.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/02_decoding/plot_haxby_glm_decoding.py" .. LINE NUMBERS ARE GIVEN BELOW. .. 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 .. GENERATED FROM PYTHON SOURCE LINES 23-27 Fetch example Haxby dataset ---------------------------- We download the Haxby dataset This is a study of visual object category representation .. GENERATED FROM PYTHON SOURCE LINES 27-37 .. 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 .. GENERATED FROM PYTHON SOURCE LINES 38-40 Load the behavioral data ------------------------- .. GENERATED FROM PYTHON SOURCE LINES 40-52 .. 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] .. GENERATED FROM PYTHON SOURCE LINES 53-55 Build a proper event structure for each session ------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 55-73 .. 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'] .. GENERATED FROM PYTHON SOURCE LINES 74-78 Instantiate and run FirstLevelModel ------------------------------------ We generate a list of z-maps together with their session and condition index .. GENERATED FROM PYTHON SOURCE LINES 78-91 .. 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') .. GENERATED FROM PYTHON SOURCE LINES 92-94 Run the glm on data from each session -------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 94-110 .. 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.maskers.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([[-0.114769, ..., -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 - 0.1s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.070285, ..., -1.222001]), ) ___________________________________________________________unmask - 0.1s, 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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.310409, ..., 0.196496]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-2.341707, ..., 2.094528]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.097247, ..., -0.496306]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.118812, ..., 1.58503 ]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.801345, ..., -1.648133]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.621172, ..., -0.678871]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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([[ 5.205584, ..., 26.587189], ..., [-6.836576, ..., 10.676956]], dtype=float32), array([[0., ..., 1.], ..., [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1) __________________________________________________________run_glm - 1.1s, 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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.034083, ..., 0.35299 ]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 1.2s, 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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 1.538844, ..., -0.797649]), ) ___________________________________________________________unmask - 0.2s, 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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 0.2s, 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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.232015, ..., -0.420629]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.672634, ..., -0.24818 ]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.099764, ..., -1.722951]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.260282, ..., -0.346342]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([1.775541, ..., 1.603123]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([3.391727, ..., 0.653312]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 1.2s, 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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.134169, ..., -0.621367]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.234171, ..., -1.497943]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 1.2s, 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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-2.463833, ..., -0.151504]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.230311, ..., 0.198537]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 1.2s, 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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.032135, ..., -1.290339]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([ 0.417132, ..., -1.258094]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.maskers.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 - 1.1s, 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 - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([0.726779, ..., 0.687642]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-0.809306, ..., 0.496974]), ) ___________________________________________________________unmask - 0.2s, 0.0min ________________________________________________________________________________ [Memory] Calling nilearn.masking.unmask... unmask(array([-1.116436, ..., -0.811166]), ) ___________________________________________________________unmask - 0.2s, 0.0min .. GENERATED FROM PYTHON SOURCE LINES 111-115 Generating a report -------------------- Since we have already computed the FirstLevelModel and have the contrast, we can quickly create a summary report. .. GENERATED FROM PYTHON SOURCE LINES 115-126 .. 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 0
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
32 -40.25 69.38 13.12 4.25 49
33 47.25 24.38 -1.88 4.23 49
34 1.75 1.88 31.88 4.22 147
35 1.75 13.12 -16.88 4.20 49
36 1.75 -1.88 -76.88 4.20 98
37 -40.25 -9.38 -5.62 4.17 49
38 15.75 88.12 16.88 4.16 147
39 -12.25 1.88 -28.12 4.15 49
40 15.75 -16.88 46.88 4.15 49
41 47.25 24.38 9.38 4.14 49
42 -5.25 -9.38 31.88 4.13 49
43 40.25 28.12 -43.12 4.12 49
44 1.75 76.88 16.88 4.11 196
45 50.75 31.88 -31.88 4.10 49
46 1.75 -5.62 -80.62 4.08 98
47 1.75 -28.12 -76.88 4.07 49
48 -54.25 9.38 -16.88 4.05 49
49 50.75 20.62 -1.88 4.05 49
50 -40.25 61.88 5.62 4.05 49
51 -26.25 -24.38 20.62 4.04 49
52 -47.25 -5.62 -28.12 4.03 49
53 -50.75 -9.38 -20.62 4.03 196
54 -22.75 -5.62 -20.62 4.01 49
55 -5.25 -28.12 -76.88 4.01 49
56 54.25 5.62 50.62 4.01 98
57 40.25 5.62 31.88 4.00 49
58 29.75 76.88 13.12 3.98 49
59 22.75 -54.38 -43.12 3.98 49
60 -26.25 -1.88 -9.38 3.96 98
61 50.75 50.62 20.62 3.93 49
62 -12.25 88.12 9.38 3.91 49
63 -36.75 -1.88 -20.62 3.91 147
64 -29.75 -69.38 9.38 3.91 98
65 47.25 -1.88 20.62 3.90 147
66 19.25 -16.88 -54.38 3.90 49
67 57.75 -24.38 5.62 3.89 49
68 -19.25 50.62 13.12 3.88 49
69 33.25 24.38 -43.12 3.88 98
70 12.25 80.62 35.62 3.87 98
71 -26.25 -80.62 -5.62 3.86 49
72 -26.25 31.88 -39.38 3.86 49
73 -68.25 -5.62 5.62 3.86 98
74 -36.75 65.62 20.62 3.85 98
75 -40.25 -16.88 -65.62 3.85 49
76 22.75 -58.12 -50.62 3.85 49
77 19.25 80.62 1.88 3.85 98
78 -5.25 1.88 -76.88 3.85 49
79 12.25 -61.88 9.38 3.84 49
80 -36.75 69.38 16.88 3.83 49
81 15.75 50.62 -9.38 3.83 49
82 33.25 -35.62 -35.62 3.83 49
83 22.75 -1.88 9.38 3.83 49
84 -5.25 5.62 -69.38 3.82 49
85 5.25 13.12 31.88 3.82 98
86 -33.25 -65.62 -16.88 3.82 49
87 -5.25 -16.88 -39.38 3.80 49
88 22.75 43.12 -9.38 3.80 49
89 -50.75 -13.12 5.62 3.80 49
90 43.75 -61.88 5.62 3.78 49
91 19.25 -1.88 -1.88 3.76 49
92 22.75 -35.62 -9.38 3.75 49
93 12.25 24.38 -35.62 3.75 98
94 -8.75 24.38 -35.62 3.74 49
95 -40.25 43.12 -31.88 3.74 49
96 40.25 -24.38 -35.62 3.73 49
97 -8.75 16.88 -43.12 3.72 49
98 15.75 88.12 9.38 3.72 98
99 29.75 -1.88 24.38 3.72 49
100 54.25 1.88 43.12 3.70 49
101 1.75 65.62 46.88 3.70 49
102 22.75 -20.62 -5.62 3.70 49
103 29.75 58.12 -16.88 3.69 49
104 -1.75 73.12 35.62 3.69 49
105 40.25 -39.38 58.12 3.68 49
106 26.25 -50.62 -54.38 3.68 49
107 -8.75 28.12 54.38 3.68 196
108 -22.75 -39.38 -54.38 3.67 49
109 -1.75 -35.62 -65.62 3.66 98
110 1.75 39.38 -16.88 3.65 49
111 8.75 -9.38 -76.88 3.65 49
112 33.25 -5.62 -1.88 3.64 98
113 -1.75 28.12 -39.38 3.64 49
114 1.75 80.62 24.38 3.62 98
115 19.25 -46.88 -35.62 3.62 49
116 1.75 -9.38 1.88 3.61 49
117 -47.25 24.38 20.62 3.61 49
118 36.75 24.38 -28.12 3.60 49
119 22.75 -43.12 -61.88 3.59 49
120 -22.75 58.12 -20.62 3.59 98
121 43.75 -31.88 -13.12 3.57 49
122 -22.75 54.38 -13.12 3.55 49
123 1.75 -5.62 -20.62 3.54 49
124 -22.75 39.38 31.88 3.54 49
125 -29.75 31.88 -9.38 3.53 49
126 57.75 16.88 16.88 3.53 49
127 15.75 -76.88 -16.88 3.52 49
128 15.75 46.88 -20.62 3.52 49
129 33.25 -39.38 24.38 3.52 49
130 -43.75 -24.38 35.62 3.52 49
131 -12.25 65.62 -9.38 3.52 49
132 -33.25 39.38 54.38 3.51 49
133 -26.25 16.88 28.12 3.51 49
134 29.75 -1.88 13.12 3.50 49
135 12.25 1.88 -69.38 3.50 49
136 19.25 54.38 50.62 3.49 49
137 8.75 76.88 16.88 3.49 49
138 -26.25 -28.12 31.88 3.49 49
139 29.75 -9.38 9.38 3.48 49
140 22.75 58.12 -5.62 3.48 49
141 -19.25 -20.62 -1.88 3.48 49
142 -12.25 -43.12 -13.12 3.48 49
143 -26.25 65.62 35.62 3.48 49
144 15.75 76.88 20.62 3.47 98
145 -5.25 -1.88 -28.12 3.47 49
146 -33.25 69.38 24.38 3.46 98
147 -19.25 -5.62 16.88 3.46 49
148 -26.25 61.88 -5.62 3.46 49
149 -22.75 69.38 35.62 3.46 49
150 29.75 -46.88 -61.88 3.46 49
151 -8.75 20.62 31.88 3.45 49
152 33.25 24.38 61.88 3.45 49
153 40.25 -1.88 61.88 3.44 98
154 -33.25 43.12 -13.12 3.44 49
155 54.25 35.62 35.62 3.44 49
156 22.75 -20.62 20.62 3.44 49
157 -19.25 31.88 -28.12 3.43 49
158 -61.25 -46.88 9.38 3.43 49
159 19.25 16.88 -46.88 3.43 49
160 -1.75 -46.88 -1.88 3.43 49
161 43.75 -9.38 -16.88 3.43 49
162 57.75 -31.88 46.88 3.42 49
163 -19.25 -1.88 -35.62 3.42 49
164 22.75 20.62 24.38 3.42 49
165 -19.25 9.38 -35.62 3.42 49
166 -22.75 20.62 -46.88 3.41 49
167 -57.75 -5.62 -1.88 3.41 49
168 -5.25 39.38 58.12 3.40 49
169 -47.25 -16.88 -13.12 3.40 49
170 -29.75 -13.12 -35.62 3.40 49
171 33.25 -39.38 -43.12 3.40 49
172 1.75 35.62 -24.38 3.40 49
173 29.75 9.38 16.88 3.39 49
174 15.75 -28.12 -54.38 3.38 49
175 22.75 -39.38 13.12 3.38 49
176 29.75 -20.62 -5.62 3.38 49
177 40.25 -43.12 -5.62 3.37 49
178 33.25 -39.38 -20.62 3.37 49
179 -26.25 9.38 20.62 3.37 49
180 50.75 -16.88 -13.12 3.36 49
181 -26.25 43.12 -28.12 3.36 49
182 15.75 -39.38 24.38 3.36 49
183 -12.25 1.88 9.38 3.35 49
184 26.25 -16.88 -35.62 3.35 49
185 33.25 5.62 16.88 3.35 49
186 -19.25 -1.88 -9.38 3.35 49
187 15.75 -50.62 46.88 3.35 49
188 -57.75 -9.38 -13.12 3.35 49
189 -15.75 -65.62 5.62 3.35 49
190 15.75 5.62 9.38 3.35 49
191 29.75 -1.88 1.88 3.35 49
192 8.75 1.88 24.38 3.35 49
193 -33.25 28.12 -39.38 3.34 49
194 26.25 1.88 24.38 3.34 49
195 -22.75 -88.12 -1.88 3.34 49
196 -29.75 -16.88 -1.88 3.34 49
197 33.25 -31.88 -43.12 3.34 49
198 -1.75 -13.12 31.88 3.33 49
199 40.25 13.12 -39.38 3.33 49
200 33.25 -20.62 43.12 3.33 49
201 22.75 -9.38 -5.62 3.32 49
202 29.75 -1.88 50.62 3.32 49
203 5.25 -1.88 5.62 3.32 49
204 -12.25 -50.62 -50.62 3.31 49
205 -15.75 1.88 28.12 3.31 49
206 -33.25 73.12 9.38 3.31 49
207 1.75 -1.88 -24.38 3.31 49
208 29.75 -50.62 -50.62 3.30 49
209 8.75 16.88 -39.38 3.30 49
210 -19.25 -20.62 -20.62 3.30 49
211 26.25 13.12 -39.38 3.30 49
212 50.75 -16.88 46.88 3.30 49
213 29.75 54.38 -5.62 3.29 49

Built using Nilearn. Source code on GitHub. File bugs & feature requests here.


.. GENERATED FROM PYTHON SOURCE LINES 127-129 In a jupyter notebook, the report will be automatically inserted, as above. We have several other ways to access the report: .. GENERATED FROM PYTHON SOURCE LINES 129-133 .. code-block:: default # report.save_as_html('report.html') # report.open_in_browser() .. GENERATED FROM PYTHON SOURCE LINES 134-156 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). .. GENERATED FROM PYTHON SOURCE LINES 156-169 .. 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 35.648 seconds) **Estimated memory usage:** 1104 MB .. _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 `_