.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_02_decoding_plot_haxby_glm_decoding.py: Decoding of a dataset after GLM fit for signal extraction ========================================================= Full step-by-step example of fitting a GLM to perform a decoding experiment. We use the data from one subject of the Haxby dataset. More specifically: 1. Download the Haxby dataset. 2. Extract the information to generate a glm representing the blocks of stimuli. 3. Analyze the decoding performance using a classifier. To run this example, you must launch IPython via ``ipython --matplotlib`` in a terminal, or use the Jupyter notebook. .. contents:: **Contents** :local: :depth: 1 Fetch example Haxby dataset ---------------------------- We download the Haxby dataset This is a study of visual object category representation .. code-block:: default # By default 2nd subject will be fetched import numpy as np import pandas as pd from nilearn import datasets haxby_dataset = datasets.fetch_haxby() # repetition has to be known TR = 2.5 Load the behavioral data ------------------------- .. code-block:: default # Load target information as string and give a numerical identifier to each behavioral = pd.read_csv(haxby_dataset.session_target[0], sep=' ') conditions = behavioral['labels'].values # Record these as an array of sessions sessions = behavioral['chunks'].values unique_sessions = behavioral['chunks'].unique() # fMRI data: a unique file for each session func_filename = haxby_dataset.func[0] Build a proper event structure for each session ------------------------------------------------ .. code-block:: default events = {} # events will take the form of a dictionary of Dataframes, one per session for session in unique_sessions: # get the condition label per session conditions_session = conditions[sessions == session] # get the number of scans per session, then the corresponding # vector of frame times n_scans = len(conditions_session) frame_times = TR * np.arange(n_scans) # each event last the full TR duration = TR * np.ones(n_scans) # Define the events object events_ = pd.DataFrame( {'onset': frame_times, 'trial_type': conditions_session, 'duration': duration}) # remove the rest condition and insert into the dictionary events[session] = events_[events_.trial_type != 'rest'] Instantiate and run FirstLevelModel ------------------------------------ We generate a list of z-maps together with their session and condition index .. code-block:: default z_maps = [] conditions_label = [] session_label = [] # Instantiate the glm from nilearn.glm.first_level import FirstLevelModel glm = FirstLevelModel(t_r=TR, mask_img=haxby_dataset.mask, high_pass=.008, smoothing_fwhm=4, memory='nilearn_cache') Run the glm on data from each session -------------------------------------- .. code-block:: default events[session].trial_type.unique() from nilearn.image import index_img for session in unique_sessions: # grab the fmri data for that particular session fmri_session = index_img(func_filename, sessions == session) # fit the glm glm.fit(fmri_session, events=events[session]) # set up contrasts: one per condition conditions = events[session].trial_type.unique() for condition_ in conditions: z_maps.append(glm.compute_contrast(condition_)) conditions_label.append(condition_) session_label.append(session) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.0s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-0.114769, ..., -2.149296], ..., [ 2.367151, ..., 0.779998]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.256013, ..., 0.308334]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.038572, ..., 0.855077]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.070285, ..., -1.222001]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.1724 , ..., 0.033508]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-2.96724 , ..., -1.474856]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.432136, ..., -1.197805]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.420626, ..., -0.443207]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.310409, ..., 0.196496]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[ 12.660587, ..., -13.536042], ..., [ -3.254408, ..., -33.842804]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.705774, ..., -0.934083]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.476069, ..., -1.29404 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.210752, ..., -1.441079]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-2.341707, ..., 2.094528]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.097247, ..., -0.496306]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.118812, ..., 1.58503 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.801345, ..., -1.648133]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.621172, ..., -0.678871]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.0s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[ 5.205584, ..., 26.587189], ..., [-6.836576, ..., 10.676956]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.3s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 1.733742, ..., -0.435687]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.502666, ..., 1.789333]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.690828, ..., 0.731519]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-2.403789, ..., 0.257657]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.472767, ..., -1.160892]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-2.07507 , ..., -2.203006]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.445359, ..., -0.36161 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.034083, ..., 0.35299 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-2.026206, ..., 5.974948], ..., [ 2.616334, ..., 0.104535]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.3s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.596132, ..., -1.910225]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.217544, ..., -0.003551]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.337751, ..., 0.789979]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.161412, ..., -0.265537]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 1.538844, ..., -0.797649]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.589617, ..., -0.790328]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.306331, ..., -0.003053]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.20477 , ..., -0.804061]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[ 53.033577, ..., -55.45955 ], ..., [-51.57195 , ..., -55.994713]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.161983, ..., -0.068078]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.467058, ..., -0.494102]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.008358, ..., 0.250096]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 1.127065, ..., -0.241985]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.066426, ..., 0.493324]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.049242, ..., -1.008997]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.336253, ..., -0.596381]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.707037, ..., 1.358865]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-27.150482, ..., -5.81308 ], ..., [-30.204891, ..., 7.417917]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.197916, ..., 1.581644]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-2.576467, ..., 0.392603]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.969252, ..., 1.15444 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.716041, ..., 1.094486]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.280537, ..., 0.542739]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.331329, ..., -0.019244]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.298501, ..., -0.355821]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.232015, ..., -0.420629]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[129.51173 , ..., -15.279282], ..., [-18.911755, ..., 21.839058]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.490124, ..., -0.665442]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.374685, ..., -0.980248]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 1.654133, ..., -0.288692]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 1.244312, ..., -1.462072]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.665202, ..., 1.793466]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.036326, ..., 0.693956]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.672634, ..., -0.24818 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.099764, ..., -1.722951]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-15.915996, ..., 22.07737 ], ..., [-16.981215, ..., 3.372383]], dtype=float32), array([[ 0. , ..., 1. ], ..., [-0.259023, ..., 1. ]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.033872, ..., -0.317176]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.98478 , ..., -0.770334]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.127461, ..., 0.929068]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.489347, ..., -0.230229]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.673052, ..., 0.6757 ]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.260282, ..., -0.346342]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.775541, ..., 1.603123]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([3.391727, ..., 0.653312]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-0.292987, ..., 18.392956], ..., [-3.935719, ..., 0.602484]], dtype=float32), array([[ 0. , ..., 1. ], ..., [-0.259023, ..., 1. ]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.4s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.620911, ..., 2.309993]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.44548 , ..., 0.576334]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.483355, ..., -0.068969]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.166576, ..., 0.713549]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.612744, ..., 1.441012]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.254934, ..., -1.288018]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.134169, ..., -0.621367]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.234171, ..., -1.497943]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-5.223948, ..., -5.959582], ..., [-7.677519, ..., 16.024363]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.3s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.01344 , ..., 1.283233]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.79114 , ..., 1.031069]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.583575, ..., 0.488828]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.265741, ..., -0.623721]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.20672, ..., 1.09668]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-2.045178, ..., 0.822339]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-2.463833, ..., -0.151504]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.230311, ..., 0.198537]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-19.66533 , ..., -6.299562], ..., [-24.647343, ..., 2.331865]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.3s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.080165, ..., -3.497239]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-3.083184, ..., -3.071235]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.724401, ..., -2.892927]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.044506, ..., 0.285176]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.257372, ..., 1.802962]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.485481, ..., 0.284696]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.032135, ..., -1.290339]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.417132, ..., -1.258094]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask... filter_and_mask(, , { 'detrend': False, 'dtype': None, 'high_pass': None, 'high_variance_confounds': False, 'low_pass': None, 'reports': True, 'runs': None, 'smoothing_fwhm': 4, 'standardize': False, 'standardize_confounds': True, 't_r': 2.5, 'target_affine': None, 'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None) __________________________________________________filter_and_mask - 1.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.glm.first_level.first_level.run_glm... run_glm(array([[-1.095605, ..., 16.449202], ..., [ 2.59974 , ..., -2.179998]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.545625, ..., -1.041515]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.152042, ..., 1.145641]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.55236 , ..., 0.696758]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.684509, ..., 0.226524]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.423531, ..., -0.641954]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.726779, ..., 0.687642]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.809306, ..., 0.496974]), ) ___________________________________________________________unmask - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.116436, ..., -0.811166]), ) ___________________________________________________________unmask - 0.1s, 0.0min Generating a report -------------------- Since we have already computed the FirstLevelModel and have the contrast, we can quickly create a summary report. .. code-block:: default from nilearn.image import mean_img from nilearn.reporting import make_glm_report mean_img_ = mean_img(func_filename) report = make_glm_report(glm, contrasts=conditions, bg_img=mean_img_, ) report # This report can be viewed in a notebook .. raw:: html

Statistical Report for bottle, cat, chair, face, house, scissors, scrambledpix, shoe

First Level Model

Model details:

drift_model cosine
drift_order 1
fir_delays [0]
high_pass (Hz) 0.008
hrf_model glover
noise_model ar1
scaling_axis 0
signal_scaling True
slice_time_ref 0
smoothing_fwhm 4
standardize False
subject_label None
t_r (s) 2.5
target_affine None
target_shape None

Design Matrix:

Plot of Design Matrix used in Session 1.

Contrasts

Plot of the contrast: bottle. Plot of the contrast: house. Plot of the contrast: chair. Plot of the contrast: scrambledpix. Plot of the contrast: face. Plot of the contrast: shoe. Plot of the contrast: cat. Plot of the contrast: scissors.

Mask

Model did not supply a mask image.

Stat Maps with Cluster Tables

bottle

Stat map plot for the contrast: bottle
Contrast Plot Plot of the contrast: bottle.

Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.3
Cluster size threshold (voxels) 0
Minimum distance (mm) 8

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -36.75 -46.88 -46.88 6.61 147
2 -54.25 13.12 5.62 6.47 98
3 -1.75 -39.38 13.12 6.45 196
4 -57.75 -39.38 9.38 6.17 246
5 -40.25 -35.62 -35.62 6.12 393
6 33.25 -43.12 -43.12 6.11 1722
6a 22.75 -35.62 -43.12 5.78
6b 15.75 -46.88 -39.38 4.33
6c 12.25 -46.88 -46.88 4.11
7 -19.25 -24.38 -43.12 5.99 492
7a -19.25 -31.88 -39.38 5.47
8 29.75 -20.62 -24.38 5.98 393
9 -43.75 28.12 -9.38 5.96 196
10 8.75 -50.62 -35.62 5.88 492
11 19.25 -54.38 -43.12 5.78 1476
11a 12.25 -58.12 -28.12 5.44
11b 8.75 -58.12 -39.38 4.44
12 40.25 16.88 -20.62 5.73 49
13 22.75 -46.88 -61.88 5.70 492
13a 22.75 -58.12 -61.88 5.38
14 26.25 -46.88 -46.88 5.69 147
15 -1.75 -50.62 -39.38 5.66 147
16 -26.25 -16.88 -24.38 5.64 147
17 12.25 -28.12 -73.12 5.60 246
18 -50.75 35.62 -16.88 5.58 246
19 -33.25 -73.12 31.88 5.56 49
20 -15.75 -28.12 -43.12 5.55 49
21 19.25 -76.88 -13.12 5.50 492
22 -12.25 -24.38 -76.88 5.46 196
23 -47.25 -13.12 13.12 5.42 98
24 26.25 -20.62 -13.12 5.41 344
25 -29.75 9.38 -28.12 5.38 246
26 -57.75 -16.88 1.88 5.34 98
27 22.75 -43.12 9.38 5.33 246
28 -47.25 -61.88 -5.62 5.29 98
29 -40.25 -39.38 31.88 5.27 344
29a -29.75 -39.38 31.88 4.36
30 -19.25 -50.62 -39.38 5.26 147
31 -12.25 5.62 -16.88 5.25 49
32 -29.75 -43.12 -50.62 5.24 393
33 8.75 -91.88 5.62 5.24 49
34 1.75 50.62 -16.88 5.19 344
35 -50.75 -50.62 20.62 5.17 147
36 43.75 -50.62 -5.62 5.17 935
36a 47.25 -43.12 -1.88 4.41
36b 50.75 -50.62 -5.62 4.15
37 26.25 9.38 -16.88 5.16 196
38 5.25 -76.88 24.38 5.15 49
39 -29.75 -84.38 1.88 5.14 49
40 26.25 -69.38 -16.88 5.14 246
41 -54.25 -31.88 -13.12 5.14 49
42 -47.25 -9.38 5.62 5.10 295
43 33.25 5.62 -13.12 5.10 246
44 -40.25 24.38 -9.38 5.08 49
45 12.25 -84.38 -28.12 5.07 98
46 -29.75 -80.62 -24.38 5.05 49
47 -54.25 -46.88 -5.62 5.02 98
48 26.25 31.88 -31.88 5.02 147
49 12.25 -13.12 -28.12 5.02 98
50 29.75 1.88 -9.38 5.01 98
51 -47.25 -39.38 -20.62 5.01 885
51a -50.75 -39.38 -9.38 4.50
52 -19.25 -58.12 -9.38 5.00 196
53 33.25 20.62 -20.62 5.00 98
54 5.25 -61.88 -20.62 4.97 295
55 -33.25 13.12 28.12 4.93 246
56 -47.25 -28.12 13.12 4.90 147
57 1.75 -43.12 -1.88 4.90 49
58 -22.75 -58.12 -46.88 4.89 147
59 -15.75 -76.88 28.12 4.88 196
60 33.25 9.38 -24.38 4.88 98
61 -61.25 -5.62 20.62 4.87 147
62 -40.25 -20.62 -31.88 4.87 49
63 -50.75 16.88 -1.88 4.86 147
64 -15.75 -50.62 -1.88 4.86 49
65 40.25 -35.62 -24.38 4.86 2067
65a 43.75 -28.12 -9.38 4.70
65b 40.25 -20.62 -13.12 4.57
65c 47.25 -35.62 -20.62 4.13
66 22.75 -20.62 -35.62 4.84 98
67 36.75 28.12 -35.62 4.84 49
68 -15.75 16.88 43.12 4.82 147
69 -43.75 50.62 16.88 4.82 49
70 12.25 -65.62 -24.38 4.81 246
71 -47.25 -28.12 5.62 4.81 98
72 1.75 -58.12 -24.38 4.80 98
73 29.75 -50.62 -50.62 4.77 196
74 19.25 -43.12 -20.62 4.75 98
75 36.75 -65.62 -13.12 4.75 344
76 -1.75 -58.12 -54.38 4.73 49
77 19.25 35.62 28.12 4.72 49
78 -40.25 -61.88 -16.88 4.70 295
79 -22.75 1.88 -1.88 4.69 49
80 22.75 -16.88 -9.38 4.69 98
81 33.25 31.88 -9.38 4.69 98
82 -8.75 -46.88 -65.62 4.68 246
83 5.25 -73.12 16.88 4.67 344
83a -1.75 -65.62 16.88 3.95
84 22.75 -20.62 -28.12 4.67 295
84a 12.25 -20.62 -31.88 4.46
85 -15.75 -1.88 -69.38 4.65 49
86 -47.25 35.62 16.88 4.65 196
87 12.25 20.62 35.62 4.64 49
88 -26.25 -20.62 -13.12 4.64 98
89 -29.75 13.12 -13.12 4.63 147
90 -15.75 -31.88 -61.88 4.63 49
91 -47.25 -54.38 -9.38 4.62 344
92 50.75 43.12 -5.62 4.61 49
93 40.25 31.88 13.12 4.59 49
94 12.25 -54.38 -46.88 4.59 98
95 -15.75 -39.38 -50.62 4.58 49
96 -5.25 -31.88 58.12 4.57 49
97 -1.75 58.12 13.12 4.57 49
98 22.75 -35.62 54.38 4.57 246
99 -12.25 -58.12 -16.88 4.56 49
100 -12.25 -13.12 -46.88 4.55 246
101 -19.25 -76.88 -28.12 4.55 147
102 8.75 -20.62 -13.12 4.55 147
103 1.75 -76.88 35.62 4.55 49
104 -36.75 -76.88 1.88 4.55 246
105 -61.25 -20.62 13.12 4.54 49
106 -1.75 -9.38 -20.62 4.54 49
107 -5.25 -13.12 -69.38 4.53 49
108 -57.75 -9.38 9.38 4.53 295
108a -61.25 -1.88 9.38 4.20
109 -47.25 -28.12 43.12 4.52 49
110 -47.25 16.88 5.62 4.52 98
111 8.75 -16.88 46.88 4.52 49
112 -12.25 -58.12 -46.88 4.52 49
113 -36.75 43.12 24.38 4.51 98
114 -22.75 -76.88 -16.88 4.51 49
115 -29.75 -58.12 -9.38 4.51 49
116 1.75 13.12 9.38 4.50 49
117 15.75 -1.88 -16.88 4.50 196
118 33.25 -9.38 -58.12 4.50 98
119 50.75 -5.62 5.62 4.49 49
120 33.25 -50.62 -20.62 4.49 246
121 -50.75 9.38 -16.88 4.48 196
122 40.25 35.62 -1.88 4.48 49
123 8.75 -50.62 -20.62 4.48 49
124 -1.75 -46.88 1.88 4.46 98
125 -47.25 20.62 -13.12 4.46 49
126 15.75 9.38 43.12 4.46 98
127 -40.25 -65.62 -5.62 4.45 49
128 -15.75 9.38 -20.62 4.45 98
129 12.25 -9.38 13.12 4.45 49
130 50.75 1.88 43.12 4.44 98
131 -26.25 -54.38 46.88 4.43 98
132 -15.75 -76.88 20.62 4.43 49
133 -50.75 -16.88 24.38 4.42 49
134 -50.75 -5.62 -5.62 4.42 49
135 40.25 5.62 -24.38 4.42 49
136 -50.75 9.38 9.38 4.41 49
137 -50.75 16.88 -13.12 4.41 147
138 -40.25 9.38 -28.12 4.41 147
139 19.25 -1.88 28.12 4.38 49
140 33.25 24.38 39.38 4.38 49
141 -15.75 24.38 -9.38 4.38 49
142 22.75 -61.88 -46.88 4.38 147
143 -40.25 -46.88 1.88 4.38 147
144 -12.25 54.38 -9.38 4.37 98
145 26.25 28.12 -20.62 4.37 98
146 57.75 35.62 28.12 4.37 98
147 5.25 -88.12 -1.88 4.37 49
148 -15.75 13.12 50.62 4.36 98
149 -36.75 28.12 31.88 4.36 246
150 68.25 -13.12 -9.38 4.36 246
150a 68.25 -1.88 -9.38 4.29
151 1.75 -5.62 -13.12 4.35 49
152 -47.25 -24.38 -16.88 4.35 98
153 -26.25 13.12 46.88 4.35 49
154 5.25 -35.62 -58.12 4.35 98
155 36.75 -73.12 5.62 4.34 98
156 -33.25 -9.38 -13.12 4.34 344
157 -15.75 -9.38 28.12 4.34 295
158 -19.25 -54.38 -61.88 4.33 196
159 -8.75 5.62 -24.38 4.33 49
160 19.25 -35.62 69.38 4.32 49
161 -29.75 -80.62 -9.38 4.32 49
162 19.25 -9.38 20.62 4.32 49
163 -1.75 -1.88 1.88 4.32 196
164 -15.75 -54.38 43.12 4.31 49
165 -40.25 -28.12 -20.62 4.31 49
166 19.25 -31.88 16.88 4.31 49
167 15.75 -1.88 -1.88 4.31 49
168 -15.75 -16.88 24.38 4.30 98
169 -36.75 58.12 35.62 4.29 49
170 12.25 -80.62 13.12 4.29 147
171 -1.75 -24.38 9.38 4.29 49
172 -1.75 -54.38 -9.38 4.28 98
173 -43.75 43.12 -28.12 4.28 98
174 5.25 -58.12 -54.38 4.28 49
175 54.25 -24.38 -5.62 4.28 344
176 -57.75 -31.88 -5.62 4.28 49
177 15.75 50.62 58.12 4.27 98
178 36.75 -61.88 -5.62 4.27 49
179 33.25 -50.62 -28.12 4.27 49
180 -5.25 31.88 -20.62 4.26 147
181 15.75 -35.62 31.88 4.26 98
182 -47.25 -65.62 1.88 4.26 196
183 -54.25 -20.62 -24.38 4.25 49
184 26.25 -80.62 -9.38 4.25 49
185 22.75 16.88 5.62 4.24 147
186 5.25 -65.62 -13.12 4.23 196
187 -47.25 24.38 20.62 4.23 98
188 -47.25 -58.12 13.12 4.23 98
189 40.25 9.38 -20.62 4.23 98
190 19.25 28.12 31.88 4.23 49
191 12.25 50.62 -1.88 4.23 98
192 47.25 -54.38 -16.88 4.23 49
193 12.25 61.88 5.62 4.22 49
194 19.25 -9.38 5.62 4.22 49
195 -57.75 -31.88 24.38 4.22 98
196 -19.25 -20.62 -35.62 4.21 98
197 19.25 -84.38 -20.62 4.21 49
198 12.25 43.12 -16.88 4.21 49
199 -1.75 -9.38 -46.88 4.21 49
200 15.75 -24.38 -35.62 4.21 246
201 -5.25 -24.38 -20.62 4.20 147
202 -1.75 -50.62 -1.88 4.20 49
203 15.75 -73.12 -31.88 4.20 49
204 -47.25 -28.12 28.12 4.20 49
205 36.75 -31.88 -9.38 4.20 246
206 12.25 5.62 43.12 4.19 49
207 -1.75 76.88 5.62 4.19 49
208 -29.75 20.62 -24.38 4.18 49
209 47.25 -54.38 -1.88 4.18 49
210 1.75 -5.62 -54.38 4.18 49
211 40.25 -58.12 5.62 4.17 98
212 26.25 5.62 16.88 4.17 246
213 19.25 -5.62 43.12 4.17 98
214 -22.75 -69.38 -13.12 4.16 196
215 -1.75 -58.12 -28.12 4.16 98
216 19.25 65.62 9.38 4.16 98
217 -61.25 -16.88 9.38 4.16 49
218 36.75 43.12 -28.12 4.15 49
219 -1.75 -39.38 1.88 4.15 98
220 12.25 1.88 28.12 4.15 147
221 -15.75 -20.62 -58.12 4.15 49
222 29.75 -28.12 -24.38 4.15 49
223 19.25 16.88 28.12 4.15 49
224 40.25 -35.62 -43.12 4.15 49
225 -22.75 -50.62 54.38 4.15 49
226 -22.75 16.88 5.62 4.15 49
227 5.25 -80.62 -1.88 4.14 49
228 -36.75 20.62 35.62 4.14 246
229 -26.25 -73.12 -35.62 4.14 98
230 -43.75 24.38 -1.88 4.14 98
231 -8.75 9.38 24.38 4.13 196
232 8.75 20.62 -24.38 4.13 147
233 -57.75 5.62 13.12 4.13 98
234 26.25 -5.62 -1.88 4.12 49
235 -54.25 1.88 13.12 4.12 49
236 -33.25 -31.88 -65.62 4.12 98
237 -8.75 -46.88 -31.88 4.12 49
238 -1.75 -50.62 -24.38 4.12 147
239 -12.25 54.38 54.38 4.11 98
240 -47.25 46.88 20.62 4.11 49
241 -15.75 -1.88 16.88 4.10 49
242 -29.75 24.38 13.12 4.10 98
243 -57.75 -46.88 -1.88 4.10 246
244 -50.75 -20.62 9.38 4.10 98
245 19.25 -13.12 28.12 4.10 49
246 22.75 46.88 -16.88 4.10 49
247 19.25 -39.38 -13.12 4.09 49
248 -19.25 65.62 39.38 4.09 49
249 -1.75 -9.38 -5.62 4.09 49
250 -8.75 -39.38 -31.88 4.08 49
251 -40.25 -16.88 -50.62 4.08 49
252 -43.75 -39.38 -16.88 4.07 147
253 19.25 13.12 -1.88 4.07 98
254 22.75 5.62 -1.88 4.07 49
255 -8.75 -39.38 -39.38 4.06 442
255a -12.25 -46.88 -43.12 3.77
256 -19.25 61.88 5.62 4.06 49
257 54.25 -20.62 39.38 4.06 49
258 54.25 -9.38 20.62 4.06 147
259 22.75 -69.38 1.88 4.06 147
260 -19.25 -5.62 -24.38 4.04 393
261 -1.75 1.88 13.12 4.04 49
262 -40.25 16.88 24.38 4.03 98
263 33.25 5.62 28.12 4.03 49
264 -36.75 43.12 1.88 4.02 147
265 19.25 -69.38 -13.12 4.02 49
266 12.25 -9.38 5.62 4.02 49
267 12.25 -88.12 5.62 4.02 98
268 -26.25 -76.88 35.62 4.02 49
269 -47.25 -43.12 9.38 4.02 147
270 -50.75 39.38 9.38 4.02 49
271 33.25 -24.38 -13.12 4.01 49
272 40.25 -43.12 -28.12 4.01 49
273 29.75 -24.38 -16.88 4.01 49
274 26.25 -20.62 -65.62 4.00 98
275 -43.75 20.62 20.62 4.00 49
276 -15.75 -73.12 -13.12 4.00 49
277 -1.75 13.12 -20.62 3.99 98
278 57.75 -39.38 5.62 3.99 49
279 -29.75 -65.62 -20.62 3.99 49
280 -19.25 -50.62 16.88 3.99 49
281 5.25 -61.88 -46.88 3.98 49
282 22.75 31.88 9.38 3.98 49
283 -19.25 -65.62 -20.62 3.98 49
284 15.75 -16.88 -58.12 3.98 49
285 19.25 -13.12 -28.12 3.98 98
286 -47.25 -13.12 -54.38 3.97 98
287 -47.25 -39.38 16.88 3.97 49
288 1.75 -1.88 31.88 3.97 98
289 36.75 -39.38 -69.38 3.97 49
290 -1.75 -35.62 -61.88 3.96 98
291 -8.75 39.38 5.62 3.96 49
292 26.25 20.62 -24.38 3.96 98
293 -5.25 -84.38 -20.62 3.96 49
294 8.75 76.88 35.62 3.95 147
295 -22.75 31.88 -24.38 3.95 49
296 -26.25 46.88 5.62 3.95 147
297 -22.75 -9.38 13.12 3.95 49
298 -33.25 13.12 13.12 3.94 98
299 5.25 -43.12 -9.38 3.94 49
300 -47.25 -13.12 35.62 3.93 49
301 -68.25 -24.38 1.88 3.93 147
302 -8.75 -54.38 -31.88 3.93 49
303 -54.25 -28.12 -16.88 3.92 49
304 50.75 -54.38 -9.38 3.92 49
305 68.25 -31.88 9.38 3.91 196
306 12.25 -1.88 -69.38 3.91 98
307 19.25 16.88 16.88 3.91 147
308 -33.25 -16.88 -5.62 3.91 49
309 5.25 -28.12 -9.38 3.91 49
310 -5.25 46.88 54.38 3.90 98
311 -43.75 16.88 58.12 3.90 49
312 -33.25 1.88 -16.88 3.90 147
313 15.75 88.12 9.38 3.90 98
314 5.25 -65.62 54.38 3.90 147
315 -64.75 -13.12 -24.38 3.90 49
316 29.75 -43.12 -58.12 3.90 49
317 1.75 -43.12 -16.88 3.90 295
317a 12.25 -39.38 -13.12 3.81
318 -47.25 -58.12 -28.12 3.90 49
319 33.25 5.62 35.62 3.89 98
320 50.75 -13.12 5.62 3.89 49
321 19.25 -24.38 16.88 3.89 49
322 -33.25 -69.38 39.38 3.89 49
323 15.75 -73.12 -20.62 3.89 49
324 64.75 -43.12 5.62 3.89 49
325 15.75 -46.88 20.62 3.88 49
326 -36.75 -31.88 -46.88 3.88 49
327 -12.25 -20.62 -61.88 3.88 49
328 -15.75 -73.12 39.38 3.88 98
329 -43.75 50.62 28.12 3.87 49
330 12.25 -13.12 31.88 3.87 98
331 19.25 -84.38 24.38 3.87 49
332 -5.25 -46.88 -16.88 3.87 147
333 -8.75 -65.62 -43.12 3.87 49
334 1.75 -58.12 9.38 3.87 49
335 -47.25 1.88 -28.12 3.87 49
336 8.75 46.88 -5.62 3.87 49
337 26.25 -80.62 1.88 3.87 49
338 -33.25 35.62 -24.38 3.86 98
339 -19.25 -13.12 61.88 3.86 49
340 12.25 -35.62 73.12 3.86 98
341 43.75 31.88 5.62 3.86 49
342 -57.75 1.88 -13.12 3.86 98
343 8.75 13.12 50.62 3.85 98
344 -26.25 5.62 -13.12 3.85 49
345 8.75 -24.38 9.38 3.85 49
346 -12.25 -20.62 -46.88 3.85 49
347 -26.25 1.88 43.12 3.85 98
348 12.25 24.38 61.88 3.85 49
349 -47.25 -61.88 20.62 3.85 49
350 19.25 -58.12 -13.12 3.85 49
351 -22.75 -5.62 -1.88 3.85 49
352 26.25 -61.88 31.88 3.85 49
353 -29.75 35.62 -9.38 3.85 49
354 47.25 13.12 -20.62 3.85 49
355 1.75 -9.38 -50.62 3.85 49
356 -15.75 -50.62 -31.88 3.84 196
357 -12.25 -31.88 -50.62 3.84 196
358 50.75 50.62 -1.88 3.84 49
359 29.75 -50.62 54.38 3.83 147
360 61.25 1.88 24.38 3.83 49
361 1.75 -31.88 1.88 3.82 49
362 36.75 -54.38 -20.62 3.82 49
363 26.25 -16.88 -58.12 3.82 49
364 -15.75 -46.88 -5.62 3.81 49
365 47.25 20.62 5.62 3.81 49
366 22.75 -28.12 69.38 3.81 98
367 40.25 -20.62 20.62 3.81 49
368 -43.75 -65.62 24.38 3.81 49
369 -19.25 -61.88 20.62 3.81 49
370 15.75 16.88 -16.88 3.80 49
371 29.75 -31.88 39.38 3.80 147
372 -12.25 46.88 28.12 3.80 49
373 12.25 69.38 5.62 3.80 49
374 15.75 -20.62 -20.62 3.80 49
375 22.75 -24.38 1.88 3.79 49
376 -29.75 -20.62 -69.38 3.79 49
377 -47.25 -1.88 -20.62 3.79 49
378 43.75 -69.38 -1.88 3.79 49
379 5.25 -28.12 73.12 3.79 49
380 19.25 35.62 1.88 3.78 98
381 47.25 13.12 5.62 3.78 98
382 -15.75 -5.62 61.88 3.78 98
383 -8.75 84.38 20.62 3.77 147
384 26.25 24.38 13.12 3.77 98
385 19.25 -9.38 -5.62 3.77 49
386 15.75 -16.88 5.62 3.77 49
387 -1.75 -28.12 58.12 3.76 49
388 -29.75 -1.88 -1.88 3.75 49
389 26.25 1.88 13.12 3.75 49
390 29.75 -58.12 -46.88 3.74 49
391 -40.25 35.62 31.88 3.74 49
392 36.75 50.62 1.88 3.74 98
393 -1.75 80.62 1.88 3.74 49
394 -57.75 -9.38 16.88 3.74 147
395 40.25 -9.38 -31.88 3.74 49
396 1.75 24.38 31.88 3.73 49
397 33.25 43.12 35.62 3.73 49
398 43.75 9.38 -39.38 3.73 49
399 26.25 13.12 9.38 3.73 49
400 33.25 -9.38 -9.38 3.73 49
401 40.25 -28.12 -1.88 3.73 49
402 22.75 -76.88 -35.62 3.73 98
403 -8.75 -46.88 -24.38 3.73 49
404 1.75 -20.62 -35.62 3.73 49
405 19.25 54.38 1.88 3.73 49
406 -26.25 -31.88 16.88 3.72 49
407 47.25 31.88 -24.38 3.72 49
408 -54.25 13.12 20.62 3.72 147
409 -33.25 -43.12 -61.88 3.72 49
410 19.25 -16.88 16.88 3.72 49
411 -19.25 -84.38 1.88 3.72 49
412 47.25 16.88 -39.38 3.72 49
413 40.25 -31.88 54.38 3.71 98
414 61.25 -31.88 39.38 3.71 49
415 -54.25 -24.38 -39.38 3.71 49
416 26.25 -20.62 65.62 3.71 49
417 36.75 -73.12 -9.38 3.71 49
418 29.75 -1.88 -20.62 3.71 49
419 15.75 -31.88 -50.62 3.71 49
420 -15.75 5.62 65.62 3.71 49
421 1.75 9.38 -16.88 3.70 49
422 -22.75 -20.62 -31.88 3.70 49
423 8.75 -73.12 35.62 3.70 49
424 36.75 -54.38 -28.12 3.70 49
425 43.75 -39.38 -1.88 3.70 49
426 -33.25 -35.62 -9.38 3.70 49
427 -50.75 28.12 46.88 3.69 49
428 -15.75 -43.12 1.88 3.69 49
429 -1.75 -43.12 -31.88 3.69 98
430 50.75 -16.88 28.12 3.68 49
431 -15.75 20.62 -43.12 3.68 98
432 43.75 -5.62 -13.12 3.68 49
433 29.75 -16.88 -61.88 3.68 49
434 57.75 -20.62 28.12 3.68 49
435 -47.25 46.88 28.12 3.68 49
436 15.75 -20.62 73.12 3.68 49
437 1.75 65.62 -16.88 3.68 98
438 -22.75 13.12 -24.38 3.67 49
439 -33.25 16.88 50.62 3.67 98
440 -15.75 -43.12 -43.12 3.67 98
441 -12.25 -9.38 -24.38 3.67 49
442 -1.75 20.62 -16.88 3.67 98
443 -26.25 -28.12 -31.88 3.67 49
444 -15.75 -69.38 28.12 3.66 49
445 -47.25 9.38 -9.38 3.66 49
446 -15.75 -1.88 -31.88 3.66 49
447 -29.75 -1.88 -24.38 3.66 98
448 26.25 1.88 -13.12 3.66 49
449 33.25 -24.38 -58.12 3.66 49
450 -40.25 -58.12 9.38 3.66 98
451 1.75 20.62 -39.38 3.65 98
452 47.25 16.88 39.38 3.65 49
453 -26.25 -35.62 54.38 3.65 49
454 22.75 -16.88 43.12 3.65 49
455 12.25 24.38 -39.38 3.65 49
456 -1.75 -16.88 -28.12 3.64 49
457 33.25 -39.38 -58.12 3.64 49
458 26.25 -20.62 20.62 3.64 49
459 19.25 5.62 -13.12 3.64 49
460 33.25 43.12 24.38 3.63 49
461 19.25 -9.38 35.62 3.63 49
462 -54.25 -20.62 -9.38 3.63 49
463 -5.25 -39.38 5.62 3.63 49
464 22.75 -54.38 61.88 3.62 49
465 19.25 -35.62 -20.62 3.62 147
466 -22.75 -80.62 1.88 3.62 49
467 40.25 -35.62 50.62 3.62 49
468 22.75 -1.88 5.62 3.62 49
469 43.75 -69.38 9.38 3.61 98
470 33.25 -58.12 -16.88 3.61 49
471 8.75 -65.62 20.62 3.61 49
472 26.25 -9.38 13.12 3.61 98
473 1.75 65.62 -9.38 3.61 49
474 -12.25 88.12 16.88 3.61 49
475 -47.25 61.88 1.88 3.61 49
476 -26.25 13.12 13.12 3.61 49
477 -33.25 -1.88 13.12 3.61 49
478 -8.75 -61.88 -46.88 3.61 49
479 40.25 28.12 -39.38 3.61 49
480 43.75 24.38 -5.62 3.61 49
481 -50.75 -58.12 1.88 3.61 49
482 26.25 -76.88 -16.88 3.60 49
483 -1.75 -9.38 -28.12 3.60 49
484 -5.25 -54.38 -54.38 3.60 49
485 -47.25 -9.38 43.12 3.60 49
486 -22.75 43.12 -28.12 3.59 49
487 -36.75 -16.88 -46.88 3.59 49
488 19.25 -31.88 -16.88 3.58 49
489 47.25 39.38 13.12 3.58 98
490 29.75 16.88 35.62 3.58 49
491 22.75 46.88 13.12 3.58 49
492 -29.75 -80.62 20.62 3.58 49
493 68.25 -28.12 16.88 3.57 49
494 -19.25 43.12 -1.88 3.57 49
495 19.25 -24.38 -39.38 3.57 49
496 -8.75 -9.38 -20.62 3.57 49
497 26.25 -80.62 -28.12 3.57 49
498 15.75 20.62 43.12 3.56 49
499 19.25 -46.88 31.88 3.56 49
500 22.75 73.12 5.62 3.56 49
501 50.75 -43.12 9.38 3.56 49
502 -57.75 -24.38 5.62 3.56 49
503 -1.75 -9.38 31.88 3.56 49
504 -15.75 -50.62 39.38 3.56 49
505 -57.75 39.38 16.88 3.56 98
506 12.25 -9.38 50.62 3.56 49
507 -12.25 -13.12 -73.12 3.55 98
508 -26.25 -61.88 31.88 3.55 49
509 -33.25 39.38 54.38 3.55 49
510 54.25 1.88 35.62 3.55 49
511 29.75 -31.88 -31.88 3.55 49
512 -33.25 65.62 20.62 3.55 49
513 -54.25 -9.38 -5.62 3.54 98
514 36.75 50.62 -16.88 3.54 49
515 15.75 28.12 -39.38 3.54 49
516 1.75 -1.88 -20.62 3.53 49
517 12.25 -65.62 1.88 3.53 49
518 29.75 65.62 43.12 3.53 49
519 47.25 -35.62 16.88 3.53 49
520 -40.25 -50.62 28.12 3.53 49
521 33.25 -28.12 65.62 3.53 49
522 5.25 5.62 -69.38 3.53 49
523 26.25 -58.12 -13.12 3.53 49
524 -1.75 -1.88 -5.62 3.52 49
525 -1.75 -46.88 -65.62 3.52 49
526 12.25 -54.38 -54.38 3.52 49
527 -5.25 43.12 -16.88 3.52 49
528 12.25 -9.38 -24.38 3.52 49
529 -50.75 -1.88 16.88 3.52 49
530 12.25 -35.62 9.38 3.52 49
531 36.75 35.62 16.88 3.52 147
532 19.25 20.62 20.62 3.52 49
533 -15.75 -69.38 -43.12 3.52 49
534 -33.25 -58.12 -1.88 3.52 49
535 -40.25 -69.38 31.88 3.52 49
536 -54.25 -13.12 13.12 3.51 98
537 8.75 28.12 -24.38 3.51 49
538 36.75 -24.38 -31.88 3.50 49
539 -36.75 -13.12 9.38 3.50 49
540 -54.25 -20.62 -50.62 3.50 49
541 36.75 20.62 31.88 3.50 49
542 8.75 -76.88 9.38 3.50 49
543 -47.25 -24.38 -5.62 3.49 49
544 -5.25 -61.88 20.62 3.49 49
545 -36.75 61.88 9.38 3.49 49
546 15.75 76.88 35.62 3.49 49
547 -50.75 -16.88 -13.12 3.49 98
548 5.25 -73.12 -16.88 3.49 49
549 15.75 28.12 -5.62 3.49 49
550 19.25 -28.12 -24.38 3.49 49
551 19.25 -50.62 -73.12 3.49 49
552 -36.75 35.62 -13.12 3.49 49
553 1.75 -16.88 -24.38 3.49 49
554 -29.75 35.62 -1.88 3.48 49
555 12.25 -84.38 -5.62 3.48 49
556 33.25 -35.62 -5.62 3.48 49
557 50.75 39.38 -9.38 3.48 49
558 22.75 -5.62 20.62 3.48 49
559 -50.75 46.88 9.38 3.48 98
560 54.25 1.88 -31.88 3.48 49
561 22.75 -9.38 -50.62 3.48 49
562 40.25 -31.88 -58.12 3.48 49
563 40.25 -76.88 -9.38 3.47 49
564 -36.75 -31.88 20.62 3.47 49
565 36.75 1.88 20.62 3.47 49
566 40.25 -9.38 1.88 3.47 49
567 22.75 13.12 35.62 3.47 98
568 33.25 5.62 50.62 3.47 49
569 -50.75 5.62 -9.38 3.47 49
570 40.25 -35.62 -1.88 3.47 49
571 -50.75 -9.38 -13.12 3.46 49
572 -29.75 24.38 5.62 3.46 49
573 -26.25 -35.62 -9.38 3.46 49
574 -15.75 -16.88 -28.12 3.46 98
575 -47.25 -50.62 5.62 3.46 49
576 -36.75 -69.38 5.62 3.46 49
577 26.25 -65.62 46.88 3.45 49
578 22.75 5.62 5.62 3.45 98
579 19.25 -5.62 13.12 3.45 49
580 -5.25 9.38 9.38 3.45 98
581 -33.25 1.88 31.88 3.45 49
582 -19.25 -50.62 -46.88 3.45 49
583 -1.75 -16.88 -58.12 3.45 49
584 36.75 24.38 -13.12 3.45 49
585 -1.75 -35.62 -13.12 3.45 49
586 -15.75 9.38 35.62 3.45 147
587 -43.75 -35.62 -9.38 3.45 49
588 22.75 -9.38 43.12 3.45 49
589 33.25 50.62 24.38 3.45 49
590 1.75 -65.62 -43.12 3.45 49
591 -29.75 16.88 -5.62 3.44 49
592 43.75 -31.88 -39.38 3.44 49
593 26.25 9.38 24.38 3.44 49
594 -54.25 24.38 24.38 3.44 49
595 43.75 1.88 -1.88 3.44 49
596 29.75 -16.88 58.12 3.44 49
597 54.25 -16.88 1.88 3.44 98
598 -36.75 -69.38 13.12 3.44 49
599 -5.25 -43.12 69.38 3.44 49
600 15.75 -50.62 9.38 3.43 49
601 -26.25 -31.88 -58.12 3.43 49
602 15.75 1.88 35.62 3.43 49
603 -29.75 -43.12 58.12 3.43 49
604 -1.75 43.12 -20.62 3.43 49
605 -19.25 -65.62 9.38 3.43 49
606 12.25 -35.62 -61.88 3.43 49
607 -33.25 -9.38 -46.88 3.43 49
608 -1.75 -16.88 24.38 3.43 49
609 47.25 28.12 -39.38 3.43 49
610 -36.75 16.88 1.88 3.43 49
611 47.25 -9.38 5.62 3.42 49
612 -19.25 1.88 58.12 3.42 49
613 -26.25 -24.38 -5.62 3.42 49
614 36.75 -31.88 13.12 3.42 49
615 12.25 50.62 9.38 3.42 49
616 8.75 -50.62 -5.62 3.41 49
617 -33.25 -35.62 46.88 3.41 49
618 19.25 43.12 -5.62 3.41 49
619 12.25 -1.88 24.38 3.41 49
620 22.75 39.38 20.62 3.40 49
621 -1.75 -39.38 -16.88 3.40 49
622 -15.75 -1.88 1.88 3.40 49
623 -54.25 -16.88 -5.62 3.40 49
624 1.75 -84.38 -16.88 3.40 49
625 -47.25 43.12 9.38 3.40 49
626 5.25 -9.38 -20.62 3.40 49
627 -57.75 1.88 20.62 3.40 49
628 -43.75 13.12 16.88 3.40 98
629 5.25 80.62 31.88 3.40 49
630 -64.75 -35.62 -1.88 3.40 49
631 -1.75 -43.12 -9.38 3.40 49
632 -54.25 -9.38 20.62 3.40 49
633 1.75 -69.38 46.88 3.40 49
634 -12.25 -5.62 -20.62 3.39 49
635 -8.75 43.12 24.38 3.39 49
636 12.25 -9.38 43.12 3.39 49
637 12.25 -35.62 -16.88 3.39 49
638 50.75 -16.88 35.62 3.39 49
639 -5.25 1.88 -58.12 3.39 49
640 12.25 -35.62 65.62 3.39 49
641 33.25 -13.12 65.62 3.38 49
642 19.25 -69.38 20.62 3.38 49
643 19.25 -31.88 -54.38 3.38 49
644 29.75 -39.38 -54.38 3.38 49
645 -54.25 -35.62 -5.62 3.37 49
646 -47.25 -16.88 -24.38 3.37 49
647 -36.75 9.38 54.38 3.37 49
648 50.75 -50.62 9.38 3.37 49
649 -26.25 -5.62 65.62 3.37 49
650 -33.25 -39.38 -28.12 3.36 49
651 33.25 43.12 -16.88 3.36 49
652 43.75 -58.12 -1.88 3.36 49
653 -47.25 16.88 -24.38 3.36 49
654 -19.25 58.12 39.38 3.36 49
655 -40.25 -5.62 35.62 3.36 49
656 -36.75 61.88 16.88 3.36 49
657 -1.75 -28.12 20.62 3.35 98
658 -8.75 -54.38 9.38 3.35 49
659 12.25 -69.38 5.62 3.35 49
660 -19.25 -43.12 31.88 3.35 98
661 -1.75 -76.88 1.88 3.35 49
662 33.25 -20.62 -9.38 3.35 49
663 -12.25 20.62 73.12 3.35 49
664 -8.75 13.12 -13.12 3.35 49
665 61.25 -35.62 35.62 3.35 49
666 43.75 -9.38 46.88 3.34 49
667 1.75 28.12 -5.62 3.34 49
668 47.25 5.62 -1.88 3.34 98
669 15.75 -5.62 28.12 3.34 49
670 15.75 -13.12 -73.12 3.34 49
671 54.25 1.88 9.38 3.34 49
672 22.75 9.38 46.88 3.34 49
673 40.25 -5.62 -9.38 3.33 49
674 61.25 5.62 31.88 3.33 49
675 29.75 -1.88 -54.38 3.33 49
676 1.75 -9.38 -16.88 3.33 49
677 19.25 -13.12 73.12 3.33 49
678 -12.25 16.88 20.62 3.33 49
679 15.75 46.88 46.88 3.33 49
680 -43.75 -61.88 9.38 3.33 49
681 47.25 -20.62 20.62 3.33 49
682 -5.25 -61.88 54.38 3.33 49
683 -1.75 -69.38 -13.12 3.32 49
684 50.75 -20.62 -43.12 3.32 49
685 54.25 -39.38 -24.38 3.32 49
686 29.75 -43.12 -20.62 3.32 49
687 -12.25 -76.88 -9.38 3.32 49
688 -15.75 20.62 20.62 3.32 49
689 -33.25 9.38 16.88 3.32 49
690 -29.75 9.38 61.88 3.31 49
691 40.25 13.12 -46.88 3.31 49
692 1.75 -58.12 -13.12 3.31 49
693 1.75 1.88 -16.88 3.31 49
694 12.25 -50.62 39.38 3.31 49
695 -36.75 5.62 -9.38 3.31 49
696 26.25 16.88 -20.62 3.31 49
697 12.25 -61.88 16.88 3.31 49
698 22.75 -50.62 35.62 3.31 49
699 19.25 -46.88 -5.62 3.31 49
700 12.25 -50.62 -1.88 3.30 49
701 -12.25 -16.88 -65.62 3.30 49
702 -19.25 24.38 31.88 3.30 49
703 47.25 5.62 31.88 3.30 49
704 5.25 46.88 13.12 3.30 49
705 -22.75 -28.12 31.88 3.30 49
706 33.25 -76.88 -28.12 3.30 49
707 22.75 20.62 16.88 3.29 49
708 5.25 -16.88 -20.62 3.29 49
709 -54.25 -46.88 13.12 3.29 49

house

Stat map plot for the contrast: house
Contrast Plot Plot of the contrast: house.

Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.3
Cluster size threshold (voxels) 0
Minimum distance (mm) 8

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 22.75 20.62 9.38 5.62 639
2 -22.75 24.38 13.12 5.01 147
3 47.25 -54.38 -16.88 5.00 98
4 22.75 -5.62 20.62 4.99 49
5 22.75 13.12 20.62 4.88 98
6 -15.75 24.38 9.38 4.79 639
7 33.25 28.12 28.12 4.64 98
8 -22.75 -39.38 -1.88 4.59 246
8a -22.75 -28.12 -1.88 4.38
9 12.25 24.38 20.62 4.57 147
10 -29.75 28.12 5.62 4.56 49
11 -22.75 16.88 5.62 4.39 98
12 1.75 31.88 -13.12 4.33 49
13 -22.75 5.62 24.38 4.29 147
14 19.25 -16.88 24.38 4.28 98
15 -15.75 -35.62 -1.88 4.25 98
16 19.25 5.62 43.12 4.25 98
17 -12.25 -43.12 -50.62 4.25 49
18 8.75 -24.38 24.38 4.21 49
19 -50.75 9.38 -28.12 4.20 49
20 -33.25 -24.38 35.62 4.19 49
21 -12.25 -16.88 -20.62 4.18 49
22 -8.75 35.62 43.12 4.16 49
23 -22.75 39.38 46.88 4.15 147
24 26.25 -24.38 -69.38 4.14 49
25 1.75 13.12 -16.88 4.14 49
26 50.75 50.62 -1.88 4.14 49
27 22.75 -31.88 1.88 4.11 49
28 -12.25 13.12 1.88 4.11 98
29 -15.75 20.62 20.62 4.10 49
30 8.75 -9.38 31.88 4.09 98
31 -12.25 -28.12 -76.88 4.09 49
32 -15.75 -46.88 20.62 4.08 98
33 26.25 -61.88 31.88 4.05 49
34 36.75 -54.38 -20.62 4.01 49
35 15.75 -54.38 5.62 4.00 49
36 -12.25 1.88 -61.88 4.00 98
37 -33.25 -58.12 35.62 3.99 49
38 -54.25 -20.62 -9.38 3.97 49
39 -26.25 9.38 20.62 3.97 98
40 -15.75 20.62 1.88 3.96 49
41 15.75 -28.12 24.38 3.92 49
42 15.75 -39.38 13.12 3.92 49
43 22.75 -16.88 16.88 3.91 49
44 22.75 16.88 5.62 3.90 49
45 33.25 -43.12 -35.62 3.89 49
46 -29.75 16.88 9.38 3.89 49
47 8.75 -16.88 31.88 3.89 49
48 -40.25 -58.12 28.12 3.89 49
49 26.25 9.38 -31.88 3.87 49
50 43.75 -1.88 20.62 3.86 49
51 22.75 -43.12 -31.88 3.84 49
52 -22.75 20.62 20.62 3.83 98
53 22.75 -43.12 9.38 3.83 49
54 -15.75 31.88 5.62 3.83 49
55 36.75 -13.12 -31.88 3.83 49
56 -54.25 -13.12 -1.88 3.82 49
57 5.25 -20.62 -13.12 3.82 49
58 -29.75 -31.88 35.62 3.81 49
59 26.25 13.12 13.12 3.79 49
60 -26.25 13.12 16.88 3.78 49
61 33.25 5.62 -39.38 3.75 49
62 5.25 -65.62 54.38 3.73 49
63 -15.75 -20.62 -28.12 3.73 49
64 26.25 -1.88 13.12 3.72 49
65 -15.75 58.12 5.62 3.72 49
66 26.25 5.62 9.38 3.72 49
67 -15.75 -50.62 24.38 3.71 49
68 40.25 31.88 -24.38 3.71 49
69 50.75 -31.88 -50.62 3.70 49
70 -8.75 1.88 28.12 3.68 98
71 33.25 54.38 28.12 3.67 49
72 -47.25 -76.88 9.38 3.67 49
73 33.25 13.12 -5.62 3.66 49
74 8.75 20.62 20.62 3.65 49
75 -33.25 -73.12 -31.88 3.64 98
76 12.25 -31.88 -73.12 3.63 49
77 5.25 31.88 50.62 3.63 49
78 -12.25 -39.38 46.88 3.63 49
79 -19.25 61.88 20.62 3.62 49
80 40.25 -39.38 13.12 3.62 49
81 40.25 -13.12 -43.12 3.62 49
82 -22.75 -24.38 46.88 3.61 49
83 -33.25 9.38 5.62 3.61 49
84 33.25 -76.88 -24.38 3.60 49
85 29.75 -50.62 -39.38 3.60 49
86 -19.25 31.88 20.62 3.60 49
87 -1.75 -61.88 -39.38 3.60 49
88 15.75 -1.88 -16.88 3.59 49
89 -33.25 13.12 -1.88 3.58 49
90 26.25 9.38 -24.38 3.57 49
91 -36.75 -39.38 -46.88 3.57 49
92 8.75 43.12 -5.62 3.56 49
93 8.75 -61.88 -50.62 3.56 49
94 -5.25 39.38 -20.62 3.56 98
95 22.75 -13.12 20.62 3.56 49
96 40.25 -50.62 -54.38 3.56 98
97 -1.75 -58.12 -28.12 3.55 49
98 -43.75 -9.38 39.38 3.55 49
99 33.25 28.12 -46.88 3.54 49
100 -15.75 31.88 24.38 3.54 49
101 57.75 -54.38 1.88 3.53 49
102 -40.25 -1.88 24.38 3.52 49
103 33.25 9.38 1.88 3.52 49
104 1.75 -46.88 -5.62 3.52 49
105 22.75 -39.38 46.88 3.52 49
106 -26.25 1.88 13.12 3.51 49
107 -12.25 -5.62 -13.12 3.51 98
108 -36.75 -65.62 -16.88 3.51 49
109 -26.25 -50.62 -46.88 3.51 49
110 26.25 -73.12 31.88 3.50 49
111 26.25 -13.12 54.38 3.50 49
112 15.75 -50.62 9.38 3.50 49
113 -36.75 24.38 13.12 3.50 49
114 -33.25 61.88 39.38 3.50 49
115 19.25 -73.12 -16.88 3.49 98
116 -40.25 -69.38 20.62 3.49 49
117 -12.25 -43.12 -16.88 3.48 49
118 64.75 1.88 35.62 3.48 49
119 22.75 -76.88 20.62 3.47 49
120 -22.75 -9.38 -65.62 3.47 49
121 -50.75 31.88 9.38 3.47 49
122 -8.75 -24.38 1.88 3.44 49
123 19.25 -58.12 20.62 3.44 49
124 50.75 13.12 9.38 3.43 49
125 57.75 -16.88 16.88 3.43 49
126 -64.75 -5.62 28.12 3.43 49
127 -15.75 -24.38 -9.38 3.43 49
128 43.75 20.62 16.88 3.43 49
129 -64.75 -24.38 -20.62 3.42 49
130 5.25 -43.12 -1.88 3.42 49
131 1.75 54.38 -9.38 3.42 49
132 -8.75 -46.88 -31.88 3.42 49
133 -8.75 -28.12 5.62 3.42 49
134 -15.75 -61.88 43.12 3.42 49
135 29.75 1.88 13.12 3.42 49
136 36.75 28.12 39.38 3.41 49
137 50.75 -43.12 -1.88 3.41 49
138 29.75 -65.62 -24.38 3.41 49
139 22.75 -9.38 13.12 3.41 49
140 36.75 -50.62 -24.38 3.40 49
141 36.75 -61.88 -13.12 3.40 49
142 -15.75 -31.88 -50.62 3.40 49
143 -40.25 -28.12 -24.38 3.40 49
144 -40.25 -46.88 39.38 3.40 49
145 22.75 -73.12 -13.12 3.39 49
146 22.75 -9.38 35.62 3.39 49
147 22.75 -65.62 20.62 3.39 49
148 29.75 -16.88 46.88 3.39 49
149 -26.25 20.62 -1.88 3.38 49
150 15.75 -1.88 69.38 3.38 49
151 -22.75 -58.12 24.38 3.37 49
152 57.75 16.88 -1.88 3.37 49
153 -40.25 24.38 35.62 3.37 49
154 26.25 5.62 -28.12 3.36 49
155 -15.75 16.88 13.12 3.35 49
156 12.25 1.88 28.12 3.35 49
157 -5.25 28.12 13.12 3.35 49
158 15.75 24.38 -13.12 3.34 49
159 -8.75 28.12 28.12 3.34 49
160 -12.25 -58.12 -46.88 3.34 49
161 -12.25 -65.62 -39.38 3.34 49
162 19.25 24.38 5.62 3.34 49
163 -22.75 -16.88 24.38 3.33 49
164 -12.25 43.12 46.88 3.33 49
165 -15.75 -69.38 46.88 3.33 49
166 26.25 -16.88 -9.38 3.32 49
167 -26.25 -31.88 -69.38 3.32 49
168 26.25 -50.62 -35.62 3.32 49
169 -36.75 -24.38 1.88 3.32 49
170 19.25 -35.62 1.88 3.32 49
171 36.75 20.62 -13.12 3.31 49
172 8.75 -80.62 -5.62 3.31 49
173 12.25 -5.62 -5.62 3.30 49
174 -40.25 50.62 1.88 3.30 49
175 -15.75 -28.12 -5.62 3.29 49
176 29.75 -54.38 -24.38 3.29 49

chair

Stat map plot for the contrast: chair
Contrast Plot Plot of the contrast: chair.

Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.3
Cluster size threshold (voxels) 0
Minimum distance (mm) 8

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 40.25 -46.88 13.12 4.86 147
2 -36.75 -76.88 1.88 4.23 49
3 12.25 35.62 -5.62 4.20 49
4 -50.75 39.38 16.88 4.17 49
5 -50.75 -39.38 -1.88 3.94 147
6 -54.25 -43.12 46.88 3.90 49
7 33.25 -58.12 -1.88 3.89 49
8 -29.75 -43.12 -58.12 3.88 49
9 -36.75 -61.88 43.12 3.84 49
10 19.25 20.62 20.62 3.81 49
11 12.25 5.62 -24.38 3.77 98
12 22.75 24.38 16.88 3.76 49
13 33.25 43.12 -16.88 3.75 49
14 26.25 -46.88 -35.62 3.75 49
15 26.25 16.88 16.88 3.73 49
16 -15.75 -1.88 -16.88 3.70 49
17 -5.25 43.12 50.62 3.67 49
18 33.25 13.12 -1.88 3.66 49
19 -57.75 -9.38 -1.88 3.62 49
20 -22.75 -31.88 -69.38 3.61 49
21 -22.75 24.38 13.12 3.57 49
22 -57.75 13.12 1.88 3.52 49
23 33.25 -58.12 -16.88 3.50 49
24 -29.75 20.62 46.88 3.48 49
25 12.25 16.88 -20.62 3.44 49
26 33.25 28.12 -31.88 3.43 49
27 36.75 -24.38 20.62 3.43 49
28 43.75 -28.12 -13.12 3.40 49
29 29.75 13.12 -43.12 3.39 49
30 12.25 28.12 -16.88 3.38 49
31 -22.75 -43.12 -31.88 3.37 49
32 -1.75 5.62 31.88 3.37 49
33 33.25 9.38 -13.12 3.36 49
34 33.25 -20.62 -58.12 3.35 49
35 -15.75 -35.62 -24.38 3.34 49
36 29.75 -43.12 -35.62 3.34 49
37 -22.75 9.38 24.38 3.34 49
38 -36.75 -46.88 -54.38 3.33 49
39 1.75 28.12 -35.62 3.32 49
40 36.75 -28.12 -9.38 3.29 49

scrambledpix

Stat map plot for the contrast: scrambledpix
Contrast Plot Plot of the contrast: scrambledpix.

Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.3
Cluster size threshold (voxels) 0
Minimum distance (mm) 8

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 1.75 24.38 -13.12 5.74 49
2 -22.75 -46.88 43.12 4.82 98
3 -15.75 61.88 -9.38 4.79 147
4 -29.75 -61.88 -31.88 4.65 49
5 50.75 16.88 -28.12 4.14 147
6 -22.75 -54.38 -9.38 3.94 49
7 5.25 43.12 -28.12 3.92 49
8 -22.75 -9.38 20.62 3.82 49
9 40.25 -16.88 -39.38 3.80 49
10 -64.75 -31.88 39.38 3.78 49
11 -26.25 -69.38 39.38 3.59 49
12 22.75 -24.38 46.88 3.56 49
13 22.75 46.88 -5.62 3.54 49
14 -1.75 5.62 31.88 3.53 49
15 -40.25 -16.88 -54.38 3.46 49
16 33.25 35.62 -28.12 3.44 49
17 -15.75 -43.12 -16.88 3.41 49
18 26.25 -24.38 -69.38 3.40 49
19 1.75 13.12 31.88 3.34 49
20 33.25 -20.62 43.12 3.33 49

face

Stat map plot for the contrast: face
Contrast Plot Plot of the contrast: face.

Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.3
Cluster size threshold (voxels) 0
Minimum distance (mm) 8

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 1.75 24.38 -13.12 5.17 98
2 -50.75 -50.62 13.12 5.09 196
2a -47.25 -58.12 13.12 3.37
3 -47.25 -43.12 -35.62 4.74 49
4 68.25 -13.12 -9.38 4.74 147
5 -47.25 -39.38 16.88 4.47 98
6 29.75 -46.88 35.62 4.40 49
7 29.75 -54.38 46.88 4.38 49
8 -33.25 50.62 16.88 4.37 147
9 -57.75 -16.88 1.88 4.37 147
10 -36.75 31.88 16.88 4.22 98
11 -12.25 -58.12 -16.88 4.22 49
12 -26.25 24.38 -13.12 4.16 147
13 -40.25 46.88 13.12 4.14 98
14 22.75 -50.62 -24.38 4.11 49
15 -22.75 39.38 13.12 4.09 49
16 40.25 28.12 39.38 3.98 98
17 -15.75 13.12 73.12 3.97 98
18 5.25 20.62 65.62 3.95 49
19 5.25 -24.38 -20.62 3.93 49
20 -47.25 -50.62 20.62 3.88 49
21 43.75 35.62 35.62 3.83 98
22 -47.25 -28.12 28.12 3.80 147
23 -1.75 31.88 61.88 3.79 98
24 -54.25 -43.12 20.62 3.77 49
25 -8.75 13.12 65.62 3.77 49
26 1.75 -43.12 -1.88 3.75 49
27 -8.75 -46.88 -31.88 3.73 49
28 -47.25 -61.88 -5.62 3.72 49
29 -19.25 -73.12 39.38 3.72 49
30 -26.25 -88.12 -9.38 3.71 98
31 12.25 28.12 61.88 3.71 49
32 -50.75 -35.62 43.12 3.70 49
33 -36.75 -43.12 39.38 3.70 147
34 -47.25 24.38 20.62 3.69 49
35 -36.75 -31.88 -46.88 3.69 49
36 -47.25 -54.38 -9.38 3.69 49
37 40.25 43.12 16.88 3.69 49
38 -57.75 -9.38 -1.88 3.67 49
39 -57.75 31.88 31.88 3.67 49
40 5.25 -58.12 -24.38 3.66 98
41 -36.75 31.88 35.62 3.65 147
42 33.25 -46.88 -39.38 3.64 49
43 -43.75 -31.88 35.62 3.62 49
44 -40.25 -43.12 -16.88 3.60 49
45 -12.25 -76.88 28.12 3.60 49
46 -8.75 -28.12 61.88 3.58 49
47 19.25 -16.88 39.38 3.56 49
48 26.25 -43.12 35.62 3.56 49
49 43.75 -54.38 9.38 3.53 49
50 19.25 -46.88 -20.62 3.53 49
51 -29.75 31.88 -9.38 3.52 49
52 40.25 -39.38 -24.38 3.51 49
53 36.75 61.88 28.12 3.51 49
54 -15.75 -73.12 16.88 3.50 49
55 8.75 -1.88 9.38 3.50 49
56 -47.25 -46.88 16.88 3.49 49
57 -22.75 -46.88 -50.62 3.49 49
58 22.75 31.88 28.12 3.49 49
59 12.25 -58.12 -20.62 3.48 49
60 5.25 -65.62 13.12 3.48 49
61 -47.25 -16.88 -35.62 3.48 49
62 -19.25 -61.88 -9.38 3.47 49
63 -15.75 -13.12 24.38 3.47 98
64 -26.25 -43.12 -43.12 3.47 49
65 -12.25 -39.38 58.12 3.46 49
66 -1.75 39.38 -20.62 3.46 49
67 26.25 -43.12 -31.88 3.46 49
68 33.25 -61.88 -13.12 3.45 49
69 5.25 31.88 -35.62 3.45 49
70 -40.25 50.62 20.62 3.43 49
71 -40.25 -35.62 -35.62 3.43 49
72 -50.75 -28.12 35.62 3.42 49
73 15.75 -54.38 -20.62 3.41 49
74 36.75 -43.12 5.62 3.40 49
75 -33.25 39.38 54.38 3.39 49
76 -15.75 -54.38 50.62 3.38 49
77 29.75 -35.62 35.62 3.38 49
78 -43.75 -39.38 -9.38 3.36 49
79 -29.75 28.12 13.12 3.35 49
80 -1.75 -20.62 24.38 3.35 49
81 -40.25 20.62 54.38 3.35 49
82 33.25 -58.12 -16.88 3.34 49
83 -47.25 -31.88 5.62 3.34 49
84 57.75 -35.62 43.12 3.33 49
85 -29.75 28.12 39.38 3.32 49
86 -47.25 -46.88 -31.88 3.32 49
87 26.25 -54.38 -20.62 3.32 49
88 -33.25 31.88 46.88 3.32 49
89 -8.75 -58.12 -43.12 3.30 49
90 -33.25 -58.12 20.62 3.29 49

shoe

Stat map plot for the contrast: shoe
Contrast Plot Plot of the contrast: shoe.

Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.3
Cluster size threshold (voxels) 0
Minimum distance (mm) 8

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 22.75 -9.38 35.62 4.74 49
2 61.25 24.38 31.88 4.22 147
3 36.75 46.88 -16.88 3.95 49
4 50.75 -35.62 -35.62 3.83 49
5 22.75 -69.38 -28.12 3.82 49
6 19.25 -20.62 -28.12 3.81 49
7 19.25 73.12 20.62 3.76 49
8 -54.25 -65.62 16.88 3.70 49
9 -19.25 20.62 43.12 3.69 49
10 -47.25 -16.88 -24.38 3.66 49
11 -1.75 -28.12 -73.12 3.65 49
12 19.25 -28.12 -54.38 3.59 49
13 15.75 -24.38 -73.12 3.57 49
14 -68.25 -20.62 24.38 3.56 49
15 -68.25 -9.38 9.38 3.53 49
16 26.25 -46.88 -24.38 3.52 49
17 -1.75 -20.62 24.38 3.52 49
18 15.75 16.88 58.12 3.45 49
19 36.75 28.12 39.38 3.45 49
20 33.25 9.38 61.88 3.43 49
21 -12.25 1.88 -69.38 3.37 49
22 26.25 -61.88 13.12 3.36 49
23 -29.75 9.38 35.62 3.36 49
24 1.75 -24.38 -39.38 3.36 49
25 -12.25 39.38 65.62 3.36 49
26 12.25 5.62 31.88 3.33 49
27 64.75 -1.88 31.88 3.32 49
28 26.25 35.62 28.12 3.30 49

cat

Stat map plot for the contrast: cat
Contrast Plot Plot of the contrast: cat.

Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.3
Cluster size threshold (voxels) 0
Minimum distance (mm) 8

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -12.25 -76.88 31.88 4.68 98
2 43.75 -5.62 16.88 4.53 196
3 -15.75 -20.62 -58.12 4.45 98
4 -40.25 -65.62 13.12 4.36 49
5 1.75 35.62 -13.12 4.29 49
6 -12.25 -58.12 -16.88 4.27 49
7 5.25 -61.88 -24.38 4.27 98
8 43.75 5.62 1.88 4.26 344
9 -1.75 50.62 -9.38 4.25 246
10 5.25 -24.38 -73.12 4.22 49
11 22.75 -61.88 -54.38 4.04 49
12 15.75 -16.88 46.88 4.02 49
13 36.75 9.38 -35.62 3.99 49
14 19.25 -31.88 -69.38 3.98 49
15 22.75 -24.38 -58.12 3.98 49
16 -64.75 -9.38 -24.38 3.98 49
17 36.75 69.38 5.62 3.91 98
18 12.25 -61.88 -31.88 3.86 98
19 1.75 28.12 -35.62 3.85 49
20 47.25 1.88 -46.88 3.82 49
21 8.75 -65.62 -24.38 3.82 49
22 -29.75 39.38 54.38 3.81 49
23 15.75 -31.88 -61.88 3.76 49
24 5.25 -80.62 9.38 3.75 49
25 43.75 24.38 9.38 3.72 49
26 40.25 -5.62 -5.62 3.70 49
27 50.75 28.12 24.38 3.70 49
28 22.75 -58.12 -61.88 3.70 49
29 -40.25 -46.88 16.88 3.66 49
30 5.25 43.12 50.62 3.63 49
31 -12.25 -73.12 -31.88 3.62 49
32 -26.25 -80.62 13.12 3.57 98
33 50.75 1.88 -24.38 3.57 49
34 -54.25 -46.88 5.62 3.57 98
35 -40.25 -69.38 35.62 3.53 49
36 36.75 28.12 -20.62 3.53 49
37 8.75 16.88 -16.88 3.53 49
38 5.25 20.62 50.62 3.53 49
39 -47.25 -31.88 -43.12 3.52 49
40 -40.25 -5.62 50.62 3.50 49
41 -5.25 -50.62 -13.12 3.50 49
42 -29.75 -46.88 5.62 3.48 49
43 29.75 1.88 -31.88 3.47 49
44 -8.75 -35.62 -24.38 3.44 49
45 61.25 -31.88 43.12 3.44 49
46 5.25 -31.88 -69.38 3.43 49
47 -22.75 65.62 39.38 3.42 98
48 36.75 28.12 43.12 3.38 147
49 -8.75 -46.88 -28.12 3.36 49
50 12.25 -39.38 -31.88 3.36 49
51 -8.75 -28.12 -61.88 3.35 49
52 61.25 -31.88 20.62 3.35 49
53 -40.25 -31.88 -58.12 3.34 49
54 40.25 -20.62 -39.38 3.32 49
55 5.25 -73.12 -20.62 3.32 49
56 -29.75 -20.62 -35.62 3.32 49
57 15.75 39.38 -20.62 3.31 49
58 -36.75 -50.62 -39.38 3.31 49
59 -47.25 -43.12 43.12 3.31 49
60 26.25 -65.62 13.12 3.30 49

scissors

Stat map plot for the contrast: scissors
Contrast Plot Plot of the contrast: scissors.

Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.3
Cluster size threshold (voxels) 0
Minimum distance (mm) 8

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -50.75 20.62 -24.38 6.55 1132
2 -19.25 84.38 20.62 6.20 1476
2a -26.25 80.62 16.88 4.90
2b -15.75 76.88 31.88 4.42
3 -26.25 24.38 28.12 5.83 147
4 36.75 20.62 -31.88 5.58 246
5 29.75 5.62 -5.62 5.32 98
6 -12.25 20.62 -46.88 5.25 344
7 12.25 -35.62 -69.38 5.14 98
8 15.75 76.88 28.12 5.05 49
9 5.25 24.38 -46.88 5.04 393
10 -12.25 76.88 20.62 5.02 935
10a -5.25 84.38 1.88 4.38
10b -5.25 84.38 20.62 4.38
10c -5.25 80.62 13.12 4.22
11 -1.75 1.88 -61.88 5.02 295
12 -19.25 61.88 50.62 5.00 49
13 12.25 13.12 -43.12 4.98 49
14 33.25 5.62 -46.88 4.92 49
15 1.75 -28.12 -61.88 4.88 49
16 -54.25 -39.38 -31.88 4.79 49
17 -22.75 -54.38 -50.62 4.66 295
18 12.25 -61.88 -46.88 4.56 49
19 12.25 80.62 -5.62 4.49 49
20 36.75 28.12 -35.62 4.46 295
21 -26.25 -13.12 1.88 4.45 49
22 -15.75 -43.12 -46.88 4.42 98
23 -29.75 20.62 -46.88 4.41 98
24 -12.25 9.38 -58.12 4.41 98
25 26.25 -46.88 1.88 4.36 49
26 -5.25 80.62 31.88 4.32 196
27 15.75 -35.62 -73.12 4.32 49
28 50.75 24.38 -35.62 4.30 98
29 40.25 -58.12 -5.62 4.26 98
30 -19.25 31.88 -35.62 4.26 147
31 12.25 13.12 -50.62 4.26 344
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In a jupyter notebook, the report will be automatically inserted, as above. We have several other ways to access the report: .. code-block:: default # report.save_as_html('report.html') # report.open_in_browser() Build the decoding pipeline ---------------------------- To define the decoding pipeline we use Decoder object, we choose : * a prediction model, here a Support Vector Classifier, with a linear kernel * the mask to use, here a ventral temporal ROI in the visual cortex * although it usually helps to decode better, z-maps time series don't need to be rescaled to a 0 mean, variance of 1 so we use standardize=False. * we use univariate feature selection to reduce the dimension of the problem keeping only 5% of voxels which are most informative. * a cross-validation scheme, here we use LeaveOneGroupOut cross-validation on the sessions which corresponds to a leave-one-session-out We fit directly this pipeline on the Niimgs outputs of the GLM, with corresponding conditions labels and session labels (for the cross validation). .. code-block:: default from nilearn.decoding import Decoder from sklearn.model_selection import LeaveOneGroupOut decoder = Decoder(estimator='svc', mask=haxby_dataset.mask, standardize=False, screening_percentile=5, cv=LeaveOneGroupOut()) decoder.fit(z_maps, conditions_label, groups=session_label) # Return the corresponding mean prediction accuracy compared to chance classification_accuracy = np.mean(list(decoder.cv_scores_.values())) chance_level = 1. / len(np.unique(conditions)) print('Classification accuracy: {:.4f} / Chance level: {}'.format( classification_accuracy, chance_level)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Classification accuracy: 0.6890 / Chance level: 0.125 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 2 minutes 38.379 seconds) .. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_glm_decoding.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/02_decoding/plot_haxby_glm_decoding.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_haxby_glm_decoding.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_haxby_glm_decoding.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_