Examples of masker reports

Nifti masker reports

NiftiMasker

Adapted from Simple example of NiftiMasker use

NiftiMasker unfitted Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask :term:`voxels` is necessary. Use case: working with time series of :term:`resting-state` or task maps.

WARNING

• This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.

This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.

NiftiMasker(mask_img=<nibabel.nifti1.Nifti1Image object at 0x7fa7b581f7f0>,
            memory='nilearn_cache', standardize='zscore_sample',
            target_affine=array([[   1.,    0.,    0.,  -98.],
       [   0.,    1.,    0., -134.],
       [   0.,    0.,    1.,  -72.],
       [   0.,    0.,    0.,    1.]]),
            target_shape=(197, 233, 189))

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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NiftiMasker Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask :term:`voxels` is necessary. Use case: working with time series of :term:`resting-state` or task maps.

This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image. To see the input Nifti image before resampling, hover over the displayed image.

The mask includes 1961850 voxels (22.6 %) of the image.

NiftiMasker(mask_img=<nibabel.nifti1.Nifti1Image object at 0x7fa7b581f7f0>,
            memory='nilearn_cache', standardize='zscore_sample',
            target_affine=array([[   1.,    0.,    0.,  -98.],
       [   0.,    1.,    0., -134.],
       [   0.,    0.,    1.,  -72.],
       [   0.,    0.,    0.,    1.]]),
            target_shape=(197, 233, 189))

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

This report was generated based on information provided at instantiation and fit time. Note that the masker can potentially perform resampling at transform time.

NiftiLabelsMasker

Adapted from Extracting signals from brain regions using the NiftiLabelsMasker

NiftiLabelsMasker unfitted Class for extracting data from Niimg-like objects using labels of non-overlapping brain regions. NiftiLabelsMasker is useful when data from non-overlapping volumes should be extracted (contrarily to :class:`nilearn.maskers.NiftiMapsMasker`). Use case: summarize brain signals from clusters that were obtained by prior K-means or Ward clustering. For more details on the definitions of labels in Nilearn, see the :ref:`region` section.

WARNING

• This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.

This report shows the regions defined by the labels of the mask.

Regions summary

NiftiLabelsMasker(labels_img='/home/runner/nilearn_data/schaefer_2018/Schaefer2018_400Parcels_7Networks_order_FSLMNI152_1mm.nii.gz',
                  lut=     index                            name    color
0        0                      Background  #000000
1        1              7Networks_LH_Vis_1  #781180
2        2              7Networks_LH_Vis_2  #781181
3        3              7Networks_LH_Vis_3  #781182
4        4              7Networks_LH_Vis_4  #781183
..     ...                             ...      ...
396    396  7Networks_RH_Default_pCunPCC_5  #d03f51
397    397  7Networks_RH_Default_pCunPCC_6  #d03f52
398    398  7Networks_RH_Default_pCunPCC_7  #d03f53
399    399  7Networks_RH_Default_pCunPCC_8  #d03f54
400    400  7Networks_RH_Default_pCunPCC_9  #d0404d

[401 rows x 3 columns],
                  standardize='zscore_sample')

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

NiftiLabelsMasker Class for extracting data from Niimg-like objects using labels of non-overlapping brain regions. NiftiLabelsMasker is useful when data from non-overlapping volumes should be extracted (contrarily to :class:`nilearn.maskers.NiftiMapsMasker`). Use case: summarize brain signals from clusters that were obtained by prior K-means or Ward clustering. For more details on the definitions of labels in Nilearn, see the :ref:`region` section.

This report shows the regions defined by the labels of the mask.

The masker has 400 different non-overlapping regions.

Regions summary

label value	region name	size (in mm^3)	relative size (in %)
1	7Networks_LH_Vis_1	2624	0.25
2	7Networks_LH_Vis_2	3392	0.32
3	7Networks_LH_Vis_3	2368	0.22
4	7Networks_LH_Vis_4	3264	0.31
5	7Networks_LH_Vis_5	3712	0.35
6	7Networks_LH_Vis_6	2944	0.28
7	7Networks_LH_Vis_7	1536	0.15
8	7Networks_LH_Vis_8	3520	0.33
9	7Networks_LH_Vis_9	2560	0.24
10	7Networks_LH_Vis_10	2048	0.19
11	7Networks_LH_Vis_11	2432	0.23
12	7Networks_LH_Vis_12	4224	0.40
13	7Networks_LH_Vis_13	960	0.09
14	7Networks_LH_Vis_14	3008	0.28
15	7Networks_LH_Vis_15	3968	0.38
16	7Networks_LH_Vis_16	1536	0.15
17	7Networks_LH_Vis_17	3584	0.34
18	7Networks_LH_Vis_18	3520	0.33
19	7Networks_LH_Vis_19	4672	0.44
20	7Networks_LH_Vis_20	896	0.08
21	7Networks_LH_Vis_21	3776	0.36
22	7Networks_LH_Vis_22	1856	0.18
23	7Networks_LH_Vis_23	4416	0.42
24	7Networks_LH_Vis_24	4928	0.47
25	7Networks_LH_Vis_25	3840	0.36
26	7Networks_LH_Vis_26	2112	0.20
27	7Networks_LH_Vis_27	2304	0.22
28	7Networks_LH_Vis_28	1728	0.16
29	7Networks_LH_Vis_29	2304	0.22
30	7Networks_LH_Vis_30	1984	0.19
31	7Networks_LH_Vis_31	2176	0.21
32	7Networks_LH_SomMot_1	3136	0.30
33	7Networks_LH_SomMot_2	3840	0.36
34	7Networks_LH_SomMot_3	1216	0.12
35	7Networks_LH_SomMot_4	1920	0.18
36	7Networks_LH_SomMot_5	1472	0.14
37	7Networks_LH_SomMot_6	2624	0.25
38	7Networks_LH_SomMot_7	1728	0.16
39	7Networks_LH_SomMot_8	1984	0.19
40	7Networks_LH_SomMot_9	1920	0.18
41	7Networks_LH_SomMot_10	1728	0.16
42	7Networks_LH_SomMot_11	1536	0.15
43	7Networks_LH_SomMot_12	2304	0.22
44	7Networks_LH_SomMot_13	1344	0.13
45	7Networks_LH_SomMot_14	1600	0.15
46	7Networks_LH_SomMot_15	1472	0.14
47	7Networks_LH_SomMot_16	2560	0.24
48	7Networks_LH_SomMot_17	1600	0.15
49	7Networks_LH_SomMot_18	2240	0.21
50	7Networks_LH_SomMot_19	1472	0.14
51	7Networks_LH_SomMot_20	1536	0.15
52	7Networks_LH_SomMot_21	2816	0.27
53	7Networks_LH_SomMot_22	1024	0.10
54	7Networks_LH_SomMot_23	1024	0.10
55	7Networks_LH_SomMot_24	1792	0.17
56	7Networks_LH_SomMot_25	2432	0.23
57	7Networks_LH_SomMot_26	3776	0.36
58	7Networks_LH_SomMot_27	2944	0.28
59	7Networks_LH_SomMot_28	2240	0.21
60	7Networks_LH_SomMot_29	2240	0.21
61	7Networks_LH_SomMot_30	3072	0.29
62	7Networks_LH_SomMot_31	1152	0.11
63	7Networks_LH_SomMot_32	2240	0.21
64	7Networks_LH_SomMot_33	2816	0.27
65	7Networks_LH_SomMot_34	1920	0.18
66	7Networks_LH_SomMot_35	2112	0.20
67	7Networks_LH_SomMot_36	2048	0.19
68	7Networks_LH_SomMot_37	832	0.08
69	7Networks_LH_DorsAttn_Post_1	2752	0.26
70	7Networks_LH_DorsAttn_Post_2	5056	0.48
71	7Networks_LH_DorsAttn_Post_3	4480	0.42
72	7Networks_LH_DorsAttn_Post_4	2880	0.27
73	7Networks_LH_DorsAttn_Post_5	1344	0.13
74	7Networks_LH_DorsAttn_Post_6	2368	0.22
75	7Networks_LH_DorsAttn_Post_7	1920	0.18
76	7Networks_LH_DorsAttn_Post_8	1856	0.18
77	7Networks_LH_DorsAttn_Post_9	1856	0.18
78	7Networks_LH_DorsAttn_Post_10	1408	0.13
79	7Networks_LH_DorsAttn_Post_11	2496	0.24
80	7Networks_LH_DorsAttn_Post_12	1600	0.15
81	7Networks_LH_DorsAttn_Post_13	3136	0.30
82	7Networks_LH_DorsAttn_Post_14	1664	0.16
83	7Networks_LH_DorsAttn_Post_15	2368	0.22
84	7Networks_LH_DorsAttn_Post_16	2752	0.26
85	7Networks_LH_DorsAttn_Post_17	960	0.09
86	7Networks_LH_DorsAttn_FEF_1	2432	0.23
87	7Networks_LH_DorsAttn_FEF_2	2624	0.25
88	7Networks_LH_DorsAttn_FEF_3	1536	0.15
89	7Networks_LH_DorsAttn_FEF_4	3840	0.36
90	7Networks_LH_DorsAttn_PrCv_1	2624	0.25
91	7Networks_LH_DorsAttn_PrCv_2	2048	0.19
92	7Networks_LH_SalVentAttn_ParOper_1	3200	0.30
93	7Networks_LH_SalVentAttn_ParOper_2	2112	0.20
94	7Networks_LH_SalVentAttn_ParOper_3	3264	0.31
95	7Networks_LH_SalVentAttn_ParOper_4	2432	0.23
96	7Networks_LH_SalVentAttn_TempOcc_1	2688	0.25
97	7Networks_LH_SalVentAttn_FrOperIns_1	2304	0.22
98	7Networks_LH_SalVentAttn_FrOperIns_2	4096	0.39
99	7Networks_LH_SalVentAttn_FrOperIns_3	1856	0.18
100	7Networks_LH_SalVentAttn_FrOperIns_4	1920	0.18
101	7Networks_LH_SalVentAttn_FrOperIns_5	1920	0.18
102	7Networks_LH_SalVentAttn_FrOperIns_6	1152	0.11
103	7Networks_LH_SalVentAttn_FrOperIns_7	1280	0.12
104	7Networks_LH_SalVentAttn_FrOperIns_8	2112	0.20
105	7Networks_LH_SalVentAttn_FrOperIns_9	2240	0.21
106	7Networks_LH_SalVentAttn_PFCl_1	5184	0.49
107	7Networks_LH_SalVentAttn_Med_1	3264	0.31
108	7Networks_LH_SalVentAttn_Med_2	3648	0.35
109	7Networks_LH_SalVentAttn_Med_3	2176	0.21
110	7Networks_LH_SalVentAttn_Med_4	3136	0.30
111	7Networks_LH_SalVentAttn_Med_5	1344	0.13
112	7Networks_LH_SalVentAttn_Med_6	1984	0.19
113	7Networks_LH_SalVentAttn_Med_7	1536	0.15
114	7Networks_LH_Limbic_OFC_1	2368	0.22
115	7Networks_LH_Limbic_OFC_2	3904	0.37
116	7Networks_LH_Limbic_OFC_3	4736	0.45
117	7Networks_LH_Limbic_OFC_4	3328	0.31
118	7Networks_LH_Limbic_OFC_5	3648	0.35
119	7Networks_LH_Limbic_TempPole_1	4352	0.41
120	7Networks_LH_Limbic_TempPole_2	3200	0.30
121	7Networks_LH_Limbic_TempPole_3	2432	0.23
122	7Networks_LH_Limbic_TempPole_4	3840	0.36
123	7Networks_LH_Limbic_TempPole_5	3328	0.31
124	7Networks_LH_Limbic_TempPole_6	5952	0.56
125	7Networks_LH_Limbic_TempPole_7	3456	0.33
126	7Networks_LH_Limbic_TempPole_8	1792	0.17
127	7Networks_LH_Cont_Par_1	2176	0.21
128	7Networks_LH_Cont_Par_2	1664	0.16
129	7Networks_LH_Cont_Par_3	2560	0.24
130	7Networks_LH_Cont_Par_4	2688	0.25
131	7Networks_LH_Cont_Par_5	1344	0.13
132	7Networks_LH_Cont_Par_6	2816	0.27
133	7Networks_LH_Cont_Temp_1	3008	0.28
134	7Networks_LH_Cont_OFC_1	2944	0.28
135	7Networks_LH_Cont_PFCl_1	4608	0.44
136	7Networks_LH_Cont_PFCl_2	2944	0.28
137	7Networks_LH_Cont_PFCl_3	3456	0.33
138	7Networks_LH_Cont_PFCl_4	3136	0.30
139	7Networks_LH_Cont_PFCl_5	4352	0.41
140	7Networks_LH_Cont_PFCl_6	5952	0.56
141	7Networks_LH_Cont_PFCl_7	2176	0.21
142	7Networks_LH_Cont_PFCl_8	3008	0.28
143	7Networks_LH_Cont_PFCv_1	1216	0.12
144	7Networks_LH_Cont_pCun_1	2240	0.21
145	7Networks_LH_Cont_pCun_2	2304	0.22
146	7Networks_LH_Cont_Cing_1	1088	0.10
147	7Networks_LH_Cont_Cing_2	1216	0.12
148	7Networks_LH_Cont_PFCmp_1	4032	0.38
149	7Networks_LH_Default_Temp_1	4224	0.40
150	7Networks_LH_Default_Temp_2	3584	0.34
151	7Networks_LH_Default_Temp_3	4352	0.41
152	7Networks_LH_Default_Temp_4	4480	0.42
153	7Networks_LH_Default_Temp_5	3328	0.31
154	7Networks_LH_Default_Temp_6	4352	0.41
155	7Networks_LH_Default_Temp_7	1920	0.18
156	7Networks_LH_Default_Temp_8	1920	0.18
157	7Networks_LH_Default_Temp_9	2624	0.25
158	7Networks_LH_Default_Temp_10	1856	0.18
159	7Networks_LH_Default_Par_1	1984	0.19
160	7Networks_LH_Default_Par_2	2496	0.24
161	7Networks_LH_Default_Par_3	3712	0.35
162	7Networks_LH_Default_Par_4	3328	0.31
163	7Networks_LH_Default_Par_5	2880	0.27
164	7Networks_LH_Default_Par_6	3520	0.33
165	7Networks_LH_Default_Par_7	2496	0.24
166	7Networks_LH_Default_PFC_1	3392	0.32
167	7Networks_LH_Default_PFC_2	1856	0.18
168	7Networks_LH_Default_PFC_3	3072	0.29
169	7Networks_LH_Default_PFC_4	3712	0.35
170	7Networks_LH_Default_PFC_5	2496	0.24
171	7Networks_LH_Default_PFC_6	2560	0.24
172	7Networks_LH_Default_PFC_7	3456	0.33
173	7Networks_LH_Default_PFC_8	1920	0.18
174	7Networks_LH_Default_PFC_9	4480	0.42
175	7Networks_LH_Default_PFC_10	4224	0.40
176	7Networks_LH_Default_PFC_11	2688	0.25
177	7Networks_LH_Default_PFC_12	3712	0.35
178	7Networks_LH_Default_PFC_13	6848	0.65
179	7Networks_LH_Default_PFC_14	4352	0.41
180	7Networks_LH_Default_PFC_15	3456	0.33
181	7Networks_LH_Default_PFC_16	2176	0.21
182	7Networks_LH_Default_PFC_17	4160	0.39
183	7Networks_LH_Default_PFC_18	1792	0.17
184	7Networks_LH_Default_PFC_19	1856	0.18
185	7Networks_LH_Default_PFC_20	3264	0.31
186	7Networks_LH_Default_PFC_21	2432	0.23
187	7Networks_LH_Default_PFC_22	1984	0.19
188	7Networks_LH_Default_PFC_23	3328	0.31
189	7Networks_LH_Default_PFC_24	3200	0.30
190	7Networks_LH_Default_pCunPCC_1	1408	0.13
191	7Networks_LH_Default_pCunPCC_2	2048	0.19
192	7Networks_LH_Default_pCunPCC_3	1728	0.16
193	7Networks_LH_Default_pCunPCC_4	704	0.07
194	7Networks_LH_Default_pCunPCC_5	2304	0.22
195	7Networks_LH_Default_pCunPCC_6	3072	0.29
196	7Networks_LH_Default_pCunPCC_7	1984	0.19
197	7Networks_LH_Default_pCunPCC_8	1920	0.18
198	7Networks_LH_Default_pCunPCC_9	1792	0.17
199	7Networks_LH_Default_pCunPCC_10	1280	0.12
200	7Networks_LH_Default_pCunPCC_11	3392	0.32
201	7Networks_RH_Vis_1	2816	0.27
202	7Networks_RH_Vis_2	3136	0.30
203	7Networks_RH_Vis_3	2880	0.27
204	7Networks_RH_Vis_4	2944	0.28
205	7Networks_RH_Vis_5	3456	0.33
206	7Networks_RH_Vis_6	3776	0.36
207	7Networks_RH_Vis_7	1024	0.10
208	7Networks_RH_Vis_8	2112	0.20
209	7Networks_RH_Vis_9	4928	0.47
210	7Networks_RH_Vis_10	1984	0.19
211	7Networks_RH_Vis_11	2496	0.24
212	7Networks_RH_Vis_12	2688	0.25
213	7Networks_RH_Vis_13	3584	0.34
214	7Networks_RH_Vis_14	1728	0.16
215	7Networks_RH_Vis_15	5760	0.55
216	7Networks_RH_Vis_16	4992	0.47
217	7Networks_RH_Vis_17	1024	0.10
218	7Networks_RH_Vis_18	3008	0.28
219	7Networks_RH_Vis_19	4928	0.47
220	7Networks_RH_Vis_20	2304	0.22
221	7Networks_RH_Vis_21	2304	0.22
222	7Networks_RH_Vis_22	4608	0.44
223	7Networks_RH_Vis_23	3712	0.35
224	7Networks_RH_Vis_24	2752	0.26
225	7Networks_RH_Vis_25	2176	0.21
226	7Networks_RH_Vis_26	3776	0.36
227	7Networks_RH_Vis_27	2496	0.24
228	7Networks_RH_Vis_28	2816	0.27
229	7Networks_RH_Vis_29	3584	0.34
230	7Networks_RH_Vis_30	2688	0.25
231	7Networks_RH_SomMot_1	3904	0.37
232	7Networks_RH_SomMot_2	3072	0.29
233	7Networks_RH_SomMot_3	3264	0.31
234	7Networks_RH_SomMot_4	1216	0.12
235	7Networks_RH_SomMot_5	896	0.08
236	7Networks_RH_SomMot_6	768	0.07
237	7Networks_RH_SomMot_7	2688	0.25
238	7Networks_RH_SomMot_8	1856	0.18
239	7Networks_RH_SomMot_9	1088	0.10
240	7Networks_RH_SomMot_10	2112	0.20
241	7Networks_RH_SomMot_11	1024	0.10
242	7Networks_RH_SomMot_12	2048	0.19
243	7Networks_RH_SomMot_13	1472	0.14
244	7Networks_RH_SomMot_14	1536	0.15
245	7Networks_RH_SomMot_15	1088	0.10
246	7Networks_RH_SomMot_16	3968	0.38
247	7Networks_RH_SomMot_17	1856	0.18
248	7Networks_RH_SomMot_18	1984	0.19
249	7Networks_RH_SomMot_19	1152	0.11
250	7Networks_RH_SomMot_20	1088	0.10
251	7Networks_RH_SomMot_21	1600	0.15
252	7Networks_RH_SomMot_22	1792	0.17
253	7Networks_RH_SomMot_23	2240	0.21
254	7Networks_RH_SomMot_24	3264	0.31
255	7Networks_RH_SomMot_25	1600	0.15
256	7Networks_RH_SomMot_26	2880	0.27
257	7Networks_RH_SomMot_27	1472	0.14
258	7Networks_RH_SomMot_28	1664	0.16
259	7Networks_RH_SomMot_29	1600	0.15
260	7Networks_RH_SomMot_30	1984	0.19
261	7Networks_RH_SomMot_31	2048	0.19
262	7Networks_RH_SomMot_32	2048	0.19
263	7Networks_RH_SomMot_33	1152	0.11
264	7Networks_RH_SomMot_34	1152	0.11
265	7Networks_RH_SomMot_35	1216	0.12
266	7Networks_RH_SomMot_36	1920	0.18
267	7Networks_RH_SomMot_37	1984	0.19
268	7Networks_RH_SomMot_38	2304	0.22
269	7Networks_RH_SomMot_39	2176	0.21
270	7Networks_RH_SomMot_40	1152	0.11
271	7Networks_RH_DorsAttn_Post_1	5952	0.56
272	7Networks_RH_DorsAttn_Post_2	2624	0.25
273	7Networks_RH_DorsAttn_Post_3	2688	0.25
274	7Networks_RH_DorsAttn_Post_4	3328	0.31
275	7Networks_RH_DorsAttn_Post_5	1984	0.19
276	7Networks_RH_DorsAttn_Post_6	1216	0.12
277	7Networks_RH_DorsAttn_Post_7	1088	0.10
278	7Networks_RH_DorsAttn_Post_8	1856	0.18
279	7Networks_RH_DorsAttn_Post_9	1728	0.16
280	7Networks_RH_DorsAttn_Post_10	1792	0.17
281	7Networks_RH_DorsAttn_Post_11	2240	0.21
282	7Networks_RH_DorsAttn_Post_12	2880	0.27
283	7Networks_RH_DorsAttn_Post_13	1920	0.18
284	7Networks_RH_DorsAttn_Post_14	2944	0.28
285	7Networks_RH_DorsAttn_Post_15	2048	0.19
286	7Networks_RH_DorsAttn_Post_16	3136	0.30
287	7Networks_RH_DorsAttn_Post_17	3776	0.36
288	7Networks_RH_DorsAttn_Post_18	2048	0.19
289	7Networks_RH_DorsAttn_Post_19	2816	0.27
290	7Networks_RH_DorsAttn_FEF_1	2560	0.24
291	7Networks_RH_DorsAttn_FEF_2	2432	0.23
292	7Networks_RH_DorsAttn_FEF_3	1408	0.13
293	7Networks_RH_DorsAttn_PrCv_1	3328	0.31
294	7Networks_RH_SalVentAttn_TempOccPar_1	1984	0.19
295	7Networks_RH_SalVentAttn_TempOccPar_2	1536	0.15
296	7Networks_RH_SalVentAttn_TempOccPar_3	1600	0.15
297	7Networks_RH_SalVentAttn_TempOccPar_4	2880	0.27
298	7Networks_RH_SalVentAttn_TempOccPar_5	2560	0.24
299	7Networks_RH_SalVentAttn_TempOccPar_6	2496	0.24
300	7Networks_RH_SalVentAttn_TempOccPar_7	2944	0.28
301	7Networks_RH_SalVentAttn_PrC_1	2880	0.27
302	7Networks_RH_SalVentAttn_FrOperIns_1	2496	0.24
303	7Networks_RH_SalVentAttn_FrOperIns_2	2304	0.22
304	7Networks_RH_SalVentAttn_FrOperIns_3	2304	0.22
305	7Networks_RH_SalVentAttn_FrOperIns_4	1280	0.12
306	7Networks_RH_SalVentAttn_FrOperIns_5	3264	0.31
307	7Networks_RH_SalVentAttn_FrOperIns_6	1152	0.11
308	7Networks_RH_SalVentAttn_FrOperIns_7	2496	0.24
309	7Networks_RH_SalVentAttn_FrOperIns_8	2368	0.22
310	7Networks_RH_SalVentAttn_PFCl_1	5312	0.50
311	7Networks_RH_SalVentAttn_Med_1	4224	0.40
312	7Networks_RH_SalVentAttn_Med_2	3264	0.31
313	7Networks_RH_SalVentAttn_Med_3	1856	0.18
314	7Networks_RH_SalVentAttn_Med_4	1536	0.15
315	7Networks_RH_SalVentAttn_Med_5	1408	0.13
316	7Networks_RH_SalVentAttn_Med_6	1472	0.14
317	7Networks_RH_SalVentAttn_Med_7	2240	0.21
318	7Networks_RH_SalVentAttn_Med_8	1280	0.12
319	7Networks_RH_Limbic_OFC_1	2880	0.27
320	7Networks_RH_Limbic_OFC_2	3008	0.28
321	7Networks_RH_Limbic_OFC_3	2752	0.26
322	7Networks_RH_Limbic_OFC_4	1856	0.18
323	7Networks_RH_Limbic_OFC_5	2368	0.22
324	7Networks_RH_Limbic_OFC_6	2432	0.23
325	7Networks_RH_Limbic_TempPole_1	5568	0.53
326	7Networks_RH_Limbic_TempPole_2	4736	0.45
327	7Networks_RH_Limbic_TempPole_3	3136	0.30
328	7Networks_RH_Limbic_TempPole_4	4032	0.38
329	7Networks_RH_Limbic_TempPole_5	3648	0.35
330	7Networks_RH_Limbic_TempPole_6	3264	0.31
331	7Networks_RH_Limbic_TempPole_7	2304	0.22
332	7Networks_RH_Cont_Par_1	3584	0.34
333	7Networks_RH_Cont_Par_2	1728	0.16
334	7Networks_RH_Cont_Par_3	2112	0.20
335	7Networks_RH_Cont_Par_4	1280	0.12
336	7Networks_RH_Cont_Par_5	1216	0.12
337	7Networks_RH_Cont_Par_6	3072	0.29
338	7Networks_RH_Cont_Temp_1	4480	0.42
339	7Networks_RH_Cont_Temp_2	3648	0.35
340	7Networks_RH_Cont_PFCv_1	1600	0.15
341	7Networks_RH_Cont_PFCl_1	3200	0.30
342	7Networks_RH_Cont_PFCl_2	4160	0.39
343	7Networks_RH_Cont_PFCl_3	7552	0.71
344	7Networks_RH_Cont_PFCl_4	2368	0.22
345	7Networks_RH_Cont_PFCl_5	5440	0.51
346	7Networks_RH_Cont_PFCl_6	2432	0.23
347	7Networks_RH_Cont_PFCl_7	2112	0.20
348	7Networks_RH_Cont_PFCl_8	3328	0.31
349	7Networks_RH_Cont_PFCl_9	3584	0.34
350	7Networks_RH_Cont_PFCl_10	2432	0.23
351	7Networks_RH_Cont_PFCl_11	3136	0.30
352	7Networks_RH_Cont_PFCl_12	3776	0.36
353	7Networks_RH_Cont_PFCl_13	2432	0.23
354	7Networks_RH_Cont_PFCl_14	4544	0.43
355	7Networks_RH_Cont_PFCl_15	3584	0.34
356	7Networks_RH_Cont_pCun_1	2624	0.25
357	7Networks_RH_Cont_pCun_2	1792	0.17
358	7Networks_RH_Cont_Cing_1	1024	0.10
359	7Networks_RH_Cont_Cing_2	960	0.09
360	7Networks_RH_Cont_PFCmp_1	3072	0.29
361	7Networks_RH_Cont_PFCmp_2	2752	0.26
362	7Networks_RH_Default_Par_1	1408	0.13
363	7Networks_RH_Default_Par_2	3776	0.36
364	7Networks_RH_Default_Par_3	3904	0.37
365	7Networks_RH_Default_Par_4	1984	0.19
366	7Networks_RH_Default_Par_5	3328	0.31
367	7Networks_RH_Default_Temp_1	4544	0.43
368	7Networks_RH_Default_Temp_2	4480	0.42
369	7Networks_RH_Default_Temp_3	2752	0.26
370	7Networks_RH_Default_Temp_4	4736	0.45
371	7Networks_RH_Default_Temp_5	2304	0.22
372	7Networks_RH_Default_Temp_6	1216	0.12
373	7Networks_RH_Default_Temp_7	1344	0.13
374	7Networks_RH_Default_Temp_8	2560	0.24
375	7Networks_RH_Default_PFCv_1	3072	0.29
376	7Networks_RH_Default_PFCv_2	2368	0.22
377	7Networks_RH_Default_PFCv_3	3712	0.35
378	7Networks_RH_Default_PFCv_4	3840	0.36
379	7Networks_RH_Default_PFCdPFCm_1	4992	0.47
380	7Networks_RH_Default_PFCdPFCm_2	2816	0.27
381	7Networks_RH_Default_PFCdPFCm_3	3968	0.38
382	7Networks_RH_Default_PFCdPFCm_4	4416	0.42
383	7Networks_RH_Default_PFCdPFCm_5	2880	0.27
384	7Networks_RH_Default_PFCdPFCm_6	1280	0.12
385	7Networks_RH_Default_PFCdPFCm_7	2240	0.21
386	7Networks_RH_Default_PFCdPFCm_8	3008	0.28
387	7Networks_RH_Default_PFCdPFCm_9	2112	0.20
388	7Networks_RH_Default_PFCdPFCm_10	3008	0.28
389	7Networks_RH_Default_PFCdPFCm_11	3392	0.32
390	7Networks_RH_Default_PFCdPFCm_12	3072	0.29
391	7Networks_RH_Default_PFCdPFCm_13	3072	0.29
392	7Networks_RH_Default_pCunPCC_1	3200	0.30
393	7Networks_RH_Default_pCunPCC_2	448	0.04
394	7Networks_RH_Default_pCunPCC_3	1280	0.12
395	7Networks_RH_Default_pCunPCC_4	2176	0.21
396	7Networks_RH_Default_pCunPCC_5	1088	0.10
397	7Networks_RH_Default_pCunPCC_6	2816	0.27
398	7Networks_RH_Default_pCunPCC_7	1984	0.19
399	7Networks_RH_Default_pCunPCC_8	1344	0.13
400	7Networks_RH_Default_pCunPCC_9	1920	0.18

NiftiLabelsMasker(labels_img='/home/runner/nilearn_data/schaefer_2018/Schaefer2018_400Parcels_7Networks_order_FSLMNI152_1mm.nii.gz',
                  lut=     index                            name    color
0        0                      Background  #000000
1        1              7Networks_LH_Vis_1  #781180
2        2              7Networks_LH_Vis_2  #781181
3        3              7Networks_LH_Vis_3  #781182
4        4              7Networks_LH_Vis_4  #781183
..     ...                             ...      ...
396    396  7Networks_RH_Default_pCunPCC_5  #d03f51
397    397  7Networks_RH_Default_pCunPCC_6  #d03f52
398    398  7Networks_RH_Default_pCunPCC_7  #d03f53
399    399  7Networks_RH_Default_pCunPCC_8  #d03f54
400    400  7Networks_RH_Default_pCunPCC_9  #d0404d

[401 rows x 3 columns],
                  standardize='zscore_sample')

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This report was generated based on information provided at instantiation and fit time. Note that the masker can potentially perform resampling at transform time.

NiftiMapsMasker

Adapted from Extracting signals of a probabilistic atlas of functional regions

NiftiMapsMasker unfitted Class for extracting data from Niimg-like objects using maps of potentially overlapping brain regions. NiftiMapsMasker is useful when data from overlapping volumes should be extracted (contrarily to :class:`nilearn.maskers.NiftiLabelsMasker`). Use case: summarize brain signals from large-scale networks obtained by prior PCA or :term:`ICA`. .. note:: Inf or NaN present in the given input images are automatically put to zero rather than considered as missing data. For more details on the definitions of maps in Nilearn, see the :ref:`region` section.

WARNING

• This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.

This report shows the spatial maps provided to the mask.

NiftiMapsMasker(maps_img='/home/runner/nilearn_data/difumo_atlases/64/2mm/maps.nii.gz',
                memory='nilearn_cache', memory_level=1,
                standardize='zscore_sample')

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NiftiMapsMasker Class for extracting data from Niimg-like objects using maps of potentially overlapping brain regions. NiftiMapsMasker is useful when data from overlapping volumes should be extracted (contrarily to :class:`nilearn.maskers.NiftiLabelsMasker`). Use case: summarize brain signals from large-scale networks obtained by prior PCA or :term:`ICA`. .. note:: Inf or NaN present in the given input images are automatically put to zero rather than considered as missing data. For more details on the definitions of maps in Nilearn, see the :ref:`region` section.

Previous Map

Map

Next Map

This report shows the spatial maps provided to the mask.

The masker has 64 spatial maps in total.

Only 3 spatial maps are shown in this report.

NiftiMapsMasker(maps_img='/home/runner/nilearn_data/difumo_atlases/64/2mm/maps.nii.gz',
                memory='nilearn_cache', memory_level=1,
                standardize='zscore_sample')

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This report was generated based on information provided at instantiation and fit time. Note that the masker can potentially perform resampling at transform time.

NiftiSpheresMasker

Adapted from Default Mode Network extraction of ADHD dataset

NiftiSpheresMasker unfitted Class for masking of Niimg-like objects using seeds. NiftiSpheresMasker is useful when data from given seeds should be extracted. Use case: summarize brain signals from seeds that were obtained from prior knowledge.

WARNING

• This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.

This report shows the regions defined by the spheres of the masker.

Regions summary

NiftiSpheresMasker(detrend=True, high_pass=0.01, low_pass=0.1,
                   memory='nilearn_cache', radius=10,
                   seeds=[(0, -53, 26), (5, 53, -26), (0, 0, 0)],
                   standardize='zscore_sample', t_r=2)

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NiftiSpheresMasker Class for masking of Niimg-like objects using seeds. NiftiSpheresMasker is useful when data from given seeds should be extracted. Use case: summarize brain signals from seeds that were obtained from prior knowledge.

Previous Sphere

Next Sphere

This report shows the regions defined by the spheres of the masker.

The masker has 3 spheres in total (displayed together on the first image).

Only 3 specific spheres can be browsed in this report with Previous and Next buttons.

Regions summary

seed number	coordinates	position	radius	size (in mm^3)	size (in voxels)	relative size (in %)
0	[0, -53, 26]	[24, 20, 26]	10	4188.79	not implemented	not implemented
1	[5, 53, -26]	[25, 46, 13]	10	4188.79	not implemented	not implemented
2	[0, 0, 0]	[24, 33, 20]	10	4188.79	not implemented	not implemented

NiftiSpheresMasker(detrend=True, high_pass=0.01, low_pass=0.1,
                   memory='nilearn_cache', radius=10,
                   seeds=[(0, -53, 26), (5, 53, -26), (0, 0, 0)],
                   standardize='zscore_sample', t_r=2)

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Surface masker reports

SurfaceMasker

SurfaceMasker unfitted Extract data from a :obj:`~nilearn.surface.SurfaceImage`. .. nilearn_versionadded:: 0.11.0

WARNING

• This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.

This report shows the input surface image overlaid with the outlines of the mask. We recommend to inspect the report for the overlap between the mask and its input image.

SurfaceMasker(mask_img=<SurfaceImage (20484,)>)

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SurfaceMasker Extract data from a :obj:`~nilearn.surface.SurfaceImage`. .. nilearn_versionadded:: 0.11.0

This report shows the input surface image overlaid with the outlines of the mask. We recommend to inspect the report for the overlap between the mask and its input image.

The mask includes 341 vertices (1.7 %) of the image.

Hemisphere	Number of vertices
left	10242
right	10242

SurfaceMasker(mask_img=<SurfaceImage (20484,)>)

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SurfaceLabelsMasker

SurfaceLabelsMasker unfitted Extract data from a SurfaceImage, averaging over atlas regions. .. nilearn_versionadded:: 0.11.0

WARNING

• This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.

This report shows the input surface image overlaid with the outlines of the mask. We recommend to inspect the report for the overlap between the mask and its input image.

SurfaceLabelsMasker(labels_img=<SurfaceImage (20484,)>,
                    lut=    index                   name
0       0                Unknown
1       1   G_and_S_frontomargin
2       2  G_and_S_occipital_inf
3       3    G_and_S_paracentral
4       4     G_and_S_subcentral
..    ...                    ...
71     71           S_suborbital
72     72          S_subparietal
73     73         S_temporal_inf
74     74         S_temporal_sup
75     75  S_temporal_transverse

[76 rows x 2 columns])

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SurfaceLabelsMasker Extract data from a SurfaceImage, averaging over atlas regions. .. nilearn_versionadded:: 0.11.0

This report shows the input surface image overlaid with the outlines of the mask. We recommend to inspect the report for the overlap between the mask and its input image.

Hemisphere	Number of vertices
left	10242
right	10242

Regions summary - left

index	name	size	relative size
1	G_and_S_frontomargin	59	0.58
2	G_and_S_occipital_inf	81	0.79
3	G_and_S_paracentral	160	1.6
4	G_and_S_subcentral	141	1.4
5	G_and_S_transv_frontopol	38	0.37
6	G_and_S_cingul-Ant	167	1.6
7	G_and_S_cingul-Mid-Ant	118	1.2
8	G_and_S_cingul-Mid-Post	158	1.5
9	G_cingul-Post-dorsal	66	0.64
10	G_cingul-Post-ventral	26	0.25
11	G_cuneus	94	0.92
12	G_front_inf-Opercular	119	1.2
13	G_front_inf-Orbital	30	0.29
14	G_front_inf-Triangul	68	0.66
15	G_front_middle	269	2.6
16	G_front_sup	494	4.8
17	G_Ins_lg_and_S_cent_ins	49	0.48
18	G_insular_short	50	0.49
19	G_occipital_middle	132	1.3
20	G_occipital_sup	84	0.82
21	G_oc-temp_lat-fusifor	113	1.1
22	G_oc-temp_med-Lingual	153	1.5
23	G_oc-temp_med-Parahip	137	1.3
24	G_orbital	153	1.5
25	G_pariet_inf-Angular	171	1.7
26	G_pariet_inf-Supramar	291	2.8
27	G_parietal_sup	260	2.5
28	G_postcentral	217	2.1
29	G_precentral	244	2.4
30	G_precuneus	219	2.1
31	G_rectus	77	0.75
32	G_subcallosal	14	0.14
33	G_temp_sup-G_T_transv	46	0.45
34	G_temp_sup-Lateral	180	1.8
35	G_temp_sup-Plan_polar	42	0.41
36	G_temp_sup-Plan_tempo	83	0.81
37	G_temporal_inf	163	1.6
38	G_temporal_middle	183	1.8
39	Lat_Fis-ant-Horizont	24	0.23
40	Lat_Fis-ant-Vertical	18	0.18
41	Lat_Fis-post	129	1.3
42	Medial_wall	888	8.7
43	Pole_occipital	112	1.1
44	Pole_temporal	105	1.0
45	S_calcarine	198	1.9
46	S_central	307	3.0
47	S_cingul-Marginalis	129	1.3
48	S_circular_insula_ant	57	0.56
49	S_circular_insula_inf	151	1.5
50	S_circular_insula_sup	193	1.9
51	S_collat_transv_ant	55	0.54
52	S_collat_transv_post	17	0.17
53	S_front_inf	143	1.4
54	S_front_middle	86	0.84
55	S_front_sup	200	2.0
56	S_interm_prim-Jensen	31	0.3
57	S_intrapariet_and_P_trans	274	2.7
58	S_oc_middle_and_Lunatus	62	0.61
59	S_oc_sup_and_transversal	89	0.87
60	S_occipital_ant	56	0.55
61	S_oc-temp_lat	61	0.6
62	S_oc-temp_med_and_Lingual	169	1.7
63	S_orbital_lateral	21	0.21
64	S_orbital_med-olfact	46	0.45
65	S_orbital-H_Shaped	77	0.75
66	S_parieto_occipital	138	1.3
67	S_pericallosal	118	1.2
68	S_postcentral	270	2.6
69	S_precentral-inf-part	120	1.2
70	S_precentral-sup-part	117	1.1
71	S_suborbital	25	0.24
72	S_subparietal	88	0.86
73	S_temporal_inf	67	0.65
74	S_temporal_sup	426	4.2
75	S_temporal_transverse	26	0.25

Regions summary - right

index	name	size	relative size
1	G_and_S_frontomargin	51	0.5
2	G_and_S_occipital_inf	72	0.7
3	G_and_S_paracentral	136	1.3
4	G_and_S_subcentral	139	1.4
5	G_and_S_transv_frontopol	50	0.49
6	G_and_S_cingul-Ant	191	1.9
7	G_and_S_cingul-Mid-Ant	141	1.4
8	G_and_S_cingul-Mid-Post	144	1.4
9	G_cingul-Post-dorsal	50	0.49
10	G_cingul-Post-ventral	41	0.4
11	G_cuneus	95	0.93
12	G_front_inf-Opercular	87	0.85
13	G_front_inf-Orbital	25	0.24
14	G_front_inf-Triangul	69	0.67
15	G_front_middle	262	2.6
16	G_front_sup	494	4.8
17	G_Ins_lg_and_S_cent_ins	59	0.58
18	G_insular_short	48	0.47
19	G_occipital_middle	124	1.2
20	G_occipital_sup	103	1.0
21	G_oc-temp_lat-fusifor	110	1.1
22	G_oc-temp_med-Lingual	142	1.4
23	G_oc-temp_med-Parahip	131	1.3
24	G_orbital	170	1.7
25	G_pariet_inf-Angular	242	2.4
26	G_pariet_inf-Supramar	250	2.4
27	G_parietal_sup	193	1.9
28	G_postcentral	199	1.9
29	G_precentral	237	2.3
30	G_precuneus	259	2.5
31	G_rectus	62	0.61
32	G_subcallosal	15	0.15
33	G_temp_sup-G_T_transv	39	0.38
34	G_temp_sup-Lateral	161	1.6
35	G_temp_sup-Plan_polar	55	0.54
36	G_temp_sup-Plan_tempo	64	0.62
37	G_temporal_inf	141	1.4
38	G_temporal_middle	188	1.8
39	Lat_Fis-ant-Horizont	28	0.27
40	Lat_Fis-ant-Vertical	19	0.19
41	Lat_Fis-post	158	1.5
42	Medial_wall	881	8.6
43	Pole_occipital	168	1.6
44	Pole_temporal	117	1.1
45	S_calcarine	177	1.7
46	S_central	283	2.8
47	S_cingul-Marginalis	151	1.5
48	S_circular_insula_ant	59	0.58
49	S_circular_insula_inf	120	1.2
50	S_circular_insula_sup	145	1.4
51	S_collat_transv_ant	70	0.68
52	S_collat_transv_post	27	0.26
53	S_front_inf	144	1.4
54	S_front_middle	125	1.2
55	S_front_sup	190	1.9
56	S_interm_prim-Jensen	35	0.34
57	S_intrapariet_and_P_trans	299	2.9
58	S_oc_middle_and_Lunatus	58	0.57
59	S_oc_sup_and_transversal	96	0.94
60	S_occipital_ant	49	0.48
61	S_oc-temp_lat	57	0.56
62	S_oc-temp_med_and_Lingual	134	1.3
63	S_orbital_lateral	23	0.22
64	S_orbital_med-olfact	58	0.57
65	S_orbital-H_Shaped	88	0.86
66	S_parieto_occipital	125	1.2
67	S_pericallosal	130	1.3
68	S_postcentral	245	2.4
69	S_precentral-inf-part	121	1.2
70	S_precentral-sup-part	119	1.2
71	S_suborbital	13	0.13
72	S_subparietal	117	1.1
73	S_temporal_inf	76	0.74
74	S_temporal_sup	479	4.7
75	S_temporal_transverse	19	0.19

SurfaceLabelsMasker(labels_img=<SurfaceImage (20484,)>,
                    lut=    index                   name
0       0                Unknown
1       1   G_and_S_frontomargin
2       2  G_and_S_occipital_inf
3       3    G_and_S_paracentral
4       4     G_and_S_subcentral
..    ...                    ...
71     71           S_suborbital
72     72          S_subparietal
73     73         S_temporal_inf
74     74         S_temporal_sup
75     75  S_temporal_transverse

[76 rows x 2 columns])

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SurfaceMapsMasker

SurfaceMapsMasker unfitted Extract data from a SurfaceImage, using maps of potentially overlapping brain regions. .. nilearn_versionadded:: 0.11.1

WARNING

• This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.

This report shows the input surface image (if provided via img) overlaid with the regions provided via maps_img.

SurfaceMapsMasker(maps_img=<SurfaceImage (20484, 64)>)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

SurfaceMapsMasker Extract data from a SurfaceImage, using maps of potentially overlapping brain regions. .. nilearn_versionadded:: 0.11.1

WARNING

• SurfaceMapsMasker has not been transformed (via transform() method) on any image yet. Plotting only maps for reporting.

Previous Map

Map

Next Map

This report shows the input surface image (if provided via img) overlaid with the regions provided via maps_img.

The masker has 64 different potentially overlapping regions.

Only 2 regions are shown in this report.

Hemisphere	Number of vertices
left	10242
right	10242

SurfaceMapsMasker(maps_img=<SurfaceImage (20484, 64)>)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

SurfaceMapsMasker Extract data from a SurfaceImage, using maps of potentially overlapping brain regions. .. nilearn_versionadded:: 0.11.1

WARNING

• SurfaceMapsMasker has not been transformed (via transform() method) on any image yet. Plotting only maps for reporting.

Previous Map

Map

Next Map

This report shows the input surface image (if provided via img) overlaid with the regions provided via maps_img.

The masker has 64 different potentially overlapping regions.

Only 2 regions are shown in this report.

Hemisphere	Number of vertices
left	10242
right	10242

SurfaceMapsMasker(maps_img=<SurfaceImage (20484, 64)>)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Masker reports in notebooks

Warning

The reports in this page may appear slightly different than they would in a real report. If you suspect a bug, double check by running in a local notebook.

View HERE.