SPM for M/EEG
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Vladimir Litvak showing slides from Guillaume Flandin
Wellcome Centre for Human Neuroimaging
University College London
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SPMclassic, SPM’94, SPM’96,
SPM’99, SPM2, SPM5, SPM8
and SPM12
represent the
ongoing theoretical advances
and technical improvements of
the original version.
“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.”
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{t
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An area specialised for the processing
of colour, the“colour centre” (V4)
highlighted by cognitive substraction
using PET.
•
Three subjects:
•
Compatible with earlier findings on
monkeys using electrophysiology.
Colour trials
(2 scans)
Grey trials
(2 scans)
Normalisation
Normalisation
Statistical Parametric Map
Statistical Parametric Map
Image time-series
Image time-series
Parameter estimates
Parameter estimates
General Linear Model
General Linear Model
Realignment
Realignment
Smoothing
Smoothing
Design matrix
Anatomical
Anatomical
reference
reference
Spatial filter
Spatial filter
Statistical
Statistical
Inference
Inference
RFT
RFT
p <0.05
p <0.05
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Pedobarographic statistical parametric
mapping (pSPM)
, T. Pataky, Journal of
Foot and Ankle Research, 2008.
Quantitative 3D analysis of bone
in hip osteoarthritis using clinical
computed tomography
,
Tom
Turmezei et al, European
Radiology, 2016.
Contrast
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Topological inference for EEG and
MEG
, J. Kilner and K.J. Friston,
Annals of Applied Statistics, 2010.
Statistical Parametric Mapping for
Event-Related Potentials I: Generic
Considerations.
S.J. Kiebel and K.J.
Friston. NeuroImage, 2004.
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M/EEG Source Analysis
Likelihood Prior
Posterior Evidence
Forward Problem
Inverse Problem
Data
Parameters
~
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DCM for cross-spectral density
DCM for event-related potentials
DCM for induced responses
DCM for phase coupling
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Free and Open Source Software
Development on GitHub
Requirements:
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Supported platforms:
Linux, Windows and Mac
Standalone version available.
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SPM software
Documentation &
Bibliography
Example data sets
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https://dx.doi.org/10.1155/2011/852961
https://doi.org/10.3389/fnins.2019.00300
SPM Manual
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SPM Book
Open Source
https://www.fil.ion.ucl.ac.uk/spm/docs/
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PET, fMRI (1
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and 2
nd
level), PPI, DCM, EEG, MEG, LFP.
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User-contributed SPM extensions
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•
Jesper Andersson
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John Ashburner
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Yael Balbastre
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Nelson Trujillo-Barreto
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Gareth Barnes
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Matthew Brett
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Mikael Brudfors
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Christian Buchel
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CC Chen
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Justin Chumbley
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Jean Daunizeau
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Olivier David
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Guillaume Flandin
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Darren Gitelman
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George O’Neil
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Robert Oostenveld
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Thomas Parr
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Will Penny
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Christophe Phillips
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Dimitris Pinotsis
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Jean-Baptiste Poline
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Ged Ridgway
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Holly Rossiter
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Mohamed Seghier
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Klaas Enno Stephan
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Sungho Tak
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Tim Tierney
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Bernadette Van Wijk
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Peter Zeidman
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Daniel Glaser
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Volkmar Glauche
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Lee Harrison
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Rik Henson
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Andrew Holmes
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Chloe Hutton
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Amirhossein Jafarian
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Maria Joao
•
Stefan Kiebel
•
James Kilner
•
Vladimir Litvak
•
Andre Marreiros
•
J
é
rémie Mattout
•
Rosalyn Moran
•
Tom Nichols
The powerful SPM software for routine statistical analysis of functional neuroimaging data from M/EEG. Learn about its origins, advancements, and applications in a comprehensive short course.
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Presentation Transcript
SPM for M/EEG Vladimir Litvak showing slides from Guillaume Flandin Wellcome Centre for Human Neuroimaging University College London SPM Short Course London, 2023
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, SPM2, SPM5, SPM8 and SPM12 represent the ongoing theoretical advances and technical improvements of the original version.
The very first SPM{t} An area specialised for the processing of colour, the colourcentre (V4) highlighted by cognitive substraction using PET. Three subjects: Colour trials (2 scans) Grey trials (2 scans) Compatible with earlier findings on monkeys using electrophysiology.
Image time-series Statistical Parametric Map Design matrix Spatial filter Realignment General Linear Model Smoothing Statistical Inference RFT Normalisation p <0.05 Anatomical reference Parameter estimates
Statistical Parametric Mapping refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. Quantitative 3D analysis of bone in hip osteoarthritis using clinical computed tomography, Tom Turmezei et al, European Radiology, 2016. Pedobarographic statistical parametric mapping (pSPM), T. Pataky, Journal of Foot and Ankle Research, 2008.
Random Field Theory Contrast c ? = ? ? + ? General Linear Model Pre- Statistical Inference processings ? = ??? 1??? ?? ? ?2= ???{?,?} ????(?)
Day 1 Statistical Parametric Mapping refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. Sensor to voxel transform Time Statistical Parametric Mapping for Event-Related Potentials I: Generic Considerations. S.J. Kiebel and K.J. Friston. NeuroImage, 2004. Topological inference for EEG and MEG, J. Kilner and K.J. Friston, Annals of Applied Statistics, 2010.
~Day 2 M/EEG Source Analysis Forward Problem p ( | ) ( | , ) m p Y m Y Data Likelihood Prior Posterior Evidence ) , | ( m Y p ( | ) p Y m Parameters Inverse Problem
Day 3 Dynamic Causal Modelling for M/EEG DCM for event-related potentials DCM for cross-spectral density DCM for induced responses DCM for phase coupling
Software: SPM Free and Open Source Software Development on GitHub Requirements: MATLAB: 7.4 (R2007a) to 9.14 (R2023a) no MathWorks toolboxes required Supported platforms: Linux, Windows and Mac Standalone version available.
SPM Website https://www.fil.ion.ucl.ac.uk/spm/ SPM software Documentation & Bibliography Example data sets
SPM documentation SPM Manual https://dx.doi.org/10.1155/2011/852961 SPM Book Open Source https://doi.org/10.3389/fnins.2019.00300
New documentation https://www.fil.ion.ucl.ac.uk/spm/docs/
SPM datasets PET, fMRI (1st and 2nd level), PPI, DCM, EEG, MEG, LFP.
SPM Toolboxes User-contributed SPM extensions
SPM Mailing List https://www.fil.ion.ucl.ac.uk/spm/support/ spm@jiscmail.ac.uk
The SPM co-authors Jesper Andersson John Ashburner Yael Balbastre Nelson Trujillo-Barreto Gareth Barnes Matthew Brett Mikael Brudfors 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 Amirhossein Jafarian Maria Joao Stefan Kiebel James Kilner Vladimir Litvak Andre Marreiros J r mie Mattout Rosalyn Moran Tom Nichols George O Neil Robert Oostenveld Thomas Parr Will Penny Christophe Phillips Dimitris Pinotsis Jean-Baptiste Poline Ged Ridgway Holly Rossiter Mohamed Seghier Klaas Enno Stephan Sungho Tak Tim Tierney Bernadette Van Wijk Peter Zeidman
