Examples of GLM reports¶
First level report¶
ADHD¶
Adapted from Default Mode Network extraction of ADHD dataset
Statistical Report - First Level Model
ADHD DMN Report
Implement the General Linear Model for single run :term:`fMRI` data.
Description
Data were analyzed using Nilearn (version= 0.13.1.dev41+g36e303c92; 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
First Level ModelIn 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.
Parameters
| t_r | None | |
| slice_time_ref | 0 | |
| hrf_model | 'glover' | |
| drift_model | 'cosine' | |
| high_pass | 0.01 | |
| drift_order | 1 | |
| fir_delays | None | |
| min_onset | -24 | |
| mask_img | None | |
| target_affine | None | |
| target_shape | None | |
| smoothing_fwhm | None | |
| memory | None | |
| memory_level | 1 | |
| standardize | False | |
| signal_scaling | 0 | |
| noise_model | 'ar1' | |
| verbose | 0 | |
| n_jobs | 1 | |
| minimize_memory | True | |
| subject_label | None | |
| random_state | None |
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 | 10.44 | 783 |
| 4a | -3.0 | -87.0 | -15.0 | 7.59 | |
| 5 | -6.0 | -87.0 | -21.0 | 10.44 | 432 |
| 5a | -12.0 | -93.0 | -21.0 | 10.44 | |
| 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
Statistical Report - First Level Model
FLM Bids Features Stat maps
Implement the General Linear Model for single run :term:`fMRI` data.
Description
Data were analyzed using Nilearn (version= 0.13.1.dev41+g36e303c92; 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
First Level ModelIn 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.
Parameters
| t_r | 2 | |
| slice_time_ref | 0.0 | |
| hrf_model | 'glover' | |
| drift_model | 'cosine' | |
| high_pass | 0.01 | |
| drift_order | 1 | |
| fir_delays | None | |
| min_onset | -24 | |
| mask_img | None | |
| target_affine | None | |
| target_shape | None | |
| smoothing_fwhm | 5.0 | |
| memory | Memory(location=None) | |
| memory_level | 1 | |
| standardize | False | |
| signal_scaling | 0 | |
| noise_model | 'ar1' | |
| verbose | 0 | |
| n_jobs | 1 | |
| minimize_memory | True | |
| subject_label | '10159' | |
| random_state | None |
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
Statistical Report - First Level Model Implement the General Linear Model for single run :term:`fMRI` data.
Description
Data were analyzed using Nilearn (version= 0.13.1.dev41+g36e303c92; 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
First Level ModelIn 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.
Parameters
| t_r | None | |
| slice_time_ref | 0.0 | |
| hrf_model | 'glover' | |
| drift_model | 'cosine' | |
| high_pass | 0.01 | |
| drift_order | 1 | |
| fir_delays | None | |
| min_onset | -24 | |
| mask_img | '/home/runner/nilearn_d.../fiac/fiac0/mask.nii.gz' | |
| target_affine | None | |
| target_shape | None | |
| smoothing_fwhm | None | |
| memory | None | |
| memory_level | 1 | |
| standardize | False | |
| signal_scaling | 0 | |
| noise_model | 'ar1' | |
| verbose | 0 | |
| n_jobs | 1 | |
| minimize_memory | True | |
| subject_label | None | |
| random_state | None |
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
SStSSp_minus_DStDSp
No suprathreshold cluster
Cluster Table
| Height control | fdr |
|---|---|
| α | 0.001 |
| Threshold (computed) | inf |
| Cluster size threshold (voxels) | 0 |
| Minimum distance (mm) | 8.0 |
No suprathreshold cluster
Deactivation
Cluster Table
| Height control | fdr |
|---|---|
| α | 0.001 |
| Threshold (computed) | 5.0 |
| Cluster size threshold (voxels) | 0 |
| Minimum distance (mm) | 8.0 |
| Cluster ID | X | Y | Z | Peak Stat | Cluster Size (mm3) |
|---|---|---|---|---|---|
| 1 | -39.0 | -114.0 | 60.0 | 6.01 | 36 |
| 2 | -51.0 | -57.0 | 56.0 | 5.39 | 36 |
| 3 | -51.0 | -96.0 | 64.0 | 5.38 | 36 |
| 4 | -42.0 | -93.0 | 64.0 | 5.36 | 36 |
| 5 | -126.0 | -108.0 | 60.0 | 5.35 | 36 |
| 6 | -45.0 | -93.0 | 52.0 | 5.23 | 36 |
| 7 | -135.0 | -111.0 | 60.0 | 5.21 | 36 |
| 8 | -45.0 | -60.0 | 60.0 | 5.21 | 36 |
| 9 | -57.0 | -57.0 | 60.0 | 5.00 | 36 |
Effects_of_interest
Cluster Table
| Height control | fdr |
|---|---|
| α | 0.001 |
| Threshold (computed) | 3.974 |
| Cluster size threshold (voxels) | 0 |
| Minimum distance (mm) | 8.0 |
| Cluster ID | X | Y | Z | Peak Stat | Cluster Size (mm3) |
|---|---|---|---|---|---|
| 1 | -33.0 | -111.0 | 56.0 | 16.62 | 7020 |
| 1a | -45.0 | -99.0 | 64.0 | 13.71 | |
| 1b | -42.0 | -111.0 | 56.0 | 13.50 | |
| 1c | -33.0 | -96.0 | 56.0 | 12.10 | |
| 2 | -45.0 | -75.0 | 56.0 | 13.99 | 1188 |
| 2a | -42.0 | -66.0 | 52.0 | 10.15 | |
| 2b | -51.0 | -84.0 | 56.0 | 9.47 | |
| 3 | -138.0 | -111.0 | 56.0 | 13.17 | 5508 |
| 3a | -126.0 | -117.0 | 56.0 | 11.96 | |
| 3b | -141.0 | -105.0 | 48.0 | 11.50 | |
| 3c | -135.0 | -96.0 | 48.0 | 11.43 | |
| 4 | -57.0 | -90.0 | 48.0 | 9.65 | 540 |
| 4a | -57.0 | -78.0 | 40.0 | 9.43 | |
| 5 | -60.0 | -72.0 | 36.0 | 8.94 | 72 |
| 6 | -48.0 | -57.0 | 56.0 | 8.58 | 1224 |
| 6a | -45.0 | -60.0 | 68.0 | 6.97 | |
| 6b | -57.0 | -57.0 | 60.0 | 5.11 | |
| 7 | -123.0 | -84.0 | 32.0 | 8.47 | 36 |
| 8 | -126.0 | -150.0 | 56.0 | 8.13 | 360 |
| 9 | -141.0 | -126.0 | 36.0 | 8.04 | 144 |
| 10 | -135.0 | -126.0 | 76.0 | 7.95 | 540 |
| 11 | -135.0 | -84.0 | 44.0 | 7.85 | 180 |
| 12 | -87.0 | -111.0 | 88.0 | 7.73 | 360 |
| 13 | -99.0 | -141.0 | 20.0 | 7.63 | 36 |
| 14 | -96.0 | -138.0 | 104.0 | 7.43 | 108 |
| 15 | -144.0 | -108.0 | 64.0 | 7.05 | 36 |
| 16 | -60.0 | -30.0 | 48.0 | 6.70 | 144 |
| 17 | -90.0 | -150.0 | 92.0 | 6.68 | 1116 |
| 18 | -114.0 | -135.0 | 20.0 | 6.64 | 36 |
| 19 | -90.0 | -129.0 | 100.0 | 6.63 | 360 |
| 20 | -117.0 | -111.0 | 56.0 | 6.57 | 72 |
| 21 | -90.0 | -114.0 | 112.0 | 6.49 | 36 |
| 22 | -126.0 | -150.0 | 80.0 | 6.45 | 288 |
| 23 | -138.0 | -117.0 | 40.0 | 6.43 | 108 |
| 24 | -120.0 | -48.0 | 68.0 | 6.43 | 216 |
| 25 | -117.0 | -114.0 | 60.0 | 6.34 | 144 |
| 26 | -117.0 | -69.0 | 80.0 | 6.34 | 72 |
| 27 | -57.0 | -75.0 | 88.0 | 6.33 | 72 |
| 28 | -138.0 | -87.0 | 48.0 | 6.31 | 36 |
| 29 | -84.0 | -135.0 | 100.0 | 6.29 | 540 |
| 30 | -57.0 | -78.0 | 16.0 | 6.29 | 72 |
| 31 | -54.0 | -72.0 | 16.0 | 6.28 | 144 |
| 32 | -93.0 | -135.0 | 108.0 | 6.26 | 108 |
| 33 | -93.0 | -147.0 | 4.0 | 6.23 | 108 |
| 34 | -108.0 | -141.0 | 0.0 | 6.19 | 252 |
| 35 | -51.0 | -63.0 | 76.0 | 6.19 | 324 |
| 35a | -60.0 | -63.0 | 72.0 | 4.47 | |
| 36 | -99.0 | -141.0 | 60.0 | 6.17 | 36 |
| 37 | -81.0 | -132.0 | 60.0 | 6.17 | 612 |
| 37a | -84.0 | -126.0 | 52.0 | 6.11 | |
| 37b | -84.0 | -123.0 | 60.0 | 4.66 | |
| 38 | -45.0 | -108.0 | 36.0 | 6.14 | 36 |
| 39 | -60.0 | -36.0 | 52.0 | 6.12 | 72 |
| 40 | -69.0 | -108.0 | 112.0 | 6.09 | 72 |
| 41 | -126.0 | -150.0 | 48.0 | 6.08 | 36 |
| 42 | -129.0 | -60.0 | 48.0 | 6.07 | 108 |
| 43 | -117.0 | -90.0 | 104.0 | 6.05 | 72 |
| 44 | -93.0 | -150.0 | 80.0 | 6.05 | 72 |
| 45 | -141.0 | -102.0 | 40.0 | 6.01 | 72 |
| 46 | -51.0 | -63.0 | 44.0 | 6.01 | 72 |
| 47 | -93.0 | -69.0 | 80.0 | 6.00 | 144 |
| 48 | -48.0 | -60.0 | 44.0 | 5.96 | 36 |
| 49 | -144.0 | -99.0 | 44.0 | 5.94 | 72 |
| 50 | -126.0 | -54.0 | 80.0 | 5.94 | 72 |
| 51 | -66.0 | -24.0 | 52.0 | 5.92 | 72 |
| 52 | -48.0 | -78.0 | 92.0 | 5.92 | 72 |
| 53 | -141.0 | -99.0 | 56.0 | 5.91 | 36 |
| 54 | -60.0 | -99.0 | 64.0 | 5.86 | 72 |
| 55 | -120.0 | -132.0 | 24.0 | 5.86 | 36 |
| 56 | -120.0 | -36.0 | 56.0 | 5.86 | 144 |
| 57 | -60.0 | -51.0 | 56.0 | 5.82 | 72 |
| 58 | -75.0 | -57.0 | 76.0 | 5.78 | 144 |
| 59 | -72.0 | -147.0 | 8.0 | 5.77 | 108 |
| 60 | -123.0 | -90.0 | 36.0 | 5.76 | 36 |
| 61 | -78.0 | -144.0 | 96.0 | 5.74 | 288 |
| 62 | -138.0 | -78.0 | 64.0 | 5.74 | 72 |
| 63 | -132.0 | -69.0 | 88.0 | 5.73 | 36 |
| 64 | -114.0 | -144.0 | 8.0 | 5.71 | 180 |
| 65 | -102.0 | -144.0 | 80.0 | 5.70 | 72 |
| 66 | -42.0 | -120.0 | 24.0 | 5.68 | 36 |
| 67 | -141.0 | -78.0 | 56.0 | 5.68 | 36 |
| 68 | -96.0 | -60.0 | 72.0 | 5.67 | 504 |
| 69 | -48.0 | -114.0 | 64.0 | 5.67 | 108 |
| 70 | -96.0 | -45.0 | 60.0 | 5.64 | 72 |
| 71 | -126.0 | -72.0 | 88.0 | 5.61 | 72 |
| 72 | -102.0 | -75.0 | 104.0 | 5.61 | 108 |
| 73 | -120.0 | -66.0 | 96.0 | 5.61 | 36 |
| 74 | -48.0 | -108.0 | 32.0 | 5.60 | 108 |
| 75 | -102.0 | -33.0 | 56.0 | 5.59 | 108 |
| 76 | -87.0 | -42.0 | 88.0 | 5.57 | 72 |
| 77 | -96.0 | -42.0 | 92.0 | 5.55 | 36 |
| 78 | -99.0 | -75.0 | 108.0 | 5.53 | 36 |
| 79 | -108.0 | -30.0 | 80.0 | 5.52 | 36 |
| 80 | -78.0 | -51.0 | 76.0 | 5.50 | 36 |
| 81 | -135.0 | -105.0 | 88.0 | 5.49 | 72 |
| 82 | -93.0 | -87.0 | 84.0 | 5.48 | 36 |
| 83 | -111.0 | -120.0 | 32.0 | 5.48 | 180 |
| 84 | -123.0 | -153.0 | 68.0 | 5.47 | 36 |
| 85 | -123.0 | -84.0 | 100.0 | 5.47 | 108 |
| 86 | -120.0 | -24.0 | 60.0 | 5.47 | 72 |
| 87 | -78.0 | -117.0 | 84.0 | 5.47 | 144 |
| 88 | -72.0 | -156.0 | 20.0 | 5.46 | 36 |
| 89 | -99.0 | -108.0 | 80.0 | 5.40 | 36 |
| 90 | -87.0 | -138.0 | 64.0 | 5.39 | 36 |
| 91 | -135.0 | -60.0 | 72.0 | 5.36 | 72 |
| 92 | -69.0 | -84.0 | 24.0 | 5.36 | 72 |
| 93 | -114.0 | -48.0 | 88.0 | 5.33 | 36 |
| 94 | -90.0 | -126.0 | 84.0 | 5.32 | 144 |
| 95 | -63.0 | -147.0 | 12.0 | 5.28 | 36 |
| 96 | -51.0 | -114.0 | 28.0 | 5.26 | 144 |
| 97 | -105.0 | -147.0 | 92.0 | 5.25 | 108 |
| 98 | -51.0 | -78.0 | 88.0 | 5.22 | 72 |
| 99 | -54.0 | -69.0 | 48.0 | 5.21 | 36 |
| 100 | -54.0 | -120.0 | 24.0 | 5.21 | 36 |
| 101 | -120.0 | -60.0 | 56.0 | 5.19 | 72 |
| 102 | -93.0 | -150.0 | 24.0 | 5.16 | 36 |
| 103 | -90.0 | -117.0 | 92.0 | 5.16 | 144 |
| 104 | -90.0 | -123.0 | 112.0 | 5.16 | 36 |
| 105 | -138.0 | -81.0 | 60.0 | 5.15 | 36 |
| 106 | -81.0 | -30.0 | 72.0 | 5.14 | 108 |
| 107 | -102.0 | -36.0 | 84.0 | 5.14 | 108 |
| 108 | -60.0 | -90.0 | 56.0 | 5.14 | 36 |
| 109 | -90.0 | -132.0 | 64.0 | 5.13 | 108 |
| 110 | -111.0 | -123.0 | 108.0 | 5.12 | 36 |
| 111 | -123.0 | -102.0 | 64.0 | 5.11 | 36 |
| 112 | -120.0 | -36.0 | 72.0 | 5.09 | 36 |
| 113 | -105.0 | -141.0 | 24.0 | 5.06 | 144 |
| 114 | -132.0 | -141.0 | 32.0 | 5.05 | 36 |
| 115 | -87.0 | -18.0 | 60.0 | 5.03 | 36 |
| 116 | -132.0 | -102.0 | 96.0 | 5.03 | 72 |
| 117 | -90.0 | -96.0 | 112.0 | 5.03 | 36 |
| 118 | -93.0 | -42.0 | 56.0 | 5.02 | 36 |
| 119 | -99.0 | -33.0 | 84.0 | 5.01 | 36 |
| 120 | -96.0 | -147.0 | 16.0 | 5.01 | 36 |
| 121 | -114.0 | -150.0 | 88.0 | 5.00 | 72 |
| 122 | -75.0 | -108.0 | 112.0 | 4.98 | 108 |
| 123 | -51.0 | -75.0 | 44.0 | 4.98 | 72 |
| 124 | -126.0 | -138.0 | 92.0 | 4.97 | 72 |
| 125 | -45.0 | -81.0 | 64.0 | 4.97 | 72 |
| 126 | -135.0 | -54.0 | 36.0 | 4.94 | 36 |
| 127 | -93.0 | -78.0 | 84.0 | 4.94 | 144 |
| 128 | -81.0 | -84.0 | 24.0 | 4.94 | 36 |
| 129 | -117.0 | -114.0 | 104.0 | 4.92 | 72 |
| 130 | -57.0 | -75.0 | 96.0 | 4.92 | 36 |
| 131 | -123.0 | -87.0 | 40.0 | 4.91 | 36 |
| 132 | -78.0 | -48.0 | 80.0 | 4.91 | 72 |
| 133 | -45.0 | -117.0 | 24.0 | 4.90 | 36 |
| 134 | -54.0 | -93.0 | 56.0 | 4.89 | 72 |
| 135 | -48.0 | -135.0 | 12.0 | 4.88 | 36 |
| 136 | -60.0 | -57.0 | 52.0 | 4.87 | 36 |
| 137 | -51.0 | -42.0 | 64.0 | 4.86 | 36 |
| 138 | -48.0 | -102.0 | 96.0 | 4.85 | 36 |
| 139 | -51.0 | -138.0 | 80.0 | 4.85 | 72 |
| 140 | -129.0 | -63.0 | 84.0 | 4.84 | 36 |
| 141 | -51.0 | -141.0 | 88.0 | 4.84 | 36 |
| 142 | -99.0 | -51.0 | 96.0 | 4.83 | 36 |
| 143 | -102.0 | -144.0 | 12.0 | 4.82 | 108 |
| 144 | -90.0 | -141.0 | 96.0 | 4.82 | 36 |
| 145 | -138.0 | -132.0 | 88.0 | 4.81 | 36 |
| 146 | -90.0 | -69.0 | 96.0 | 4.80 | 72 |
| 147 | -135.0 | -126.0 | 64.0 | 4.80 | 36 |
| 148 | -87.0 | -33.0 | 52.0 | 4.79 | 36 |
| 149 | -51.0 | -66.0 | 36.0 | 4.78 | 72 |
| 150 | -51.0 | -78.0 | 16.0 | 4.77 | 36 |
| 151 | -93.0 | -72.0 | 96.0 | 4.75 | 36 |
| 152 | -96.0 | -72.0 | 84.0 | 4.75 | 72 |
| 153 | -129.0 | -57.0 | 64.0 | 4.74 | 36 |
| 154 | -72.0 | -132.0 | 64.0 | 4.71 | 36 |
| 155 | -63.0 | -78.0 | 40.0 | 4.71 | 36 |
| 156 | -60.0 | -90.0 | 64.0 | 4.71 | 36 |
| 157 | -63.0 | -57.0 | 48.0 | 4.69 | 36 |
| 158 | -60.0 | -75.0 | 40.0 | 4.68 | 36 |
| 159 | -105.0 | -93.0 | 112.0 | 4.68 | 36 |
| 160 | -114.0 | -123.0 | 104.0 | 4.67 | 36 |
| 161 | -87.0 | -42.0 | 64.0 | 4.67 | 36 |
| 162 | -51.0 | -75.0 | 52.0 | 4.65 | 36 |
| 163 | -93.0 | -60.0 | 100.0 | 4.60 | 36 |
| 164 | -75.0 | -39.0 | 80.0 | 4.60 | 36 |
| 165 | -63.0 | -54.0 | 92.0 | 4.60 | 36 |
| 166 | -84.0 | -126.0 | 64.0 | 4.59 | 36 |
| 167 | -90.0 | -141.0 | 104.0 | 4.58 | 36 |
| 168 | -72.0 | -33.0 | 48.0 | 4.58 | 36 |
| 169 | -99.0 | -45.0 | 92.0 | 4.57 | 36 |
| 170 | -114.0 | -147.0 | 92.0 | 4.57 | 72 |
| 171 | -102.0 | -120.0 | 28.0 | 4.56 | 36 |
| 172 | -138.0 | -135.0 | 48.0 | 4.55 | 72 |
| 173 | -114.0 | -111.0 | 108.0 | 4.55 | 72 |
| 174 | -129.0 | -105.0 | 88.0 | 4.55 | 36 |
| 175 | -60.0 | -72.0 | 96.0 | 4.54 | 36 |
| 176 | -93.0 | -150.0 | 72.0 | 4.54 | 36 |
| 177 | -57.0 | -33.0 | 48.0 | 4.54 | 36 |
| 178 | -96.0 | -138.0 | 96.0 | 4.53 | 36 |
| 179 | -84.0 | -147.0 | 100.0 | 4.52 | 108 |
| 180 | -117.0 | -147.0 | 88.0 | 4.52 | 36 |
| 181 | -123.0 | -144.0 | 72.0 | 4.52 | 36 |
| 182 | -48.0 | -78.0 | 24.0 | 4.50 | 36 |
| 183 | -111.0 | -93.0 | 108.0 | 4.49 | 72 |
| 184 | -51.0 | -135.0 | 16.0 | 4.49 | 36 |
| 185 | -135.0 | -48.0 | 44.0 | 4.49 | 36 |
| 186 | -126.0 | -63.0 | 52.0 | 4.48 | 36 |
| 187 | -54.0 | -63.0 | 48.0 | 4.46 | 36 |
| 188 | -87.0 | -36.0 | 48.0 | 4.45 | 72 |
| 189 | -111.0 | -108.0 | 112.0 | 4.45 | 36 |
| 190 | -63.0 | -114.0 | 28.0 | 4.44 | 36 |
| 191 | -84.0 | -153.0 | 80.0 | 4.44 | 36 |
| 192 | -93.0 | -108.0 | 72.0 | 4.43 | 36 |
| 193 | -141.0 | -90.0 | 56.0 | 4.42 | 36 |
| 194 | -84.0 | -150.0 | 16.0 | 4.41 | 36 |
| 195 | -48.0 | -57.0 | 76.0 | 4.40 | 36 |
| 196 | -132.0 | -129.0 | 92.0 | 4.39 | 72 |
| 197 | -78.0 | -60.0 | 80.0 | 4.38 | 36 |
| 198 | -72.0 | -78.0 | 96.0 | 4.38 | 36 |
| 199 | -54.0 | -39.0 | 48.0 | 4.37 | 72 |
| 200 | -96.0 | -144.0 | 12.0 | 4.37 | 72 |
| 201 | -69.0 | -132.0 | 68.0 | 4.36 | 36 |
| 202 | -111.0 | -114.0 | 112.0 | 4.35 | 36 |
| 203 | -81.0 | -126.0 | 104.0 | 4.35 | 36 |
| 204 | -117.0 | -54.0 | 72.0 | 4.35 | 36 |
| 205 | -75.0 | -150.0 | 20.0 | 4.35 | 36 |
| 206 | -72.0 | -141.0 | 96.0 | 4.33 | 36 |
| 207 | -60.0 | -87.0 | 100.0 | 4.32 | 36 |
| 208 | -90.0 | -147.0 | 56.0 | 4.32 | 36 |
| 209 | -60.0 | -78.0 | 100.0 | 4.32 | 36 |
| 210 | -63.0 | -72.0 | 40.0 | 4.32 | 36 |
| 211 | -126.0 | -69.0 | 92.0 | 4.30 | 36 |
| 212 | -141.0 | -120.0 | 68.0 | 4.30 | 36 |
| 213 | -117.0 | -30.0 | 68.0 | 4.29 | 36 |
| 214 | -120.0 | -147.0 | 72.0 | 4.28 | 36 |
| 215 | -48.0 | -120.0 | 24.0 | 4.28 | 36 |
| 216 | -78.0 | -111.0 | 80.0 | 4.27 | 36 |
| 217 | -135.0 | -132.0 | 40.0 | 4.27 | 36 |
| 218 | -90.0 | -123.0 | 68.0 | 4.26 | 36 |
| 219 | -102.0 | -78.0 | 108.0 | 4.26 | 36 |
| 220 | -45.0 | -66.0 | 64.0 | 4.25 | 36 |
| 221 | -135.0 | -129.0 | 36.0 | 4.24 | 36 |
| 222 | -66.0 | -105.0 | 24.0 | 4.23 | 36 |
| 223 | -129.0 | -48.0 | 72.0 | 4.23 | 36 |
| 224 | -126.0 | -48.0 | 76.0 | 4.22 | 36 |
| 225 | -99.0 | -147.0 | 12.0 | 4.22 | 36 |
| 226 | -60.0 | -60.0 | 60.0 | 4.22 | 36 |
| 227 | -99.0 | -144.0 | 76.0 | 4.22 | 72 |
| 228 | -126.0 | -81.0 | 96.0 | 4.21 | 36 |
| 229 | -39.0 | -75.0 | 72.0 | 4.21 | 36 |
| 230 | -102.0 | -111.0 | 80.0 | 4.20 | 36 |
| 231 | -72.0 | -78.0 | 104.0 | 4.20 | 72 |
| 232 | -99.0 | -48.0 | 60.0 | 4.19 | 36 |
| 233 | -135.0 | -78.0 | 44.0 | 4.19 | 36 |
| 234 | -129.0 | -81.0 | 48.0 | 4.18 | 36 |
| 235 | -102.0 | -135.0 | 28.0 | 4.18 | 36 |
| 236 | -123.0 | -108.0 | 84.0 | 4.18 | 72 |
| 237 | -114.0 | -150.0 | 28.0 | 4.18 | 36 |
| 238 | -57.0 | -132.0 | 68.0 | 4.17 | 36 |
| 239 | -66.0 | -126.0 | 108.0 | 4.16 | 36 |
| 240 | -33.0 | -105.0 | 40.0 | 4.16 | 36 |
| 241 | -96.0 | -150.0 | 12.0 | 4.15 | 36 |
| 242 | -132.0 | -66.0 | 36.0 | 4.15 | 36 |
| 243 | -93.0 | -51.0 | 96.0 | 4.14 | 36 |
| 244 | -135.0 | -57.0 | 40.0 | 4.14 | 36 |
| 245 | -54.0 | -93.0 | 72.0 | 4.14 | 72 |
| 246 | -99.0 | -63.0 | 96.0 | 4.14 | 36 |
| 247 | -54.0 | -141.0 | 28.0 | 4.13 | 36 |
| 248 | -45.0 | -126.0 | 56.0 | 4.13 | 36 |
| 249 | -111.0 | -138.0 | 20.0 | 4.13 | 36 |
| 250 | -39.0 | -132.0 | 60.0 | 4.12 | 36 |
| 251 | -87.0 | -147.0 | 96.0 | 4.12 | 36 |
| 252 | -114.0 | -21.0 | 64.0 | 4.11 | 36 |
| 253 | -60.0 | -108.0 | 12.0 | 4.11 | 36 |
| 254 | -63.0 | -153.0 | 24.0 | 4.10 | 36 |
| 255 | -102.0 | -42.0 | 88.0 | 4.10 | 36 |
| 256 | -135.0 | -138.0 | 32.0 | 4.10 | 36 |
| 257 | -57.0 | -162.0 | 40.0 | 4.09 | 36 |
| 258 | -93.0 | -51.0 | 68.0 | 4.09 | 36 |
| 259 | -87.0 | -48.0 | 64.0 | 4.09 | 36 |
| 260 | -51.0 | -147.0 | 68.0 | 4.08 | 36 |
| 261 | -81.0 | -21.0 | 60.0 | 4.07 | 36 |
| 262 | -63.0 | -96.0 | 56.0 | 4.07 | 36 |
| 263 | -93.0 | -147.0 | 76.0 | 4.07 | 36 |
| 264 | -84.0 | -39.0 | 80.0 | 4.07 | 36 |
| 265 | -120.0 | -90.0 | 100.0 | 4.06 | 36 |
| 266 | -45.0 | -138.0 | 56.0 | 4.05 | 36 |
| 267 | -69.0 | -27.0 | 44.0 | 4.05 | 36 |
| 268 | -105.0 | -150.0 | 12.0 | 4.04 | 36 |
| 269 | -93.0 | -147.0 | 96.0 | 4.04 | 36 |
| 270 | -66.0 | -147.0 | 20.0 | 4.03 | 36 |
| 271 | -126.0 | -57.0 | 40.0 | 4.03 | 36 |
| 272 | -111.0 | -45.0 | 88.0 | 4.03 | 36 |
| 273 | -75.0 | -135.0 | 84.0 | 4.02 | 36 |
| 274 | -120.0 | -30.0 | 56.0 | 4.01 | 36 |
| 275 | -135.0 | -63.0 | 76.0 | 4.01 | 36 |
| 276 | -111.0 | -153.0 | 84.0 | 4.01 | 36 |
| 277 | -51.0 | -147.0 | 80.0 | 4.00 | 36 |
| 278 | -102.0 | -135.0 | 92.0 | 4.00 | 36 |
| 279 | -126.0 | -60.0 | 84.0 | 4.00 | 36 |
| 280 | -126.0 | -69.0 | 28.0 | 4.00 | 36 |
| 281 | -87.0 | -33.0 | 80.0 | 3.99 | 36 |
| 282 | -75.0 | -87.0 | 108.0 | 3.99 | 36 |
| 283 | -105.0 | -33.0 | 84.0 | 3.98 | 36 |
| 284 | -120.0 | -141.0 | 20.0 | 3.98 | 36 |
| 285 | -45.0 | -111.0 | 96.0 | 3.98 | 36 |
| 286 | -54.0 | -69.0 | 72.0 | 3.97 | 36 |
About
- Date preprocessed:
Surface GLM: empty¶
Statistical Report - First Level Model Implement the General Linear Model for single run :term:`fMRI` data.
WARNING
- No contrast passed during report generation.
- This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.
Description
Data were analyzed using Nilearn (version= 0.13.1.dev41+g36e303c92; 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
First Level ModelIn 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.
Parameters
| t_r | None | |
| slice_time_ref | 0.0 | |
| hrf_model | 'glover' | |
| drift_model | 'cosine' | |
| high_pass | 0.01 | |
| drift_order | 1 | |
| fir_delays | None | |
| min_onset | -24 | |
| mask_img | SurfaceMasker() | |
| target_affine | None | |
| target_shape | None | |
| smoothing_fwhm | None | |
| memory | None | |
| memory_level | 1 | |
| standardize | False | |
| signal_scaling | 0 | |
| noise_model | 'ar1' | |
| verbose | 0 | |
| n_jobs | 1 | |
| minimize_memory | True | |
| subject_label | None | |
| random_state | None |
SurfaceMasker()
Parameters
| mask_img | None | |
| smoothing_fwhm | None | |
| standardize | False | |
| standardize_confounds | True | |
| detrend | False | |
| high_variance_confounds | False | |
| low_pass | None | |
| high_pass | None | |
| t_r | None | |
| memory | None | |
| memory_level | 1 | |
| verbose | 0 | |
| reports | True | |
| cmap | 'inferno' | |
| clean_args | None |
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
Statistical Report - First Level Model Implement the General Linear Model for single run :term:`fMRI` data.
Description
Data were analyzed using Nilearn (version= 0.13.1.dev41+g36e303c92; 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:
- visual_computation
- visual_right_hand_button_press
- sentence_reading
- vertical_checkerboard
- horizontal_checkerboard
- visual_left_hand_button_press
- audio_computation
- audio_left_hand_button_press
- audio_right_hand_button_press
- sentence_listening
Model details
First Level ModelIn 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.
Parameters
| t_r | 2.4 | |
| slice_time_ref | 0.5 | |
| hrf_model | 'glover + derivative' | |
| drift_model | 'cosine' | |
| high_pass | 0.01 | |
| drift_order | 1 | |
| fir_delays | None | |
| min_onset | -24 | |
| mask_img | None | |
| target_affine | None | |
| target_shape | None | |
| smoothing_fwhm | None | |
| memory | None | |
| memory_level | 1 | |
| standardize | False | |
| signal_scaling | 0 | |
| noise_model | 'ar1' | |
| verbose | 1 | |
| n_jobs | 1 | |
| minimize_memory | False | |
| subject_label | None | |
| random_state | None |
Design Matrix
correlation matrix
Contrasts
Mask
The mask includes 20484 voxels (100.0 %) of the image.
Statistical Maps
(left - right) button press
Cluster Table
| Height control | None |
|---|---|
| Threshold Z | 3.0 |
| Cluster ID | Hemisphere | Peak Stat | Cluster Size (vertices) |
|---|---|---|---|
| 1 | left | 3.92 | 4 |
| 2 | right | 7.36 | 140 |
| 3 | right | 3.59 | 13 |
| 4 | right | 3.31 | 1 |
| 5 | right | 3.08 | 2 |
| 6 | right | 3.06 | 1 |
About
- Date preprocessed:
Second level report¶
Volume GLM¶
Adapted from Voxel-Based Morphometry on OASIS dataset
Statistical Report - Second Level Model
Implement the :term:`General Linear Model` for multiple subject :term:`fMRI` data.
Description
Data were analyzed using Nilearn (version= 0.13.1.dev41+g36e303c92; 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
Second Level ModelIn 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.
Parameters
| mask_img | <nibabel.nift...x7f9e1fd6d120> | |
| target_affine | None | |
| target_shape | None | |
| smoothing_fwhm | 2.0 | |
| memory | None | |
| memory_level | 1 | |
| verbose | 0 | |
| n_jobs | 1 | |
| minimize_memory | True |
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¶
Statistical Report - Second Level Model
Implement the :term:`General Linear Model` for multiple subject :term:`fMRI` data.
WARNING
- No contrast passed during report generation.
- This estimator has not been fit yet. Make sure to run `fit` before inspecting reports.
Description
Data were analyzed using Nilearn (version= 0.13.1.dev41+g36e303c92; 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
Second Level ModelIn 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.
Parameters
| mask_img | SurfaceMasker() | |
| target_affine | None | |
| target_shape | None | |
| smoothing_fwhm | None | |
| memory | None | |
| memory_level | 1 | |
| verbose | 0 | |
| n_jobs | 1 | |
| minimize_memory | True |
SurfaceMasker()
Parameters
| mask_img | None | |
| smoothing_fwhm | None | |
| standardize | False | |
| standardize_confounds | True | |
| detrend | False | |
| high_variance_confounds | False | |
| low_pass | None | |
| high_pass | None | |
| t_r | None | |
| memory | None | |
| memory_level | 1 | |
| verbose | 0 | |
| reports | True | |
| cmap | 'inferno' | |
| clean_args | None |
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.