SPM for MEEG Guillaume Flandin Wellcome Centre for

SPM for M/EEG Guillaume Flandin Wellcome Centre for Human Neuroimaging University College London https: //www. fil. ion. ucl. ac. uk/spm/course/london/material/ SPM Course London, May 2019

Random Contrast c Field Theory General Linear Model Preprocessings Statistical Inference

Image time-series Realignment Spatial filter Design matrix Smoothing General Linear Model Statistical Parametric Map Statistical Inference Normalisation Anatomical reference Parameter estimates RFT p <0. 05

q Statistical Parametric Mapping refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. Time Sensor to voxel transform Statistical Parametric Mapping for Event -Related Potentials I: Generic Considerations. S. J. Kiebel and K. J. Friston. Neuro. Image, 2004. Topological inference for EEG and MEG, J. Kilner and K. J. Friston, Annals of Applied Statistics, 2010.

M/EEG Source Analysis Forward Problem Data Likelihood Prior Posterior Evidence Parameters Inverse Problem

Dynamic Causal Modelling for M/EEG DCM for event-related potentials DCM for cross-spectral density DCM for induced responses DCM for phase coupling

SPM Software “The SPM software was originally developed by Karl Friston for the routine statistical analysis of functional neuroimaging data from PET while at the Hammersmith Hospital in the UK, and made available to the emerging functional imaging community in 1991 to promote collaboration and a common analysis scheme across laboratories. ” SPMclassic, SPM’ 94, SPM’ 96, SPM’ 99, SPM 2, SPM 5, SPM 8 and SPM 12 represent the ongoing theoretical advances and technical improvements of the original version.

Software: SPM 12 q Free and Open Source Software (GPL) q Requirements: Ø MATLAB: 7. 4 (R 2007 a) to 9. 6 (R 2019 a) no Math. Works toolboxes required Ø Supported platforms: Linux, Windows and Mac q File formats: – Volumetric images: NIf. TI (DICOM import) – Geometric images: GIf. TI – M/EEG: most manufacturers (Field. Trip’s fileio) q Standalone version available.

Brain Imaging Data Structure (BIDS) “A simple and intuitive way to organise and describe your neuroimaging and behavioural data. ” https: //bids. neuroimaging. io/ K. J. Gorgolewski et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data (2016) G. J. G. Niso et al. MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography. Scientific Data (2018). C. Pernet et al. EEG-BIDS: an extension to the Brain Imaging Data Structure for electroencephalography. Scientific Data (2019).

SPM Website https: //www. fil. ion. ucl. ac. uk/spm/ q SPM software q Documentation & Bibliography q Example data sets

SPM Manual https: //dx. doi. org/10. 1155/2011/852961 https: //doi. org/10. 3389/fnins. 2019. 00300

SPM Toolboxes q User-contributed SPM extensions: http: //www. fil. ion. ucl. ac. uk/spm/ext/

SPM Mailing List https: //www. fil. ion. ucl. ac. uk/spm/support/ spm@jiscmail. ac. uk

The SPM co-authors • Jesper Andersson • John Ashburner • Nelson Trujillo-Barreto • Gareth Barnes • Matthew Brett • Christian Buchel • CC Chen • Justin Chumbley • Jean Daunizeau • Olivier David • Guillaume Flandin • Karl Friston • Darren Gitelman • Daniel Glaser • Volkmar Glauche • Lee Harrison • Rik Henson • Andrew Holmes • Chloe Hutton • Maria Joao • Stefan Kiebel • James Kilner • Vladimir Litvak • Andre Marreiros • Jérémie Mattout • Rosalyn Moran • Tom Nichols • Robert Oostenveld • Will Penny • Christophe Phillips • Dimitris Pinotsis • Jean-Baptiste Poline • Ged Ridgway • Holly Rossiter • Mohamed Seghier • Klaas Enno Stephan • Sungho Tak • Bernadette Van Wijk • Peter Zeidman