Statistical Parametric Mapping

SPM (Statistical Parametric Mapping) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. These ideas have been instantiated in software that is called SPM.

The SPM software package has been designed for the analysis of brain imaging data sequences. The sequences can be a series of images from different cohorts, or time-series from the same subject. The current release is designed for the analysis of fMRI, PET, SPECT, EEG and MEG.

SPM is made freely available to the [neuro]imaging community, to promote collaboration and a common analysis scheme across laboratories. The software represents the implementation of the theoretical concepts of Statistical Parametric Mapping in a complete analysis package, as a suite of MATLAB (The MathWorks, Inc) functions and subroutines with some externally compiled C routines.

Overview

• SPM website at the Wellcome Trust Centre for Neuroimaging, UCL, UK.
• Overview of SPM from Wikipedia
• Overview of SPM from Scholarpedia
• Introduction to Statistical Parametric Mapping
• History of Statistical Parametric Mapping in functional neuroimaging

Installation

Overview of SPM installation:

• Download

System Requirements (hardware and software):

• MATLAB version
• Advice on hardware selection

Detailed installation and compilation on:

• Windows 64bit
• Linux 64bit
• macOS 64bit

Instructions for older platforms: Windows 32bit, Linux 32bit, MacOS (Intel), MacOS (PowerPc), SunOS / Solaris.

Standalone version using the MATLAB Compiler:

• Standalone SPM

Optimising your installation:

• Optimising MATLAB/SPM

Containerisation:

• Docker

Miscellaneous:

• GNU Octave
• Continuous Integration

Experimental Design for fMRI

• Design efficiency
• Block design
• Event related design

Data Formats

• Importing data from the scanner
• DICOM Import

Preprocessing

• Slice Timing
• Realignment
• Unwarp
• Coregistration
• Normalisation
• Segmentation
• Spatial smoothing

Modelling

• Haemodynamic Response Function
• Basis functions
• General Linear Model
• Correlation and Regression
• Linear Hierarchical Models
• Covariance
• Autocorrelation
• Non-sphericity
• Session concatenation
• Group analysis

Statistical Inference

• F and T tests
• Contrasts
• Inference
• Conjunction
• Power Analysis
• About Correlation
• Bayesian Inference
• Results Table
• Information to include in papers
• Calculating Percentage signal change
• Comparing a single patient versus a group of controls
• Timeseries extraction

Voxel Based Morphometry

• VBM (Voxel Based Morphometry)

Connectivity Analysis

• Structural Equation Modelling
• Psychophysiological Interactions (PPI)
• Dynamic Causal Modelling (DCM)
  • The DCM Equation. 1. Motivation
  • The DCM Equation. 2. Dynamical Systems
  • The DCM Equation. 3. Networks and Matrices
  • The DCM Equation. 4. The State Equation
  • Two-State DCM for fMRI
  • Bayesian Parameter Averaging (BPA)
  • Parametric Empirical Bayes (PEB)

Misc

• How-tos
• Atlases
• Datasets
• BIDS
• Working with 4D data
• Programming intro
• Writing batch scripts
• No Display Mode
• Learning SPM: Courses, books and websites

Other tools

• Cogent a MATLAB-based stimulus presentation software
• MRIcron and MRIcroGL medical images viewers
• MarsBaR region of interest toolbox for SPM
• SnPM Statistical nonParametric Mapping
• Anatomy SPM Anatomy toolbox
• AAL Anatomical Automatic Labeling
• WFU_PickAtlas a region of interest toolbox for SPM based on the Talairach Daemon Database
• Physiological noise correction
• Diffusion tools

The physics of imaging technologies

• functional Magnetic Resonance Imaging (fMRI)
• Positron Emission Tomography (PET)
• Single Photon Emission Computed Tomography (SPECT)
• Electroencephalography (EEG)
• Magnetoencephalography (MEG)