Examples of GLM reports¶
First level report¶
ADHD¶
Adapted from Default Mode Network extraction of ADHD dataset
ADHD DMN Report Implement the General Linear Model for single run :term:`fMRI` data.
Description
Data were analyzed using Nilearn (version= 0.14.0; RRID:SCR_001362).
At the subject level, a mass univariate analysis was performed with a linear regression at each voxel of the brain, using generalized least squares with a global ar1 noise model to account for temporal auto-correlation and a cosine drift model (high pass filter=0.01 Hz).
Model details
| Value | |
|---|---|
| Parameter | |
| drift_model | cosine |
| high_pass (Hertz) | 0.01 |
| hrf_model | glover |
| noise_model | ar1 |
| signal_scaling | 0 |
| slice_time_ref | 0 |
| standardize | False |
Design Matrix
correlation matrix
Contrasts
Mask
The mask includes 62546 voxels (23.0 %) of the image.
Statistical Maps
seed_based_glm
Cluster Table
| Height control | bonferroni |
|---|---|
| α | 9.00E-4 |
| Threshold (computed) | 5.549 |
| Cluster size threshold (voxels) | 15 |
| Minimum distance (mm) | 8.0 |
| Cluster ID | X | Y | Z | Peak Stat | Cluster Size (mm3) |
|---|---|---|---|---|---|
| 1 | 3.0 | -54.0 | 18.0 | 13.01 | 13284 |
| 1a | 0.0 | -57.0 | 30.0 | 12.68 | |
| 1b | 0.0 | -48.0 | 30.0 | 11.96 | |
| 1c | -3.0 | -51.0 | 18.0 | 11.91 | |
| 2 | 0.0 | 51.0 | -6.0 | 11.08 | 5832 |
| 2a | 3.0 | 69.0 | 3.0 | 10.62 | |
| 2b | 0.0 | 57.0 | 3.0 | 10.31 | |
| 2c | 0.0 | 63.0 | 15.0 | 10.12 | |
| 3 | 57.0 | -66.0 | 27.0 | 10.91 | 1215 |
| 3a | 48.0 | -57.0 | 30.0 | 8.99 | |
| 3b | 51.0 | -69.0 | 27.0 | 8.51 | |
| 4 | 6.0 | -84.0 | -18.0 | 10.72 | 783 |
| 4a | -3.0 | -87.0 | -15.0 | 7.59 | |
| 5 | -6.0 | -87.0 | -21.0 | 10.72 | 432 |
| 5a | -15.0 | -93.0 | -21.0 | 10.72 | |
| 6 | 18.0 | 42.0 | 51.0 | 9.99 | 594 |
| 7 | -63.0 | -12.0 | -9.0 | 9.20 | 567 |
| 7a | -63.0 | -18.0 | -15.0 | 9.05 | |
| 8 | -48.0 | -69.0 | 42.0 | 8.93 | 1431 |
| 8a | -42.0 | -72.0 | 33.0 | 8.72 | |
| 8b | -54.0 | -66.0 | 36.0 | 8.71 | |
| 8c | -48.0 | -75.0 | 30.0 | 7.57 | |
| 9 | -12.0 | 39.0 | 54.0 | 8.52 | 432 |
| 9a | -15.0 | 30.0 | 51.0 | 7.87 | |
| 10 | -24.0 | 30.0 | 48.0 | 8.51 | 702 |
| 10a | -18.0 | 30.0 | 42.0 | 8.39 | |
| 11 | -36.0 | -54.0 | -24.0 | 8.04 | 783 |
| 11a | -45.0 | -54.0 | -21.0 | 7.61 | |
| 12 | -51.0 | -60.0 | 24.0 | 7.50 | 891 |
About
- Date preprocessed:
BIDS features¶
Adapted from First level analysis of a complete BIDS dataset from openneuro
FLM Bids Features Stat maps Implement the General Linear Model for single run :term:`fMRI` data.
Description
Data were analyzed using Nilearn (version= 0.14.0; RRID:SCR_001362).
At the subject level, a mass univariate analysis was performed with a linear regression at each voxel of the brain, using generalized least squares with a global ar1 noise model to account for temporal auto-correlation and a cosine drift model (high pass filter=0.01 Hz).
Input images were smoothed with gaussian kernel (full-width at half maximum=5.0 mm).
The following contrasts were computed using a fixed-effect approach across runs :
- StopSuccess - Go
Model details
| Value | |
|---|---|
| Parameter | |
| drift_model | cosine |
| high_pass (Hertz) | 0.01 |
| hrf_model | glover |
| noise_model | ar1 |
| signal_scaling | 0 |
| slice_time_ref | 0.0 |
| smoothing_fwhm (mm) | 5.0 |
| standardize | False |
| subject_label | 10159 |
| t_r (seconds) | 2 |
Design Matrix
correlation matrix
Contrasts
Mask
The mask includes 49554 voxels (20.2 %) of the image.
Statistical Maps
StopSuccess - Go
Cluster Table
| Height control | fpr |
|---|---|
| α | 0.001 |
| Threshold (computed) | 3.09 |
| Cluster size threshold (voxels) | 3 |
| Minimum distance (mm) | 8.0 |
| Cluster ID | X | Y | Z | Peak Stat | Cluster Size (mm3) |
|---|---|---|---|---|---|
| 1 | -66.0 | -45.0 | 22.0 | 5.31 | 6300 |
| 1a | -66.0 | -33.0 | 18.0 | 4.67 | |
| 1b | -48.0 | -36.0 | 14.0 | 4.53 | |
| 1c | -57.0 | -48.0 | 10.0 | 4.25 | |
| 2 | -42.0 | 15.0 | 26.0 | 4.92 | 2520 |
| 2a | -51.0 | 9.0 | 34.0 | 4.72 | |
| 2b | -42.0 | 9.0 | 30.0 | 4.68 | |
| 2c | -57.0 | 12.0 | 38.0 | 4.59 | |
| 3 | -45.0 | -12.0 | 26.0 | 4.84 | 252 |
| 4 | 57.0 | -27.0 | 2.0 | 4.69 | 504 |
| 4a | 66.0 | -27.0 | 2.0 | 3.66 | |
| 5 | 54.0 | 9.0 | 14.0 | 4.65 | 180 |
| 6 | -66.0 | -30.0 | 6.0 | 4.51 | 216 |
| 7 | -6.0 | -15.0 | 34.0 | 4.47 | 108 |
| 8 | 42.0 | 9.0 | 34.0 | 4.46 | 540 |
| 9 | 36.0 | 15.0 | 10.0 | 4.32 | 288 |
| 10 | -60.0 | -27.0 | -2.0 | 4.32 | 108 |
| 11 | 6.0 | 18.0 | 34.0 | 4.26 | 2520 |
| 11a | -3.0 | 15.0 | 46.0 | 4.08 | |
| 11b | 0.0 | 0.0 | 38.0 | 3.82 | |
| 11c | 3.0 | 9.0 | 50.0 | 3.80 | |
| 12 | 6.0 | 6.0 | 54.0 | 4.21 | 468 |
| 12a | 6.0 | 3.0 | 62.0 | 3.35 | |
| 13 | -45.0 | 21.0 | 2.0 | 4.19 | 504 |
| 13a | -54.0 | 21.0 | 6.0 | 3.39 | |
| 14 | 45.0 | -21.0 | 42.0 | 4.16 | 432 |
| 15 | 63.0 | -9.0 | 2.0 | 4.16 | 288 |
| 16 | 63.0 | -24.0 | 30.0 | 4.08 | 360 |
| 17 | -12.0 | 6.0 | 6.0 | 4.06 | 792 |
| 17a | -9.0 | -3.0 | 10.0 | 3.73 | |
| 17b | -9.0 | 6.0 | 14.0 | 3.71 | |
| 18 | -30.0 | 24.0 | 2.0 | 4.05 | 288 |
| 19 | 6.0 | -3.0 | 74.0 | 4.05 | 144 |
| 20 | -27.0 | 45.0 | 18.0 | 4.04 | 432 |
| 21 | 54.0 | -39.0 | 34.0 | 4.02 | 108 |
| 22 | -15.0 | -66.0 | 38.0 | 3.99 | 324 |
| 23 | -18.0 | -63.0 | 6.0 | 3.99 | 180 |
| 24 | -60.0 | 6.0 | -2.0 | 3.96 | 144 |
| 25 | 3.0 | -24.0 | 30.0 | 3.95 | 360 |
| 26 | 12.0 | -72.0 | 22.0 | 3.94 | 360 |
| 27 | 36.0 | -48.0 | -26.0 | 3.93 | 108 |
| 28 | 33.0 | 42.0 | 34.0 | 3.91 | 756 |
| 28a | 30.0 | 45.0 | 26.0 | 3.88 | |
| 29 | 9.0 | 6.0 | 6.0 | 3.88 | 216 |
| 30 | -12.0 | -24.0 | 10.0 | 3.82 | 180 |
| 31 | 51.0 | -30.0 | 14.0 | 3.78 | 648 |
| 32 | -54.0 | -57.0 | -2.0 | 3.77 | 108 |
| 33 | 9.0 | -15.0 | 14.0 | 3.70 | 108 |
| 34 | 0.0 | 0.0 | 66.0 | 3.63 | 180 |
| 35 | 36.0 | 36.0 | 30.0 | 3.63 | 108 |
| 36 | -39.0 | -45.0 | 38.0 | 3.57 | 108 |
| 37 | 9.0 | 30.0 | 26.0 | 3.44 | 144 |
| 38 | 45.0 | 18.0 | 2.0 | 3.40 | 144 |
| 39 | 60.0 | -18.0 | 10.0 | 3.39 | 108 |
| 40 | 15.0 | -30.0 | 2.0 | 3.39 | 252 |
| 41 | -12.0 | 27.0 | 30.0 | 3.39 | 108 |
| 42 | -42.0 | -21.0 | -2.0 | 3.32 | 216 |
| 43 | -54.0 | 21.0 | -2.0 | 3.24 | 108 |
| 44 | 3.0 | -18.0 | 46.0 | 3.20 | 108 |
About
- Date preprocessed:
FIAC¶
Adapted from Simple example of two-runs fMRI model fitting
FirstLevelModel Implement the General Linear Model for single run :term:`fMRI` data.
WARNING
- No contrast passed during report generation.
Description
Data were analyzed using Nilearn (version= 0.14.0; RRID:SCR_001362).
At the subject level, a mass univariate analysis was performed with a linear regression at each voxel of the brain, using generalized least squares with a global ar1 noise model to account for temporal auto-correlation and a cosine drift model (high pass filter=0.01 Hz).
Model details
| Value | |
|---|---|
| Parameter | |
| drift_model | cosine |
| high_pass (Hertz) | 0.01 |
| hrf_model | glover |
| noise_model | ar1 |
| signal_scaling | 0 |
| slice_time_ref | 0.0 |
| standardize | False |
You can cycle through the different runs using your left and right arrow keys.
Mask
The mask includes 28008 voxels (22.8 %) of the image.
Statistical Maps
No statistical map was provided.
About
- Date preprocessed:
Surface GLM: empty¶
FirstLevelModel Implement the General Linear Model for single run :term:`fMRI` data.
WARNING
- This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.
- No contrast passed during report generation.
Description
Data were analyzed using Nilearn (version= 0.14.0; RRID:SCR_001362).
At the subject level, a mass univariate analysis was performed with a linear regression at each voxel of the brain, using generalized least squares with a global ar1 noise model to account for temporal auto-correlation None.
Model details
| Value | |
|---|---|
| Parameter | |
| drift_model | cosine |
| high_pass (Hertz) | 0.01 |
| hrf_model | glover |
| noise_model | ar1 |
| signal_scaling | 0 |
| slice_time_ref | 0.0 |
| standardize | False |
Mask
No mask was provided.
Statistical Maps
No statistical map was provided.
About
- Date preprocessed:
Surface GLM¶
Adapted from Example of surface-based first-level analysis
FirstLevelModel Implement the General Linear Model for single run :term:`fMRI` data.
WARNING
- No contrast passed during report generation.
Description
Data were analyzed using Nilearn (version= 0.14.0; RRID:SCR_001362).
At the subject level, a mass univariate analysis was performed with a linear regression at each voxel of the brain, using generalized least squares with a global ar1 noise model to account for temporal auto-correlation and a cosine drift model (high pass filter=0.01 Hz).
Regressors were entered into run-specific design matrices and onsets were convolved with a glover + derivative canonical hemodynamic response function for the following conditions:
- audio_right_hand_button_press
- audio_computation
- horizontal_checkerboard
- visual_left_hand_button_press
- sentence_reading
- audio_left_hand_button_press
- visual_right_hand_button_press
- vertical_checkerboard
- sentence_listening
- visual_computation
Model details
| Value | |
|---|---|
| Parameter | |
| drift_model | cosine |
| high_pass (Hertz) | 0.01 |
| hrf_model | glover + derivative |
| noise_model | ar1 |
| signal_scaling | 0 |
| slice_time_ref | 0.5 |
| standardize | False |
| t_r (seconds) | 2.4 |
Design Matrix
correlation matrix
Contrasts
No contrast was provided.
Mask
The mask includes 20484 voxels (100.0 %) of the image.
Statistical Maps
No statistical map was provided.
About
- Date preprocessed:
Second level report¶
Volume GLM¶
Adapted from Voxel-Based Morphometry on OASIS dataset
SecondLevelModel
Implement the :term:`General Linear Model` for multiple subject :term:`fMRI` data.
Description
Data were analyzed using Nilearn (version= 0.14.0; RRID:SCR_001362).
At the group level, a mass univariate analysis was performed with a linear regression at each voxel of the brain.
Input images were smoothed with gaussian kernel (full-width at half maximum=2.0 mm).
The following contrasts were computed :
- age
- sex
Model details
| Value | |
|---|---|
| Parameter | |
| smoothing_fwhm (mm) | 2.0 |
Design Matrix
correlation matrix
Contrasts
Mask
The mask includes 191002 voxels (21.2 %) of the image.
Statistical Maps
age
Cluster Table
| Height control | None |
|---|---|
| Threshold Z | 3.09 |
| Cluster size threshold (voxels) | 0 |
| Minimum distance (mm) | 8.0 |
| Cluster ID | X | Y | Z | Peak Stat | Cluster Size (mm3) |
|---|---|---|---|---|---|
| 1 | -10.0 | -66.0 | -4.0 | 4.53 | 8 |
| 2 | -2.0 | -2.0 | -8.0 | 4.46 | 8 |
| 3 | -14.0 | -54.0 | 48.0 | 4.25 | 8 |
| 4 | -20.0 | -6.0 | -34.0 | 4.25 | 8 |
| 5 | 6.0 | 48.0 | 34.0 | 4.14 | 8 |
| 6 | 50.0 | 16.0 | -4.0 | 4.09 | 8 |
| 7 | -8.0 | -18.0 | -2.0 | 3.89 | 8 |
| 8 | 18.0 | 26.0 | -20.0 | 3.88 | 8 |
| 9 | -2.0 | 26.0 | 32.0 | 3.87 | 8 |
| 10 | -44.0 | 28.0 | 0.0 | 3.86 | 8 |
| 11 | 60.0 | -4.0 | -8.0 | 3.79 | 8 |
| 12 | 0.0 | 32.0 | -2.0 | 3.78 | 8 |
| 13 | 32.0 | 46.0 | -18.0 | 3.78 | 8 |
| 14 | -42.0 | -64.0 | -8.0 | 3.76 | 8 |
| 15 | -26.0 | -34.0 | 14.0 | 3.75 | 8 |
| 16 | -6.0 | 40.0 | -8.0 | 3.71 | 8 |
| 17 | -8.0 | -58.0 | 22.0 | 3.69 | 8 |
| 18 | -42.0 | 12.0 | 26.0 | 3.69 | 8 |
| 19 | 26.0 | -32.0 | -10.0 | 3.68 | 8 |
| 20 | 46.0 | -26.0 | -8.0 | 3.68 | 8 |
| 21 | -30.0 | 18.0 | -42.0 | 3.67 | 8 |
| 22 | 20.0 | 66.0 | 4.0 | 3.66 | 8 |
| 23 | -48.0 | 8.0 | 40.0 | 3.65 | 8 |
| 24 | -44.0 | 16.0 | 40.0 | 3.65 | 8 |
| 25 | -62.0 | -8.0 | 28.0 | 3.65 | 8 |
| 26 | -60.0 | -56.0 | 20.0 | 3.64 | 8 |
| 27 | -16.0 | -48.0 | 70.0 | 3.62 | 8 |
| 28 | -16.0 | 70.0 | 12.0 | 3.62 | 8 |
| 29 | 38.0 | -74.0 | -44.0 | 3.62 | 8 |
| 30 | -2.0 | 24.0 | -12.0 | 3.60 | 8 |
| 31 | -4.0 | -6.0 | 24.0 | 3.60 | 8 |
| 32 | -8.0 | 52.0 | 34.0 | 3.58 | 8 |
| 33 | 64.0 | -50.0 | 32.0 | 3.57 | 8 |
| 34 | -2.0 | 10.0 | 28.0 | 3.57 | 8 |
| 35 | 64.0 | -26.0 | 24.0 | 3.57 | 8 |
| 36 | -4.0 | -28.0 | -2.0 | 3.57 | 8 |
| 37 | 56.0 | -8.0 | 40.0 | 3.56 | 8 |
| 38 | 16.0 | 0.0 | 16.0 | 3.56 | 8 |
| 39 | 0.0 | -16.0 | -36.0 | 3.55 | 8 |
| 40 | -36.0 | 34.0 | 50.0 | 3.55 | 8 |
| 41 | 6.0 | -58.0 | 0.0 | 3.54 | 8 |
| 42 | 22.0 | 34.0 | -24.0 | 3.54 | 8 |
| 43 | -16.0 | 32.0 | -24.0 | 3.54 | 8 |
| 44 | 54.0 | -44.0 | 52.0 | 3.53 | 8 |
| 45 | 12.0 | -8.0 | 14.0 | 3.53 | 8 |
| 46 | -58.0 | -14.0 | 46.0 | 3.52 | 8 |
| 47 | -10.0 | 6.0 | 12.0 | 3.51 | 16 |
| 48 | 46.0 | -48.0 | -20.0 | 3.50 | 8 |
| 49 | -20.0 | 38.0 | -16.0 | 3.49 | 8 |
| 50 | 12.0 | 14.0 | 2.0 | 3.48 | 8 |
| 51 | 0.0 | 0.0 | 36.0 | 3.48 | 8 |
| 52 | 2.0 | -48.0 | 66.0 | 3.47 | 8 |
| 53 | 2.0 | -40.0 | 60.0 | 3.47 | 8 |
| 54 | 26.0 | 64.0 | 16.0 | 3.47 | 8 |
| 55 | -8.0 | -14.0 | 26.0 | 3.46 | 8 |
| 56 | -46.0 | -66.0 | -18.0 | 3.46 | 8 |
| 57 | -52.0 | -34.0 | 44.0 | 3.45 | 8 |
| 58 | 56.0 | -14.0 | -32.0 | 3.45 | 8 |
| 59 | 2.0 | 4.0 | 52.0 | 3.44 | 8 |
| 60 | 12.0 | -22.0 | 4.0 | 3.44 | 8 |
| 61 | 66.0 | -6.0 | 18.0 | 3.44 | 8 |
| 62 | 62.0 | -10.0 | 8.0 | 3.44 | 8 |
| 63 | 4.0 | 22.0 | 22.0 | 3.44 | 8 |
| 64 | 64.0 | 2.0 | -16.0 | 3.43 | 8 |
| 65 | 6.0 | -46.0 | 38.0 | 3.42 | 8 |
| 66 | 4.0 | -56.0 | 22.0 | 3.42 | 8 |
| 67 | -10.0 | -18.0 | 40.0 | 3.41 | 8 |
| 68 | 6.0 | -58.0 | 28.0 | 3.41 | 8 |
| 69 | 8.0 | -2.0 | 56.0 | 3.41 | 8 |
| 70 | 2.0 | 0.0 | -2.0 | 3.41 | 8 |
| 71 | 56.0 | -4.0 | -32.0 | 3.41 | 8 |
| 72 | -18.0 | 24.0 | 52.0 | 3.41 | 8 |
| 73 | 0.0 | 26.0 | 56.0 | 3.40 | 8 |
| 74 | 6.0 | -76.0 | -2.0 | 3.39 | 8 |
| 75 | -12.0 | 42.0 | -20.0 | 3.38 | 8 |
| 76 | -4.0 | -54.0 | -8.0 | 3.38 | 8 |
| 77 | -26.0 | -30.0 | 60.0 | 3.37 | 8 |
| 78 | 32.0 | -22.0 | -28.0 | 3.37 | 8 |
| 79 | -6.0 | 2.0 | -16.0 | 3.36 | 8 |
| 80 | -18.0 | 28.0 | -2.0 | 3.36 | 8 |
| 81 | 24.0 | 34.0 | 40.0 | 3.36 | 8 |
| 82 | 30.0 | -32.0 | -14.0 | 3.35 | 8 |
| 83 | 2.0 | 32.0 | -18.0 | 3.35 | 8 |
| 84 | -26.0 | 32.0 | -16.0 | 3.35 | 8 |
| 85 | -18.0 | -28.0 | -26.0 | 3.34 | 8 |
| 86 | -6.0 | -10.0 | 12.0 | 3.34 | 8 |
| 87 | 10.0 | -76.0 | -48.0 | 3.34 | 8 |
| 88 | 34.0 | -56.0 | -50.0 | 3.33 | 8 |
| 89 | 2.0 | -34.0 | 58.0 | 3.33 | 8 |
| 90 | 52.0 | -18.0 | -8.0 | 3.33 | 8 |
| 91 | -2.0 | 46.0 | 38.0 | 3.32 | 8 |
| 92 | -2.0 | -4.0 | 14.0 | 3.32 | 8 |
| 93 | 60.0 | -50.0 | 2.0 | 3.32 | 8 |
| 94 | 42.0 | 16.0 | -46.0 | 3.32 | 8 |
| 95 | 30.0 | -30.0 | -4.0 | 3.31 | 8 |
| 96 | 28.0 | 40.0 | 42.0 | 3.31 | 8 |
| 97 | -40.0 | 30.0 | 42.0 | 3.31 | 8 |
| 98 | 2.0 | -32.0 | -22.0 | 3.30 | 8 |
| 99 | 24.0 | 44.0 | 28.0 | 3.30 | 8 |
| 100 | -4.0 | -30.0 | -8.0 | 3.30 | 8 |
| 101 | -20.0 | 58.0 | 18.0 | 3.30 | 8 |
| 102 | 6.0 | -92.0 | 32.0 | 3.29 | 8 |
| 103 | -28.0 | -38.0 | 16.0 | 3.29 | 8 |
| 104 | 12.0 | 48.0 | 10.0 | 3.29 | 8 |
| 105 | -6.0 | 38.0 | 4.0 | 3.29 | 8 |
| 106 | 4.0 | -20.0 | 22.0 | 3.29 | 8 |
| 107 | -6.0 | -80.0 | 36.0 | 3.28 | 8 |
| 108 | -12.0 | -44.0 | 68.0 | 3.28 | 8 |
| 109 | 62.0 | 6.0 | -22.0 | 3.28 | 8 |
| 110 | 64.0 | 2.0 | 26.0 | 3.28 | 8 |
| 111 | 8.0 | 54.0 | -22.0 | 3.27 | 8 |
| 112 | -34.0 | 12.0 | -46.0 | 3.27 | 8 |
| 113 | -4.0 | -12.0 | 26.0 | 3.27 | 8 |
| 114 | 28.0 | -38.0 | -26.0 | 3.27 | 8 |
| 115 | -2.0 | -18.0 | 54.0 | 3.27 | 8 |
| 116 | 10.0 | -16.0 | 16.0 | 3.27 | 8 |
| 117 | -64.0 | -20.0 | 20.0 | 3.27 | 8 |
| 118 | 2.0 | 34.0 | 20.0 | 3.26 | 8 |
| 119 | -2.0 | 20.0 | 26.0 | 3.26 | 8 |
| 120 | -10.0 | -86.0 | 28.0 | 3.25 | 8 |
| 121 | 20.0 | -24.0 | -4.0 | 3.25 | 8 |
| 122 | 0.0 | 42.0 | 12.0 | 3.25 | 8 |
| 123 | 42.0 | -10.0 | 20.0 | 3.24 | 8 |
| 124 | 24.0 | 0.0 | 56.0 | 3.24 | 8 |
| 125 | 18.0 | -26.0 | 12.0 | 3.23 | 8 |
| 126 | 8.0 | 12.0 | 62.0 | 3.23 | 8 |
| 127 | 4.0 | -16.0 | -18.0 | 3.22 | 8 |
| 128 | -16.0 | 10.0 | 10.0 | 3.22 | 8 |
| 129 | -24.0 | -42.0 | 70.0 | 3.22 | 8 |
| 130 | 52.0 | -36.0 | -4.0 | 3.21 | 8 |
| 131 | -18.0 | 66.0 | 8.0 | 3.21 | 8 |
| 132 | 22.0 | -38.0 | -2.0 | 3.21 | 8 |
| 133 | 12.0 | 70.0 | 6.0 | 3.21 | 8 |
| 134 | 24.0 | 4.0 | 66.0 | 3.21 | 8 |
| 135 | -66.0 | -20.0 | 32.0 | 3.20 | 8 |
| 136 | 44.0 | -2.0 | 56.0 | 3.20 | 8 |
| 137 | -58.0 | 4.0 | -18.0 | 3.20 | 8 |
| 138 | -4.0 | -48.0 | -8.0 | 3.20 | 8 |
| 139 | 54.0 | -18.0 | 28.0 | 3.20 | 8 |
| 140 | -10.0 | 4.0 | 66.0 | 3.19 | 8 |
| 141 | 8.0 | 14.0 | -12.0 | 3.19 | 8 |
| 142 | 6.0 | -58.0 | -16.0 | 3.19 | 8 |
| 143 | 10.0 | 10.0 | 12.0 | 3.19 | 8 |
| 144 | -50.0 | -8.0 | -12.0 | 3.19 | 8 |
| 145 | 54.0 | -16.0 | 34.0 | 3.19 | 8 |
| 146 | -40.0 | -66.0 | 50.0 | 3.19 | 8 |
| 147 | -2.0 | -46.0 | 66.0 | 3.18 | 8 |
| 148 | -42.0 | 16.0 | -12.0 | 3.18 | 8 |
| 149 | -14.0 | -38.0 | 14.0 | 3.18 | 8 |
| 150 | 66.0 | -10.0 | 18.0 | 3.18 | 8 |
| 151 | -24.0 | -44.0 | -2.0 | 3.18 | 8 |
| 152 | 42.0 | 24.0 | -6.0 | 3.18 | 8 |
| 153 | -60.0 | -14.0 | 36.0 | 3.18 | 8 |
| 154 | 56.0 | -4.0 | -8.0 | 3.18 | 8 |
| 155 | -20.0 | -26.0 | 14.0 | 3.17 | 8 |
| 156 | 30.0 | -40.0 | 8.0 | 3.17 | 8 |
| 157 | 6.0 | 8.0 | 30.0 | 3.17 | 8 |
| 158 | 4.0 | -40.0 | 78.0 | 3.17 | 8 |
| 159 | 16.0 | 34.0 | 56.0 | 3.17 | 8 |
| 160 | 68.0 | -4.0 | 14.0 | 3.17 | 8 |
| 161 | 24.0 | -74.0 | 44.0 | 3.17 | 8 |
| 162 | 32.0 | -38.0 | -28.0 | 3.17 | 8 |
| 163 | -36.0 | -62.0 | 38.0 | 3.16 | 8 |
| 164 | 34.0 | 34.0 | 46.0 | 3.16 | 8 |
| 165 | -16.0 | -40.0 | 8.0 | 3.16 | 8 |
| 166 | 10.0 | -86.0 | 34.0 | 3.16 | 8 |
| 167 | 10.0 | -32.0 | 18.0 | 3.15 | 8 |
| 168 | -6.0 | -46.0 | 28.0 | 3.15 | 8 |
| 169 | -42.0 | -30.0 | 46.0 | 3.15 | 8 |
| 170 | -36.0 | 6.0 | 44.0 | 3.15 | 8 |
| 171 | 58.0 | -52.0 | 4.0 | 3.15 | 8 |
| 172 | 10.0 | 0.0 | 14.0 | 3.15 | 8 |
| 173 | -38.0 | -14.0 | -42.0 | 3.15 | 8 |
| 174 | 2.0 | -54.0 | 32.0 | 3.15 | 8 |
| 175 | 16.0 | 58.0 | 32.0 | 3.15 | 8 |
| 176 | -8.0 | -10.0 | 26.0 | 3.14 | 8 |
| 177 | 56.0 | -18.0 | 34.0 | 3.14 | 8 |
| 178 | 40.0 | 22.0 | -6.0 | 3.14 | 8 |
| 179 | 40.0 | -28.0 | 40.0 | 3.14 | 8 |
| 180 | 54.0 | -14.0 | -14.0 | 3.14 | 8 |
| 181 | 44.0 | 14.0 | 44.0 | 3.14 | 8 |
| 182 | -28.0 | -76.0 | -58.0 | 3.13 | 8 |
| 183 | 14.0 | -28.0 | 14.0 | 3.13 | 8 |
| 184 | 36.0 | 24.0 | 4.0 | 3.13 | 8 |
| 185 | -2.0 | 12.0 | 30.0 | 3.13 | 8 |
| 186 | 48.0 | -42.0 | 24.0 | 3.13 | 8 |
| 187 | -46.0 | -24.0 | 10.0 | 3.13 | 8 |
| 188 | -42.0 | 4.0 | -6.0 | 3.13 | 8 |
| 189 | -32.0 | -34.0 | 46.0 | 3.13 | 8 |
| 190 | -14.0 | -10.0 | 10.0 | 3.13 | 8 |
| 191 | -26.0 | -10.0 | -46.0 | 3.12 | 8 |
| 192 | -42.0 | 10.0 | -8.0 | 3.12 | 8 |
| 193 | 44.0 | -40.0 | 50.0 | 3.11 | 8 |
| 194 | -60.0 | -40.0 | -26.0 | 3.11 | 8 |
| 195 | 2.0 | 10.0 | 56.0 | 3.11 | 8 |
| 196 | 32.0 | 34.0 | -14.0 | 3.11 | 8 |
| 197 | -42.0 | -66.0 | 54.0 | 3.11 | 8 |
| 198 | -22.0 | -46.0 | 70.0 | 3.11 | 8 |
| 199 | -28.0 | -34.0 | -10.0 | 3.10 | 8 |
| 200 | 6.0 | 26.0 | 54.0 | 3.10 | 8 |
| 201 | 14.0 | -48.0 | -6.0 | 3.10 | 8 |
| 202 | -28.0 | 30.0 | -22.0 | 3.10 | 8 |
| 203 | 2.0 | -34.0 | 2.0 | 3.10 | 8 |
| 204 | -10.0 | -54.0 | 68.0 | 3.10 | 8 |
| 205 | -58.0 | -2.0 | 8.0 | 3.10 | 8 |
| 206 | 16.0 | -6.0 | -34.0 | 3.10 | 8 |
| 207 | 14.0 | -28.0 | -22.0 | 3.09 | 8 |
| 208 | 10.0 | 34.0 | 42.0 | 3.09 | 8 |
| 209 | 34.0 | -94.0 | 6.0 | 3.09 | 8 |
| 210 | 18.0 | -6.0 | 22.0 | 3.09 | 8 |
| 211 | 14.0 | -76.0 | -36.0 | 3.09 | 8 |
sex
Cluster Table
| Height control | None |
|---|---|
| Threshold Z | 3.09 |
| Cluster size threshold (voxels) | 0 |
| Minimum distance (mm) | 8.0 |
| Cluster ID | X | Y | Z | Peak Stat | Cluster Size (mm3) |
|---|---|---|---|---|---|
| 1 | -34.0 | -10.0 | -18.0 | 4.73 | 8 |
| 2 | -4.0 | -22.0 | 18.0 | 4.62 | 8 |
| 3 | 52.0 | 32.0 | 0.0 | 4.27 | 8 |
| 4 | -40.0 | -76.0 | -24.0 | 4.20 | 8 |
| 5 | 38.0 | -74.0 | -44.0 | 4.14 | 16 |
| 6 | 24.0 | -74.0 | -48.0 | 4.10 | 8 |
| 7 | 2.0 | -44.0 | -56.0 | 3.98 | 8 |
| 8 | -24.0 | -90.0 | 8.0 | 3.89 | 8 |
| 9 | -30.0 | -72.0 | -24.0 | 3.87 | 8 |
| 10 | -24.0 | -78.0 | -38.0 | 3.86 | 8 |
| 11 | -4.0 | 46.0 | -26.0 | 3.80 | 8 |
| 12 | -12.0 | 8.0 | 20.0 | 3.78 | 8 |
| 13 | 32.0 | 6.0 | 32.0 | 3.75 | 8 |
| 14 | -20.0 | -78.0 | -44.0 | 3.73 | 8 |
| 15 | -58.0 | -8.0 | 48.0 | 3.72 | 8 |
| 16 | 10.0 | -34.0 | -28.0 | 3.72 | 8 |
| 17 | 44.0 | -66.0 | -40.0 | 3.72 | 8 |
| 18 | -36.0 | -76.0 | -28.0 | 3.71 | 8 |
| 19 | -2.0 | -44.0 | -54.0 | 3.68 | 8 |
| 20 | 8.0 | -82.0 | -36.0 | 3.67 | 8 |
| 21 | -12.0 | -34.0 | -28.0 | 3.67 | 8 |
| 22 | -14.0 | -74.0 | -46.0 | 3.66 | 8 |
| 23 | 64.0 | -32.0 | -24.0 | 3.66 | 8 |
| 24 | 20.0 | -70.0 | -52.0 | 3.65 | 8 |
| 25 | -38.0 | 2.0 | 30.0 | 3.65 | 8 |
| 26 | -16.0 | -74.0 | -48.0 | 3.64 | 8 |
| 27 | -40.0 | 32.0 | -8.0 | 3.62 | 8 |
| 28 | -2.0 | -32.0 | -20.0 | 3.62 | 8 |
| 29 | 2.0 | 52.0 | 18.0 | 3.60 | 8 |
| 30 | -46.0 | -64.0 | -34.0 | 3.59 | 8 |
| 31 | -24.0 | -72.0 | -36.0 | 3.59 | 16 |
| 32 | -18.0 | -80.0 | -42.0 | 3.59 | 8 |
| 33 | -16.0 | -28.0 | 36.0 | 3.59 | 8 |
| 34 | -24.0 | -94.0 | 10.0 | 3.59 | 8 |
| 35 | 16.0 | 64.0 | -14.0 | 3.59 | 8 |
| 36 | 24.0 | -68.0 | -40.0 | 3.58 | 16 |
| 37 | -58.0 | -2.0 | 48.0 | 3.58 | 8 |
| 38 | 10.0 | -76.0 | -48.0 | 3.58 | 8 |
| 39 | -6.0 | -52.0 | -56.0 | 3.57 | 8 |
| 40 | 40.0 | -70.0 | -32.0 | 3.56 | 8 |
| 41 | 30.0 | -80.0 | -36.0 | 3.56 | 8 |
| 42 | 40.0 | -66.0 | -38.0 | 3.53 | 8 |
| 43 | -36.0 | -8.0 | -26.0 | 3.51 | 8 |
| 44 | -48.0 | -52.0 | 56.0 | 3.50 | 8 |
| 45 | -34.0 | -38.0 | -14.0 | 3.50 | 8 |
| 46 | 0.0 | -2.0 | 18.0 | 3.50 | 8 |
| 47 | 42.0 | -60.0 | -42.0 | 3.50 | 8 |
| 48 | 34.0 | -62.0 | -40.0 | 3.48 | 8 |
| 49 | -6.0 | -18.0 | 62.0 | 3.48 | 8 |
| 50 | 22.0 | -64.0 | -28.0 | 3.48 | 8 |
| 51 | -4.0 | -8.0 | 20.0 | 3.47 | 8 |
| 52 | -16.0 | -74.0 | -42.0 | 3.46 | 8 |
| 53 | -8.0 | 0.0 | 40.0 | 3.46 | 8 |
| 54 | -38.0 | -74.0 | -28.0 | 3.46 | 8 |
| 55 | -40.0 | 34.0 | 28.0 | 3.46 | 8 |
| 56 | 28.0 | -70.0 | -36.0 | 3.45 | 8 |
| 57 | 42.0 | -46.0 | -22.0 | 3.45 | 8 |
| 58 | -10.0 | -8.0 | 38.0 | 3.44 | 8 |
| 59 | -16.0 | -74.0 | -32.0 | 3.44 | 8 |
| 60 | 54.0 | 36.0 | -10.0 | 3.43 | 8 |
| 61 | -32.0 | -66.0 | -38.0 | 3.43 | 8 |
| 62 | 26.0 | 2.0 | 12.0 | 3.43 | 8 |
| 63 | -16.0 | -34.0 | -46.0 | 3.42 | 8 |
| 64 | -10.0 | -76.0 | -38.0 | 3.42 | 16 |
| 65 | 36.0 | -54.0 | 40.0 | 3.42 | 8 |
| 66 | 38.0 | -52.0 | -46.0 | 3.42 | 8 |
| 67 | -16.0 | -78.0 | 44.0 | 3.41 | 8 |
| 68 | -34.0 | -76.0 | 36.0 | 3.41 | 8 |
| 69 | -8.0 | 34.0 | 46.0 | 3.41 | 8 |
| 70 | -10.0 | 20.0 | 8.0 | 3.41 | 8 |
| 71 | -36.0 | -66.0 | -56.0 | 3.40 | 8 |
| 72 | -26.0 | -2.0 | -12.0 | 3.40 | 8 |
| 73 | 32.0 | -92.0 | -8.0 | 3.39 | 8 |
| 74 | -22.0 | -72.0 | -44.0 | 3.39 | 8 |
| 75 | -58.0 | 0.0 | 38.0 | 3.39 | 8 |
| 76 | 34.0 | -34.0 | -38.0 | 3.37 | 8 |
| 77 | -34.0 | -54.0 | -48.0 | 3.37 | 8 |
| 78 | 38.0 | -18.0 | 46.0 | 3.36 | 8 |
| 79 | 20.0 | -74.0 | -44.0 | 3.36 | 8 |
| 80 | 34.0 | -72.0 | -36.0 | 3.36 | 8 |
| 81 | 20.0 | 50.0 | 46.0 | 3.36 | 8 |
| 82 | -30.0 | -58.0 | -28.0 | 3.35 | 8 |
| 83 | 60.0 | -36.0 | -2.0 | 3.34 | 8 |
| 84 | 10.0 | -70.0 | -46.0 | 3.34 | 16 |
| 85 | 38.0 | 38.0 | 6.0 | 3.33 | 8 |
| 86 | 34.0 | -60.0 | -30.0 | 3.33 | 8 |
| 87 | 24.0 | -52.0 | 10.0 | 3.33 | 8 |
| 88 | 18.0 | -80.0 | -26.0 | 3.32 | 8 |
| 89 | -14.0 | 2.0 | 24.0 | 3.32 | 8 |
| 90 | -40.0 | -20.0 | 48.0 | 3.31 | 8 |
| 91 | 12.0 | -78.0 | -48.0 | 3.30 | 8 |
| 92 | 32.0 | -78.0 | -36.0 | 3.30 | 8 |
| 93 | -34.0 | -68.0 | -28.0 | 3.30 | 8 |
| 94 | 30.0 | -64.0 | -4.0 | 3.29 | 8 |
| 95 | 2.0 | -80.0 | -10.0 | 3.29 | 16 |
| 96 | 42.0 | -50.0 | -44.0 | 3.29 | 8 |
| 97 | 8.0 | -84.0 | -12.0 | 3.29 | 8 |
| 98 | 16.0 | -70.0 | -52.0 | 3.27 | 8 |
| 99 | -38.0 | 24.0 | -24.0 | 3.26 | 8 |
| 100 | -26.0 | 4.0 | 48.0 | 3.26 | 8 |
| 101 | 28.0 | -84.0 | 22.0 | 3.26 | 8 |
| 102 | -16.0 | -82.0 | -28.0 | 3.24 | 8 |
| 103 | -36.0 | -40.0 | 58.0 | 3.23 | 8 |
| 104 | -44.0 | -10.0 | 6.0 | 3.23 | 8 |
| 105 | -14.0 | -44.0 | -46.0 | 3.23 | 8 |
| 106 | 14.0 | -72.0 | -46.0 | 3.23 | 8 |
| 107 | 34.0 | -48.0 | 0.0 | 3.22 | 8 |
| 108 | -14.0 | 42.0 | 20.0 | 3.21 | 8 |
| 109 | 48.0 | -18.0 | 58.0 | 3.21 | 8 |
| 110 | -10.0 | 10.0 | 18.0 | 3.20 | 8 |
| 111 | 16.0 | -66.0 | -46.0 | 3.20 | 8 |
| 112 | -40.0 | -48.0 | -36.0 | 3.20 | 8 |
| 113 | 2.0 | -82.0 | -16.0 | 3.20 | 8 |
| 114 | -46.0 | -6.0 | 0.0 | 3.19 | 8 |
| 115 | 36.0 | 34.0 | 12.0 | 3.19 | 8 |
| 116 | 20.0 | -86.0 | -14.0 | 3.19 | 8 |
| 117 | -42.0 | -4.0 | 56.0 | 3.19 | 8 |
| 118 | -10.0 | -24.0 | 20.0 | 3.18 | 8 |
| 119 | 34.0 | -56.0 | -50.0 | 3.18 | 8 |
| 120 | 0.0 | -24.0 | 46.0 | 3.17 | 8 |
| 121 | -16.0 | -70.0 | -30.0 | 3.17 | 8 |
| 122 | 18.0 | -66.0 | -32.0 | 3.17 | 8 |
| 123 | 38.0 | -44.0 | -16.0 | 3.17 | 8 |
| 124 | -32.0 | -70.0 | -56.0 | 3.17 | 8 |
| 125 | 40.0 | -60.0 | 44.0 | 3.16 | 8 |
| 126 | 34.0 | 12.0 | 34.0 | 3.16 | 8 |
| 127 | 62.0 | -38.0 | 4.0 | 3.16 | 8 |
| 128 | 36.0 | -66.0 | -42.0 | 3.15 | 8 |
| 129 | 40.0 | -64.0 | -44.0 | 3.15 | 8 |
| 130 | -6.0 | -42.0 | 8.0 | 3.15 | 8 |
| 131 | -28.0 | -8.0 | -12.0 | 3.15 | 8 |
| 132 | -14.0 | 16.0 | 34.0 | 3.15 | 8 |
| 133 | 32.0 | -58.0 | -44.0 | 3.15 | 8 |
| 134 | -22.0 | -94.0 | -10.0 | 3.14 | 8 |
| 135 | 22.0 | -80.0 | -50.0 | 3.14 | 8 |
| 136 | 54.0 | -4.0 | 50.0 | 3.14 | 8 |
| 137 | 12.0 | -58.0 | -34.0 | 3.13 | 8 |
| 138 | 40.0 | -64.0 | -36.0 | 3.13 | 8 |
| 139 | -6.0 | -14.0 | 22.0 | 3.13 | 8 |
| 140 | -48.0 | -8.0 | 54.0 | 3.13 | 8 |
| 141 | 12.0 | -78.0 | 10.0 | 3.13 | 8 |
| 142 | 2.0 | -44.0 | -44.0 | 3.13 | 8 |
| 143 | 24.0 | -16.0 | 12.0 | 3.12 | 8 |
| 144 | -16.0 | -70.0 | -6.0 | 3.12 | 8 |
| 145 | -28.0 | -22.0 | 58.0 | 3.11 | 8 |
| 146 | -34.0 | -76.0 | -36.0 | 3.11 | 16 |
| 147 | -36.0 | -60.0 | -56.0 | 3.11 | 8 |
| 148 | -46.0 | -8.0 | 34.0 | 3.11 | 8 |
| 149 | 18.0 | -50.0 | -62.0 | 3.11 | 8 |
| 150 | -20.0 | -42.0 | -10.0 | 3.11 | 8 |
| 151 | -18.0 | -32.0 | -28.0 | 3.10 | 8 |
| 152 | -52.0 | -4.0 | -38.0 | 3.10 | 8 |
| 153 | 28.0 | -56.0 | 10.0 | 3.10 | 8 |
| 154 | 44.0 | -62.0 | -42.0 | 3.10 | 8 |
| 155 | -32.0 | -68.0 | -58.0 | 3.10 | 8 |
| 156 | -40.0 | -66.0 | -26.0 | 3.10 | 8 |
| 157 | 40.0 | -64.0 | 10.0 | 3.10 | 8 |
| 158 | 24.0 | -80.0 | -38.0 | 3.09 | 8 |
| 159 | 34.0 | -76.0 | -24.0 | 3.09 | 8 |
About
- Date preprocessed:
Surface GLM¶
SecondLevelModel
Implement the :term:`General Linear Model` for multiple subject :term:`fMRI` data.
WARNING
- This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.
- No contrast passed during report generation.
Description
Data were analyzed using Nilearn (version= 0.14.0; RRID:SCR_001362).
At the group level, a mass univariate analysis was performed with a linear regression at each voxel of the brain.
Model details
Mask
No mask was provided.
Statistical Maps
No statistical map was provided.
About
- Date preprocessed:
GLM 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.