";
</script>
<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
<symbol id="svg-toc" viewBox="0 0 24 24">
<title>Contents</title>
<svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
<path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
</svg>
</symbol>
<symbol id="svg-menu" viewBox="0 0 24 24">
<title>Menu</title>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
<line x1="3" y1="12" x2="21" y2="12"></line>
<line x1="3" y1="6" x2="21" y2="6"></line>
<line x1="3" y1="18" x2="21" y2="18"></line>
</svg>
</symbol>
<symbol id="svg-arrow-right" viewBox="0 0 24 24">
<title>Expand</title>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
<polyline points="9 18 15 12 9 6"></polyline>
</svg>
</symbol>
<symbol id="svg-sun" viewBox="0 0 24 24">
<title>Light mode</title>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
<circle cx="12" cy="12" r="5"></circle>
<line x1="12" y1="1" x2="12" y2="3"></line>
<line x1="12" y1="21" x2="12" y2="23"></line>
<line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
<line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
<line x1="1" y1="12" x2="3" y2="12"></line>
<line x1="21" y1="12" x2="23" y2="12"></line>
<line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
<line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
</svg>
</symbol>
<symbol id="svg-moon" viewBox="0 0 24 24">
<title>Dark mode</title>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
<path stroke="none" d="M0 0h24v24H0z" fill="none" />
<path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
</svg>
</symbol>
<symbol id="svg-sun-half" viewBox="0 0 24 24">
<title>Auto light/dark mode</title>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
<path stroke="none" d="M0 0h24v24H0z" fill="none"/>
<circle cx="12" cy="12" r="9" />
<path d="M13 12h5" />
<path d="M13 15h4" />
<path d="M13 18h1" />
<path d="M13 9h4" />
<path d="M13 6h1" />
</svg>
</symbol>
</svg>
<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
<label class="overlay sidebar-overlay" for="__navigation">
<div class="visually-hidden">Hide navigation sidebar</div>
</label>
<label class="overlay toc-overlay" for="__toc">
<div class="visually-hidden">Hide table of contents sidebar</div>
</label>
<div class="page">
<header class="mobile-header">
<div class="header-left">
<label class="nav-overlay-icon" for="__navigation">
<div class="visually-hidden">Toggle site navigation sidebar</div>
<i class="icon"><svg><use href="#svg-menu"></use></svg></i>
</label>
</div>
<div class="header-center">
<a href="../index.html"><div class="brand">Nilearn</div></a>
</div>
<div class="header-right">
<div class="theme-toggle-container theme-toggle-header">
<button class="theme-toggle">
<div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
<svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
<svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
<svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
</button>
</div>
<label class="toc-overlay-icon toc-header-icon" for="__toc">
<div class="visually-hidden">Toggle table of contents sidebar</div>
<i class="icon"><svg><use href="#svg-toc"></use></svg></i>
</label>
</div>
</header>
<aside class="sidebar-drawer">
<div class="sidebar-container">
<div class="sidebar-sticky"><a class="sidebar-brand" href="../index.html">
<div class="sidebar-logo-container">
<img class="sidebar-logo" src="../_static/nilearn-transparent.png" alt="Logo"/>
</div>
<span class="sidebar-brand-text">Nilearn</span>
</a><form class="sidebar-search-container" method="get" action="../search.html" role="search">
<input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
<input type="hidden" name="check_keywords" value="yes">
<input type="hidden" name="area" value="default">
</form>
<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../quickstart.html">Quickstart</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../auto_examples/index.html">Examples</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of Examples</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/00_tutorials/index.html">Basic tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Basic tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/00_tutorials/plot_python_101.html">Basic numerics and plotting with Python</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/00_tutorials/plot_nilearn_101.html">Basic nilearn example: manipulating and looking at data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/00_tutorials/plot_3d_and_4d_niimg.html">3D and 4D niimgs: handling and visualizing</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/00_tutorials/plot_decoding_tutorial.html">A introduction tutorial to fMRI decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/00_tutorials/plot_single_subject_single_run.html">Intro to GLM Analysis: a single-session, single-subject fMRI dataset</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/01_plotting/index.html">Visualization of brain images</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Visualization of brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_demo_glass_brain.html">Glass brain plotting in nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_visualize_megatrawls_netmats.html">Visualizing Megatrawls Network Matrices from Human Connectome Project</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_prob_atlas.html">Visualizing 4D probabilistic atlas maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_atlas.html">Basic Atlas plotting</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_overlay.html">Visualizing a probabilistic atlas: the default mode in the MSDL atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_dim_plotting.html">Controlling the contrast of the background when plotting</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_multiscale_parcellations.html">Visualizing multiscale functional brain parcellations</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_colormaps.html">Matplotlib colormaps in Nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_visualization.html">NeuroImaging volumes visualization</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_carpet.html">Visualizing global patterns with a carpet plot</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_haxby_masks.html">Plot Haxby masks</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_surface_projection_strategies.html">Technical point: Illustration of the volume to surface sampling schemes</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_demo_plotting.html">Plotting tools in nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_surf_atlas.html">Loading and plotting of a cortical surface atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_demo_glass_brain_extensive.html">Glass brain plotting in nilearn (all options)</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_demo_more_plotting.html">More plotting tools from nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_3d_map_to_surface_projection.html">Making a surface plot of a 3D statistical map</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_surf_stat_map.html">Seed-based connectivity on the surface</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/02_decoding/index.html">Decoding and predicting from brain images</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Decoding and predicting from brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_stimuli.html">Show stimuli of Haxby et al. dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_mixed_gambles_frem.html">FREM on Jimura et al “mixed gambles” dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_oasis_vbm_space_net.html">Voxel-Based Morphometry on Oasis dataset with Space-Net prior</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_searchlight_surface.html">Cortical surface-based searchlight decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_anova_svm.html">Decoding with ANOVA + SVM: face vs house in the Haxby dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_frem.html">Decoding with FREM: face vs house vs chair object recognition</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_multiclass.html">The haxby dataset: different multi-class strategies</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_searchlight.html">Searchlight analysis of face vs house recognition</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_glm_decoding.html">Decoding of a dataset after GLM fit for signal extraction</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_full_analysis.html">ROI-based decoding analysis in Haxby et al. dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_grid_search.html">Setting a parameter by cross-validation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_oasis_vbm.html">Voxel-Based Morphometry on Oasis dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_different_estimators.html">Different classifiers in decoding the Haxby dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_simulated_data.html">Example of pattern recognition on simulated data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_miyawaki_encoding.html">Encoding models for visual stimuli from Miyawaki et al. 2008</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_miyawaki_reconstruction.html">Reconstruction of visual stimuli from Miyawaki et al. 2008</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/03_connectivity/index.html">Functional connectivity</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Functional connectivity</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_inverse_covariance_connectome.html">Computing a connectome with sparse inverse covariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_probabilistic_atlas_extraction.html">Extracting signals of a probabilistic atlas of functional regions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_simulated_connectome.html">Connectivity structure estimation on simulated data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_compare_decomposition.html">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_seed_to_voxel_correlation.html">Producing single subject maps of seed-to-voxel correlation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_multi_subject_connectome.html">Group Sparse inverse covariance for multi-subject connectome</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.html">Regions extraction using dictionary learning and functional connectomes</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_atlas_comparison.html">Comparing connectomes on different reference atlases</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_group_level_connectivity.html">Classification of age groups using functional connectivity</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_signal_extraction.html">Extracting signals from a brain parcellation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_sphere_based_connectome.html">Extract signals on spheres and plot a connectome</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_data_driven_parcellations.html">Clustering methods to learn a brain parcellation from fMRI</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/04_glm_first_level/index.html">GLM: First level analysis</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of GLM: First level analysis</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_fixed_effects.html">Example of explicit fixed effects fMRI model fitting</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_write_events_file.html">Generate an events.tsv file for the NeuroSpin localizer task</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_adhd_dmn.html">Default Mode Network extraction of ADHD dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_design_matrix.html">Examples of design matrices</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_fir_model.html">Analysis of an fMRI dataset with a Finite Impule Response (FIR) model</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_spm_multimodal_faces.html">Single-subject data (two sessions) in native space</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_hrf.html">Example of MRI response functions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_fiac_analysis.html">Simple example of two-session fMRI model fitting</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_predictions_residuals.html">Predicted time series and residuals</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_bids_features.html">First level analysis of a complete BIDS dataset from openneuro</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_localizer_surface_analysis.html">Example of surface-based first-level analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_first_level_details.html">Understanding parameters of the first-level model</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/05_glm_second_level/index.html">GLM: Second level analysis</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of GLM: Second level analysis</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_second_level_design_matrix.html">Example of second level design matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_proportion_activated_voxels.html">Second-level fMRI model: true positive proportion in clusters</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_thresholding.html">Statistical testing of a second-level analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_oasis.html">Voxel-Based Morphometry on OASIS dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_second_level_two_sample_test.html">Second-level fMRI model: two-sample test, unpaired and paired</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_second_level_one_sample_test.html">Second-level fMRI model: one sample test</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_second_level_association_test.html">Example of generic design in second-level models</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/06_manipulating_images/index.html">Manipulating brain image volumes</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of Manipulating brain image volumes</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_negate_image.html">Negating an image with math_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_compare_mean_image.html">Comparing the means of 2 images</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_smooth_mean_image.html">Smoothing an image</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_extract_regions_labels_image.html">Breaking an atlas of labels in separated regions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_resample_to_template.html">Resample an image to a template</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_extract_rois_smith_atlas.html">Regions Extraction of Default Mode Networks using Smith Atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_nifti_simple.html">Simple example of NiftiMasker use</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_nifti_labels_simple.html">Extracting signals from brain regions using the NiftiLabelsMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_extract_rois_statistical_maps.html">Region Extraction using a t-statistical map (3D)</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_mask_computation.html">Understanding NiftiMasker and mask computation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_affine_transformation.html">Visualization of affine resamplings</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_roi_extraction.html">Computing a Region of Interest (ROI) mask manually</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/07_advanced/index.html">Advanced statistical analysis of brain images</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of Advanced statistical analysis of brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_localizer_simple_analysis.html">Massively univariate analysis of a calculation task from the Localizer dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_ica_resting_state.html">Multivariate decompositions: Independent component analysis of fMRI</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_bids_analysis.html">BIDS dataset first and second level analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_neurovault_meta_analysis.html">NeuroVault meta-analysis of stop-go paradigm studies</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_age_group_prediction_cross_val.html">Functional connectivity predicts age group</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_localizer_mass_univariate_methods.html">Massively univariate analysis of a motor task from the Localizer dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_surface_bids_analysis.html">Surface-based dataset first and second level analysis of a dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_ica_neurovault.html">NeuroVault cross-study ICA maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_haxby_mass_univariate.html">Massively univariate analysis of face vs house recognition</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_advanced_decoding_scikit.html">Advanced decoding using scikit learn</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_beta_series.html">Beta-Series Modeling for Task-Based Functional Connectivity and Decoding</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/08_experimental/index.html">Examples for experimental modules</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of Examples for experimental modules</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/08_experimental/plot_surface_image_and_maskers.html">A short demo of the surface images & maskers</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1 current has-children"><a class="reference internal" href="../user_guide.html">User guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of User guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../introduction.html">1. Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../introduction.html#what-is-nilearn">2. What is <code class="docutils literal notranslate"><span class="pre">nilearn</span></code>?</a></li>
<li class="toctree-l2"><a class="reference internal" href="../introduction.html#using-nilearn-for-the-first-time">3. Using <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> for the first time</a></li>
<li class="toctree-l2"><a class="reference internal" href="../introduction.html#machine-learning-applications-to-neuroimaging">4. Machine learning applications to Neuroimaging</a></li>
<li class="toctree-l2 current has-children"><a class="reference internal" href="index.html">5. Decoding and MVPA: predicting from brain images</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of 5. Decoding and MVPA: predicting from brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html">5.1. An introduction to decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="estimator_choice.html">5.2. Choosing the right predictive model for neuroimaging</a></li>
<li class="toctree-l3"><a class="reference internal" href="frem.html">5.3. FREM: fast ensembling of regularized models for robust decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="space_net.html">5.4. SpaceNet: decoding with spatial structure for better maps</a></li>
<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">5.5. Searchlight : finding voxels containing information</a></li>
<li class="toctree-l3"><a class="reference internal" href="going_further.html">5.6. Running scikit-learn functions for more control on the analysis</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../connectivity/index.html">6. Functional connectivity and resting state</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of 6. Functional connectivity and resting state</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../connectivity/functional_connectomes.html">6.1. Extracting times series to build a functional connectome</a></li>
<li class="toctree-l3 has-children"><a class="reference internal" href="../connectivity/connectome_extraction.html">6.2. Connectome extraction: inverse covariance for direct connections</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of 6.2. Connectome extraction: inverse covariance for direct connections</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l4"><a class="reference internal" href="../developers/group_sparse_covariance.html">6.2.3.1. Group-sparse covariance estimation</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../connectivity/resting_state_networks.html">6.3. Extracting functional brain networks: ICA and related</a></li>
<li class="toctree-l3"><a class="reference internal" href="../connectivity/region_extraction.html">6.4. Region Extraction for better brain parcellations</a></li>
<li class="toctree-l3"><a class="reference internal" href="../connectivity/parcellating.html">6.5. Clustering to parcellate the brain in regions</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../plotting/index.html">7. Plotting brain images</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../glm/index.html">8. Analyzing fMRI using GLMs</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of 8. Analyzing fMRI using GLMs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../glm/glm_intro.html">8.1. An introduction to GLMs in fMRI statistical analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../glm/first_level_model.html">8.2. First level models</a></li>
<li class="toctree-l3"><a class="reference internal" href="../glm/second_level_model.html">8.3. Second level models</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../manipulating_images/index.html">9. Manipulation brain volumes with nilearn</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of 9. Manipulation brain volumes with nilearn</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../manipulating_images/input_output.html">9.1. Input and output: neuroimaging data representation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../manipulating_images/manipulating_images.html">9.2. Manipulating images: resampling, smoothing, masking, ROIs…</a></li>
<li class="toctree-l3"><a class="reference internal" href="../manipulating_images/masker_objects.html">9.3. From neuroimaging volumes to data matrices: the masker objects</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../building_blocks/index.html">10. Advanced usage: manual pipelines and scaling up</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of 10. Advanced usage: manual pipelines and scaling up</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../building_blocks/manual_pipeline.html">10.1. Building your own neuroimaging machine-learning pipeline</a></li>
<li class="toctree-l3"><a class="reference internal" href="../building_blocks/neurovault.html">10.2. Downloading statistical maps from the Neurovault repository</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../modules/index.html">API References</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of API References</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/connectome.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.connectome</span></code>: Functional Connectivity</a><input class="toctree-checkbox" id="toctree-checkbox-19" name="toctree-checkbox-19" role="switch" type="checkbox"/><label for="toctree-checkbox-19"><div class="visually-hidden">Toggle navigation of nilearn.connectome: Functional Connectivity</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.ConnectivityMeasure.html">nilearn.connectome.ConnectivityMeasure</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.GroupSparseCovariance.html">nilearn.connectome.GroupSparseCovariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.GroupSparseCovarianceCV.html">nilearn.connectome.GroupSparseCovarianceCV</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.sym_matrix_to_vec.html">nilearn.connectome.sym_matrix_to_vec</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.vec_to_sym_matrix.html">nilearn.connectome.vec_to_sym_matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.group_sparse_covariance.html">nilearn.connectome.group_sparse_covariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.cov_to_corr.html">nilearn.connectome.cov_to_corr</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.prec_to_partial.html">nilearn.connectome.prec_to_partial</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/datasets.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.datasets</span></code>: Automatic Dataset Fetching</a><input class="toctree-checkbox" id="toctree-checkbox-20" name="toctree-checkbox-20" role="switch" type="checkbox"/><label for="toctree-checkbox-20"><div class="visually-hidden">Toggle navigation of nilearn.datasets: Automatic Dataset Fetching</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_icbm152_2009.html">nilearn.datasets.fetch_icbm152_2009</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_icbm152_brain_gm_mask.html">nilearn.datasets.fetch_icbm152_brain_gm_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_surf_fsaverage.html">nilearn.datasets.fetch_surf_fsaverage</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.load_mni152_brain_mask.html">nilearn.datasets.load_mni152_brain_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.load_mni152_gm_mask.html">nilearn.datasets.load_mni152_gm_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.load_mni152_gm_template.html">nilearn.datasets.load_mni152_gm_template</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.load_mni152_template.html">nilearn.datasets.load_mni152_template</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.load_mni152_wm_mask.html">nilearn.datasets.load_mni152_wm_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.load_mni152_wm_template.html">nilearn.datasets.load_mni152_wm_template</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_aal.html">nilearn.datasets.fetch_atlas_aal</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_allen_2011.html">nilearn.datasets.fetch_atlas_allen_2011</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_basc_multiscale_2015.html">nilearn.datasets.fetch_atlas_basc_multiscale_2015</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_craddock_2012.html">nilearn.datasets.fetch_atlas_craddock_2012</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_destrieux_2009.html">nilearn.datasets.fetch_atlas_destrieux_2009</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_difumo.html">nilearn.datasets.fetch_atlas_difumo</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_harvard_oxford.html">nilearn.datasets.fetch_atlas_harvard_oxford</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_juelich.html">nilearn.datasets.fetch_atlas_juelich</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_msdl.html">nilearn.datasets.fetch_atlas_msdl</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_pauli_2017.html">nilearn.datasets.fetch_atlas_pauli_2017</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_schaefer_2018.html">nilearn.datasets.fetch_atlas_schaefer_2018</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_smith_2009.html">nilearn.datasets.fetch_atlas_smith_2009</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_surf_destrieux.html">nilearn.datasets.fetch_atlas_surf_destrieux</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_talairach.html">nilearn.datasets.fetch_atlas_talairach</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_atlas_yeo_2011.html">nilearn.datasets.fetch_atlas_yeo_2011</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_coords_dosenbach_2010.html">nilearn.datasets.fetch_coords_dosenbach_2010</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_coords_power_2011.html">nilearn.datasets.fetch_coords_power_2011</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_coords_seitzman_2018.html">nilearn.datasets.fetch_coords_seitzman_2018</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_abide_pcp.html">nilearn.datasets.fetch_abide_pcp</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_adhd.html">nilearn.datasets.fetch_adhd</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_bids_langloc_dataset.html">nilearn.datasets.fetch_bids_langloc_dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_development_fmri.html">nilearn.datasets.fetch_development_fmri</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_ds000030_urls.html">nilearn.datasets.fetch_ds000030_urls</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_fiac_first_level.html">nilearn.datasets.fetch_fiac_first_level</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_haxby.html">nilearn.datasets.fetch_haxby</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_language_localizer_demo_dataset.html">nilearn.datasets.fetch_language_localizer_demo_dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_localizer_first_level.html">nilearn.datasets.fetch_localizer_first_level</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_miyawaki2008.html">nilearn.datasets.fetch_miyawaki2008</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_openneuro_dataset_index.html">nilearn.datasets.fetch_openneuro_dataset_index</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_spm_auditory.html">nilearn.datasets.fetch_spm_auditory</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_spm_multimodal_fmri.html">nilearn.datasets.fetch_spm_multimodal_fmri</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_surf_nki_enhanced.html">nilearn.datasets.fetch_surf_nki_enhanced</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_localizer_button_task.html">nilearn.datasets.fetch_localizer_button_task</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_localizer_calculation_task.html">nilearn.datasets.fetch_localizer_calculation_task</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_localizer_contrasts.html">nilearn.datasets.fetch_localizer_contrasts</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_megatrawls_netmats.html">nilearn.datasets.fetch_megatrawls_netmats</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_mixed_gambles.html">nilearn.datasets.fetch_mixed_gambles</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_oasis_vbm.html">nilearn.datasets.fetch_oasis_vbm</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_neurovault_auditory_computation_task.html">nilearn.datasets.fetch_neurovault_auditory_computation_task</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_neurovault_motor_task.html">nilearn.datasets.fetch_neurovault_motor_task</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_neurovault.html">nilearn.datasets.fetch_neurovault</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_neurovault_ids.html">nilearn.datasets.fetch_neurovault_ids</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_openneuro_dataset.html">nilearn.datasets.fetch_openneuro_dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.get_data_dirs.html">nilearn.datasets.get_data_dirs</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.patch_openneuro_dataset.html">nilearn.datasets.patch_openneuro_dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.select_from_index.html">nilearn.datasets.select_from_index</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.datasets.load_sample_motor_activation_image.html">nilearn.datasets.load_sample_motor_activation_image</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/decoding.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.decoding</span></code>: Decoding</a><input class="toctree-checkbox" id="toctree-checkbox-21" name="toctree-checkbox-21" role="switch" type="checkbox"/><label for="toctree-checkbox-21"><div class="visually-hidden">Toggle navigation of nilearn.decoding: Decoding</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.decoding.Decoder.html">nilearn.decoding.Decoder</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.decoding.DecoderRegressor.html">nilearn.decoding.DecoderRegressor</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.decoding.FREMClassifier.html">nilearn.decoding.FREMClassifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.decoding.FREMRegressor.html">nilearn.decoding.FREMRegressor</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.decoding.SpaceNetClassifier.html">nilearn.decoding.SpaceNetClassifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.decoding.SpaceNetRegressor.html">nilearn.decoding.SpaceNetRegressor</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html">nilearn.decoding.SearchLight</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/decomposition.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.decomposition</span></code>: Multivariate Decompositions</a><input class="toctree-checkbox" id="toctree-checkbox-22" name="toctree-checkbox-22" role="switch" type="checkbox"/><label for="toctree-checkbox-22"><div class="visually-hidden">Toggle navigation of nilearn.decomposition: Multivariate Decompositions</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.decomposition.CanICA.html">nilearn.decomposition.CanICA</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.decomposition.DictLearning.html">nilearn.decomposition.DictLearning</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/experimental.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.experimental</span></code>: Experimental Modules</a><input class="toctree-checkbox" id="toctree-checkbox-23" name="toctree-checkbox-23" role="switch" type="checkbox"/><label for="toctree-checkbox-23"><div class="visually-hidden">Toggle navigation of nilearn.experimental: Experimental Modules</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.FileMesh.html">nilearn.experimental.surface.FileMesh</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.InMemoryMesh.html">nilearn.experimental.surface.InMemoryMesh</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.Mesh.html">nilearn.experimental.surface.Mesh</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.PolyMesh.html">nilearn.experimental.surface.PolyMesh</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.SurfaceImage.html">nilearn.experimental.surface.SurfaceImage</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.SurfaceLabelsMasker.html">nilearn.experimental.surface.SurfaceLabelsMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.SurfaceMasker.html">nilearn.experimental.surface.SurfaceMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.fetch_destrieux.html">nilearn.experimental.surface.fetch_destrieux</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.fetch_nki.html">nilearn.experimental.surface.fetch_nki</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.experimental.surface.load_fsaverage.html">nilearn.experimental.surface.load_fsaverage</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/glm.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.glm</span></code>: Generalized Linear Models</a><input class="toctree-checkbox" id="toctree-checkbox-24" name="toctree-checkbox-24" role="switch" type="checkbox"/><label for="toctree-checkbox-24"><div class="visually-hidden">Toggle navigation of nilearn.glm: Generalized Linear Models</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.Contrast.html">nilearn.glm.Contrast</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.FContrastResults.html">nilearn.glm.FContrastResults</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.TContrastResults.html">nilearn.glm.TContrastResults</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.ARModel.html">nilearn.glm.ARModel</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.OLSModel.html">nilearn.glm.OLSModel</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.LikelihoodModelResults.html">nilearn.glm.LikelihoodModelResults</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.RegressionResults.html">nilearn.glm.RegressionResults</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.SimpleRegressionResults.html">nilearn.glm.SimpleRegressionResults</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.compute_contrast.html">nilearn.glm.compute_contrast</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.compute_fixed_effects.html">nilearn.glm.compute_fixed_effects</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.expression_to_contrast_vector.html">nilearn.glm.expression_to_contrast_vector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.fdr_threshold.html">nilearn.glm.fdr_threshold</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.cluster_level_inference.html">nilearn.glm.cluster_level_inference</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.threshold_stats_img.html">nilearn.glm.threshold_stats_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.FirstLevelModel.html">nilearn.glm.first_level.FirstLevelModel</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.check_design_matrix.html">nilearn.glm.first_level.check_design_matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.compute_regressor.html">nilearn.glm.first_level.compute_regressor</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.first_level_from_bids.html">nilearn.glm.first_level.first_level_from_bids</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.glover_dispersion_derivative.html">nilearn.glm.first_level.glover_dispersion_derivative</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.glover_hrf.html">nilearn.glm.first_level.glover_hrf</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.glover_time_derivative.html">nilearn.glm.first_level.glover_time_derivative</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.make_first_level_design_matrix.html">nilearn.glm.first_level.make_first_level_design_matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.mean_scaling.html">nilearn.glm.first_level.mean_scaling</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.run_glm.html">nilearn.glm.first_level.run_glm</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.spm_dispersion_derivative.html">nilearn.glm.first_level.spm_dispersion_derivative</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.spm_hrf.html">nilearn.glm.first_level.spm_hrf</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.first_level.spm_time_derivative.html">nilearn.glm.first_level.spm_time_derivative</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.second_level.SecondLevelModel.html">nilearn.glm.second_level.SecondLevelModel</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.second_level.make_second_level_design_matrix.html">nilearn.glm.second_level.make_second_level_design_matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.glm.second_level.non_parametric_inference.html">nilearn.glm.second_level.non_parametric_inference</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/image.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.image</span></code>: Image Processing and Resampling Utilities</a><input class="toctree-checkbox" id="toctree-checkbox-25" name="toctree-checkbox-25" role="switch" type="checkbox"/><label for="toctree-checkbox-25"><div class="visually-hidden">Toggle navigation of nilearn.image: Image Processing and Resampling Utilities</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.binarize_img.html">nilearn.image.binarize_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.clean_img.html">nilearn.image.clean_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.concat_imgs.html">nilearn.image.concat_imgs</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.coord_transform.html">nilearn.image.coord_transform</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.copy_img.html">nilearn.image.copy_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.crop_img.html">nilearn.image.crop_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.get_data.html">nilearn.image.get_data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.high_variance_confounds.html">nilearn.image.high_variance_confounds</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.index_img.html">nilearn.image.index_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.iter_img.html">nilearn.image.iter_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.largest_connected_component_img.html">nilearn.image.largest_connected_component_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.load_img.html">nilearn.image.load_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.math_img.html">nilearn.image.math_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.mean_img.html">nilearn.image.mean_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.new_img_like.html">nilearn.image.new_img_like</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.resample_img.html">nilearn.image.resample_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.resample_to_img.html">nilearn.image.resample_to_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.reorder_img.html">nilearn.image.reorder_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.smooth_img.html">nilearn.image.smooth_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.swap_img_hemispheres.html">nilearn.image.swap_img_hemispheres</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.image.threshold_img.html">nilearn.image.threshold_img</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/interfaces.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces</span></code>: Loading components from interfaces</a><input class="toctree-checkbox" id="toctree-checkbox-26" name="toctree-checkbox-26" role="switch" type="checkbox"/><label for="toctree-checkbox-26"><div class="visually-hidden">Toggle navigation of nilearn.interfaces: Loading components from interfaces</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.interfaces.bids.get_bids_files.html">nilearn.interfaces.bids.get_bids_files</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.interfaces.bids.parse_bids_filename.html">nilearn.interfaces.bids.parse_bids_filename</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.interfaces.bids.save_glm_to_bids.html">nilearn.interfaces.bids.save_glm_to_bids</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.interfaces.fmriprep.load_confounds.html">nilearn.interfaces.fmriprep.load_confounds</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.interfaces.fmriprep.load_confounds_strategy.html">nilearn.interfaces.fmriprep.load_confounds_strategy</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.interfaces.fsl.get_design_from_fslmat.html">nilearn.interfaces.fsl.get_design_from_fslmat</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/maskers.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.maskers</span></code>: Extracting Signals from Brain Images</a><input class="toctree-checkbox" id="toctree-checkbox-27" name="toctree-checkbox-27" role="switch" type="checkbox"/><label for="toctree-checkbox-27"><div class="visually-hidden">Toggle navigation of nilearn.maskers: Extracting Signals from Brain Images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.maskers.BaseMasker.html">nilearn.maskers.BaseMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.maskers.NiftiMasker.html">nilearn.maskers.NiftiMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.maskers.MultiNiftiMasker.html">nilearn.maskers.MultiNiftiMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.maskers.NiftiLabelsMasker.html">nilearn.maskers.NiftiLabelsMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.maskers.MultiNiftiLabelsMasker.html">nilearn.maskers.MultiNiftiLabelsMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.maskers.NiftiMapsMasker.html">nilearn.maskers.NiftiMapsMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.maskers.MultiNiftiMapsMasker.html">nilearn.maskers.MultiNiftiMapsMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.maskers.NiftiSpheresMasker.html">nilearn.maskers.NiftiSpheresMasker</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/masking.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.masking</span></code>: Data Masking Utilities</a><input class="toctree-checkbox" id="toctree-checkbox-28" name="toctree-checkbox-28" role="switch" type="checkbox"/><label for="toctree-checkbox-28"><div class="visually-hidden">Toggle navigation of nilearn.masking: Data Masking Utilities</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.masking.compute_epi_mask.html">nilearn.masking.compute_epi_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.masking.compute_multi_epi_mask.html">nilearn.masking.compute_multi_epi_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.masking.compute_brain_mask.html">nilearn.masking.compute_brain_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.masking.compute_multi_brain_mask.html">nilearn.masking.compute_multi_brain_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.masking.compute_background_mask.html">nilearn.masking.compute_background_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.masking.compute_multi_background_mask.html">nilearn.masking.compute_multi_background_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.masking.intersect_masks.html">nilearn.masking.intersect_masks</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.masking.apply_mask.html">nilearn.masking.apply_mask</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.masking.unmask.html">nilearn.masking.unmask</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/mass_univariate.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.mass_univariate</span></code>: Mass-Univariate Analysis</a><input class="toctree-checkbox" id="toctree-checkbox-29" name="toctree-checkbox-29" role="switch" type="checkbox"/><label for="toctree-checkbox-29"><div class="visually-hidden">Toggle navigation of nilearn.mass_univariate: Mass-Univariate Analysis</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.mass_univariate.permuted_ols.html">nilearn.mass_univariate.permuted_ols</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/plotting.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.plotting</span></code>: Plotting Brain Data</a><input class="toctree-checkbox" id="toctree-checkbox-30" name="toctree-checkbox-30" role="switch" type="checkbox"/><label for="toctree-checkbox-30"><div class="visually-hidden">Toggle navigation of nilearn.plotting: Plotting Brain Data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.find_cut_slices.html">nilearn.plotting.find_cut_slices</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.find_xyz_cut_coords.html">nilearn.plotting.find_xyz_cut_coords</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.find_parcellation_cut_coords.html">nilearn.plotting.find_parcellation_cut_coords</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.find_probabilistic_atlas_cut_coords.html">nilearn.plotting.find_probabilistic_atlas_cut_coords</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_anat.html">nilearn.plotting.plot_anat</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_img.html">nilearn.plotting.plot_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_epi.html">nilearn.plotting.plot_epi</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_matrix.html">nilearn.plotting.plot_matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_roi.html">nilearn.plotting.plot_roi</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_stat_map.html">nilearn.plotting.plot_stat_map</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_glass_brain.html">nilearn.plotting.plot_glass_brain</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_connectome.html">nilearn.plotting.plot_connectome</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_markers.html">nilearn.plotting.plot_markers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_prob_atlas.html">nilearn.plotting.plot_prob_atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_carpet.html">nilearn.plotting.plot_carpet</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_surf.html">nilearn.plotting.plot_surf</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_surf_roi.html">nilearn.plotting.plot_surf_roi</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_surf_contours.html">nilearn.plotting.plot_surf_contours</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_surf_stat_map.html">nilearn.plotting.plot_surf_stat_map</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_img_on_surf.html">nilearn.plotting.plot_img_on_surf</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_img_comparison.html">nilearn.plotting.plot_img_comparison</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_design_matrix.html">nilearn.plotting.plot_design_matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_event.html">nilearn.plotting.plot_event</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_contrast_matrix.html">nilearn.plotting.plot_contrast_matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.view_surf.html">nilearn.plotting.view_surf</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.view_img_on_surf.html">nilearn.plotting.view_img_on_surf</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.view_connectome.html">nilearn.plotting.view_connectome</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.view_markers.html">nilearn.plotting.view_markers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.view_img.html">nilearn.plotting.view_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.show.html">nilearn.plotting.show</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.get_projector.html">nilearn.plotting.displays.get_projector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.get_slicer.html">nilearn.plotting.displays.get_slicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.OrthoProjector.html">nilearn.plotting.displays.OrthoProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.XZProjector.html">nilearn.plotting.displays.XZProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.YZProjector.html">nilearn.plotting.displays.YZProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.YXProjector.html">nilearn.plotting.displays.YXProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.XProjector.html">nilearn.plotting.displays.XProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.YProjector.html">nilearn.plotting.displays.YProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.ZProjector.html">nilearn.plotting.displays.ZProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.LZRYProjector.html">nilearn.plotting.displays.LZRYProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.LYRZProjector.html">nilearn.plotting.displays.LYRZProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.LYRProjector.html">nilearn.plotting.displays.LYRProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.LZRProjector.html">nilearn.plotting.displays.LZRProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.LRProjector.html">nilearn.plotting.displays.LRProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.LProjector.html">nilearn.plotting.displays.LProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.RProjector.html">nilearn.plotting.displays.RProjector</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.BaseAxes.html">nilearn.plotting.displays.BaseAxes</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.CutAxes.html">nilearn.plotting.displays.CutAxes</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.GlassBrainAxes.html">nilearn.plotting.displays.GlassBrainAxes</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.BaseSlicer.html">nilearn.plotting.displays.BaseSlicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.OrthoSlicer.html">nilearn.plotting.displays.OrthoSlicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.PlotlySurfaceFigure.html">nilearn.plotting.displays.PlotlySurfaceFigure</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.TiledSlicer.html">nilearn.plotting.displays.TiledSlicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.MosaicSlicer.html">nilearn.plotting.displays.MosaicSlicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.XZSlicer.html">nilearn.plotting.displays.XZSlicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.YZSlicer.html">nilearn.plotting.displays.YZSlicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.YXSlicer.html">nilearn.plotting.displays.YXSlicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.XSlicer.html">nilearn.plotting.displays.XSlicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.YSlicer.html">nilearn.plotting.displays.YSlicer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.plotting.displays.ZSlicer.html">nilearn.plotting.displays.ZSlicer</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/regions.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.regions</span></code>: Operating on Regions</a><input class="toctree-checkbox" id="toctree-checkbox-31" name="toctree-checkbox-31" role="switch" type="checkbox"/><label for="toctree-checkbox-31"><div class="visually-hidden">Toggle navigation of nilearn.regions: Operating on Regions</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.connected_regions.html">nilearn.regions.connected_regions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.connected_label_regions.html">nilearn.regions.connected_label_regions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.img_to_signals_labels.html">nilearn.regions.img_to_signals_labels</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.signals_to_img_labels.html">nilearn.regions.signals_to_img_labels</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.img_to_signals_maps.html">nilearn.regions.img_to_signals_maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.signals_to_img_maps.html">nilearn.regions.signals_to_img_maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.recursive_neighbor_agglomeration.html">nilearn.regions.recursive_neighbor_agglomeration</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.RegionExtractor.html">nilearn.regions.RegionExtractor</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.Parcellations.html">nilearn.regions.Parcellations</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.ReNA.html">nilearn.regions.ReNA</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.regions.HierarchicalKMeans.html">nilearn.regions.HierarchicalKMeans</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/reporting.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.reporting</span></code>: Reporting Functions</a><input class="toctree-checkbox" id="toctree-checkbox-32" name="toctree-checkbox-32" role="switch" type="checkbox"/><label for="toctree-checkbox-32"><div class="visually-hidden">Toggle navigation of nilearn.reporting: Reporting Functions</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.reporting.HTMLReport.html">nilearn.reporting.HTMLReport</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.reporting.get_clusters_table.html">nilearn.reporting.get_clusters_table</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.reporting.make_glm_report.html">nilearn.reporting.make_glm_report</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/signal.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.signal</span></code>: Preprocessing Time Series</a><input class="toctree-checkbox" id="toctree-checkbox-33" name="toctree-checkbox-33" role="switch" type="checkbox"/><label for="toctree-checkbox-33"><div class="visually-hidden">Toggle navigation of nilearn.signal: Preprocessing Time Series</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.signal.butterworth.html">nilearn.signal.butterworth</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.signal.clean.html">nilearn.signal.clean</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.signal.high_variance_confounds.html">nilearn.signal.high_variance_confounds</a></li>
</ul>
</li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/surface.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.surface</span></code>: Manipulating Surface Data</a><input class="toctree-checkbox" id="toctree-checkbox-34" name="toctree-checkbox-34" role="switch" type="checkbox"/><label for="toctree-checkbox-34"><div class="visually-hidden">Toggle navigation of nilearn.surface: Manipulating Surface Data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.surface.load_surf_data.html">nilearn.surface.load_surf_data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.surface.load_surf_mesh.html">nilearn.surface.load_surf_mesh</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.surface.vol_to_surf.html">nilearn.surface.vol_to_surf</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../glossary.html">Glossary</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Development</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../development.html">Contributing</a></li>
<li class="toctree-l1"><a class="reference internal" href="../maintenance.html">Maintenance</a></li>
<li class="toctree-l1"><a class="reference internal" href="../changes/whats_new.html">What’s new</a></li>
<li class="toctree-l1"><a class="reference internal" href="../authors.html">Team</a></li>
<li class="toctree-l1"><a class="reference external" href="https://github.com/nilearn/nilearn">GitHub Repository</a></li>
</ul>
</div>
</div>
</div>
</div>
</aside>
<div class="main">
<div class="content">
<div class="article-container">
<a href="#" class="back-to-top muted-link">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
<path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
</svg>
<span>Back to top</span>
</a>
<div class="content-icon-container">
<div class="edit-this-page">
<a class="muted-link" href="https://github.com/nilearn/nilearn/edit/main/doc/decoding/searchlight.rst" title="Edit this page">
<svg aria-hidden="true" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
<path stroke="none" d="M0 0h24v24H0z" fill="none"/>
<path d="M4 20h4l10.5 -10.5a1.5 1.5 0 0 0 -4 -4l-10.5 10.5v4" />
<line x1="13.5" y1="6.5" x2="17.5" y2="10.5" />
</svg>
<span class="visually-hidden">Edit this page</span>
</a>
</div><div class="theme-toggle-container theme-toggle-content">
<button class="theme-toggle">
<div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
<svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
<svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
<svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
</button>
</div>
<label class="toc-overlay-icon toc-content-icon" for="__toc">
<div class="visually-hidden">Toggle table of contents sidebar</div>
<i class="icon"><svg><use href="#svg-toc"></use></svg></i>
</label>
</div>
<article role="main">
<section id="searchlight-finding-voxels-containing-information">
<span id="searchlight"></span><h1><span class="section-number">5.5. </span>Searchlight : finding voxels containing information<a class="headerlink" href="#searchlight-finding-voxels-containing-information" title="Permalink to this heading">#</a></h1>
<p>This page overviews searchlight analyses and how they are approached
in nilearn with the <a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a> estimator.</p>
<section id="principle-of-the-searchlight">
<h2><span class="section-number">5.5.1. </span>Principle of the Searchlight<a class="headerlink" href="#principle-of-the-searchlight" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a> analysis was introduced in [Kriegeskorte <em>et al.</em><a class="footnote-reference brackets" href="#footcite-kriegeskorte2006" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>], and consists of scanning the brain with a <em>searchlight</em>.
Briefly, a ball of given radius is scanned across the brain volume and the prediction accuracy of a classifier trained on the corresponding <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a> is measured.</p>
<p>Searchlights are also not limited to <a class="reference internal" href="../glossary.html#term-classification"><span class="xref std std-term">classification</span></a>; <a class="reference internal" href="../glossary.html#term-regression"><span class="xref std std-term">regression</span></a> (e.g., [Kahnt <em>et al.</em><a class="footnote-reference brackets" href="#footcite-kahnt2011" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>]) and representational similarity analysis (e.g., [Clarke and Tyler<a class="footnote-reference brackets" href="#footcite-clarke2014" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>]) are other uses of searchlights.
Currently, only <a class="reference internal" href="../glossary.html#term-classification"><span class="xref std std-term">classification</span></a> and <a class="reference internal" href="../glossary.html#term-regression"><span class="xref std std-term">regression</span></a> are supported in nilearn.</p>
<aside class="topic">
<p class="topic-title"><strong>Further Reading</strong></p>
<p>For a critical review on searchlights, see [Etzel <em>et al.</em><a class="footnote-reference brackets" href="#footcite-etzel2013" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>6<span class="fn-bracket">]</span></a>].</p>
</aside>
</section>
<section id="preparing-the-data">
<h2><span class="section-number">5.5.2. </span>Preparing the data<a class="headerlink" href="#preparing-the-data" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a> requires a series of brain volumes as input, <code class="docutils literal notranslate"><span class="pre">X</span></code>, each with
a corresponding label, <code class="docutils literal notranslate"><span class="pre">y</span></code>. The number of brain volumes therefore correspond to
the number of samples used for decoding.</p>
<section id="masking">
<h3><span class="section-number">5.5.2.1. </span>Masking<a class="headerlink" href="#masking" title="Permalink to this heading">#</a></h3>
<p>One of the main elements that distinguish <a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a> from other
algorithms is the notion of structuring element that scans the entire volume.
This has an impact on the masking procedure.</p>
<p>Two masks are used with <a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a>:</p>
<ul class="simple">
<li><p><em>mask_img</em> is the anatomical mask</p></li>
<li><p><em>process_mask_img</em> is a subset of the brain mask and defines the boundaries
of where the searchlight scans the volume. Often times we are interested in
only performing a searchlight within a specific area of the brain (e.g.,
frontal cortex). If no <em>process_mask_img</em> is set, then <a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.SearchLight</span></code></a>
defaults to performing a searchlight over the whole brain.</p></li>
</ul>
<p><em>mask_img</em> ensures that only <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a> with usable signals are included in the
searchlight. This could be a full-brain mask or a gray-matter mask.</p>
</section>
</section>
<section id="setting-up-the-searchlight">
<h2><span class="section-number">5.5.3. </span>Setting up the searchlight<a class="headerlink" href="#setting-up-the-searchlight" title="Permalink to this heading">#</a></h2>
<section id="classifier">
<h3><span class="section-number">5.5.3.1. </span>Classifier<a class="headerlink" href="#classifier" title="Permalink to this heading">#</a></h3>
<p>The classifier used by default by <a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a> is LinearSVC with C=1 but
this can be customized easily by passing an estimator parameter to the
Searchlight. See scikit-learn documentation for <a class="reference external" href="https://scikit-learn.org/stable/supervised_learning.html">other classifiers</a>. You can
also pass scikit-learn <a class="reference external" href="https://scikit-learn.org/stable/modules/compose.html">Pipelines</a>
to the <a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a> in order to combine estimators and preprocessing steps
(e.g., feature scaling) for your searchlight.</p>
</section>
<section id="score-function">
<h3><span class="section-number">5.5.3.2. </span>Score function<a class="headerlink" href="#score-function" title="Permalink to this heading">#</a></h3>
<p>Metrics can be specified by the “scoring” argument to the <a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a>, as
detailed in the <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#the-scoring-parameter-defining-model-evaluation-rules">scikit-learn documentation</a></p>
</section>
<section id="cross-validation">
<h3><span class="section-number">5.5.3.3. </span>Cross validation<a class="headerlink" href="#cross-validation" title="Permalink to this heading">#</a></h3>
<p><a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a> will iterate on the volume and give a score to each <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxel</span></a>.
This score is computed by running a classifier on selected <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a>.
In order to make this score as accurate as possible (and avoid overfitting),
cross-validation is used.</p>
<p>Cross-validation can be defined using the “cv” argument. As it
is computationally costly, <em>K</em>-Fold cross validation with <em>K</em> = 3 is set as the
default. A <a class="reference external" href="https://scikit-learn.org/stable/modules/classes.html#splitter-classes">scikit-learn cross-validation generator</a> can also
be passed to set a specific type of cross-validation.</p>
<p>Leave-one-run-out cross-validation (LOROCV) is a common approach for searchlights.
This approach is a specific use-case of grouped cross-validation, where the
cross-validation folds are determined by the acquisition runs. The held-out fold
in a given iteration of cross-validation consist of data from a separate run,
which keeps training and validation sets properly independent. For this reason,
LOROCV is often recommended. This can be performed by using <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html">LeaveOneGroupOut</a>,
and then setting the group/run labels when fitting the estimator.</p>
</section>
<section id="sphere-radius">
<h3><span class="section-number">5.5.3.4. </span>Sphere radius<a class="headerlink" href="#sphere-radius" title="Permalink to this heading">#</a></h3>
<p>An important parameter is the radius of the sphere that will run through
the data. The sphere size determines the number of voxels/features to use
for <a class="reference internal" href="../glossary.html#term-classification"><span class="xref std std-term">classification</span></a> (i.e. more <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a> are included with larger spheres).</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a> defines sphere radius in millimeters; the number
of <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a> included in the sphere will therefore depend on the
<a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxel</span></a> size.</p>
<p>For reference, [Kriegeskorte <em>et al.</em><a class="footnote-reference brackets" href="#footcite-kriegeskorte2006" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>] use a 4mm radius because it yielded
the best detection performance in their simulation of 2mm isovoxel data.</p>
</div>
</section>
</section>
<section id="visualization">
<h2><span class="section-number">5.5.4. </span>Visualization<a class="headerlink" href="#visualization" title="Permalink to this heading">#</a></h2>
<section id="id6">
<h3><span class="section-number">5.5.4.1. </span>Searchlight<a class="headerlink" href="#id6" title="Permalink to this heading">#</a></h3>
<p>The results of the searchlight can be found in the <code class="docutils literal notranslate"><span class="pre">scores_</span></code> attribute of the
<a class="reference internal" href="../modules/generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-class docutils literal notranslate"><span class="pre">SearchLight</span></code></a> object after fitting it to the data. Below is a
visualization of the results from <a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_searchlight.html#sphx-glr-auto-examples-02-decoding-plot-haxby-searchlight-py"><span class="std std-ref">Searchlight analysis of face
vs house recognition</span></a>.
The searchlight was restricted to a slice in the back of the brain. Within
this slice, we can see that a cluster of <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a> in visual cortex
contains information to distinguish pictures showed to the volunteers,
which was the expected result.</p>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/02_decoding/plot_haxby_searchlight.html"><img alt="../_images/sphx_glr_plot_haxby_searchlight_001.png" src="../_images/sphx_glr_plot_haxby_searchlight_001.png" style="width: 176.0px; height: 256.0px;" /></a>
</figure>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<ul class="simple">
<li><p><a class="reference internal" href="../plotting/index.html#plotting"><span class="std std-ref">Plotting brain images</span></a></p></li>
</ul>
</div>
</section>
<section id="comparing-to-massively-univariate-analysis-f-score-or-spm">
<h3><span class="section-number">5.5.4.2. </span>Comparing to massively univariate analysis: F_score or SPM<a class="headerlink" href="#comparing-to-massively-univariate-analysis-f-score-or-spm" title="Permalink to this heading">#</a></h3>
<p>The standard approach to brain mapping is performed using <em>Statistical
Parametric Mapping</em> (<a class="reference internal" href="../glossary.html#term-SPM"><span class="xref std std-term">SPM</span></a>), using <a class="reference internal" href="../glossary.html#term-ANOVA"><span class="xref std std-term">ANOVA</span></a> (analysis of
variance), and parametric tests (F-tests ot t-tests).
Here we compute the <em>p-values</em> of the <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a> <a class="footnote-reference brackets" href="#id9" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
To display the results, we use the negative log of the p-value.</p>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/02_decoding/plot_haxby_searchlight.html"><img alt="../_images/sphx_glr_plot_haxby_searchlight_002.png" src="../_images/sphx_glr_plot_haxby_searchlight_002.png" style="width: 176.0px; height: 256.0px;" /></a>
</figure>
<p>Parametric scores can be converted into p-values using a reference
theoretical distribution, which is known under specific assumptions
(hence the name <em>parametric</em>). In practice, neuroimaging signal has a
complex structure that might not match these assumptions. An exact,
non-parametric <em>permutation test</em> can be performed as an alternative
to the parametric test: the residuals of the model are permuted so as
to break any effect and the corresponding decision statistic is
recomputed. One thus builds the distribution of the decision statistic
under the hypothesis that there is no relationship between the tested
variates and the target variates. In neuroimaging, this is generally
done by swapping the signal values of all <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a> while the tested
variables remain unchanged <a class="footnote-reference brackets" href="#id10" id="id8" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. A voxel-wise analysis is then
performed on the permuted data. The relationships between the image
descriptors and the tested variates are broken while the value of the
signal in each particular <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxel</span></a> can be observed with the same
probability than the original value associated to that <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxel</span></a>.
Note that it is hereby assumed that the signal distribution is the same in
every <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxel</span></a>. Several data permutations are performed (typically
10,000) while the scores for every <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxel</span></a> and every data permutation
is stored. The empirical distribution of the scores is thus
constructed (under the hypothesis that there is no relationship
between the tested variates and the neuroimaging signal, the so-called
<em>null-hypothesis</em>) and we can compare the original scores to that
distribution: The higher the rank of the original score, the smaller
is its associated p-value. The
<a class="reference internal" href="../modules/generated/nilearn.mass_univariate.permuted_ols.html#nilearn.mass_univariate.permuted_ols" title="nilearn.mass_univariate.permuted_ols"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.mass_univariate.permuted_ols</span></code></a> function returns the
p-values computed with a permutation test.</p>
<p>The number of tests performed is generally large when full-brain
analysis is performed (> 50,000 voxels). This increases the
probability of finding a significant activation by chance, a
phenomenon that is known to statisticians as the <em>multiple comparisons
problem</em>. It is therefore recommended to correct the p-values to take
into account the multiple tests. <em>Bonferroni correction</em> consists of
multiplying the p-values by the number of tests (while making sure the
p-values remain smaller than 1). Thus, we control the occurrence of one
false detection <em>at most</em>, the so-called <em>family-wise error control</em>.
A similar control can be performed when performing a permutation test:
For each permutation, only the maximum value of the F-statistic across
<a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a> is considered and is used to build the null distribution.
It is crucial to assume that the distribution of the signal is the same in
every <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxel</span></a> so that the F-statistics are comparable.
This correction strategy is applied in nilearn
<a class="reference internal" href="../modules/generated/nilearn.mass_univariate.permuted_ols.html#nilearn.mass_univariate.permuted_ols" title="nilearn.mass_univariate.permuted_ols"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.mass_univariate.permuted_ols</span></code></a> function.</p>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/07_advanced/plot_haxby_mass_univariate.html"><img alt="../_images/sphx_glr_plot_haxby_mass_univariate_001.png" src="../_images/sphx_glr_plot_haxby_mass_univariate_001.png" style="width: 174.0px; height: 192.0px;" /></a>
</figure>
<p>We observe that the results obtained with a permutation test are less
conservative than the ones obtained with a Bonferroni correction
strategy.</p>
<p>In nilearn <a class="reference internal" href="../modules/generated/nilearn.mass_univariate.permuted_ols.html#nilearn.mass_univariate.permuted_ols" title="nilearn.mass_univariate.permuted_ols"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.mass_univariate.permuted_ols</span></code></a> function, we
permute a parametric t-test. Unlike F-test, a t-test can be signed
(<em>one-sided test</em>), that is both the absolute value and the sign of an
effect are considered. Thus, only positive effects
can be focused on. It is still possible to perform a two-sided test
equivalent to a permuted F-test by setting the argument
<code class="docutils literal notranslate"><span class="pre">two_sided_test</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code>. In the example above, we do perform a two-sided
test but add back the sign of the effect at the end using the t-scores obtained
on the original (non-permuted) data. Thus, we can perform two one-sided tests
(a given contrast and its opposite) for the price of one single run.
The example results can be interpreted as follows: viewing faces significantly
activates the Fusiform Face Area as compared to viewing houses, while viewing
houses does not reveal significant supplementary activations as compared to
viewing faces.</p>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id9" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id7">1</a><span class="fn-bracket">]</span></span>
<p>The <em>p-value</em> is the probability of getting the observed values
assuming that nothing happens (i.e. under the null hypothesis).
Therefore, a small <em>p-value</em> indicates that there is a small chance
of getting this data if no real difference existed, so the observed
voxel must be significant.</p>
</aside>
<aside class="footnote brackets" id="id10" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id8">2</a><span class="fn-bracket">]</span></span>
<p>When the variate tested is a scalar (test of the <em>intercept</em>)
–which corresponds to a one sample test–, no swapping can be
performed but one can estimate the null distribution by assuming
symmetry about some reference value. When this value is zero, one can
randomly swap the sign of the target variates (the imaging
signal). nilearn
<a class="reference internal" href="../modules/generated/nilearn.mass_univariate.permuted_ols.html#nilearn.mass_univariate.permuted_ols" title="nilearn.mass_univariate.permuted_ols"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.mass_univariate.permuted_ols</span></code></a> function automatically
adopts the suitable strategy according to the input data.</p>
</aside>
</aside>
<aside class="topic">
<p class="topic-title"><strong>Example code</strong></p>
<p>All the steps discussed in this section can be seen implemented in
<a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_searchlight.html#sphx-glr-auto-examples-02-decoding-plot-haxby-searchlight-py"><span class="std std-ref">a full code example</span></a>.</p>
</aside>
</section>
</section>
<section id="references">
<h2><span class="section-number">5.5.5. </span>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
<div class="docutils container" id="id11">
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="footcite-kriegeskorte2006" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id5">2</a>)</span>
<p>Nikolaus Kriegeskorte, Rainer Goebel, and Peter Bandettini. Information-based functional brain mapping. <em>Proceedings of the National Academy of Sciences</em>, 103(10):3863–3868, 2006. URL: <a class="reference external" href="https://www.pnas.org/content/103/10/3863">https://www.pnas.org/content/103/10/3863</a>, <a class="reference external" href="https://doi.org/10.1073/pnas.0600244103">doi:10.1073/pnas.0600244103</a>.</p>
</aside>
<aside class="footnote brackets" id="footcite-kahnt2011" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">4</a><span class="fn-bracket">]</span></span>
<p>Thorsten Kahnt, Marcus Grueschow, Oliver Speck, and John-Dylan Haynes. Perceptual learning and decision-making in human medial frontal cortex. <em>Neuron</em>, 70(3):549–559, 2011. URL: <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0896627311002960">https://www.sciencedirect.com/science/article/pii/S0896627311002960</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1016/j.neuron.2011.02.054">doi:https://doi.org/10.1016/j.neuron.2011.02.054</a>.</p>
</aside>
<aside class="footnote brackets" id="footcite-clarke2014" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">5</a><span class="fn-bracket">]</span></span>
<p>Alex Clarke and Lorraine K. Tyler. Object-specific semantic coding in human perirhinal cortex. <em>Journal of Neuroscience</em>, 34(14):4766–4775, 2014. URL: <a class="reference external" href="https://www.jneurosci.org/content/34/14/4766">https://www.jneurosci.org/content/34/14/4766</a>, <a class="reference external" href="https://arxiv.org/abs/https://www.jneurosci.org/content/34/14/4766.full.pdf">arXiv:https://www.jneurosci.org/content/34/14/4766.full.pdf</a>, <a class="reference external" href="https://doi.org/10.1523/JNEUROSCI.2828-13.2014">doi:10.1523/JNEUROSCI.2828-13.2014</a>.</p>
</aside>
<aside class="footnote brackets" id="footcite-etzel2013" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">6</a><span class="fn-bracket">]</span></span>
<p>Joset A. Etzel, Jeffrey M. Zacks, and Todd S. Braver. Searchlight analysis: promise, pitfalls, and potential. <em>NeuroImage</em>, 78:261–269, 2013. URL: <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1053811913002917">https://www.sciencedirect.com/science/article/pii/S1053811913002917</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1016/j.neuroimage.2013.03.041">doi:https://doi.org/10.1016/j.neuroimage.2013.03.041</a>.</p>
</aside>
</aside>
</div>
</section>
</section>
</article>
</div>
<footer>
<div class="related-pages">
<a class="next-page" href="going_further.html">
<div class="page-info">
<div class="context">
<span>Next</span>
</div>
<div class="title"><span class="section-number">5.6. </span>Running scikit-learn functions for more control on the analysis</div>
</div>
<svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
</a>
<a class="prev-page" href="space_net.html">
<svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
<div class="page-info">
<div class="context">
<span>Previous</span>
</div>
<div class="title"><span class="section-number">5.4. </span>SpaceNet: decoding with spatial structure for better maps</div>
</div>
</a>
</div>
<div class="bottom-of-page">
<div class="left-details">
<div class="copyright">
Copyright © The nilearn developers 2010-2023
</div>
Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
<a href="https://github.com/pradyunsg/furo">Furo</a>
</div>
<div class="right-details">
<div class="icons">
<a class="muted-link fa-brands fa-solid fa-github fa-2x" href="https://github.com/nilearn/nilearn" aria-label="GitHub"></a>
<a class="muted-link fa-brands fa-solid fa-twitter fa-2x" href="https://twitter.com/nilearn" aria-label="Twitter"></a>
<a class="muted-link fa-brands fa-solid fa-mastodon fa-2x" href="https://fosstodon.org/@nilearn" aria-label="Mastodon"></a>
<a class="muted-link fa-brands fa-solid fa-discord fa-2x" href="https://discord.gg/SsQABEJHkZ" aria-label="Discord"></a>
</div>
</div>
</div>
</footer>
</div>
<aside class="toc-drawer">
<div class="toc-sticky toc-scroll">
<div class="toc-title-container">
<span class="toc-title">
On this page
</span>
</div>
<div class="toc-tree-container">
<div class="toc-tree">
<ul>
<li><a class="reference internal" href="#">5.5. Searchlight : finding voxels containing information</a><ul>
<li><a class="reference internal" href="#principle-of-the-searchlight">5.5.1. Principle of the Searchlight</a></li>
<li><a class="reference internal" href="#preparing-the-data">5.5.2. Preparing the data</a><ul>
<li><a class="reference internal" href="#masking">5.5.2.1. Masking</a></li>