= Introduction tutorials: Editing process = http://neuroimage.usc.edu/brainstorm/Tutorials Redactors: * '''Francois Tadel''': Montreal Neurological Institute * '''Elizabeth Bock''': Montreal Neurological Institute Reviewers ''[current reviewing status] '' * '''Sylvain Baillet''': Montreal Neurological Institute ''[overview 1-14, no proofreading]'' * '''Richard Leahy''': University of Southern California ''[validated 1-19]'' * '''John Mosher''': Cleveland Clinic ''[not started]'' * '''Dimitrios Pantazis''': Massachusetts Institute of Technology ''[validated 1-15]'' Expected timeline (2016) * '''March''': Tutorials #1-#21 (interface and pre-processing) * '''April''': Tutorials #22-#24 (source estimation and time-frequency) * '''May-June''': Tutorials #25-#28 (statistics and workflows) * '''July-Sept''': Cross-validation of the pipeline and the results with MNE, FieldTrip and SPM * '''October''': Presentation during a satellite meeting at the Biomag 2016 conference * '''Nov-Dec''': Connectivity tutorial <<BR>><<BR>> == Tutorial 10: Power spectrum and frequency filters == [CODE] * Richard, John, Sylvain: Finish the evaluation of the band-pass filters used in Brainstorm * Francois: Mark with an extended event the time segments that we cannot trust (edge effect) * Francois: Add warning if recordings are not long enough [ONLINE DOC] * Richard, John, Sylvain: Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/ArtifactsFilter#What_filters_to_apply.3F_.5BTODO.5D|What filters to apply?]] * Richard, John, Sylvain: Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/ArtifactsFilter#Filters_specifications_.5BTODO.5D|Filters specifications]] == Tutorial 12: Artifact detection == [ONLINE DOC] * Beth: Adding examples of the different detection processes. Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/ArtifactsDetect#Other_detection_processes|Other detection processes]] == Tutorial 13: Artifact cleaning with SSP == [CODE] * Francois: Check the length needed to filter the recordings (after finishing #10) <<BR>>Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/ArtifactsSsp#SSP_Algorithm_.5BTODO.5D|SSP Algorithm]] == Tutorial 15: Import epochs == [ONLINE DOC] * Richard, Sylvain, John, Francois: Define recommendations for epoch lengths (after finishing #10) <<BR>>Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/Epoching#Epoch_length_.5BTODO.5D|Epoch length]] == Tutorial 20: Head modelling == [ONLINE DOC] * John, Richard, Sylvain: In-depth review of this sensitive tutorial * John, Richard, Sylvain: Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/AllIntroduction#Tutorials.2FHeadModel.References_.5BTODO.5D|References]] == Tutorial 22: Source estimation == [CODE] * John: Inverse code: sLORETA * John: Inverse code: NAI * John: Inverse code: Mixed head models * John, Richard, Sylvain: How to deal with '''unconstrained sources''' ? * For the Z-score normalization? * For the connectivity analysis? http://neuroimage.usc.edu/forums/showthread.php?2401 * For the statistics? * Projection on a dominant orientation? * Francois: Remove the warning messages in the interface * Francois: Call FieldTrip headmodels and beamformers [ONLINE DOC] * John, Richard, Sylvain: Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/SourceEstimation#Source_estimation_options_.5BTODO.5D|Source estimation options]] * John, Richard, Sylvain: Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/SourceEstimation#Advanced_options_.5BTODO.5D|Advanced options]] * John, Richard, Sylvain: Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/SourceEstimation#Equations_.5BTODO.5D|Equations]] * John, Richard, Sylvain: Section [[http://neuroimage.usc.edu/brainstorm/Tutorials/SourceEstimation#References_.5BTODO.5D|References]] * John, Richard, Sylvain: Why are dSPM values 2x lower than Z-score ? * John, Richard, Sylvain: In-depth review of this sensitive tutorial == Tutorial 24: Time-frequency == [CODE] * Francois: Enable option "Hide edge effects" for Hilbert (after finishing #10) [ONLINE DOC] * Francois: Hilbert: Link back to the filters tutorial (after finishing #10) == Tutorial 25: Difference == [ONLINE DOC] * Validate order of the processes: Difference sources => Zscore => Low-pass filter<<BR>>Section: [[http://neuroimage.usc.edu/brainstorm/Tutorials/Difference#Difference_deviant-standard|Difference deviant-standard]] == Tutorial 26: Statistics == [CODE] * Francois: Implementation of Brainstorm-only permutation tests * Francois: Keep edge effects map (TFmask) for the stats on time-frequency maps [ONLINE DOC] * Francois: Add a section about unconstrained sources (after finishing #27) == Tutorial 27: Workflows == * Update everything after: http://neuroimage.usc.edu/brainstorm/Tutorials/Workflows#Constrained_cortical_sources * Update: http://neuroimage.usc.edu/brainstorm/Tutorials/WorkflowGuide * Update number of pages == Tutorial 28: Scripting == * Not evaluated yet * Update number of pages == Final steps == * Francois: Check that the script tutorial_introduction.m produces the same output as tutorials * Francois: Check all the links in all the pages * Francois: Reference on ResearchGate, Academia and Google Scholar <<BR>>http://neuroimage.usc.edu/brainstorm/Tutorials/AllIntroduction <<BR>><<BR>><<BR>><<BR>><<BR>>[Additional important stuff] == Other analysis scenarios == * Francois: Update all the tutorials (100+ pages) * Francois: Remove useless images from all tutorials * Francois: Add number of pages in the tutorials * Francois: Split list in columns == Connectivity == * Not documented at all * Richard, Sylvain: Define example dataset and precise results to obtain from them * Richard: How to assess significance from connectivity matrices * Francois: Preparation of a tutorial == Parametric experiments == * '''A = B''': Parametric t-test for constrained sources. 1. '''Sources''': Compute source maps for each trial (constrained, no normalization) 1. '''First-level statistic''': Compute a t-statistic for the source maps of all the trials A vs B. * Process2 "Test > Compute t-statistic": no absolute values, independant, equal variance. * With a high number of trials (n>30), t-values follow approximately a N(0,1) distribution. 1. '''Low-pass filter''' your evoked responses (optional). [NO! SHOULD BE DONE BEFORE, BUT WHEN ? => SPLIT ANALYSIS IN TWO: ERP OR FREQUENCY/RS] 1. '''Rectify '''the individual t-statistic (we're giving up the sign across subjects). 1. '''Project '''the individual t-statistic on a template (only when using the individual brains). 1. '''Smooth '''spatially the t-statistic maps. 1. '''Second-level statistic''': Compute a one-sampled chi-square test based on the t-statistics. * Process1: "Test > Parametric test against zero": One-sampled Chi-square test * This tests for '''|A-B|'''=0 using a Chi-square test: X = sum(|t<<HTML(<SUB>)>>i<<HTML(</SUB>)>>|^2) ~ Chi2(N<<HTML(<SUB>)>>subj<<HTML(</SUB>)>>) * Indicates when and where there is a significant effect (but not in which direction). 1. After identifying the significant effects, you may want to know which condition is stronger:<<BR>>Compute and plot power maps at the time points of interest: '''average(Ai^2^) - average(Bi^2^)'''