Pattern Recognition for Neuroimaging Toolbox
London, UK
21 May 2012
Janaina Mourao-Miranda,
Machine Learning and Neuroimaging Lab,
University College London, UK
Outline
• Motivation
• PRoNTo Framework
• Future Developments
Machine Learning
community
PRoNTo
Neuroscience and
Clinical Neuroscience
communities
sMRI
fMRI
Advantages of Pattern Recognition Analysis
Accounts for the spatial correlation of the data (multivariate)
• fMRI data are multivariate by nature.
• Can yield greater sensitivity than conventional analysis.
Enable classification/prediction of individual subjects/scans
• ‘Mind-reading’ or decoding applications
• Clinical applications
Questions investigated with pattern
recognition analysis
•Does the pattern of activation in brain regions A, B and C encode information
about a variable of interest?
•Can we classify groups of subjects (e.g. patients and healthy controls) based
on brain scans?
•Which features lead to the best discrimination between a group of patients
and a group of controls?
•Can we predict continuous measures (e.g. age, performance, etc) from brain
scans?
•etc
Conventional neuroimaging studies (encoding):
- Investigate which regions of the brain are involved in a perceptual or cognitive task.
- Probe for individual voxels whether the average activity during one condition is
significantly different from the average activity during a base line condition.
- Mapping: cognitive state -> fMRI data
Pattern Recognition (‘mind-reading’) approaches (decoding):
- Address the problem of classifying the cognitive state of a subject based on the fMRI
data.
- Based on a training set identify a distributed pattern of activation that discriminates
two cognitive states and uses this pattern to make prediction for new data.
- Mapping: fMRI data -> cognitive state
OUR GOAL
“develop a toolbox based on machine learning
techniques for the analysis of neuroimaging data”
BUT
“free”, matlab based, compatible with SPM,
easy to use (with GUI),
multiple modalities (fMRI/sMRI/PET/betas),
various machines, modular code, easy to contribute
PRoNTo
7
Existing software
Freely available packages for machine learning modeling of neuroimaging
data:
• 3dsvm plugin for AFNI (LaConte et al., 2005)
• the Matlab MVPA toolbox for fMRI data (Detre et al., 2006)
• PyMVPA (Hanke et al., 2009)
• PROBID (http://www.brainmap.co.uk/probid.htm)
• 3dsvm, the Matlab MVPA toolbox for fMRI data and PyMVPA require
(advanced) programming skills, can not be directly integrated into the main
neuroimaging analysis pipelines, e.g. SPM (http://www.fil.ion.ucl.ac.uk/spm/).
• PROBID is optimized for groups classification (i.e. patients vs. healthy
controls) and therefore does not easily enables single subject analysis or
flexible cross-validation framework.
Pascal Harvest Project
• “The main idea behind Harvest is: put together in a room a
team for long enough to produce an innovative software for a
real application. PASCAL2 will pick up the bills”.
Requirements:
• Piece of software as their main objective
• Training component.
• International team.
Title: PRoNTo (Pattern Recognition for Neuroimaging Toolbox)
Coordinator: Dr. Janaina Mourao-Miranda
Participants:
Dr. Christophe Phillips (Cyclotron Research Centre, University of Liège,
Belgium)
Dr. John Ashburner (Wellcome Trust Centre for Neuroimaging, UCL)
Dr. Jane Rondina (Department of Neuroimaging, KCL)
Dr. Andre Marquand (Department of Neuroimaging, KCL)
Dr. Maria Joao Rosa (Wellcome Trust Centre for Neuroimaging, UCL)
Dr. Jonas Richiardi (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
Ms. Jessica Schrouff (Cyclotron Research Centre, University of Liège,
Belgium)
Dr. Carton Chu (National Institute of Mental Health (NIMH), NIH, USA )
Hosting site: UCL, Computer Science Department, London, UK
Toolbox based on pattern
recognition techniques for the
analysis of neuroimaging data:
- “free” (MATLAB based)
- easy to use (with GUIs)
- easy to contribute (modular
code)
- multiple modalities:
- fMRI/sMRI/PET/betas
- compatible with SPM
For users
User Interface
Matlab
Batch
Functions
Easy to use
Click buttons
Very efficient
Can be saved
and copied
Compatible
with SPM
User-specific
analysis
More
programming
skills required
User point of view
User Interface
Matlab Batch
13
For developers
Machine
learning
Machines
Specific GUI
Batch system
Script
Features
Kernel
Model
Training
Validation
Classification
(SVM, GPC, RF)
Regression
(KRR,RVR)
Wrapper
User Interface
Developer point of view
Structure containing:
-Data/Kernel
-Labels
-Etc
Structure containing:
-Function name (machine)
-Arguments
prt_machine
Structure containing:
-Predictions
-Coefficients/Weights
-etc
Machine Library
(classification and regression models)
Structure containing:
-Data/Kernel
-Labels
-Etc
prt_machine_krr
prt_machine_gpml
prt_machine_svm_bin
Structure containing:
-Predictions
-Coefficients/Weights
15
Future developments
• improved fMRI data handling (detrending & hrf estimation)
• feature selection (GP based, RFE,…)
• more machines (provided by Machine Learning community)
• etc
Download
Available here:
http://www.mlnl.cs.ucl.ac.uk/pronto/
Credits
The development of PRoNTo was possible with the financial and logistic
support of:
- PASCAL Harvest Programme
(http://www.pascal-network.org/)
- the Department of Computer Science, University College London
(http://www.cs.ucl.ac.uk);
- the Wellcome Trust;
- PASCAL2 (http://www.pascal-network.org/);
- the Fonds de la Recherche Scientifique-FNRS, Belgium
(http://www.fnrs.be);
- The Foundation for Science and Technology, Portugal
(http://www.fct.pt);
- Swiss National Science Foundation (PP00P2-123438) and Center for
Biomedical Imaging (CIBM) of the EPFL and Universities and Hospitals
of Lausanne and Geneva.
PRoNTo a team work!