From buttons to code Eamonn Walsh & Domenica Bueti Overview of SPM2 From buttons to code Eamonn Walsh & Domenica Bueti

SPM Statistical Parametric Mapping SPM examines every voxel location across all images and computes a parametric map Data reduction – condensing information from a number of individuals in a statistically meaningful way

Data transformations p <0.05 Statistical parametric map (SPM) Image time-series Kernel Design matrix Realignment Smoothing General linear model Statistical inference Gaussian field theory Normalisation p <0.05 Template Parameter estimates

Spatial Preprocessing Model Specification and Parameter Estimation

Taken in 1839, this picture of a boulevard gives the impression of empty streets, because with long exposures moving objects would not register. However there is one exception; when a man stopped to have his shoes shined (see bottom of picture).

Spatial Preprocessing

Spatial Preprocessing - Realign Aligning hundreds of 3D brain volumes per single subject To create a ‘mean image’

Spatial Preprocessing - Coregister Matching the functional scan to the structural scan for the same individual

Spatial Preprocessing – Slice Timing Take first or middle slice as reference slice Shift all other slices in time to match this reference slice

Spatial Preprocessing - Normalise The same voxels in different brains are aligned to represent the same anatomical location. Template

Spatial Preprocessing - Smooth Blur the images prior to statistical analysis FWHM Full width half maximum

Spatial Preprocessing - Segment Images are segmented into grey and white matter maps

Image File fm00223_004.img Realigned rfm00223_004.img Realigned, Coregistered,, rrfm00223_004.img Realigned, Coregistered, Normalised, wrrfm00223_004.img Realigned, Coregistered, Normalised, Smoothed swrrfm00223_004.img

Spatial Preprocessing

Spatial Preprocessing Guided Tour SPM Demo Spatial Preprocessing Guided Tour