Examples of masker reports

Nifti masker reports

NiftiMasker

Adapted from Simple example of NiftiMasker use

Nilearn - NiftiMasker unfitted report

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.

Value
Parameter
cmap gray
detrend False
high_variance_confounds False
mask_img Nifti1Image(
shape=(197, 233, 189),
affine=array([[ 1., 0., 0., -98.],
[ 0., 1., 0., -134.],
[ 0., 0., 1., -72.],
[ 0., 0., 0., 1.]])
)
mask_strategy background
memory nilearn_cache
memory_level 1
reports True
standardize zscore_sample
standardize_confounds True
target_affine [[1.0, 0.0, 0.0, -98.0], [0.0, 1.0, 0.0, -134.0], [0.0, 0.0, 1.0, -72.0], [0.0, 0.0, 0.0, 1.0]]
target_shape (197, 233, 189)
verbose 0
Nilearn - NiftiMasker report

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.

No image provided.
No overlay found.

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.

Value
Parameter
cmap gray
detrend False
high_variance_confounds False
mask_img Nifti1Image(
shape=(197, 233, 189),
affine=array([[ 1., 0., 0., -98.],
[ 0., 1., 0., -134.],
[ 0., 0., 1., -72.],
[ 0., 0., 0., 1.]])
)
mask_strategy background
memory nilearn_cache
memory_level 1
reports True
standardize zscore_sample
standardize_confounds True
target_affine [[1.0, 0.0, 0.0, -98.0], [0.0, 1.0, 0.0, -134.0], [0.0, 0.0, 1.0, -72.0], [0.0, 0.0, 0.0, 1.0]]
target_shape (197, 233, 189)
verbose 0

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

Nilearn - NiftiLabelsMasker unfitted report

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
Value
Parameter
background_label 0
cmap CMRmap_r
detrend False
high_variance_confounds False
keep_masked_labels False
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]
memory_level 1
reports True
resampling_target data
standardize zscore_sample
standardize_confounds True
strategy mean
verbose 0
Nilearn - NiftiLabelsMasker report

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.

image

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
Value
Parameter
background_label 0
cmap CMRmap_r
detrend False
high_variance_confounds False
keep_masked_labels False
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]
memory_level 1
reports True
resampling_target data
standardize zscore_sample
standardize_confounds True
strategy mean
verbose 0

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

Nilearn - NiftiMapsMasker unfitted report

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.

Value
Parameter
allow_overlap True
cmap CMRmap_r
detrend False
high_variance_confounds False
keep_masked_maps False
maps_img /home/runner/nilearn_data/difumo_atlases/64/2mm/maps.nii.gz
memory nilearn_cache
memory_level 1
reports True
resampling_target data
standardize zscore_sample
standardize_confounds True
verbose 0
Nilearn - NiftiMapsMasker report

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.

Map

No image to display

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.

Value
Parameter
allow_overlap True
cmap CMRmap_r
detrend False
high_variance_confounds False
keep_masked_maps False
maps_img /home/runner/nilearn_data/difumo_atlases/64/2mm/maps.nii.gz
memory nilearn_cache
memory_level 1
reports True
resampling_target data
standardize zscore_sample
standardize_confounds True
verbose 0

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

Nilearn - NiftiSpheresMasker unfitted report

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
Value
Parameter
allow_overlap False
detrend True
high_pass 0.01
high_variance_confounds False
low_pass 0.1
memory nilearn_cache
memory_level 1
radius 10
reports True
seeds [(0, -53, 26), (5, 53, -26), (0, 0, 0)]
standardize zscore_sample
standardize_confounds True
t_r 2
verbose 0
Nilearn - NiftiSpheresMasker report

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.

No image to display.

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
Value
Parameter
allow_overlap False
detrend True
high_pass 0.01
high_variance_confounds False
low_pass 0.1
memory nilearn_cache
memory_level 1
radius 10
reports True
seeds [(0, -53, 26), (5, 53, -26), (0, 0, 0)]
standardize zscore_sample
standardize_confounds True
t_r 2
verbose 0

Surface masker reports

SurfaceMasker

Nilearn - SurfaceMasker unfitted report

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.

Value
Parameter
cmap inferno
detrend False
high_variance_confounds False
mask_img <SurfaceImage (20484,)>
memory_level 1
reports True
standardize False
standardize_confounds True
verbose 0
Nilearn - SurfaceMasker report

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

image

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
Value
Parameter
cmap inferno
detrend False
high_variance_confounds False
mask_img <SurfaceImage (20484,)>
memory_level 1
reports True
standardize False
standardize_confounds True
verbose 0

SurfaceLabelsMasker

Nilearn - SurfaceLabelsMasker unfitted report

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.

Value
Parameter
background_label 0
cmap inferno
detrend False
high_variance_confounds False
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]
memory_level 1
reports True
standardize False
standardize_confounds True
strategy mean
verbose 0
Nilearn - SurfaceLabelsMasker report

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

image

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
Value
Parameter
background_label 0
cmap inferno
detrend False
high_variance_confounds False
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]
memory_level 1
reports True
standardize False
standardize_confounds True
strategy mean
verbose 0

SurfaceMapsMasker

Nilearn - SurfaceMapsMasker unfitted report

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.

Value
Parameter
allow_overlap True
cmap inferno
detrend False
high_variance_confounds False
maps_img <SurfaceImage (20484, 64)>
memory_level 1
reports True
standardize False
standardize_confounds True
verbose 0
Nilearn - SurfaceMapsMasker report

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.

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
Value
Parameter
allow_overlap True
cmap inferno
detrend False
high_variance_confounds False
maps_img <SurfaceImage (20484, 64)>
memory_level 1
reports True
standardize False
standardize_confounds True
verbose 0
Nilearn - SurfaceMapsMasker report

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.

Map

matplotlib image

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
Value
Parameter
allow_overlap True
cmap inferno
detrend False
high_variance_confounds False
maps_img <SurfaceImage (20484, 64)>
memory_level 1
reports True
standardize False
standardize_confounds True
verbose 0

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.