├── .codespellrc ├── .gitattributes ├── .github └── workflows │ ├── build.yml │ ├── linkcheck.yml │ ├── linting.yml │ └── spelling.yml ├── .gitignore ├── .markdown-link-check.json ├── .markdownlintrc ├── .pyspelling.yaml ├── .wordlist.txt ├── LICENSE ├── README.md ├── addons └── abbreviations.md ├── docs ├── assets │ ├── bib │ │ ├── elsevier-vancouver.csl │ │ └── spm.bib │ ├── css │ │ └── extra.css │ ├── figures │ │ ├── batch.png │ │ ├── coregistration_batch.png │ │ ├── coregistration_plot.png │ │ ├── daiss │ │ │ ├── beamforming │ │ │ │ ├── daiss_beamformer_batch_data.png │ │ │ │ ├── daiss_beamformer_batch_features.png │ │ │ │ ├── daiss_beamformer_batch_inverse.png │ │ │ │ ├── daiss_beamformer_batch_output.png │ │ │ │ ├── daiss_beamformer_batch_sources.png │ │ │ │ ├── daiss_beamformer_batch_view.png │ │ │ │ ├── daiss_beamformer_batch_write.png │ │ │ │ ├── daiss_beamformer_overlay.png │ │ │ │ ├── megin_batch_data.png │ │ │ │ ├── megin_batch_features.png │ │ │ │ ├── megin_batch_inverse.png │ │ │ │ ├── megin_batch_output_powerimage.png │ │ │ │ ├── megin_batch_output_ve.png │ │ │ │ ├── megin_batch_sources.png │ │ │ │ ├── megin_batch_whiten.png │ │ │ │ ├── megin_batch_write_powerimage.png │ │ │ │ ├── megin_batch_write_ve.png │ │ │ │ ├── megin_contrast_result.png │ │ │ │ ├── megin_eigenspectrum_post.png │ │ │ │ ├── megin_eigenspectrum_pre.png │ │ │ │ ├── megin_eigenspectrum_v2.png │ │ │ │ ├── megin_ve_results.png │ │ │ │ ├── opm_coreg.png │ │ │ │ ├── opm_data_sim_batch.png │ │ │ │ ├── opm_eigenspectrum_post.png │ │ │ │ ├── opm_eigenspectrum_pre.png │ │ │ │ ├── view_glass_brain.png │ │ │ │ └── view_glass_brain_bad.png │ │ │ ├── daiss_workflow.png │ │ │ └── daiss_workflow.svg │ │ ├── development │ │ │ ├── browse.png │ │ │ ├── commit.png │ │ │ └── download.png │ │ ├── git_clone_repo.png │ │ ├── git_config.png │ │ ├── git_pull_rebase.svg │ │ ├── git_windows_cmd.png │ │ ├── git_windows_eol.png │ │ ├── git_windows_rebase.png │ │ ├── manual │ │ │ ├── FieldMap │ │ │ │ ├── fieldmap_gui1.png │ │ │ │ ├── fieldmap_results1.png │ │ │ │ └── fieldmap_taskmgr.png │ │ │ ├── auditory │ │ │ │ ├── aud_bayes.png │ │ │ │ ├── aud_bayes2.png │ │ │ │ ├── aud_orth.png │ │ │ │ ├── bias.png │ │ │ │ ├── checkreg.png │ │ │ │ ├── command.png │ │ │ │ ├── con_man.png │ │ │ │ ├── con_man2.png │ │ │ │ ├── con_man3.png │ │ │ │ ├── contrast.png │ │ │ │ ├── coreg.png │ │ │ │ ├── design.png │ │ │ │ ├── explore.png │ │ │ │ ├── fitted.png │ │ │ │ ├── gray.png │ │ │ │ ├── interactive.png │ │ │ │ ├── realign.png │ │ │ │ ├── render.png │ │ │ │ ├── sections.png │ │ │ │ ├── slices.png │ │ │ │ ├── smooth.png │ │ │ │ ├── spm1.png │ │ │ │ └── volume.png │ │ │ ├── batch │ │ │ │ ├── batch_basicio.png │ │ │ │ ├── batch_dependencies.png │ │ │ │ ├── batch_multi_subject_template.png │ │ │ │ ├── batch_single_subject.png │ │ │ │ ├── batch_single_subject_template.png │ │ │ │ ├── batch_single_subject_template_nodeps.png │ │ │ │ ├── batch_spm.png │ │ │ │ └── flow.png │ │ │ ├── bms │ │ │ │ ├── Slide1.png │ │ │ │ ├── Slide2.png │ │ │ │ ├── Slide3.png │ │ │ │ ├── Slide4.png │ │ │ │ ├── Slide5.png │ │ │ │ └── Slide6.png │ │ │ ├── dartelguide │ │ │ │ ├── AtoB.png │ │ │ │ ├── Template2individual.png │ │ │ │ ├── VBM.png │ │ │ │ ├── averages.png │ │ │ │ ├── imported.png │ │ │ │ └── sharpening.png │ │ │ ├── dcm │ │ │ │ ├── Fig1.png │ │ │ │ ├── Fig2.png │ │ │ │ ├── Fig3.png │ │ │ │ ├── Fig4.png │ │ │ │ ├── Fig5.png │ │ │ │ ├── Fig6.png │ │ │ │ ├── Fig7.png │ │ │ │ ├── Fig8.png │ │ │ │ ├── Fig9.png │ │ │ │ ├── dcm_mod_bwd.png │ │ │ │ └── dcm_mod_fwd.png │ │ │ ├── dcm_csd │ │ │ │ ├── dcm_csd_fig1.png │ │ │ │ ├── dcm_csd_fig2.png │ │ │ │ ├── dcm_csd_fig3.png │ │ │ │ └── dcm_csd_fig4.png │ │ │ ├── dcm_erp │ │ │ │ ├── A_C_matrices.png │ │ │ │ ├── BMC.png │ │ │ │ ├── B_matrix.png │ │ │ │ ├── DCM_user_interface.png │ │ │ │ └── evoked_responses.png │ │ │ ├── dcm_fnirs │ │ │ │ ├── dcm_fit.png │ │ │ │ ├── est_conn.png │ │ │ │ ├── est_neural.png │ │ │ │ └── gui_con.png │ │ │ ├── dcm_ir │ │ │ │ ├── irfigure1.png │ │ │ │ ├── irfigure2.png │ │ │ │ ├── irfigure3.png │ │ │ │ ├── irfigure4.png │ │ │ │ ├── irfigure5_FlBl.png │ │ │ │ ├── irfigure5_FlBn.png │ │ │ │ ├── irfigure5_FnBl.png │ │ │ │ └── irfigure6.png │ │ │ ├── dcm_phase │ │ │ │ ├── bms_phase.png │ │ │ │ ├── endogenous.png │ │ │ │ ├── model4_conn.png │ │ │ │ ├── modulatory.png │ │ │ │ └── phase_models.png │ │ │ ├── dcm_rs │ │ │ │ ├── Fig1.png │ │ │ │ ├── Fig2.png │ │ │ │ ├── Fig3.png │ │ │ │ └── dcm_mod_full.png │ │ │ ├── dcm_ssr │ │ │ │ ├── fig1.png │ │ │ │ ├── fig2.png │ │ │ │ └── fig3.png │ │ │ ├── faces │ │ │ │ ├── cat3_contrast.png │ │ │ │ ├── cat3_psth.png │ │ │ │ ├── cat5_volume.png │ │ │ │ ├── cat_contrasts.png │ │ │ │ ├── cat_design.png │ │ │ │ ├── cat_explore.png │ │ │ │ ├── command.png │ │ │ │ ├── coreg.png │ │ │ │ ├── dropout.png │ │ │ │ ├── face_ar1.png │ │ │ │ ├── face_bayes.png │ │ │ │ ├── face_stim.png │ │ │ │ ├── face_timing.png │ │ │ │ ├── famous_lag.png │ │ │ │ ├── famous_lag_mip.png │ │ │ │ ├── gray.png │ │ │ │ ├── movements.png │ │ │ │ ├── par_design.png │ │ │ │ ├── realign.png │ │ │ │ └── smooth.png │ │ │ ├── faces_group │ │ │ │ ├── Fdisp.png │ │ │ │ ├── Ftemp.png │ │ │ │ ├── can_weighted_fir.png │ │ │ │ ├── f1_res.png │ │ │ │ ├── fir_covariance.png │ │ │ │ ├── fir_design.png │ │ │ │ ├── fir_results.png │ │ │ │ ├── informed_covariance.png │ │ │ │ ├── informed_design.png │ │ │ │ ├── informed_plot.png │ │ │ │ ├── informed_results.png │ │ │ │ ├── informed_t.png │ │ │ │ ├── nullall.png │ │ │ │ ├── nullcan.png │ │ │ │ └── t1.png │ │ │ ├── fmri_est │ │ │ │ ├── basis.png │ │ │ │ ├── bayes_options.png │ │ │ │ └── est_method.png │ │ │ ├── fmri_spec │ │ │ │ ├── design.png │ │ │ │ ├── fmri_model.png │ │ │ │ └── reg4.png │ │ │ ├── images │ │ │ │ ├── disp1.png │ │ │ │ ├── seg1.png │ │ │ │ └── seg2.png │ │ │ ├── meeg │ │ │ │ └── eeg_review_buttons.png │ │ │ ├── meeg_artefact │ │ │ │ ├── figure1.png │ │ │ │ ├── figure2.png │ │ │ │ ├── figure3.png │ │ │ │ ├── figure4.png │ │ │ │ ├── figure5.png │ │ │ │ └── figure6.png │ │ │ ├── meg_sloc │ │ │ │ ├── slide1.png │ │ │ │ ├── slide10.png │ │ │ │ ├── slide11.png │ │ │ │ ├── slide12.png │ │ │ │ ├── slide13.png │ │ │ │ ├── slide14.png │ │ │ │ ├── slide15.png │ │ │ │ ├── slide2.png │ │ │ │ ├── slide3.png │ │ │ │ ├── slide4.png │ │ │ │ ├── slide5.png │ │ │ │ ├── slide6.png │ │ │ │ ├── slide7.png │ │ │ │ ├── slide8.png │ │ │ │ └── slide9.png │ │ │ ├── mmn │ │ │ │ ├── 3DSPM.png │ │ │ │ ├── coreg.png │ │ │ │ ├── data_and_design.png │ │ │ │ ├── dcm_gui.png │ │ │ │ ├── electro_model.png │ │ │ │ ├── erp_c23.png │ │ │ │ ├── forward.png │ │ │ │ ├── invert.png │ │ │ │ ├── mesh.png │ │ │ │ ├── neuronal_model.png │ │ │ │ ├── source_erps.png │ │ │ │ ├── source_gui.png │ │ │ │ └── topo1.png │ │ │ ├── multi │ │ │ │ ├── figure1.png │ │ │ │ ├── figure10.png │ │ │ │ ├── figure11.png │ │ │ │ ├── figure12.png │ │ │ │ ├── figure13.png │ │ │ │ ├── figure14.png │ │ │ │ ├── figure15.png │ │ │ │ ├── figure16.png │ │ │ │ ├── figure2.png │ │ │ │ ├── figure3.png │ │ │ │ ├── figure4.png │ │ │ │ ├── figure5.png │ │ │ │ ├── figure6.png │ │ │ │ ├── figure7.png │ │ │ │ ├── figure8.png │ │ │ │ └── figure9.png │ │ │ ├── pet │ │ │ │ ├── all.png │ │ │ │ ├── all_results.png │ │ │ │ ├── cluster.png │ │ │ │ ├── conj.png │ │ │ │ ├── interactive.png │ │ │ │ ├── masked_con.png │ │ │ │ ├── masked_res.png │ │ │ │ ├── multi_design.png │ │ │ │ ├── sc_design.png │ │ │ │ ├── single_design.png │ │ │ │ ├── st_design.png │ │ │ │ ├── subject2.png │ │ │ │ ├── svc.png │ │ │ │ ├── table.png │ │ │ │ └── voi.png │ │ │ └── ppi │ │ │ │ ├── Fig1.png │ │ │ │ ├── Fig10.png │ │ │ │ ├── Fig11.png │ │ │ │ ├── Fig12.png │ │ │ │ ├── Fig13.png │ │ │ │ ├── Fig14.png │ │ │ │ ├── Fig15.png │ │ │ │ ├── Fig16.png │ │ │ │ ├── Fig2.png │ │ │ │ ├── Fig3.png │ │ │ │ ├── Fig4.png │ │ │ │ ├── Fig5.png │ │ │ │ ├── Fig6.png │ │ │ │ ├── Fig7.png │ │ │ │ ├── Fig8.png │ │ │ │ └── Fig9.png │ │ ├── matlab_setpath.png │ │ ├── normalisation_batch.png │ │ ├── opm │ │ │ ├── 90ms.png │ │ │ ├── Logo.png │ │ │ ├── aef_example_erf.PNG │ │ │ ├── aef_example_psd_after.PNG │ │ │ ├── aef_example_psd_after_filters.PNG │ │ │ ├── aef_example_psd_before.PNG │ │ │ ├── aef_example_topo.PNG │ │ │ ├── aef_example_topo_ft.PNG │ │ │ ├── badchannel_psd.png │ │ │ ├── coreg.png │ │ │ ├── coreg_new.png │ │ │ ├── coreg_template.png │ │ │ ├── coreg_template_new.PNG │ │ │ ├── evoked_field.png │ │ │ ├── mesh_select.png │ │ │ ├── mni15.png │ │ │ ├── mni15Whole.png │ │ │ ├── processed_psd.png │ │ │ ├── raw_psd.png │ │ │ ├── source_evoked_t.png │ │ │ ├── source_evoked_t_new.png │ │ │ └── t-stat.png │ │ ├── realignment_batch_1.png │ │ ├── realignment_batch_2.png │ │ ├── realignment_plot.png │ │ ├── segmentation_batch.png │ │ ├── slice_timing_batch.png │ │ ├── slice_timing_figure.png │ │ ├── smoothing_batch.png │ │ ├── spm_interface.png │ │ ├── tutorials │ │ │ ├── dcm-attention │ │ │ │ ├── Fig_BMS.png │ │ │ │ ├── Fig_estimation.png │ │ │ │ ├── GLM.png │ │ │ │ ├── V5_timeseries.png │ │ │ │ ├── connections.png │ │ │ │ ├── dcm_attention_figures.pptx │ │ │ │ ├── dcm_attention_stimuli.png │ │ │ │ ├── model_bwd.png │ │ │ │ ├── model_fwd.png │ │ │ │ └── two_models.png │ │ │ ├── dcm-motor │ │ │ │ ├── BMA_hypotheses_driven.PNG │ │ │ │ ├── BMC_hypotheses_driven.PNG │ │ │ │ ├── DCM-design.png │ │ │ │ ├── PEB_Ep.png │ │ │ │ ├── PEB_review_following_specification.PNG │ │ │ │ ├── dcm_motor_design_and_rois.png │ │ │ │ ├── dcm_motor_figures.pptx │ │ │ │ ├── design_matrix.png │ │ │ │ ├── model_space.png │ │ │ │ └── results_summary.png │ │ │ ├── dcm │ │ │ │ ├── dcm_fmri_firstlevel_Amatrix.png │ │ │ │ ├── dcm_fmri_firstlevel_Bmatrix.png │ │ │ │ ├── dcm_fmri_firstlevel_fullmodel.png │ │ │ │ ├── dcm_fmri_firstlevel_ldFcoords.png │ │ │ │ ├── dcm_fmri_firstlevel_modelcomparison.png │ │ │ │ ├── dcm_fmri_firstlevel_modelspace.png │ │ │ │ ├── dcm_fmri_firstlevel_pyramids_palmtrees.png │ │ │ │ ├── dcm_fmri_firstlevel_run_glm.png │ │ │ │ ├── dcm_fmri_firstlevel_sections.png │ │ │ │ ├── dcm_fmri_secondlevel_bma.png │ │ │ │ ├── dcm_fmri_secondlevel_bmc.png │ │ │ │ ├── dcm_fmri_secondlevel_peb_review.png │ │ │ │ ├── dcm_tutorial_figures.pptx │ │ │ │ └── dcm_tutorial_workflow.png │ │ │ ├── fmri │ │ │ │ └── group │ │ │ │ │ ├── semantic_factorial_design_matrix.png │ │ │ │ │ ├── semantic_factorial_results.png │ │ │ │ │ ├── semantic_first_level_design_matrix.png │ │ │ │ │ ├── semantic_one_sample_ttest_design_matrix.png │ │ │ │ │ ├── semantic_one_sample_ttest_results_1.png │ │ │ │ │ ├── semantic_one_sample_ttest_results_2.png │ │ │ │ │ ├── semantic_two_sample_ttest_design_matrix.png │ │ │ │ │ └── semantic_two_sample_ttest_results.png │ │ │ ├── meeg │ │ │ │ └── meeg_firstlevel │ │ │ │ │ ├── add-batch-artefacts.png │ │ │ │ │ ├── add-batch-filter.png │ │ │ │ │ ├── artefacts-options.png │ │ │ │ │ ├── auditory-scalp.png │ │ │ │ │ ├── conv-basis-set.png │ │ │ │ │ ├── conv-other-conds.png │ │ │ │ │ ├── conv-top.png │ │ │ │ │ ├── filter-options.png │ │ │ │ │ ├── prepare-options.png │ │ │ │ │ ├── prepare-post-conv.png │ │ │ │ │ ├── result-response.png │ │ │ │ │ ├── scalp.png │ │ │ │ │ ├── select-prepare-batch.png │ │ │ │ │ ├── tactile-scalp.png │ │ │ │ │ └── visual-scalp.png │ │ │ ├── vbm │ │ │ │ ├── vbm-CheckReg14.png │ │ │ │ ├── vbm-Dartel14.png │ │ │ │ ├── vbm-Display14.png │ │ │ │ ├── vbm-Imported14.png │ │ │ │ ├── vbm-Processed14.png │ │ │ │ ├── vbm-SegResults14.png │ │ │ │ ├── vbm-Segment14.png │ │ │ │ ├── vbm-Segment24.png │ │ │ │ ├── vbm-Shoot24.png │ │ │ │ ├── vbm-WriteNorm24.png │ │ │ │ ├── vbm-find-set-path.png │ │ │ │ ├── vbm-handedness.png │ │ │ │ ├── vbm-interpretation.png │ │ │ │ ├── vbm-new_segment.png │ │ │ │ ├── vbm-normalise2mni.png │ │ │ │ ├── vbm-preprocessed.png │ │ │ │ ├── vbm-results1.png │ │ │ │ ├── vbm-results2.png │ │ │ │ ├── vbm-run_dartel.png │ │ │ │ ├── vbm-set-path.png │ │ │ │ └── vbm-template8.png │ │ │ └── vbm_spm_course_2024 │ │ │ │ ├── analysis_batch_1.png │ │ │ │ ├── analysis_batch_2.png │ │ │ │ ├── analysis_batch_3.png │ │ │ │ ├── analysis_batch_4.png │ │ │ │ ├── analysis_check_reg.png │ │ │ │ ├── analysis_contrast.png │ │ │ │ ├── analysis_design_matrix.png │ │ │ │ ├── analysis_menu_1.png │ │ │ │ ├── analysis_menu_2.png │ │ │ │ ├── analysis_menu_3.png │ │ │ │ ├── analysis_menu_4.png │ │ │ │ ├── analysis_results_1.png │ │ │ │ ├── analysis_results_2.png │ │ │ │ ├── analysis_results_3.png │ │ │ │ ├── analysis_results_4.png │ │ │ │ ├── data_prep_dicom_import_batch_1.png │ │ │ │ ├── data_prep_dicom_import_batch_2.png │ │ │ │ ├── data_prep_dicom_import_menu.png │ │ │ │ ├── index_dir_structure.png │ │ │ │ ├── index_workflow.png │ │ │ │ ├── preprocessing_check_reg_1.png │ │ │ │ ├── preprocessing_check_reg_2.png │ │ │ │ ├── preprocessing_check_reg_3.png │ │ │ │ ├── preprocessing_segment_batch_1.png │ │ │ │ ├── preprocessing_segment_batch_2.png │ │ │ │ ├── preprocessing_segment_batch_3.png │ │ │ │ ├── preprocessing_segment_check_reg.png │ │ │ │ ├── preprocessing_segment_menu.png │ │ │ │ ├── preprocessing_shoot_batch_1.png │ │ │ │ ├── preprocessing_shoot_batch_2.png │ │ │ │ ├── preprocessing_shoot_batch_3.png │ │ │ │ ├── preprocessing_shoot_batch_4.png │ │ │ │ ├── preprocessing_shoot_menu.png │ │ │ │ ├── preprocessing_shoot_population_average.png │ │ │ │ ├── preprocessing_warp_batch_1.png │ │ │ │ ├── preprocessing_warp_batch_2.png │ │ │ │ ├── preprocessing_warp_check_reg.png │ │ │ │ └── preprocessing_workflow.png │ │ └── wikibooks │ │ │ ├── EP2D_BOLD_resting-state_Siemens_scanner_sequence_printout.png │ │ │ ├── ExampleDCMGraph.png │ │ │ ├── Exponential_decay_mechanism.svg │ │ │ ├── Gnumex_setup.png │ │ │ ├── Mricron-slice-order-nifti.png │ │ │ ├── SPM_Imcalc_mean_image.png │ │ │ ├── SPM_hemodynamic_response_function.png │ │ │ ├── Spm_4D_example.png │ │ │ ├── Spm_reorient.png │ │ │ ├── ThreeVertexGraph.png │ │ │ └── Two_State_DCM_for_fMRI.png │ ├── images │ │ ├── London_skyline_700b.jpg │ │ ├── spm_favicon.png │ │ ├── spm_front.png │ │ └── spm_logo_white.png │ ├── javascripts │ │ └── mathjax.js │ └── videos │ │ ├── inspecting_anatomical_data.mp4 │ │ ├── inspecting_functional_data.mp4 │ │ └── realignment.mp4 ├── courses │ ├── conduct.md │ ├── eeg_meg │ │ ├── eeg_datasets.md │ │ ├── index.md │ │ ├── programme.md │ │ └── recordings │ │ │ ├── bayesian_inference.md │ │ │ ├── bayesian_model_selection.md │ │ │ ├── daiss.md │ │ │ ├── dcm_cross_spectral_desities.md │ │ │ ├── dcm_evoked_responses.md │ │ │ ├── dcm_principles.md │ │ │ ├── glm_classical_inference.md │ │ │ ├── glm_convolutional.md │ │ │ ├── group_analysis.md │ │ │ ├── index.md │ │ │ ├── introduction.md │ │ │ ├── multiple_comparisons.md │ │ │ ├── opm_analysis.md │ │ │ ├── preprocessing.md │ │ │ ├── source_analysis.md │ │ │ └── what_measured.md │ ├── fmri_vbm │ │ ├── index.md │ │ ├── learning_objectives.md │ │ ├── maths │ │ │ ├── index.md │ │ │ └── linear_algebra.md │ │ ├── mri_datasets.md │ │ ├── preparatory_materials.md │ │ ├── programme.md │ │ ├── programme_online.md │ │ ├── recordings │ │ │ ├── bayesian_inference.md │ │ │ ├── contrasts_classical_inference.md │ │ │ ├── dcm_hypothesis_testing.md │ │ │ ├── dcm_introduction.md │ │ │ ├── event_related_fmri.md │ │ │ ├── experimental_design.md │ │ │ ├── fmri_legacy_recordings.md │ │ │ ├── glm.md │ │ │ ├── group_analysis.md │ │ │ ├── index.md │ │ │ ├── preprocessing.md │ │ │ ├── random_field_theory.md │ │ │ └── vbm.md │ │ └── slides │ │ │ ├── 2023 │ │ │ ├── 01_fmri_preprocessing.pdf │ │ │ ├── 02_general_linear_model.pdf │ │ │ ├── 03_contrasts_and_inference.pdf │ │ │ ├── 04_group_analysis.pdf │ │ │ ├── 05_thresholding.pdf │ │ │ ├── 06_voxel_based_morphometry.pdf │ │ │ ├── 07_experimental_design.pdf │ │ │ ├── 08_event_related_fmri.pdf │ │ │ ├── 09_bayesian_inference.pdf │ │ │ ├── 10_dcm_introduction.pdf │ │ │ └── 11_dcm_testing_hypotheses.pdf │ │ │ └── 2024 │ │ │ └── 07_experimental_design.pdf │ └── index.md ├── development │ ├── compilation │ │ ├── index.md │ │ ├── linux.md │ │ ├── macos.md │ │ └── windows.md │ ├── git.md │ ├── guidelines.md │ ├── index.md │ ├── material.md │ ├── release.md │ ├── ssh_authentication.md │ ├── testing.md │ └── versioning.md ├── index.md ├── installation │ ├── containers.md │ ├── index.md │ ├── octave.md │ └── standalone.md ├── manual │ ├── FieldMap │ │ └── fieldmap.md │ ├── auditory │ │ └── auditory.md │ ├── batch │ │ ├── batch.md │ │ └── devel.md │ ├── bms │ │ └── bms.md │ ├── dartelguide │ │ └── dartelguide.md │ ├── dcm │ │ └── dcm.md │ ├── dcm_fnirs │ │ └── dcm_fnirs.md │ ├── dcm_rs │ │ └── dcm_rs.md │ ├── faces_group │ │ └── face_group.md │ ├── fmri_est │ │ └── fmri_est.md │ ├── fmri_spec │ │ └── fmri_spec.md │ ├── mixed_effects │ │ └── mixed_effects.md │ ├── pet │ │ └── pet.md │ └── ppi │ │ └── ppi.md ├── reading │ └── spm_history.md ├── reference │ ├── MEEG │ │ ├── eeg_DCM.md │ │ ├── eeg_VBECD.md │ │ ├── eeg_imaging.md │ │ ├── eeg_preprocessing.md │ │ ├── eeg_sensoranalysis.md │ │ └── index.md │ ├── docs │ │ ├── index.md │ │ ├── spm.dcm.bms.inference.md │ │ ├── spm.dcm.bms.results.md │ │ ├── spm.dcm.estimate.md │ │ ├── spm.dcm.peb.compare.md │ │ ├── spm.dcm.peb.peb_review.md │ │ ├── spm.dcm.peb.predict.md │ │ ├── spm.dcm.peb.reduce_all.md │ │ ├── spm.dcm.peb.specify.md │ │ ├── spm.dcm.spec.fmri.group.md │ │ ├── spm.dcm.spec.fmri.inputs.md │ │ ├── spm.dcm.spec.fmri.regions.md │ │ ├── spm.dcm.spec.meeg.md │ │ ├── spm.meeg.OPM.create.md │ │ ├── spm.meeg.OPM.denoise.md │ │ ├── spm.meeg.OPM.epoch.md │ │ ├── spm.meeg.averaging.average.md │ │ ├── spm.meeg.averaging.contrast.md │ │ ├── spm.meeg.averaging.grandmean.md │ │ ├── spm.meeg.convert.md │ │ ├── spm.meeg.images.collapse.md │ │ ├── spm.meeg.images.convert2images.md │ │ ├── spm.meeg.modelling.convmodel.md │ │ ├── spm.meeg.modelling.eegreg.md │ │ ├── spm.meeg.other.copy.md │ │ ├── spm.meeg.other.delete.md │ │ ├── spm.meeg.other.review.md │ │ ├── spm.meeg.preproc.artefact.md │ │ ├── spm.meeg.preproc.bc.md │ │ ├── spm.meeg.preproc.combineplanar.md │ │ ├── spm.meeg.preproc.correct.md │ │ ├── spm.meeg.preproc.crop.md │ │ ├── spm.meeg.preproc.downsample.md │ │ ├── spm.meeg.preproc.epoch.md │ │ ├── spm.meeg.preproc.filter.md │ │ ├── spm.meeg.preproc.fuse.md │ │ ├── spm.meeg.preproc.merge.md │ │ ├── spm.meeg.preproc.montage.md │ │ ├── spm.meeg.preproc.prepare.md │ │ ├── spm.meeg.preproc.reduce.md │ │ ├── spm.meeg.preproc.remove.md │ │ ├── spm.meeg.preproc.sconfounds.md │ │ ├── spm.meeg.source.coregshift.md │ │ ├── spm.meeg.source.dipfit.md │ │ ├── spm.meeg.source.eeg_shp_distort.md │ │ ├── spm.meeg.source.eeg_shp_gainmat.md │ │ ├── spm.meeg.source.extract.md │ │ ├── spm.meeg.source.headmodel.md │ │ ├── spm.meeg.source.headmodelhelmet.md │ │ ├── spm.meeg.source.inv_mix.md │ │ ├── spm.meeg.source.invert.md │ │ ├── spm.meeg.source.invertiter.md │ │ ├── spm.meeg.source.momentfit.md │ │ ├── spm.meeg.source.results.md │ │ ├── spm.meeg.source.sensorshift.md │ │ ├── spm.meeg.source.simulate.md │ │ ├── spm.meeg.tf.avgfreq.md │ │ ├── spm.meeg.tf.avgtime.md │ │ ├── spm.meeg.tf.cfc.md │ │ ├── spm.meeg.tf.rescale.md │ │ ├── spm.meeg.tf.tf.md │ │ ├── spm.spatial.coreg.estimate.md │ │ ├── spm.spatial.coreg.estwrite.md │ │ ├── spm.spatial.coreg.write.md │ │ ├── spm.spatial.normalise.est.md │ │ ├── spm.spatial.normalise.estwrite.md │ │ ├── spm.spatial.normalise.write.md │ │ ├── spm.spatial.preproc.md │ │ ├── spm.spatial.realign.estimate.md │ │ ├── spm.spatial.realign.estwrite.md │ │ ├── spm.spatial.realign.write.md │ │ ├── spm.spatial.realignunwarp.md │ │ ├── spm.spatial.smooth.md │ │ ├── spm.stats.bms_map.inference.md │ │ ├── spm.stats.bms_map.results.md │ │ ├── spm.stats.con.md │ │ ├── spm.stats.factorial_design.md │ │ ├── spm.stats.fmri_data.md │ │ ├── spm.stats.fmri_design.md │ │ ├── spm.stats.fmri_est.md │ │ ├── spm.stats.fmri_spec.md │ │ ├── spm.stats.mfx.ffx.md │ │ ├── spm.stats.mfx.spec.md │ │ ├── spm.stats.ppi.md │ │ ├── spm.stats.results.md │ │ ├── spm.stats.review.md │ │ ├── spm.stats.setlevel.md │ │ ├── spm.temporal.st.md │ │ ├── spm.tools.beamforming.copy.md │ │ ├── spm.tools.beamforming.data.md │ │ ├── spm.tools.beamforming.features.md │ │ ├── spm.tools.beamforming.group.md │ │ ├── spm.tools.beamforming.inverse.md │ │ ├── spm.tools.beamforming.output.md │ │ ├── spm.tools.beamforming.sources.md │ │ ├── spm.tools.beamforming.view.md │ │ ├── spm.tools.beamforming.write.md │ │ ├── spm.tools.dartel.crt_iwarped.md │ │ ├── spm.tools.dartel.crt_warped.md │ │ ├── spm.tools.dartel.jacdet.md │ │ ├── spm.tools.dartel.kernfun.flokern.md │ │ ├── spm.tools.dartel.kernfun.reskern.md │ │ ├── spm.tools.dartel.mni_norm.md │ │ ├── spm.tools.dartel.popnorm.md │ │ ├── spm.tools.dartel.warp.md │ │ ├── spm.tools.dartel.warp1.md │ │ ├── spm.tools.fieldmap.applyvdm.md │ │ ├── spm.tools.fieldmap.calculatevdm.md │ │ ├── spm.tools.longit.pairwise.md │ │ ├── spm.tools.longit.series.md │ │ ├── spm.tools.mb.fil.md │ │ ├── spm.tools.mb.mbnorm.md │ │ ├── spm.tools.mb.merge.md │ │ ├── spm.tools.mb.out.md │ │ ├── spm.tools.mb.run.md │ │ ├── spm.tools.oldnorm.est.md │ │ ├── spm.tools.oldnorm.estwrite.md │ │ ├── spm.tools.oldnorm.write.md │ │ ├── spm.tools.oldseg.md │ │ ├── spm.tools.render.SExtract.md │ │ ├── spm.tools.render.SRender.md │ │ ├── spm.tools.shoot.kernfun.reskern.md │ │ ├── spm.tools.shoot.kernfun.scalmom.md │ │ ├── spm.tools.shoot.kernfun.velkern.md │ │ ├── spm.tools.shoot.norm.md │ │ ├── spm.tools.shoot.warp.md │ │ ├── spm.tools.shoot.warp1.md │ │ ├── spm.tools.spatial.denoise.md │ │ ├── spm.tools.spatial.scope.md │ │ ├── spm.tools.spatial.slice2vol.md │ │ ├── spm.tools.tsss.momentspace.md │ │ ├── spm.tools.tsss.tsss.md │ │ ├── spm.util.bbox.md │ │ ├── spm.util.cat.md │ │ ├── spm.util.cdir.md │ │ ├── spm.util.checkreg.md │ │ ├── spm.util.deface.md │ │ ├── spm.util.defs.md │ │ ├── spm.util.disp.md │ │ ├── spm.util.exp_frames.md │ │ ├── spm.util.imcalc.md │ │ ├── spm.util.import.dicom.md │ │ ├── spm.util.import.ecat.md │ │ ├── spm.util.import.minc.md │ │ ├── spm.util.import.parrec.md │ │ ├── spm.util.md.md │ │ ├── spm.util.print.md │ │ ├── spm.util.render.display.md │ │ ├── spm.util.render.extract.md │ │ ├── spm.util.reorient.md │ │ ├── spm.util.sendmail.md │ │ ├── spm.util.split.md │ │ ├── spm.util.tvol.md │ │ └── spm.util.voi.md │ └── index.md ├── template.md ├── tutorials │ ├── MEEG │ │ ├── artefact.md │ │ ├── index.md │ │ ├── meeg_firstlevel.md │ │ ├── meg_sloc.md │ │ ├── mmn │ │ │ └── index.md │ │ ├── multi │ │ │ ├── evoked.md │ │ │ ├── fmri.md │ │ │ ├── index.md │ │ │ ├── preprocessing.md │ │ │ ├── references.md │ │ │ ├── source.md │ │ │ └── timefrequency.md │ │ └── multimodal │ │ │ ├── eeg.md │ │ │ ├── figures │ │ │ ├── eeg_coreg.png │ │ │ ├── eeg_erp.png │ │ │ ├── eeg_forward.png │ │ │ ├── eeg_meshing.png │ │ │ ├── eeg_msp.png │ │ │ ├── eeg_recon.png │ │ │ ├── eeg_scalptime.png │ │ │ ├── eeg_scalptime_results.png │ │ │ ├── eeg_topo.png │ │ │ ├── fmri_faces_vs_scrambled.png │ │ │ ├── fused_eeg_meg_msp.png │ │ │ ├── fused_eeg_meg_msp_fmri.png │ │ │ ├── fused_eeg_msp.png │ │ │ ├── fused_meg_msp.png │ │ │ ├── meg_TF_results.png │ │ │ ├── meg_TFimage.png │ │ │ ├── meg_coreg.png │ │ │ ├── meg_msp.png │ │ │ ├── meg_plv_faces.png │ │ │ ├── meg_plv_scrambled.png │ │ │ ├── meg_pow_faces.png │ │ │ ├── meg_pow_scrambled.png │ │ │ ├── meg_scalp_erf.png │ │ │ ├── meg_topo180.png │ │ │ ├── paradigm.png │ │ │ └── select_frames.png │ │ │ ├── fmri.md │ │ │ ├── fusion.md │ │ │ ├── index.md │ │ │ └── meg.md │ ├── daiss │ │ ├── beamforming │ │ │ ├── neuromag │ │ │ │ └── index.md │ │ │ ├── opm │ │ │ │ └── index.md │ │ │ └── simulated │ │ │ │ └── index.md │ │ └── index.md │ ├── dcm │ │ ├── dcm_csd.md │ │ ├── dcm_erp.md │ │ ├── dcm_fmri_first_level.md │ │ ├── dcm_fmri_first_level_gui.md │ │ ├── dcm_fmri_second_level.md │ │ ├── dcm_fmri_second_level_motor.md │ │ ├── dcm_ir.md │ │ ├── dcm_phase.md │ │ ├── dcm_ssr.md │ │ └── index.md │ ├── fmri │ │ ├── block │ │ │ ├── index.md │ │ │ ├── modelling │ │ │ │ └── block_design.md │ │ │ └── preprocessing │ │ │ │ ├── batch.md │ │ │ │ ├── coregistration.md │ │ │ │ ├── dummy_scans.md │ │ │ │ ├── introduction.md │ │ │ │ ├── normalisation.md │ │ │ │ ├── realignment.md │ │ │ │ ├── scripting.md │ │ │ │ ├── segmentation.md │ │ │ │ ├── slice_timing.md │ │ │ │ ├── smoothing.md │ │ │ │ └── visual_inspection.md │ │ ├── event │ │ │ ├── bayesian.md │ │ │ ├── categorical.md │ │ │ ├── index.md │ │ │ ├── parametric.md │ │ │ └── preprocessing.md │ │ ├── group │ │ │ ├── factorial.md │ │ │ ├── index.md │ │ │ ├── one_sample_ttest.md │ │ │ └── two_sample_ttest.md │ │ └── index.md │ ├── index.md │ ├── opm │ │ ├── coreg │ │ │ └── index.md │ │ ├── evoked │ │ │ └── index.md │ │ ├── evoked_source │ │ │ └── index.md │ │ ├── formats │ │ │ └── index.md │ │ ├── index.md │ │ ├── neuro1_aef │ │ │ └── index.md │ │ └── simulation │ │ │ └── index.md │ ├── vbm │ │ ├── image_processing.md │ │ ├── index.md │ │ └── statistical_analysis.md │ └── vbmcourse24 │ │ ├── analysis.md │ │ ├── data_prep.md │ │ ├── index.md │ │ └── preprocessing.md └── wikibooks │ ├── Advice_on_hardware_selection.md │ ├── Atlases.md │ ├── Autocorrelation.md │ ├── BIDS.md │ ├── Basis_functions.md │ ├── Batch.md │ ├── Bayesian_Parameter_Averaging_(BPA).md │ ├── Block_design.md │ ├── CI.md │ ├── Cogent.md │ ├── Compile_the_gnumex_mex_files.md │ ├── Concatenation.md │ ├── Contrasts.md │ ├── Correlation.md │ ├── Correlation_and_Regression.md │ ├── Covariance.md │ ├── DCM_units.md │ ├── DICOM.md │ ├── DTI.md │ ├── Datasets.md │ ├── Design_efficiency.md │ ├── Docker.md │ ├── Download.md │ ├── F_and_T_tests.md │ ├── Faster_SPM.md │ ├── Group_Analysis.md │ ├── Haemodynamic_Response_Function.md │ ├── Headless.md │ ├── How-to.md │ ├── Importing_data_from_the_scanner.md │ ├── Inference.md │ ├── Installation_on_64bit_Linux.md │ ├── Installation_on_64bit_Mac_OS_(Intel).md │ ├── Installation_on_64bit_Windows.md │ ├── Installation_on_Linux.md │ ├── Installation_on_Mac_OS_(Intel).md │ ├── Installation_on_Mac_OS_(PowerPC).md │ ├── Installation_on_SunOS.md │ ├── Installation_on_Windows.md │ ├── Julia.md │ ├── Learning_SPM.md │ ├── MATLAB.md │ ├── Non-sphericity.md │ ├── Normalisation.md │ ├── Octave.md │ ├── PPI.md │ ├── Parametric_Empirical_Bayes_(PEB).md │ ├── Percentage_signal_change.md │ ├── Physio.md │ ├── Power_Analysis.md │ ├── Programming_intro.md │ ├── Python.md │ ├── SEM.md │ ├── SPM.md │ ├── SPM12_MEX_Compilation_on_Windows.md │ ├── SPM2_MEX_Compilation_on_Windows.md │ ├── SPM5_MEX_Compilation_on_Windows.md │ ├── SPM8_MEX_Compilation_on_Windows.md │ ├── Single_Case_Study.md │ ├── Spatial_smoothing.md │ ├── Standalone.md │ ├── The_DCM_Equation._1._Motivation.md │ ├── The_DCM_Equation._2._Dynamical_Systems.md │ ├── The_DCM_Equation._3._Networks_and_Matrices.md │ ├── The_DCM_Equation._4._The_State_Equation.md │ ├── Timeseries_extraction.md │ ├── Two_State_DCM.md │ ├── VBM.md │ ├── Working_with_4D_data.md │ └── fonts.md ├── mkdocs.yml └── requirements.txt /.codespellrc: -------------------------------------------------------------------------------- 1 | [codespell] 2 | ignore-words = .wordlist.txt 3 | ignore-regex = (FWE|TE|ans|Unser|theses|PhLEM|whos) 4 | skip = *.pdf,*.bib,*env*/*,.git,site 5 | quiet-level = 2 6 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | * text=auto 2 | -------------------------------------------------------------------------------- /.github/workflows/build.yml: -------------------------------------------------------------------------------- 1 | name: Build 2 | on: 3 | push: 4 | branches: 5 | - main 6 | release: 7 | types: [published] 8 | pull_request: 9 | workflow_dispatch: 10 | jobs: 11 | deploy: 12 | runs-on: ubuntu-latest 13 | steps: 14 | - uses: actions/checkout@v4 15 | - name: Setup Python 16 | uses: actions/setup-python@v4 17 | with: 18 | python-version: '3.10.6' 19 | architecture: 'x64' 20 | - name: Install dependencies 21 | run: | 22 | python3 -m pip install --upgrade pip 23 | python3 -m pip install -r requirements.txt 24 | - name: Build site 25 | run: mkdocs build 26 | -------------------------------------------------------------------------------- /.github/workflows/linkcheck.yml: -------------------------------------------------------------------------------- 1 | name: Links Check 2 | on: 3 | push: 4 | branches: 5 | - main 6 | release: 7 | types: [published] 8 | pull_request: 9 | workflow_dispatch: 10 | jobs: 11 | check-links: 12 | runs-on: ubuntu-latest 13 | steps: 14 | - uses: actions/checkout@v3 15 | - uses: gaurav-nelson/github-action-markdown-link-check@v1 16 | with: 17 | use-quiet-mode: 'yes' 18 | use-verbose-mode: 'no' 19 | config-file: '.markdown-link-check.json' 20 | folder-path: 'docs/installation, docs/tutorials' 21 | file-path: './README.md, ./docs/index.md' 22 | base-branch: main 23 | -------------------------------------------------------------------------------- /.github/workflows/linting.yml: -------------------------------------------------------------------------------- 1 | name: Linting 2 | on: 3 | push: 4 | branches: 5 | - main 6 | release: 7 | types: [published] 8 | pull_request: 9 | workflow_dispatch: 10 | jobs: 11 | lint: 12 | runs-on: ubuntu-latest 13 | steps: 14 | - uses: actions/checkout@v3 15 | - uses: actions/setup-node@v3 16 | with: 17 | node-version: 16 18 | - name: Install markdownlint 19 | run: | 20 | npm install -g markdownlint-cli 21 | - name: Run markdownlint 22 | run: | 23 | markdownlint "docs/**/*.md" || true 24 | -------------------------------------------------------------------------------- /.github/workflows/spelling.yml: -------------------------------------------------------------------------------- 1 | name: Spelling 2 | on: 3 | push: 4 | branches: 5 | - main 6 | release: 7 | types: [published] 8 | pull_request: 9 | workflow_dispatch: 10 | jobs: 11 | spell: 12 | runs-on: ubuntu-latest 13 | steps: 14 | - uses: actions/checkout@v4 15 | - name: Setup Python 16 | uses: actions/setup-python@v5 17 | - name: Install dependencies 18 | run: | 19 | python3 -m pip install --upgrade pip setuptools 20 | python3 -m pip install -r requirements.txt 21 | - name: Install Aspell 22 | run: | 23 | sudo apt-get install aspell aspell-en 24 | - name: Install Hunspell 25 | run: | 26 | sudo apt-get install hunspell hunspell-en-gb 27 | - name: codespell 28 | run: | 29 | codespell docs 30 | - name: pyspelling 31 | run: | 32 | pyspelling || true 33 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # mkdocs documentation 2 | /site 3 | 4 | # Environments 5 | .env 6 | .venv 7 | env/ 8 | venv/ 9 | ENV/ 10 | env.bak/ 11 | venv.bak/ 12 | .vs/ 13 | 14 | # Spellcheck 15 | .wordlist.dic 16 | 17 | .DS_Store 18 | -------------------------------------------------------------------------------- /.markdown-link-check.json: -------------------------------------------------------------------------------- 1 | { 2 | "ignorePatterns": [ 3 | { 4 | "pattern": "^(?!http)" 5 | } 6 | ], 7 | "aliveStatusCodes": [200,206,503], 8 | "httpHeaders": [ 9 | { 10 | "urls": ["https://github.com/", "https://guides.github.com/", "https://help.github.com/", "https://docs.github.com/"], 11 | "headers": { 12 | "Accept-Encoding": "zstd, br, gzip, deflate" 13 | } 14 | } 15 | ] 16 | } 17 | -------------------------------------------------------------------------------- /.markdownlintrc: -------------------------------------------------------------------------------- 1 | { 2 | "default": true, 3 | "MD003": { 4 | "style": "atx" 5 | }, 6 | "MD013": false, 7 | "MD033": false 8 | } 9 | -------------------------------------------------------------------------------- /.pyspelling.yaml: -------------------------------------------------------------------------------- 1 | spellchecker: aspell 2 | 3 | matrix: 4 | - name: Markdown 5 | sources: 6 | - '*.md' 7 | - 'docs/**/*.md' 8 | aspell: 9 | lang: en 10 | d: en_GB 11 | ignore-case: true 12 | hunspell: 13 | d: en_GB 14 | dictionary: 15 | wordlists: 16 | - .wordlist.txt 17 | output: .wordlist.dic 18 | encoding: utf-8 19 | pipeline: 20 | - pyspelling.filters.context: 21 | context_visible_first: true 22 | delimiters: 23 | - open: ':' 24 | content: '[\w-]*' 25 | close: ':' 26 | - pyspelling.filters.markdown: 27 | markdown_extensions: 28 | - pymdownx.snippets 29 | - markdown.extensions.extra: 30 | - pyspelling.filters.html: 31 | comments: false 32 | attributes: 33 | - title 34 | - alt 35 | ignores: 36 | - code 37 | - pre 38 | - blockquote 39 | - span 40 | - a 41 | - :matches(.csl-bib-body) 42 | - pyspelling.filters.url: 43 | 44 | -------------------------------------------------------------------------------- /addons/abbreviations.md: -------------------------------------------------------------------------------- 1 | *[BOLD]: Blood Oxygen Level Dependent 2 | *[BIDS]: Brain Imaging Data Structure 3 | *[CSV]: Comma-Separated Values 4 | *[CVA]: Canonical Variate Analysis 5 | *[DAiSS]: Data Analysis in Source Space 6 | *[DCM]: Dynamic Causal Modelling 7 | *[EEG]: Electroencephalography 8 | *[EPI]: Echo-Planar Imaging 9 | *[fMRI]: functional Magnetic Resonance Imaging 10 | *[FWHM]: Full Width at Half Maximum 11 | *[GLM]: General Linear Model 12 | *[GLMs]: General Linear Models 13 | *[HRF]: Hemodynamic Response Function 14 | *[JSON]: JavaScript Object Notation 15 | *[MEG]: Magnetoencephalography 16 | *[MRI]: Magnetic Resonance Imaging 17 | *[OPM]: Optically Pumped Magnetometers 18 | *[PET]: Positron Emission Tomography 19 | *[PSD]: Power Spectral Density 20 | *[SPECT]: Single-Photon Emission Computed Tomography 21 | *[TR]: Repetition Time 22 | *[TRs]: Repetition Times 23 | *[TSSS]: Temporal 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/docs/assets/videos/inspecting_functional_data.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/assets/videos/inspecting_functional_data.mp4 -------------------------------------------------------------------------------- /docs/assets/videos/realignment.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/assets/videos/realignment.mp4 -------------------------------------------------------------------------------- /docs/courses/eeg_meg/index.md: -------------------------------------------------------------------------------- 1 | # SPM Course - EEG & MEG 2 | 3 | This course introduces the analysis of EEG and MEG data. The first three days combine theoretical presentations with practical demonstrations of the different data analysis methods implemented in SPM. On the last day participants have the opportunity to work on SPM tutorial data sets under the supervision of the course faculty. We also invite students to use their own data for analysis. 4 | 5 | The course is suitable for both beginners and more advanced users. We advise students to gain at least some minimal familiarity with the methodology, by following data analysis examples in the [SPM tutorials](../../tutorials/MEEG/index.md) or by scanning through [Litvak et al. (2011)](https://doi.org/10.1155/2011/852961). 6 | 7 |
8 | 9 | - :material-laptop:{ .lg .middle } [Upcoming course](./programme.md) 10 | 11 | --- 12 | 13 | 19-22 May 2025, **online**. | Registration is closed. 14 |
15 | -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/bayesian_inference.md: -------------------------------------------------------------------------------- 1 | # Bayesian inference 2 | 3 | In this talk, [Dr Dimitrios Pinotsis](https://www.city.ac.uk/about/people/academics/dimitrios-pinotsis) introduces the concepts underpinning Bayesian analysis. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/bayesian_model_selection.md: -------------------------------------------------------------------------------- 1 | # Bayesian model selection and averaging 2 | 3 | In this talk, [Dr Peter Zeidman](https://peterzeidman.co.uk) describes Bayesian model selection and averaging. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/daiss.md: -------------------------------------------------------------------------------- 1 | # DAiSS: An SPM toolbox for alternate source imaging approaches (talk & demo) 2 | 3 | In this talk, [Dr George O'Neill](https://profiles.ucl.ac.uk/74645-george-o%27neill) introduces and demonstrates DAiSS, a toolbox for alternate source reconstruction approaches. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/dcm_evoked_responses.md: -------------------------------------------------------------------------------- 1 | # DCM for evoked responses 2 | 3 | In this talk, [Dr Daniel Hauke](https://profiles.ucl.ac.uk/92790-daniel-hauke) describes the application of DCM for evoked responses paradigms. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/dcm_principles.md: -------------------------------------------------------------------------------- 1 | # The principles of dynamic causal modelling (DCM) 2 | 3 | In this talk, [Dr Amirhossein Jafarian](https://ftd.neurology.cam.ac.uk/directory/A_Jafarian) introduces dynamic causal modelling (DCM). 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/glm_classical_inference.md: -------------------------------------------------------------------------------- 1 | # General linear model (GLM) and classical inference 2 | 3 | In this talk, [Dr Zita Patai](https://spierslab.com/zita-patai/) describes how to model EEG and MEG data using the general linear model (GLM) framework and how to make inferences from your data. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/glm_convolutional.md: -------------------------------------------------------------------------------- 1 | # Convolutional GLM 2 | 3 | In this talk, [Dr Ashwani Jha](https://profiles.ucl.ac.uk/4064-ashwani-jha) describes advanced applications of the GLM. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/group_analysis.md: -------------------------------------------------------------------------------- 1 | # Group M/EEG data analysis 2 | 3 | In this talk, [Dr Jason Taylor](https://research.manchester.ac.uk/en/persons/jason.taylor) describes how to collate individual participant EEG and MEG data to perform group-level analysis. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/introduction.md: -------------------------------------------------------------------------------- 1 | # Introduction & resources 2 | 3 | In this talk, [Prof Vladimir Litvak](https://profiles.ucl.ac.uk/10801-vladimir-litvak) introduces the concepts underpinning EEG & MEG analysis in SPM. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/multiple_comparisons.md: -------------------------------------------------------------------------------- 1 | # Multiple comparisons - problems & solutions 2 | 3 | In this talk, [Dr Tim Tierney](https://profiles.ucl.ac.uk/41354-tim-tierney) outlines the problem of multiple comparisons in imaging statistics and the potential solutions that can be used to account for it. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/eeg_meg/recordings/what_measured.md: -------------------------------------------------------------------------------- 1 | # What are we measuring with M/EEG? 2 | 3 | In this talk, [Dr Catharina Zich](https://www.mrcbndu.ox.ac.uk/people/dr-catharina-zich) describes the nature of signals measured with EEG and MEG. 4 | 5 | 6 | 7 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/maths/index.md: -------------------------------------------------------------------------------- 1 | # Mathematics in MATLAB tutorials 2 | * [Linear algebra](./linear_algebra.md): Simple linear algebra in MATLAB 3 | 4 | -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/contrasts_classical_inference.md: -------------------------------------------------------------------------------- 1 | # Contrasts & classical inference 2 | 3 | In this talk, [Prof Christophe Phillips](https://christophephillips.github.io) provides an overview of contrast and statistical inference in the context of the mass univariate approach. The discussion builds upon concepts presented earlier on the GLM and focuses on the latter part of the analysis pipeline. 4 | 5 | Slides are available [here](../slides/2023/03_contrasts_and_inference.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/dcm_hypothesis_testing.md: -------------------------------------------------------------------------------- 1 | # DCM: Testing hypotheses 2 | 3 | In the last lecture of the course, [Dr Peter Zeidman](https://peterzeidman.co.uk) provides a comprehensive overview of the steps involved in performing a DCM analysis for fMRI data. This talk summarises the key concepts discussed throughout the course and sheds light on the process of model inversion using Bayesian statistics. 4 | 5 | Slides are available [here](../slides/2023/11_dcm_testing_hypotheses.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/dcm_introduction.md: -------------------------------------------------------------------------------- 1 | # Introduction to DCM 2 | 3 | In this talk, [Dr Edda Bilek](https://www.zi-mannheim.de/en/research/people/person/5186.html) provides an introductory overview of the use of DCM in neuroimaging. The talk aims to give a broad understanding of DCM, its applications, and the modelling and estimation process as they are implemented in SPM. 4 | 5 | Slides are available [here](../slides/2023/10_dcm_introduction.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/event_related_fmri.md: -------------------------------------------------------------------------------- 1 | # Event-related fMRI 2 | 3 | In this talk, [Dr Sonia Medina](https://medicine.exeter.ac.uk/clinical-biomedical/people/profile/index.php?web_id=Sonia_Medina) discusses the importance of understanding the BOLD signal and HRF for fMRI data analysis. This talk delves into the importance of modelling fMRI data accurately and the key considerations when building design matrices for analysis. 4 | 5 | Slides are available [here](../slides/2023/08_event_related_fmri.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/experimental_design.md: -------------------------------------------------------------------------------- 1 | # Experimental design 2 | 3 | In this talk, [Dr Carolin Moessnang](https://www.zi-mannheim.de/en/research/people/person/6741.html) explains the fundamental concepts of experimental designs in fMRI experiments. The goal is to provide an overview of different experimental designs and highlight important considerations when planning an fMRI study. 4 | 5 | Slides are available [here](../slides/2024/07_experimental_design.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/glm.md: -------------------------------------------------------------------------------- 1 | # The general linear model 2 | 3 | In this talk, [Dr Nadège Corbin](https://scholar.google.com/citations?user=TjIzldkAAAAJ&hl=fr) discusses the concept of the GLM in the context of fMRI analysis. The GLM is a widely used statistical approach that allows researchers to analyse fMRI time series data and make inferences about brain function. The talk outlines the key components and steps involved in creating a GLM. 4 | 5 | Slides are available [here](../slides/2023/02_general_linear_model.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/group_analysis.md: -------------------------------------------------------------------------------- 1 | # Group analysis 2 | 3 | In this talk, [Dr Emma Holmes](https://emmaholmes.co.uk) provides an overview of methods for conducting group analyses in neuroimaging studies using SPM. The talk covers fixed and random effects analyses, and provides examples of different within- and between-subject designs. 4 | 5 | Slides are available [here](../slides/2023/04_group_analysis.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/preprocessing.md: -------------------------------------------------------------------------------- 1 | # Spatial preprocessing 2 | 3 | In this talk, [Prof John Ashburner](https://www.fil.ion.ucl.ac.uk/~john/) provides an overview of spatial preprocessing steps for fMRI data. The talk covers realignment, distortion correction, coregistration, normalisation, and spatial smoothing. 4 | 5 | Slides are available [here](../slides/2023/01_fmri_preprocessing.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/random_field_theory.md: -------------------------------------------------------------------------------- 1 | # Random field theory 2 | 3 | In this talk, [Prof Tom Nichols](https://www.bdi.ox.ac.uk/Team/t-e-nichols) describes how random field theory can be used to help determine the presence of real effects amidst statistical noise in neuroimaging data. The talk outlines various approaches to correcting for multiple comparisons, including family-wise error correction, false discovery rate correction, and permutation testing. 4 | 5 | Slides are available [here](../slides/2023/05_thresholding.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/recordings/vbm.md: -------------------------------------------------------------------------------- 1 | # Voxel-based morphometry 2 | 3 | In this talk, [Dr Christian Lambert](https://profiles.ucl.ac.uk/11034-christian-lambert) focuses on the concept of structural brain analysis and explores the use of VBM and its implementation in SPM. The talk covers preprocessing fundamentals, common pitfalls, and hints for effective data analysis. Additionally, the extensions to the VBM framework, including longitudinal analysis and voxel-based quantification, are explored. 4 | 5 | Slides are available [here](../slides/2023/06_voxel_based_morphometry.pdf). 6 | 7 | 8 | 9 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/01_fmri_preprocessing.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/01_fmri_preprocessing.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/02_general_linear_model.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/02_general_linear_model.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/03_contrasts_and_inference.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/03_contrasts_and_inference.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/04_group_analysis.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/04_group_analysis.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/05_thresholding.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/05_thresholding.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/06_voxel_based_morphometry.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/06_voxel_based_morphometry.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/07_experimental_design.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/07_experimental_design.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/08_event_related_fmri.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/08_event_related_fmri.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/09_bayesian_inference.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/09_bayesian_inference.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/10_dcm_introduction.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/10_dcm_introduction.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2023/11_dcm_testing_hypotheses.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2023/11_dcm_testing_hypotheses.pdf -------------------------------------------------------------------------------- /docs/courses/fmri_vbm/slides/2024/07_experimental_design.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spm/spm-docs/ac623800651e8278ec3ea561e777cc32a33f0c91/docs/courses/fmri_vbm/slides/2024/07_experimental_design.pdf -------------------------------------------------------------------------------- /docs/reference/docs/spm.dcm.bms.results.md: -------------------------------------------------------------------------------- 1 | # Review results 2 | Bayesian Model Selection for DCM (Results). Show results from BMS for DCM. 3 | 4 | * **BMS.mat** (select files) 5 | Specify the BMS.mat file obtained from previous BMS analysis (optional). Leave field empty to work on serial mode. 6 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.dcm.peb.peb_review.md: -------------------------------------------------------------------------------- 1 | # Review PEB 2 | Reviews PEB results 3 | 4 | * **Select PEB file** (select files) 5 | Select PEB_*.mat file. 6 | 7 | * **DCMs** (select files) 8 | Select group DCM file (GCM_*.mat). This is a cell array with one row per subject and one column per DCM. 9 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.dcm.spec.fmri.regions.md: -------------------------------------------------------------------------------- 1 | # Region specification 2 | Insert new regions into a DCM model. 3 | . 4 | The RT is assumed to be the same as before. 5 | . 6 | This functionality can be used, for example, to replace subject X's data by subject Y's. The model can then be re-estimated without having to go through model specification again. 7 | 8 | * **Select DCM_*.mat** (select files) 9 | Select DCM_*.mat files. 10 | 11 | * **Select VOI_*.mat** (select files) 12 | Select VOI_*.mat files. 13 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.dcm.spec.meeg.md: -------------------------------------------------------------------------------- 1 | # DCM for M/EEG 2 | Specify DCMs for multiple models and subjects 3 | 4 | * **M/EEG datasets** (select files) 5 | Select the M/EEG mat files. 6 | 7 | * **DCM files** (select files) 8 | Select pre-specified DCM files. 9 | 10 | * **Priors** (select files) 11 | Select a DCM file where priors will be taken from (DCM.M.pE, DCM.M.pC) 12 | 13 | * **Initialisation** (select files) 14 | Select a DCM file for initialising the inversion (at DCM.Ep) 15 | 16 | * **Graphical feedback** (choose from the menu) 17 | Plot intermediate results during inversions 18 | 19 | * **Output** (choose from the menu) 20 | Specify what to output 21 | 22 | * **Directory** (select files) 23 | Select the directory where the output will be written. 24 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.OPM.denoise.md: -------------------------------------------------------------------------------- 1 | # Synthetic Gradiometery 2 | Denoise will regress all channels of the selected type(s) from the input dataset. Optionally the derivatives of the selected type(s) can be used as well. This function will automatically regress on a trial by trial basis or across the whole sesison based on whether or not the dataset has been epoched. 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Confounds** (enter text) 8 | Labels of channel types to use for denoising. Will default to REF to use reference sensors for denoising 9 | 10 | * **Derivatives** (choose from the menu) 11 | Boolean to specify whether derivatives should be used for denoising. Default is true 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.OPM.epoch.md: -------------------------------------------------------------------------------- 1 | # Epoch M/EEG object on Trigger 2 | Epoch M/EEG data at the rise of every trigger in the dataset 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Time window** (enter text) 8 | Time window around trigger which to epoch(ms). e.g [-200,300] 9 | 10 | * **Condition Labels** (enter text) 11 | Labels of conditions. Enter each label on a new line. If left empty the default behaviour is to label conditions according to number(e.g. Cond1,Cond2,...) 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.averaging.contrast.md: -------------------------------------------------------------------------------- 1 | # Contrast over epochs 2 | Computes contrasts over EEG/MEG epochs. 3 | 4 | * **File Name** (select files) 5 | Select the EEG mat file. 6 | 7 | * **Contrasts** (create a list of items) 8 | Each contrast defines a new condition in the output file. 9 | 10 | * **Contrast** 11 | 12 | * **Contrast coefficients** (enter text) 13 | Enter the contrast vector. 14 | 15 | * **New condition label** (enter text) 16 | Enter the label for the condition derived by applying the contrast. 17 | 18 | * **Weight by replications** (choose from the menu) 19 | Weight the contrast by the number of replications from which the average was computed. 20 | 21 | * **Filename Prefix** (enter text) 22 | Specify the string to be prepended to the filenames of the output dataset. Default prefix is 'w'. 23 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.averaging.grandmean.md: -------------------------------------------------------------------------------- 1 | # Grandmean 2 | Average multiple evoked responses 3 | 4 | * **File Names** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Output filename** (enter text) 8 | Choose filename 9 | 10 | * **Weighted average?** (choose from the menu) 11 | Average weighted by number of replications in input. 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.images.collapse.md: -------------------------------------------------------------------------------- 1 | # Collapse time 2 | Compute within-peristimulus time (or frequency) averages (contrasts) of M/EEG data in voxel-space 3 | 4 | * **Images to average** (select files) 5 | Specify the images to average time/frequency. 6 | 7 | * **Time/frequency window** (enter text) 8 | Start and stop of the time/frequency window [ms/Hz]. 9 | 10 | * **Filename Prefix** (enter text) 11 | Specify the string to be prepended to the output images. Default prefix is 'l'. 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.other.copy.md: -------------------------------------------------------------------------------- 1 | # Copy 2 | Copying M/EEG datasets 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Output filename** (enter text) 8 | Choose filename. 9 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.other.delete.md: -------------------------------------------------------------------------------- 1 | # Delete 2 | Deleting M/EEG datasets 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file(s). 6 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.other.review.md: -------------------------------------------------------------------------------- 1 | # Display 2 | Run the reviewing tool with the given dataset as input. 3 | 4 | * **File Name** (select files) 5 | Select the EEG mat file. 6 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.preproc.bc.md: -------------------------------------------------------------------------------- 1 | # Baseline correction 2 | Baseline correction of M/EEG time data 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Baseline** (enter text) 8 | Start and stop of baseline [ms]. 9 | 10 | * **Filename Prefix** (enter text) 11 | Specify the string to be prepended to the filenames of the filtered dataset. Default prefix is 'b'. 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.preproc.combineplanar.md: -------------------------------------------------------------------------------- 1 | # Combine planar 2 | Combine planar MEG channels 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Copying mode** (choose from the menu) 8 | Select which channels to copy to the new combined dataset 9 | 10 | * **Filename Prefix** (enter text) 11 | Specify the string to be prepended to the filenames of the output dataset. Default prefix is 'P'. 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.preproc.correct.md: -------------------------------------------------------------------------------- 1 | # Correct sensor data 2 | Perform topography-based correction of artefacts 3 | 4 | * **File Name** (select files) 5 | Select the EEG mat file. 6 | 7 | * **Correction mode** (choose from the menu) 8 | Select correction method. 9 | SSP removes more but also distorts the data more 10 | Berg method requires forward model to be defined in the dataset 11 | 12 | * **Filename Prefix** (enter text) 13 | Specify the string to be prepended to the filenames of the output dataset. Default prefix is 'T'. 14 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.preproc.crop.md: -------------------------------------------------------------------------------- 1 | # Crop 2 | Cropping M/EEG data 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Time window** (enter text) 8 | Start and stop of the time window [ms]. 9 | 10 | * **Frequency window** (enter text) 11 | Start and stop of the frequency window (Hz). 12 | 13 | * **Channel selection** (create a list of items) 14 | Channel selection. 15 | 16 | * **All** 17 | 18 | * **Select channels by type** (choose from the menu) 19 | Select channels by type. 20 | 21 | * **Custom channel** (enter text) 22 | Enter a single channel name. 23 | 24 | * **Regular expression** (enter text) 25 | Enter a regular expression for matching multiple channel labels. 26 | 27 | * **Channel file** (select files) 28 | 29 | * **Filename Prefix** (enter text) 30 | Specify the string to be prepended to the filenames of the output dataset. Default prefix is 'p'. 31 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.preproc.downsample.md: -------------------------------------------------------------------------------- 1 | # Downsampling 2 | Downsample EEG/MEG data. 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **New sampling rate** (enter text) 8 | Input the new sampling rate [Hz]. 9 | 10 | * **Resampling method** (choose from the menu) 11 | Select the downsampling method. 12 | 13 | * **Filename Prefix** (enter text) 14 | Specify the string to be prepended to the filenames of the filtered dataset. Default prefix is 'd'. 15 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.preproc.filter.md: -------------------------------------------------------------------------------- 1 | # Filter 2 | Filters EEG/MEG data. 3 | 4 | * **File Name** (select files) 5 | Select the EEG mat file. 6 | 7 | * **Type** (choose from the menu) 8 | Select the filter typee. 9 | 10 | * **Band** (choose from the menu) 11 | Select the filter band. 12 | 13 | * **Cutoff(s)** (enter text) 14 | Enter the filter cutoff 15 | 16 | * **Direction** (choose from the menu) 17 | Select the filter direction. 18 | 19 | * **Order** (enter text) 20 | Enter the filter order 21 | 22 | * **Filename Prefix** (enter text) 23 | Specify the string to be prepended to the filenames of the filtered dataset. Default prefix is 'f'. 24 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.preproc.fuse.md: -------------------------------------------------------------------------------- 1 | # Fusion 2 | Fuse EEG/MEG data. 3 | 4 | * **File Names** (select files) 5 | Select the M/EEG mat files. 6 | 7 | * **Filename Prefix** (enter text) 8 | Specify the string to be prepended to the filenames of the fused dataset. Default prefix is 'u'. 9 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.preproc.remove.md: -------------------------------------------------------------------------------- 1 | # Remove bad trials 2 | Removes bad trials and re-orders trials to conform to condlist 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Filename Prefix** (enter text) 8 | Specify the string to be prepended to the filenames of the output dataset. Default prefix is 'r'. 9 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.source.eeg_shp_gainmat.md: -------------------------------------------------------------------------------- 1 | # Gain matrices for surfaces 2 | To compute new lead field/ gain matrices for multiple disto 3 | 4 | * **M/EEG datasets** (select files) 5 | Select the M/EEG mat file 6 | 7 | * **Inversion index** (enter text) 8 | Index of the cell in D.inv where the results will be stored. 9 | 10 | * **Use which head models** (choose from the menu) 11 | Select the head model to use 12 | 13 | * **Force recompute of lead fields** (choose from the menu) 14 | Will force recompute lead fields even if they exist 15 | 16 | * **Name of subdir to store leads** (enter text) 17 | Select name of the subdirectory to store leads in (normally seed001 etc) 18 | 19 | * **Removes all files in existing seed directory** (choose from the menu) 20 | Will force fresh empty directory for lead fields 21 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.source.inv_mix.md: -------------------------------------------------------------------------------- 1 | # Merge source estimates from multiple inversions 2 | To merge different source level variance estimates based on the same data 3 | 4 | * **M/EEG datasets** (select files) 5 | Select the M/EEG mat files or .mat files containing inverses 6 | 7 | * **Inversion index** (enter text) 8 | Index of the cell in D.inv (same for all files) where the forward model can be found and the results will be stored. 9 | 10 | * **Merged file prefix** (enter text) 11 | Prefix for the new filename that will contain the merged inversion results 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.source.results.md: -------------------------------------------------------------------------------- 1 | # Inversion results 2 | Export results of imaging source reconstruction. 3 | 4 | * **M/EEG datasets** (select files) 5 | Select the M/EEG mat files. 6 | 7 | * **Inversion index** (enter text) 8 | Index of the cell in D.inv where the inversion results are stored. 9 | 10 | * **Time window of interest** (enter text) 11 | Time window to average over (ms). 12 | 13 | * **Frequency window of interest** (enter text) 14 | Frequency window (Hz). 15 | 16 | * **Contrast type** (choose from the menu) 17 | Contrast type: evoked activity, induced activity or single trials. 18 | 19 | * **Output space** (choose from the menu) 20 | Output space to write the results in. 21 | 22 | * **Output file format** (choose from the menu) 23 | Output file format. 24 | 25 | * **Cortical smoothing** (enter text) 26 | Number of iterations for cortical smoothing. 27 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.tf.avgfreq.md: -------------------------------------------------------------------------------- 1 | # Average over frequency 2 | Average M/EEG data over frequency 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Frequency window** (enter text) 8 | Start and stop of the frequency window (Hz). 9 | 10 | * **Filename Prefix** (enter text) 11 | Specify the string to be prepended to the filenames of the output dataset. Default prefix is 'P'. 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.meeg.tf.avgtime.md: -------------------------------------------------------------------------------- 1 | # Average over time 2 | Average M/EEG data over time 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Time window** (enter text) 8 | Start and stop of the time window [ms]. 9 | 10 | * **Filename Prefix** (enter text) 11 | Specify the string to be prepended to the filenames of the output dataset. Default prefix is 'S'. 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.stats.fmri_data.md: -------------------------------------------------------------------------------- 1 | # fMRI data specification 2 | Select data and optional explicit mask for a specified fMRI design 3 | 4 | * **Scans** (select files) 5 | Select the fMRI scans for this session. 6 | They must all have the same image dimensions, orientation, voxel size etc. 7 | 8 | * **Select SPM.mat** (select files) 9 | Select the SPM.mat file containing the specified design matrix. 10 | 11 | * **Explicit mask** (select files) 12 | Specify an image for explicitly masking the analysis. 13 | A sensible option here is to use a segmentation of structural images to specify a within-brain mask. If you select that image as an explicit mask then only those voxels in the brain will be analysed. This both speeds the estimation and restricts SPMs/PPMs to within-brain voxels. Alternatively, if such structural images are unavailable or no masking is required, then leave this field empty. 14 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.stats.mfx.ffx.md: -------------------------------------------------------------------------------- 1 | # FFX Specification 2 | Create FFX multi-session first-level design 3 | 4 | * **Directory** (select files) 5 | Select a directory where the SPM.mat file containing the specified design matrix will be written. 6 | 7 | * **Select SPM.mat files** (select files) 8 | Select the SPM.mat files that contains first-level designs. 9 | They must have the same number of parameters for each session. 10 | These are assumed to represent session-specific realisations of 2nd-level effects. 11 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.stats.mfx.spec.md: -------------------------------------------------------------------------------- 1 | # MFX Specification 2 | MFX Specification 3 | 4 | * **Select SPM.mat** (select files) 5 | Design and estimation structure after a 1st-level analysis. 6 | 7 | * **Contrast** (enter text) 8 | Contrast used to define 2nd level design matrix. 9 | E.g. ones(n,1) contrast where n is the number of sessions/subjects. 10 | The specification of a contrast that is not ones(n,1) allows, for example, specified sessions/subjects to be ignored. 11 | If left empty (default), a contrast ones(n,1) will be specified automatically at run time. 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.stats.setlevel.md: -------------------------------------------------------------------------------- 1 | # Set Level test 2 | A set level test how likely the statistical image is a random field 3 | 4 | * **Select SPM.mat** (select files) 5 | Select the SPM.mat file that contains the design matrix specification and results. 6 | 7 | * **Contrast index** (enter text) 8 | Index of the contrast of interest 9 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.tools.beamforming.copy.md: -------------------------------------------------------------------------------- 1 | # Copy analysis 2 | Make a copy of existing analysis 3 | 4 | * **BF.mat file** (select files) 5 | Select BF.mat file. 6 | 7 | * **Output directory** (select files) 8 | Select a directory where the copied BF.mat file will be written. 9 | 10 | * **Steps to copy** (choose from the menu) 11 | Select 'all' or the last step to copy 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.tools.dartel.jacdet.md: -------------------------------------------------------------------------------- 1 | # Jacobian determinants 2 | Create Jacobian determinant fields from flowfields. 3 | 4 | * **Flow fields** (select files) 5 | The flow fields store the deformation information. The same fields can be used for both forward or backward deformations (or even, in principle, half way or exaggerated deformations). 6 | 7 | * **Time Steps** (choose from the menu) 8 | The number of time points used for solving the partial differential equations. Note that Jacobian determinants are not very accurate for very small numbers of time steps (less than about 16). 9 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.tools.dartel.popnorm.md: -------------------------------------------------------------------------------- 1 | # Population to ICBM Registration 2 | Estimate the mapping from the population average to ICBM space. This is achieved by aligning the population average with grey and white matter tissue probability maps generated by Vladimir Fonov of the McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University . 3 | 4 | * **Dartel Template** (select files) 5 | Select the final Template file generated by Dartel. 6 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.tools.mb.fil.md: -------------------------------------------------------------------------------- 1 | # Image Labelling 2 | Factorisation-based Image Labelling. 3 | Label brains according to the Neuromorphometrics protocol using the method of Yan et al . Note that the algorithm may need to download additional data from Figshare: 4 | * https://figshare.com/articles/dataset/Trained_FIL_model/17143370/2 5 | * https://figshare.com/articles/dataset/Head_Tissue_Template/17143289 6 | 7 | * **Scans** (select files) 8 | Select one NIfTI format scan for each subject. 9 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.tools.shoot.kernfun.velkern.md: -------------------------------------------------------------------------------- 1 | # Kernel from velocities 2 | Generate a kernel from velocity fields. The dot products are saved in a variable ``K`` in the resulting dp_*.mat file. 3 | 4 | * **Velocity fields** (select files) 5 | Select the velocity fields for each subject. 6 | 7 | * **Dot product Filename** (enter text) 8 | Enter a filename for results (it will be prefixed by ``dp_`` and saved in the current directory. 9 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.tools.shoot.warp.md: -------------------------------------------------------------------------------- 1 | # Run Shooting (create Templates) 2 | Run the geodesic shooting nonlinear image registration procedure . This involves iteratively matching all the selected images to a template generated from their own mean . A series of Template*.nii files are generated, which become increasingly crisp as the registration proceeds. 3 | 4 | * **Images** (create a list of items) 5 | Select the images to be warped together. Multiple sets of images can be simultaneously registered. For example, the first set may be a bunch of grey matter images, and the second set may be the white matter images of the same subjects. 6 | 7 | * **Images** (select files) 8 | Select a set of imported images of the same type to be registered by minimising a measure of difference from the template. 9 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.tools.shoot.warp1.md: -------------------------------------------------------------------------------- 1 | # Run Shoot (existing Templates) 2 | Run the Shoot nonlinear image registration procedure to match individual images to pre-existing template data. Start out with smooth templates, and select crisp templates for the later iterations. 3 | 4 | * **Images** (create a list of items) 5 | Select the images to be warped together. Multiple sets of images can be simultaneously registered. For example, the first set may be a bunch of grey matter images, and the second set may be the white matter images of the same subjects. 6 | 7 | * **Images** (select files) 8 | Select a set of imported images of the same type to be registered by minimising a measure of difference from the template. 9 | 10 | * **Templates** (select files) 11 | Select templates. Smoother templates should be used for the early iterations. Note that the template should be a 4D file, with the 4th dimension equal to the number of sets of images. 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.tools.tsss.momentspace.md: -------------------------------------------------------------------------------- 1 | # TSSS space conversion 2 | Engage the TSSS space virtual montage 3 | 4 | * **File Name** (select files) 5 | Select the M/EEG mat file. 6 | 7 | * **Condition number threshold** (enter text) 8 | Threshold on condition number applied for basis regularisation 9 | 10 | * **Extra channels to add** (choose an option) 11 | 12 | * **None** 13 | 14 | * **Channel selection** (create a list of items) 15 | Channel selection. 16 | 17 | * **All** 18 | 19 | * **Select channels by type** (choose from the menu) 20 | Select channels by type. 21 | 22 | * **Custom channel** (enter text) 23 | Enter a single channel name. 24 | 25 | * **Regular expression** (enter text) 26 | Enter a regular expression for matching multiple channel labels. 27 | 28 | * **Channel file** (select files) 29 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.bbox.md: -------------------------------------------------------------------------------- 1 | # Get Bounding Box 2 | Determine the bounding box of an image. 3 | This is the 2 x 3 array of the minimum and maximum X, Y, and Z coordinates (in mm), 4 | ``BB = [min_X min_Y min_Z 5 | max_X max_Y max_Z]`` 6 | 7 | * **Image** (select files) 8 | Image for which to determine bounding box. 9 | 10 | * **Bounding box definition** (choose an option) 11 | Bounding box for field of view (using only header information) or for the set of voxels over a specified threshold. 12 | 13 | * **Field of view** 14 | Bounding box is for entire field of view of image. 15 | 16 | * **Supra-threshold voxels** 17 | Bounding box is for voxels over specified threshold. 18 | 19 | * **Threshold** (enter text) 20 | Bounding box is for set of voxels above this value. 21 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.cat.md: -------------------------------------------------------------------------------- 1 | # 3D to 4D File Conversion 2 | Concatenate a number of 3D volumes into a single 4D file. 3 | 4 | * **3D Volumes** (select files) 5 | Select the volumes to concatenate 6 | 7 | * **Output Filename** (enter text) 8 | Specify the name of the output 4D volume file. 9 | Unless explicit, the output folder is the one containing the first image. 10 | A '.nii' extension will be added if not specified. 11 | 12 | * **Data Type** (choose from the menu) 13 | Data-type of output image. SAME indicates the same datatype as the original images. 14 | 15 | * **Interscan interval** (enter text) 16 | Interscan interval, TR, (specified in seconds). 17 | This is the time between acquiring a plane of one volume and the same plane in the next volume. It is assumed to be constant throughout. 18 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.cdir.md: -------------------------------------------------------------------------------- 1 | # Change Directory (Deprecated) 2 | This module is deprecated and has been moved to BasicIO. 3 | Jobs which are ready to run may continue using it, but the module inputs can not be changed via GUI. Please switch to the BasicIO module instead. 4 | This facility allows programming a directory change. Directories are selected in the right listbox. 5 | 6 | * **Select a directory** (select files) 7 | Select a directory to change to. 8 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.deface.md: -------------------------------------------------------------------------------- 1 | # De-face Images 2 | Strip the face from images, so individuals are more difficult to identify from surface renderings. 3 | De-faced images are prefixed by ``anon_``. Please note that this face-stripping may not always be reliable, so you are urged to visually inspect the end results prior to making data available to others. 4 | 5 | * **Images to de-face** (select files) 6 | Specify the NIfTI images to strip the face from. 7 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.exp_frames.md: -------------------------------------------------------------------------------- 1 | # Expand Image Frames 2 | Return a list of image filenames with appended frame numbers. 3 | 4 | * **NIfTI file(s)** (select files) 5 | Files to read. If the same multi-frame image is specified more than once, it will be expanded as often as it is listed. 6 | 7 | * **Frames** (enter text) 8 | Frame number(s) requested. 9 | Only frames that are actually present in the image file(s) will be listed. 10 | Enter 'Inf' to list all frames. 11 | Enter '[N Inf]' to list all frames starting from N. 12 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.import.ecat.md: -------------------------------------------------------------------------------- 1 | # ECAT Import 2 | ECAT 7 Conversion. 3 | ECAT 7 is the image data format used by the more recent CTI PET scanners. 4 | 5 | * **ECAT files** (select files) 6 | Select the ECAT files to convert. 7 | 8 | * **Options** 9 | Conversion options 10 | 11 | * **Output image format** (choose from the menu) 12 | Output files can be written as .img + .hdr, or the two can be combined into a .nii file. 13 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.import.minc.md: -------------------------------------------------------------------------------- 1 | # MINC Import 2 | MINC Conversion. 3 | MINC is the image data format used for exchanging data within the ICBM community, and the format used by the MNI software tools. It is based on NetCDF. MINC is no longer supported for reading images into SPM, so MINC files need to be converted to NIFTI format in order to use them. See http://www.bic.mni.mcgill.ca/software/ for more information. 4 | 5 | * **MINC files** (select files) 6 | Select the MINC files to convert. 7 | 8 | * **Options** 9 | Conversion options 10 | 11 | * **Data Type** (choose from the menu) 12 | Data-type of output images. Note that the number of bits used determines the accuracy, and the amount of disk space needed. 13 | 14 | * **Output image format** (choose from the menu) 15 | Output files can be written as .img + .hdr, or the two can be combined into a .nii file. 16 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.import.parrec.md: -------------------------------------------------------------------------------- 1 | # PAR/REC Import 2 | Philips PAR/REC Import. 3 | 4 | * **PAR files** (select files) 5 | Select the PAR files to convert. 6 | 7 | * **Options** 8 | Conversion options 9 | 10 | * **Output directory** (select files) 11 | Select a directory where files are written. 12 | 13 | * **Output image format** (choose from the menu) 14 | Output files can be written as .img + .hdr, or the two can be combined into a .nii file. 15 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.md.md: -------------------------------------------------------------------------------- 1 | # Make Directory (Deprecated) 2 | This facility allows programming a directory change. Directories are selected in the right listbox. 3 | This module is deprecated and has been moved to BasicIO. 4 | Jobs which are ready to run may continue using it, but the module inputs can not be changed via GUI. Please switch to the BasicIO module instead. 5 | 6 | * **Select a base directory** (select files) 7 | Select a base directory. 8 | 9 | * **Enter a directory name** (enter text) 10 | Enter a directory name 11 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.print.md: -------------------------------------------------------------------------------- 1 | # Print figure 2 | Print figure. 3 | 4 | * **Print Filename** (enter text) 5 | Filename to print to. If set to an empty string, the figure will be printed to a file named spm_*.*, in the current directory. PostScript files will be appended to, but other files will have 'page numbers' appended to them. 6 | 7 | * **Figure to print** (choose an option) 8 | Figure to print 9 | 10 | * **Figure Name** (enter text) 11 | Figure to print. The value entered here must be the 'Tag' property of a figure. 12 | 13 | * **Figure Handle** (enter text) 14 | Figure to print. The value entered here must be a figure handle. If it is a non-finite value (Inf/NaN), the SPM Graphics window is printed. 15 | 16 | * **Printing Format** (choose from the menu) 17 | Select the printing format you want. PostScript (PS) is the only format that allows to append figures to the same file. 18 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.render.extract.md: -------------------------------------------------------------------------------- 1 | # Extract Surface 2 | Surface extraction. 3 | 4 | * **Data** (select files) 5 | Images to create rendering/surface from (usually grey and white matter segmentations). 6 | 7 | * **Output** (choose from the menu) 8 | Operation mode. 9 | 10 | * **Surface isovalue(s)** (enter text) 11 | Enter one or more values at which isosurfaces through the input images will be computed. 12 | This is only relevant for extracting surfaces, not rendering. 13 | -------------------------------------------------------------------------------- /docs/reference/docs/spm.util.split.md: -------------------------------------------------------------------------------- 1 | # 4D to 3D File Conversion 2 | Convert a 4D volume file into a series of 3D volume files. 3 | 4 | * **4D Volume** (select files) 5 | Select the 4D volume file to convert into a series of 3D volume files. 6 | 7 | * **Output Directory** (select files) 8 | Specify the output directory. If no directory is given, files will be written in the same directory than the input 4D file. 9 | -------------------------------------------------------------------------------- /docs/reference/index.md: -------------------------------------------------------------------------------- 1 | # Reference 2 | 3 | These reference materials explain some of the theory and data structures used in SPM. For step-by-step guides to using SPM, please see the [tutorials section](../tutorials/index.md). 4 | 5 | !!! Update in progress 6 | The reference material in this section is being imported from the old [SPM Manual](https://www.fil.ion.ucl.ac.uk/spm/doc/spm12_manual.pdf), and there may be formatting issues and broken links. Apologies for any inconvenience. -------------------------------------------------------------------------------- /docs/tutorials/MEEG/index.md: -------------------------------------------------------------------------------- 1 | # M/EEG Tutorials 2 | 3 | ## About the tutorials 4 | 5 | These tutorials cover the following areas in M/EEG analysis. 6 | 7 | 1. [Basic preprocessing](mmn/index.md) 8 | 2. [First level convolutional modelling ](meeg_firstlevel.md) 9 | 3. [Bayesian Source localisation](meg_sloc.md) 10 | 4. [multimodal analysis](multimodal/index.md) 11 | 5. 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For this reason, it is recommended to discard the initial few volumes from datasets that do not include lead-in dummy scans. 6 | 7 | In this dataset, the first 12 volumes have been discarded. 8 | 9 | !!! info "Note about more recent datasets" 10 | Please note that for more recent datasets which include lead-in dummy scans, this step may not be necessary. Always make sure that you know the protocol used for acquisition of the data you are working with. 11 | 12 | --8<-- "addons/abbreviations.md" -------------------------------------------------------------------------------- /docs/tutorials/fmri/index.md: -------------------------------------------------------------------------------- 1 | # functional Magnetic Resonance Imaging 2 | 3 | The instructions accompanying these fMRI datasets show you how to use SPM to analyse data for a block-design and an event-related fMRI experiments. They are both single-subjects or "first level" analyses. 4 | 5 | * [Block design fMRI experiment](./block/index.md): Auditory experiment (_Mother of all Experiments_) 6 | * [Event-related fMRI experiment](./event/index.md): repetition priming for famous and non-famous faces 7 | -------------------------------------------------------------------------------- /docs/wikibooks/Autocorrelation.md: -------------------------------------------------------------------------------- 1 | ## Pages from WikiPedia 2 | 3 | [Autocorrelation](w:Autocorrelation "wikilink") 4 | 5 | ## Pages from MathWorld 6 | 7 | 8 | 9 | ## Autocorrelation in SPM 10 | -------------------------------------------------------------------------------- /docs/wikibooks/Basis_functions.md: -------------------------------------------------------------------------------- 1 | ## From WikiPedia 2 | 3 | [Basis Function](w:Basis_function "wikilink") 4 | 5 | ## Basis functions in SPM 6 | -------------------------------------------------------------------------------- /docs/wikibooks/CI.md: -------------------------------------------------------------------------------- 1 | \_\_NOTOC\_\_ 2 | 3 | ## Continuous Integration 4 | 5 | - 6 | - 7 | - 8 | - 9 | - 10 | - [Travis CI](https://travis-ci.org/), 11 | [CircleCI](https://circleci.com/), [Azure 12 | Pipelines](https://docs.microsoft.com/en-us/azure/devops/pipelines/get-started/) 13 | - [Jenkins](https://jenkins.io/), [Artenolis](https://git.io/artenolis) 14 | - [MOxUnit](https://github.com/MOxUnit/MOxUnit), 15 | [MOcov](https://github.com/MOcov/MOcov), 16 | [MOdox](https://github.com/MOdox/MOdox) 17 | -------------------------------------------------------------------------------- /docs/wikibooks/Cogent.md: -------------------------------------------------------------------------------- 1 | Cogent is a matlab based program for displaying stimuli in psychophysics 2 | and scanner experiments. It can show pictures, words, movies and play 3 | sounds, as well as use all of Matlab\'s math and randomisation 4 | functions. Timing is precise to the millisecond. 5 | 6 | Downloads and more details here: [Cogent page at the Laboratory of 7 | Neurobiology site](http://www.vislab.ucl.ac.uk/cogent.php) 8 | -------------------------------------------------------------------------------- /docs/wikibooks/Contrasts.md: -------------------------------------------------------------------------------- 1 | If you want activated regions only, then you should apply a t-test (F 2 | tests find differences in \'any\' direction while t-tests distinguish 3 | between directions). 4 | 5 | There are some predefined contrasts in SPM when you estimate a model 6 | (e.g. the \"effects of interest\" f-contrast). If you hadn\'t defined 7 | your control (or baseline) task then it would find areas activated for 8 | stimulus A and also areas activated for stimulus B. 9 | -------------------------------------------------------------------------------- /docs/wikibooks/Correlation.md: -------------------------------------------------------------------------------- 1 | See [this 2 | thread](https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=spm;7e904ea1.1302) 3 | on the SPM mailing list: 4 | 5 | 6 | 7 | 8 | 17 | 18 | 19 |

[It should be said] that in SPM (and generally) we discourage the 9 | use of correlations – they are difficult to interpret in many instances 10 | and are an odd measure of (standardised) effect size that conflates the 11 | regression slope with standard error. In fact, there are people who 12 | would like to abolish the correlation coefficient [...]:

13 |

http://societytosupressthecorrelationcoefficient.wordpress.com/about/

15 |

I am not sure whether this society is serious but it raises an 16 | interesting point of view :)

20 | -------------------------------------------------------------------------------- /docs/wikibooks/Correlation_and_Regression.md: -------------------------------------------------------------------------------- 1 | ## Pages from WikiPedia 2 | 3 | [Correlation](w:Correlation "wikilink") 4 | 5 | [Regression toward the mean](w:Regression_toward_the_mean "wikilink") 6 | -------------------------------------------------------------------------------- /docs/wikibooks/Covariance.md: -------------------------------------------------------------------------------- 1 | ## Pages from WikiPedia 2 | 3 | [Covariance](w:Covariance "wikilink") 4 | 5 | ## Covariance in SPM 6 | -------------------------------------------------------------------------------- /docs/wikibooks/DTI.md: -------------------------------------------------------------------------------- 1 | # Diffusion tools 2 | 3 | - [ACID toolbox](http://www.diffusiontools.com) 4 | - [Diffusion 5 | toolbox](http://sourceforge.net/projects/diffusion.spmtools.p/) 6 | -------------------------------------------------------------------------------- /docs/wikibooks/Datasets.md: -------------------------------------------------------------------------------- 1 | # Datasets 2 | 3 | - [SPM Datasets](http://www.fil.ion.ucl.ac.uk/spm/data/) 4 | - [Publicly available brain signals data](http://www.brainsignals.de/) 5 | -------------------------------------------------------------------------------- /docs/wikibooks/Design_efficiency.md: -------------------------------------------------------------------------------- 1 | # Design Efficiency 2 | 3 | ## General advice about how to optimise an fMRI experiment 4 | 5 | 6 | 7 | ## Tools 8 | 9 | - Tool for optimising randomised designs: 10 | 11 | 12 | 13 | - Genetic algorithm for optimising fMRI designs: 14 | 15 | 16 | 17 | - Tool for simulating fMRI designs: 18 | 19 | 20 | 21 | - MATLAB function called fMRI_GLM_efficiency.m: 22 | 23 | 24 | 25 | - Optseq: 26 | 27 | 28 | -------------------------------------------------------------------------------- /docs/wikibooks/F_and_T_tests.md: -------------------------------------------------------------------------------- 1 | ## Pages from WikiPedia 2 | 3 | [F test](w:F_Test "wikilink") 4 | 5 | [T_Test](w:Student's_t-test "wikilink") 6 | 7 | ## F and T tests in SPM 8 | -------------------------------------------------------------------------------- /docs/wikibooks/Headless.md: -------------------------------------------------------------------------------- 1 | # SPM without Graphical Output 2 | 3 | - Start MATLAB with options: 4 | - -nodisplay -nosplash 5 | - -noFigureWindows on Windows platforms 6 | 7 | 8 | 9 | - Initialise SPM with the following: 10 | 11 | `spm('defaults','fmri');` 12 | `spm_jobman('initcfg');` 13 | 14 | - Enable the Command Line mode in your SPM scripts: 15 | 16 | `spm_get_defaults('cmdline',true);` 17 | 18 | - Use [Xvfb](http://en.wikipedia.org/wiki/Xvfb) (X virtual framebuffer) 19 | on Linux platforms. 20 | -------------------------------------------------------------------------------- /docs/wikibooks/Inference.md: -------------------------------------------------------------------------------- 1 | ## Pages from WikiPedia 2 | 3 | [Inference](w:Inference "wikilink") 4 | 5 | ## Inference in SPM 6 | -------------------------------------------------------------------------------- /docs/wikibooks/Julia.md: -------------------------------------------------------------------------------- 1 | ## SPM and Julia 2 | 3 | Julia is a modern programming language with excellent numerical 4 | computing abilities. 5 | 6 | ### SPM.jl 7 | 8 | - [SPM.jl](https://github.com/spm/SPM.jl) 9 | -------------------------------------------------------------------------------- /docs/wikibooks/Learning_SPM.md: -------------------------------------------------------------------------------- 1 | # Courses 2 | 3 | See 4 | 5 | # Books 6 | 7 | See [Statistical Parametric Mapping: The Analysis of Functional Brain 8 | Images](http://www.fil.ion.ucl.ac.uk/spm/doc/#Books) 9 | 10 | # Websites 11 | 12 | ## SPM 13 | 14 | 15 | 16 | 17 | 18 | ## MATLAB 19 | 20 | - MathWorks [1](http://www.mathworks.co.uk/) 21 | - MATLAB Central [2](http://www.mathworks.co.uk/matlabcentral/) 22 | - MATLAB for Psychologists [3](http://antoniahamilton.com/matlab.html) 23 | 24 | ## Maths 25 | 26 | 27 | 28 | # Example Data Sets 29 | 30 | - SPM datasets and tutorials [4](http://www.fil.ion.ucl.ac.uk/spm/data/) 31 | 32 | # Mailing list 33 | 34 | 35 | -------------------------------------------------------------------------------- /docs/wikibooks/Normalisation.md: -------------------------------------------------------------------------------- 1 | In order to compare data from several scans and / or research subjects, 2 | all the brain images have to be in the same 3D space. In SPM this is 3 | accomplished by \"normalizing\" the images into the space defined by the 4 | Montreal Neurological Institute (MNI) template. Different versions of 5 | SPM use different algorithms for this process. In general, the closer 6 | the images start out to the MNI template orientation, the better the 7 | outcome of the normalization. 8 | 9 | ## Links 10 | 11 | - [Spatial normalization](w:Spatial_normalization "wikilink") 12 | -------------------------------------------------------------------------------- /docs/wikibooks/PPI.md: -------------------------------------------------------------------------------- 1 | # Psychophysiological Interactions (PPI) 2 | 3 | - 4 | - 5 | - 6 | - 7 | -------------------------------------------------------------------------------- /docs/wikibooks/Percentage_signal_change.md: -------------------------------------------------------------------------------- 1 | # Percentage of Signal Change 2 | 3 | - 4 | - 5 | - 6 | - 7 | - 8 | - 9 | -------------------------------------------------------------------------------- /docs/wikibooks/SEM.md: -------------------------------------------------------------------------------- 1 | # Structural Equation Modelling 2 | 3 | - [SEM toolbox by Douglas 4 | Steele](http://dslink333.dlinkddns.com/SEM.htm) 5 | - [another SEM 6 | toolbox](http://www.mrc-cbu.cam.ac.uk/people/rik.henson/personal/analysis/) 7 | - [OpenMx](http://openmx.psyc.virginia.edu/) 8 | -------------------------------------------------------------------------------- /docs/wikibooks/Single_Case_Study.md: -------------------------------------------------------------------------------- 1 | # Single Case Study 2 | 3 | - [Comparing a single patient versus a group of 4 | controls](http://www.mrc-cbu.cam.ac.uk//personal/rik.henson/personal/Henson_Singlecase_06.pdf) 5 | - [Crawford modified 6 | t-test](http://homepages.abdn.ac.uk/j.crawford/pages/dept/SingleCaseMethodology.htm) 7 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | mkdocs-material 2 | mkdocs-video 3 | mkdocs-bibtex 4 | codespell 5 | pyspelling --------------------------------------------------------------------------------