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1,202,209,216,222,238,247,248,261,263,264,266,272,275,277,280,281,282,283,284,285,286,293,295,296,297,302,303,308,311,313],Going:[102,115,312],IPS:41,Ids:138,Ins:41,Its:[48,105,113,118,119,125,191,202,209,222,225,260,261,303],NOS:138,NOT:104,Not:[53,55,56,97,145,294],OLS:[87,90,199,201,202,260,309],One:[6,25,26,38,39,46,53,60,97,100,105,106,111,112,116,118,120,121,123,127,129,209,241,246,248,298],RAS:240,Such:[81,105],TRs:[17,128],That:[196,200],The:[0,3,6,7,9,10,11,12,13,14,15,20,21,23,25,27,31,32,33,34,36,37,38,39,40,41,42,44,45,46,48,50,51,52,53,54,56,57,58,59,60,62,63,64,65,66,67,68,69,70,72,73,76,77,78,79,80,81,82,84,85,86,87,89,91,93,95,96,97,103,104,105,106,108,109,110,112,113,114,115,116,118,119,120,121,123,125,126,127,128,130,131,132,133,134,135,136,137,138,139,140,141,142,143,148,152,154,158,160,161,162,164,165,166,168,170,171,172,173,174,175,177,181,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,209,211,212,216,217,218,222,223,224,226,227,228,232,236,237,238,239,240,245,246,247,248,249,250,251,252,253,254,255,256,257,260,261,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,289,293,294,295,296,298,300,303,304,305,307,308,309,311,312,313],Then:[21,31,33,40,41,45,51,52,58,81,82,96,97,109,111,113,114,119,125,128,129,130,132,162],There:[0,13,55,98,108,111,112,113,119,125,199,201,202,223,301],These:[45,48,70,77,81,92,95,97,98,100,103,111,114,115,117,118,119,123,125,129,155,157,311,313],Uing:120,Use:[21,27,32,48,72,77,79,81,87,89,125,144,150,170,172,194,195,199,201,202,236,241,242,244,246,247,248,249,250,269,298,308,313],Used:[130,131,138,139,146,147,149,151,154,158,160,161,162,164,165,166,168,171,173,174,175,178,179,180,181,182,187,188,189,190,192,193,194,195,209,222,227,233,246,247,248,249,250,252,253,254,255,256,257,293,294,295,303],Useful:[130,131,261,288,290,291,292,313],Uses:[72,255,256,293],Using:[7,26,27,31,34,35,38,43,46,47,48,51,74,84,104,107,111,112,115,118,120,121,127,129,131,140,175,312,313],Vis:41,Will:230,With:[6,23,66,77,114,118,121,131,134,205],______________________________________________________________________:21,________________________________________________________________________________:[24,34,38,39,43,44,46,48],___________________________________________________________unmask:24,__________________________________________________________fastica:38,__________________________________________________________run_glm:24,________________________________________________________ward_tre:39,_____________________________________________________path_scor:34,_____________________________________________________resample_img:44,____________________________________________________estimator_fit:39,___________________________________________________randomized_svd:38,__________________________________________________filter_and_mask:24,_________________________________________________compute_load:38,_______________________________________________filter_and_extract:[39,43,44,46,48],_____________________________________________dict_learning_onlin:38,_____________________________________________explained_vari:38,__________________________________________high_variance_confound:43,________________________________________resample_img:42,__________________________________filter_and_extract:42,__class__:35,__doc__:[35,60,62],__init__:[130,131,132,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,209,222,246,247,248,249,250,261,293,294,295],__init___:200,__name__:35,_adjust_screening_percentil:[22,23,30,34,313],_agglom:39,_ball_sample_loc:16,_base:30,_build:119,_cach:[38,39],_classif:32,_cmap_d:4,_col_filters_available_on_serv:170,_column:121,_compute_load:38,_data:[12,13,38,40],_dict_learn:38,_empty_filt:170,_estimator_fit:39,_extractionfunctor:[39,43,44,46,48],_fastica:[38,79,89],_fetch_fil:119,_get_mask_volum:313,_graph_lasso:48,_graph_net_squared_loss:34,_line_sample_loc:16,_safe_get_data:313,_sample_locations_between_surfac:16,_scan:121,_smooth_arrai:297,_space:212,_subplot:[54,55,68,69,100],_task:212,_test:119,_univariate_select:22,_update_submatrix:118,_util:[22,23,30,37,39,42,44,47,51,60,118,310,313],_voxel:121,_warn_prf:32,a00028185:15,a00028185_left_preprocessed_fwhm6:15,a00028185_lh_preprocessed_fsaverage5_fwhm6:15,a00028185_rh_preprocessed_fsaverage5_fwhm6:15,a_model001_task002_cope011_tstat1:92,aal:[140,313],abadi:313,abbrevi:127,aben:313,aberr:66,abid:[100,138,313],abl:[7,21,23,154],about:[0,6,11,16,39,51,64,66,67,81,86,93,96,97,100,103,104,108,111,112,114,116,118,119,120,121,125,127,141,142,146,149,150,151,154,160,161,162,168,174,175,183,308,313],abov:[6,13,24,33,38,39,40,46,58,74,81,96,97,98,104,105,106,108,109,110,111,116,118,119,120,127,128,129,162,196,199,200,201,202,241,242,268,280,288,290,292,295,313],abraham:[0,109,295,313],abrupt:129,abs:[21,34,35,43,48,72,87,88,89,118,131,134,224,260,268],absenc:[121,298],absolut:[33,34,48,116,118,153,163,176,230,261,265,266,268,269,272,274,275,277,281,282,283,286,289,290,292,311,313],absolute_path:92,abstract_knowledge_regparam:88,acad:[149,168],academi:191,acc:12,acceler:[15,181],accept:[54,96,97,119,120,125,127,222,224,225,251,252,253,254,255,256,257,266,277,279,280,281,282,289,291,300,301,313],acces:38,access:[24,50,51,53,55,63,100,103,106,110,118,119,120,125,126,127,129,175,197,312],accommod:[66,313],accord:[32,35,58,81,93,100,109,116,119,131,138,148,185,187,188,189,190,192,193,196,226,260,278,283,285,286,294,295,304,311,313],accordingli:7,account:[55,64,81,100,116,129,247,248,250,295],accross:251,accumul:[93,311],accur:[23,39,48,55,58,116,120,121,130,131,132,187,188,189,190,191,192,193,194,195,198,203,209,222,246,247,248,249,250,293,294,295,313],accuraci:[20,21,22,23,24,26,28,32,41,84,85,97,115,116,125,187,189,191,192,312],accuracy_scor:[32,41],achiev:[7,45,60,80,111,118,128,129,134],acord:278,acoustic_processing_regparam:88,acq:212,acquir:[55,97,100,103,121],acquis:129,acquisit:[21,23,52,55,60,86,97,100,116,121,166,174,216,267],across:[7,8,37,40,41,46,53,54,55,60,68,69,81,84,85,88,97,100,105,106,109,110,111,112,114,116,121,130,146,187,188,189,190,192,193,194,195,244,267,293,295,308,311,313],act:[20,25,136,137,313],action:[121,166],action_perception_regparam:88,action_regparam:88,activ:[6,7,21,23,31,32,53,55,58,59,60,64,65,67,68,69,70,93,100,103,105,106,109,110,111,115,116,118,119,120,121,123,125,129,132,140,146,149,168,169,204,262,265,295,297],activation_level_regparam:88,activation_regparam:88,activation_threshold:265,active_maintenance_regparam:88,active_minus_rest:100,active_retrieval_regparam:88,active_vs_rest_eff_map:100,active_vs_rest_z_map:100,actual:[33,55,57,60,65,74,90,97,100,118,119,121,125,127,132,154,175,209,222,225,303],acuity_regparam:88,adapt:[54,103,109,112,119],adaptation_regparam:88,adaptive_control_regparam:88,add:[7,12,31,32,40,52,58,87,104,109,111,114,116,119,132,170,172,185,226,249,261,279,288,290,292,304,311,313],add_:7,add_ax:[7,10,21,27,31,33,34,38,75,87,90,91,96],add_contour:[6,10,261,311,313],add_edg:[261,311],add_mark:[31,45,50,59,261,311,313],add_overlai:[40,109,261,311,313],add_patch:31,add_reg:[50,52,93,120,216],add_reg_nam:[50,52,216],added:[12,13,187,188,189,190,192,193,224,241,260,266,268,272,274,275,278,279,280,281,282,289,313],addiction_regparam:88,adding:[1,6,7,31,55,58,72,114,120,230],addit:[0,45,52,55,58,59,74,77,86,103,104,111,114,118,119,120,125,160,169,194,195,197,209,216,222,246,249,293,294,311,313],addition:[6,48,59,128,129],address:[121,123],adelstein:174,adequ:63,adhd200:139,adhd200_40sub_preprocess:139,adhd:[50,73,105,139,313],adhd_dataset:[3,50,120],adjac:[28,313],adjacency_matrix:[268,269,288],adjust:[55,57,261,313],administr:125,admitedli:108,adopt:116,adult:[38,41,85,158,175,313],adulthood:[161,184],advanc:[45,94,111,114,125,128,160,187,233,249,282,312],advantag:[33,34,58,118,129],advic:111,advis:[89,127,162,254,293],affect:121,affect_perception_regparam:88,affect_recognition_regparam:88,affin:[31,34,39,60,77,80,81,83,95,126,129,187,188,189,190,192,193,194,195,209,226,227,228,229,231,236,237,238,239,240,241,242,244,246,247,248,249,253,257,258,287,293,298,300,301,308,309,312,313],affine_:[246,249],afni:174,after:[15,23,25,31,36,40,46,55,56,65,81,84,90,95,108,109,111,116,118,120,121,128,129,134,154,160,174,187,194,209,217,222,230,233,238,249,252,253,254,257,296,303],afterward:[96,118],again:[7,39,88,103,123,125,168,170,172],against:[51,120,170,260,279],age:[33,34,49,63,66,94,95,105,112,113,125,130,146,154,158,161,168,175,181,184,187,188,189,190,192,193,248,268,279,287,313],age_at_scan:138,age_group:158,age_pr:33,age_test:[33,34],age_train:[33,34],agency_regparam:88,ages:158,agg:[3,51,52,54,55,57,66,67,100,287],agglom:[39,108,293,313],agglomer:[39,108,189,190,293,294],agglomerativeclust:[39,293],aggreg:[22,30,48,113,141,189,190,313],aggress:[131,247,248,250,295],aging:123,agramfort:[],agre:[33,63,119,175,222,224],agreeableness_regparam:88,agreement:[33,51,63,175],ahdh:[61,95,120,139,209,216,250,261,282,303],aim:[20,25,111,119,192,193,313],ainafp:313,ajust:16,aka:[55,93,100,108,121,204],aki:313,akshai:313,alex:313,alexandr:[0,39,146,313],alexi:[65,313],alexis_chevali:313,alff:138,algebra:125,algorithm:[38,39,40,77,79,81,87,89,90,103,105,108,109,110,113,114,116,125,129,131,134,175,194,195,232,255,256,295,297,305,309],alia:311,align:[41,85,242,244,274],alik:143,all:[11,15,19,21,26,30,33,34,35,37,38,39,40,41,46,47,53,55,56,58,59,60,64,65,66,67,75,79,81,84,86,88,92,95,97,98,100,103,104,105,106,108,109,110,112,114,116,118,119,121,123,125,127,128,129,131,132,134,138,139,141,142,149,154,158,165,166,170,173,174,175,176,181,182,186,187,188,189,190,191,192,193,194,195,199,201,202,204,209,212,218,222,223,224,226,227,232,237,238,241,242,243,246,247,248,249,250,255,256,258,260,261,262,268,270,274,277,278,284,287,293,295,298,303,304,308,311,312,313],all_categori:21,all_coef_:[192,193],allel:168,allen2011:13,allen:[13,141,313],allen_rsn_2011:13,allevi:81,allow:[12,16,31,38,41,48,52,77,85,86,87,103,105,118,120,122,129,166,177,187,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yes:[187,188,189,190],yet:[0,54,68,69,89,111,174,280],yield:[22,30,65,67,72,97,104,110,111,112,113,116,117,120,121,170,191,195,205,209,211,216,225,313],ylabel:[17,21,23,33,38,45,47,48,128],ylim:23,ylorrd:48,ymean_:[192,193],ymin:[266,268,269,272,274,275,278,280,281,282],you:[0,7,14,15,16,24,30,33,34,35,38,39,48,50,51,55,59,63,74,79,84,86,88,93,95,96,98,100,103,104,105,106,108,109,110,111,112,113,114,116,117,119,123,125,127,128,129,137,139,145,146,154,158,164,165,166,170,175,181,194,195,246,249,261,266,268,272,274,275,278,280,281,282,285,289,290,292,293,308,311,313],young:[38,142,168,175],your:[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,38,39,40,41,42,43,44,45,46,47,48,50,51,52,53,54,55,56,57,58,59,60,62,63,64,65,66,67,68,69,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90,91,92,93,96,97,98,99,100,102,104,106,110,111,112,114,116,119,127,129,145,158,162,187,188,189,190,194,195,200,246,249,293,310,311,312],yslicer:[7,79],yt_big:32,yt_larg:32,yt_tall:32,ytick:[21,35,38,41,85],yxslicer:7,yzslicer:[7,30],z_image_path:53,z_img:92,z_map:[24,50,51,53,54,55,59,60,63,64,65,67,68,69,70,86,100,209,222],z_map_ftest:59,z_score:[50,53,54,55,56,58,59,60,63,64,65,67,68,69,70,93,100,197,209,222],z_score_left:93,z_scores_left:93,z_scores_right:93,z_slice:[10,33,90,91],z_val:208,z_val_left:93,z_val_right:93,z_valu:92,zaleski:106,zealand:[87,260],zero:[12,14,15,27,30,35,43,53,56,59,65,67,81,105,109,111,112,116,117,118,121,125,128,129,130,131,132,134,175,192,193,194,196,201,202,216,224,226,230,243,246,247,248,249,250,251,254,256,260,266,281,282,284,285,295,298,300,301,304,313],zero_divis:32,zeros_lik:[31,57,72,87],zijdenbo:161,zip:[12,21,23,25,29,32,37,40,41,43,48,85,86,88,93,95,109,125,146,161],zmap:[30,86],zmap_filenam:30,zollei:[148,152],zone:168,zscore:[100,246,247,248,249,250,304,313],zslicer:[7,59,76],zstat12:51,zuo:[148,174],zvi:313},titles:["People","9.2.15. Making a surface plot of a 3D statistical map","9.2.1. Basic Atlas plotting","9.2.3. Visualizing global patterns with a carpet plot","9.2.6. Matplotlib colormaps in Nilearn","9.2.2. Glass brain plotting in nilearn","9.2.18. Glass brain plotting in nilearn (all options)","9.2.19. More plotting tools from nilearn","9.2.13. Plotting tools in nilearn","9.2.9. Controling the contrast of the background when plotting","9.2.12. Plot Haxby masks","9.2.5. Visualizing multiscale functional brain parcellations","9.2.7. Visualizing a probablistic atlas: the default mode in the MSDL atlas","9.2.11. Visualizing 4D probabilistic atlas maps","9.2.17. Loading and plotting of a cortical surface atlas","9.2.16. Seed-based connectivity on the surface","9.2.14. Technical point: Illustration of the volume to surface sampling schemes","9.2.10. NeuroImaging volumes visualization","9.2.4. Visualizing Megatrawls Network Matrices from Human Connectome Project","Computation times","9.3.5. Decoding with ANOVA + SVM: face vs house in the Haxby dataset","9.3.12. Different classifiers in decoding the Haxby dataset","9.3.3. Decoding with FREM: face vs house object recognition","9.3.11. ROI-based decoding analysis in Haxby et al. dataset","9.3.9. Decoding of a dataset after GLM fit for signal extraction","9.3.10. Setting a parameter by cross-validation","9.3.7. The haxby dataset: different multi-class strategies","9.3.8. Searchlight analysis of face vs house recognition","9.3.6. Cortical surface-based searchlight decoding","9.3.1. Show stimuli of Haxby et al. dataset","9.3.2. FREM on Jimura et al \u201cmixed gambles\u201d dataset.","9.3.15. Encoding models for visual stimuli from Miyawaki et al. 2008","9.3.16. Reconstruction of visual stimuli from Miyawaki et al. 2008","9.3.13. Voxel-Based Morphometry on Oasis dataset","9.3.4. Voxel-Based Morphometry on Oasis dataset with Space-Net prior","9.3.14. Example of pattern recognition on simulated data","Computation times","9.4.6. Comparing connectomes on different reference atlases","9.4.5. Deriving spatial maps from group fMRI data using ICA and Dictionary Learning","9.4.11. Clustering methods to learn a brain parcellation from fMRI","9.4.10. Regions extraction using Dictionary Learning and functional connectomes","9.4.9. Classification of age groups using functional connectivity","9.4.3. Computing a connectome with sparse inverse covariance","9.4.8. Group Sparse inverse covariance for multi-subject connectome","9.4.1. Extracting signals of a probabilistic atlas of functional regions","9.4.7. Producing single subject maps of seed-to-voxel correlation","9.4.2. Extracting signals from a brain parcellation","9.4.4. Connectivity structure estimation on simulated data","9.4.12. Extract signals on spheres and plot a connectome","Computation times","9.5.4. Default Mode Network extraction of AHDH dataset","9.5.9. First level analysis of a complete BIDS dataset from openneuro","9.5.5. Examples of design matrices","9.5.8. Simple example of two-session fMRI model fitting","9.5.6. Analysis of an fMRI dataset with a Finite Impule Response (FIR) model","9.5.12. Understanding parameters of the first-level model","9.5.3. Example of explicit fixed effects fMRI model fitting","9.5.1. Example of hemodynamic reponse functions.","9.5.11. Example of surface-based first-level analysis","9.5.10. Predicted time series and residuals","9.5.7. Single-subject data (two sessions) in native space","Computation times","9.5.2. Example of a events.tsv file generation: the neurospin/localizer events.","9.6.6. Voxel-Based Morphometry on Oasis dataset","9.6.2. Second-level fMRI model: true positive proportion in clusters","9.6.8. Example of generic design in second-level models","9.6.1. Example of second level design matrix","9.6.7. Second-level fMRI model: one sample test","9.6.3. Second-level fMRI model: a paired-sample test","9.6.4. Second-level fMRI model: a two-sample test","9.6.5. Statistical testing of a second-level analysis","Computation times","9.7.10. Visualization of affine resamplings","9.7.2. Comparing the means of 2 images","9.7.5. Breaking an atlas of labels in separated regions","9.7.4. Regions Extraction of Default Mode Networks using Smith Atlas","9.7.8. Region Extraction using a t-statistical map (3D)","9.7.9. Understanding NiftiMasker and mask computation","9.7.1. Negating an image with math_img","9.7.7. Simple example of NiftiMasker use","9.7.6. Resample an image to a template","9.7.11. Computing a Region of Interest (ROI) mask manually","9.7.3. Smoothing an image","Computation times","9.8.10. Advanced decoding using scikit learn","9.8.4. Functional connectivity predicts age group","9.8.3. BIDS dataset first and second level analysis","9.8.9. Massively univariate analysis of face vs house recognition","9.8.8. NeuroVault cross-study ICA maps.","9.8.1. Multivariate decompositions: Independent component analysis of fMRI","9.8.6. Massively univariate analysis of a motor task from the Localizer dataset","9.8.2. Massively univariate analysis of a calculation task from the Localizer dataset","9.8.5. NeuroVault meta-analysis of stop-go paradigm studies.","9.8.7. Surface-based dataset first and second level analysis of a dataset","Computation times","9. Nilearn usage examples","9.1.3. 3D and 4D niimgs: handling and visualizing","9.1.4. A introduction tutorial to fMRI decoding","9.1.2. Basic nilearn example: manipulating and looking at data","9.1.1. Basic numerics and plotting with Python","9.1.5. Intro to GLM Analysis: a single-session, single-subject fMRI dataset","Computation times","7. Advanced usage: manual pipelines and scaling up","7.1. Building your own neuroimaging machine-learning pipeline","7.2. Downloading statistical maps from the Neurovault repository","3.2. Connectome extraction: inverse covariance for direct connections","3.1. Extracting times series to build a functional connectome","3. Functional connectivity and resting state","3.5. Clustering to parcellate the brain in regions","3.4. Region Extraction for better brain parcellations","3.3. Extracting functional brain networks: ICA and related","2.1. An introduction to decoding","2.2. Choosing the right predictive model for neuroimaging","2.3. FREM: fast ensembling of regularized models for robust decoding","2.6. Running scikit-learn functions for more control on the analysis","2. Decoding and MVPA: predicting from brain images","2.5. Searchlight : finding voxels containing information","2.4. SpaceNet: decoding with spatial structure for better maps","3.2.3.1. Group-sparse covariance estimation","Nilearn development process","5.2. First level models","5.1. An introduction to GLMs in fMRI statistical analysis","5. Analyzing fMRI using GLMs","5.3. Second level models","<no title>","1. Introduction: nilearn in a nutshell","6. Manipulation brain volumes with nilearn","6.1. Input and output: neuroimaging data representation","6.2. Manipulating images: resampling, smoothing, masking, ROIs\u2026","6.3. From neuroimaging volumes to data matrices: the masker objects","8.1.1. nilearn.connectome.ConnectivityMeasure","8.1.2. nilearn.connectome.GroupSparseCovariance","8.1.3. nilearn.connectome.GroupSparseCovarianceCV","8.1.7. nilearn.connectome.cov_to_corr","8.1.6. nilearn.connectome.group_sparse_covariance","8.1.8. nilearn.connectome.prec_to_partial","8.1.4. nilearn.connectome.sym_matrix_to_vec","8.1.5. nilearn.connectome.vec_to_sym_matrix","8.2.14. nilearn.datasets.fetch_abide_pcp","8.2.15. nilearn.datasets.fetch_adhd","8.2.9. nilearn.datasets.fetch_atlas_aal","8.2.11. nilearn.datasets.fetch_atlas_allen_2011","8.2.10. nilearn.datasets.fetch_atlas_basc_multiscale_2015","8.2.1. nilearn.datasets.fetch_atlas_craddock_2012","8.2.2. nilearn.datasets.fetch_atlas_destrieux_2009","8.2.3. nilearn.datasets.fetch_atlas_harvard_oxford","8.2.4. nilearn.datasets.fetch_atlas_msdl","8.2.12. nilearn.datasets.fetch_atlas_pauli_2017","8.2.29. nilearn.datasets.fetch_atlas_schaefer_2018","8.2.7. nilearn.datasets.fetch_atlas_smith_2009","8.2.27. nilearn.datasets.fetch_atlas_surf_destrieux","8.2.28. nilearn.datasets.fetch_atlas_talairach","8.2.8. nilearn.datasets.fetch_atlas_yeo_2011","8.2.41. nilearn.datasets.fetch_bids_langloc_dataset","8.2.32. nilearn.datasets.fetch_cobre","8.2.13. nilearn.datasets.fetch_coords_dosenbach_2010","8.2.5. nilearn.datasets.fetch_coords_power_2011","8.2.6. nilearn.datasets.fetch_coords_seitzman_2018","8.2.16. nilearn.datasets.fetch_development_fmri","8.2.49. nilearn.datasets.fetch_fiac_first_level","8.2.17. nilearn.datasets.fetch_haxby","8.2.18. nilearn.datasets.fetch_icbm152_2009","8.2.19. nilearn.datasets.fetch_icbm152_brain_gm_mask","8.2.40. nilearn.datasets.fetch_language_localizer_demo_dataset","8.2.20. nilearn.datasets.fetch_localizer_button_task","8.2.22. nilearn.datasets.fetch_localizer_calculation_task","8.2.21. nilearn.datasets.fetch_localizer_contrasts","8.2.46. nilearn.datasets.fetch_localizer_first_level","8.2.31. nilearn.datasets.fetch_megatrawls_netmats","8.2.23. nilearn.datasets.fetch_miyawaki2008","8.2.33. nilearn.datasets.fetch_neurovault","8.2.35. nilearn.datasets.fetch_neurovault_auditory_computation_task","8.2.34. nilearn.datasets.fetch_neurovault_ids","8.2.36. nilearn.datasets.fetch_neurovault_motor_task","8.2.24. nilearn.datasets.fetch_nyu_rest","8.2.30. nilearn.datasets.fetch_oasis_vbm","8.2.45. nilearn.datasets.fetch_openneuro_dataset","8.2.42. nilearn.datasets.fetch_openneuro_dataset_index","8.2.47. nilearn.datasets.fetch_spm_auditory","8.2.48. nilearn.datasets.fetch_spm_multimodal_fmri","8.2.26. nilearn.datasets.fetch_surf_fsaverage","8.2.25. nilearn.datasets.fetch_surf_nki_enhanced","8.2.37. nilearn.datasets.get_data_dirs","8.2.39. nilearn.datasets.load_mni152_brain_mask","8.2.38. nilearn.datasets.load_mni152_template","8.2.44. nilearn.datasets.patch_openneuro_dataset","8.2.43. nilearn.datasets.select_from_index","8.3.1. nilearn.decoding.Decoder","8.3.2. nilearn.decoding.DecoderRegressor","8.3.3. nilearn.decoding.FREMClassifier","8.3.4. nilearn.decoding.FREMRegressor","8.3.7. nilearn.decoding.SearchLight","8.3.5. nilearn.decoding.SpaceNetClassifier","8.3.6. nilearn.decoding.SpaceNetRegressor","8.4.1. nilearn.decomposition.CanICA","8.4.2. nilearn.decomposition.DictLearning","8.12.4. nilearn.glm.ARModel","8.12.1. nilearn.glm.Contrast","8.12.2. nilearn.glm.FContrastResults","8.12.6. nilearn.glm.LikelihoodModelResults","8.12.5. nilearn.glm.OLSModel","8.12.7. nilearn.glm.RegressionResults","8.12.8. nilearn.glm.SimpleRegressionResults","8.12.3. nilearn.glm.TContrastResults","8.12.13. nilearn.glm.cluster_level_inference","8.12.9. nilearn.glm.compute_contrast","8.12.10. nilearn.glm.compute_fixed_effects","8.12.11. nilearn.glm.expression_to_contrast_vector","8.12.12. nilearn.glm.fdr_threshold","8.12.15.1. nilearn.glm.first_level.FirstLevelModel","8.12.15.2. nilearn.glm.first_level.check_design_matrix","8.12.15.3. nilearn.glm.first_level.compute_regressor","8.12.15.4. nilearn.glm.first_level.first_level_from_bids","8.12.15.5. nilearn.glm.first_level.glover_dispersion_derivative","8.12.15.6. nilearn.glm.first_level.glover_hrf","8.12.15.7. nilearn.glm.first_level.glover_time_derivative","8.12.15.8. nilearn.glm.first_level.make_first_level_design_matrix","8.12.15.9. nilearn.glm.first_level.mean_scaling","8.12.15.10. nilearn.glm.first_level.run_glm","8.12.15.11. nilearn.glm.first_level.spm_dispersion_derivative","8.12.15.12. nilearn.glm.first_level.spm_hrf","8.12.15.13. nilearn.glm.first_level.spm_time_derivative","8.12.16.1. nilearn.glm.second_level.SecondLevelModel","8.12.16.2. nilearn.glm.second_level.make_second_level_design_matrix","8.12.16.3. nilearn.glm.second_level.non_parametric_inference","8.12.14. nilearn.glm.threshold_stats_img","8.5.1. nilearn.image.clean_img","8.5.2. nilearn.image.concat_imgs","8.5.3. nilearn.image.coord_transform","8.5.4. nilearn.image.copy_img","8.5.5. nilearn.image.crop_img","8.5.6. nilearn.image.get_data","8.5.7. nilearn.image.high_variance_confounds","8.5.8. nilearn.image.index_img","8.5.9. nilearn.image.iter_img","8.5.10. nilearn.image.largest_connected_component_img","8.5.11. nilearn.image.load_img","8.5.12. nilearn.image.math_img","8.5.13. nilearn.image.mean_img","8.5.14. nilearn.image.new_img_like","8.5.17. nilearn.image.reorder_img","8.5.15. nilearn.image.resample_img","8.5.16. nilearn.image.resample_to_img","8.5.18. nilearn.image.smooth_img","8.5.19. nilearn.image.swap_img_hemispheres","8.5.20. nilearn.image.threshold_img","8.6.2. nilearn.input_data.MultiNiftiMasker","8.6.3. nilearn.input_data.NiftiLabelsMasker","8.6.4. nilearn.input_data.NiftiMapsMasker","8.6.1. nilearn.input_data.NiftiMasker","8.6.5. nilearn.input_data.NiftiSpheresMasker","8.7.8. nilearn.masking.apply_mask","8.7.5. nilearn.masking.compute_background_mask","8.7.3. nilearn.masking.compute_brain_mask","8.7.1. nilearn.masking.compute_epi_mask","8.7.6. nilearn.masking.compute_multi_background_mask","8.7.2. nilearn.masking.compute_multi_epi_mask","8.7.4. nilearn.masking.compute_multi_gray_matter_mask","8.7.7. nilearn.masking.intersect_masks","8.7.9. nilearn.masking.unmask","8.9.1. nilearn.mass_univariate.permuted_ols","8.10.32. nilearn.plotting.displays.OrthoSlicer","8.10.1. nilearn.plotting.find_cut_slices","8.10.3. nilearn.plotting.find_parcellation_cut_coords","8.10.4. nilearn.plotting.find_probabilistic_atlas_cut_coords","8.10.2. nilearn.plotting.find_xyz_cut_coords","8.10.5. nilearn.plotting.plot_anat","8.10.16. nilearn.plotting.plot_carpet","8.10.12. nilearn.plotting.plot_connectome","8.10.13. nilearn.plotting.plot_connectome_strength","8.10.25. nilearn.plotting.plot_contrast_matrix","8.10.23. nilearn.plotting.plot_design_matrix","8.10.7. nilearn.plotting.plot_epi","8.10.24. nilearn.plotting.plot_event","8.10.11. nilearn.plotting.plot_glass_brain","8.10.6. nilearn.plotting.plot_img","8.10.22. nilearn.plotting.plot_img_comparison","8.10.21. nilearn.plotting.plot_img_on_surf","8.10.14. nilearn.plotting.plot_markers","8.10.8. nilearn.plotting.plot_matrix","8.10.15. nilearn.plotting.plot_prob_atlas","8.10.9. nilearn.plotting.plot_roi","8.10.10. nilearn.plotting.plot_stat_map","8.10.17. nilearn.plotting.plot_surf","8.10.19. nilearn.plotting.plot_surf_contours","8.10.18. nilearn.plotting.plot_surf_roi","8.10.20. nilearn.plotting.plot_surf_stat_map","8.10.31. nilearn.plotting.show","8.10.28. nilearn.plotting.view_connectome","8.10.30. nilearn.plotting.view_img","8.10.27. nilearn.plotting.view_img_on_surf","8.10.29. nilearn.plotting.view_markers","8.10.26. nilearn.plotting.view_surf","8.8.8. nilearn.regions.Parcellations","8.8.9. nilearn.regions.ReNA","8.8.7. nilearn.regions.RegionExtractor","8.8.2. nilearn.regions.connected_label_regions","8.8.1. nilearn.regions.connected_regions","8.8.3. nilearn.regions.img_to_signals_labels","8.8.5. nilearn.regions.img_to_signals_maps","8.8.4. nilearn.regions.signals_to_img_labels","8.8.6. nilearn.regions.signals_to_img_maps","8.13.1. nilearn.reporting.get_clusters_table","8.13.2. nilearn.reporting.make_glm_report","8.11.1. nilearn.signal.clean","8.11.2. nilearn.signal.high_variance_confounds","8.14.1. nilearn.surface.load_surf_data","8.14.2. nilearn.surface.load_surf_mesh","8.14.3. nilearn.surface.vol_to_surf","8. Reference documentation: all nilearn functions","A Quick Guide to Migrating Nistats Code to Nilearn","4. Plotting brain images","User guide: table of 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