London, UK 21 May 2012 Janaina Mourao -Miranda, Machine Learning and Neuroimaging Lab,

1. Pattern Recognition for Neuroimaging Toolbox London, UK 21 May 2012 Janaina Mourao-Miranda, Machine Learning and Neuroimaging Lab, University College London, UK

2. Outline • Motivation • PRoNTo Framework • Future Developments

3. Machine Learning community PRoNTo • Neuroscience and Clinical Neuroscience communities sMRI time fMRI

4. 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

5. 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

6. 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

7. 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

8. Existing software • Freely available packages for machine learning modeling of neuroimaging data: • 3dsvmplugin 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/). • PROBIDis optimized for groupsclassification (i.e. patients vs. healthy controls) and therefore does not easily enables single subject analysis or flexible cross-validation framework.

9. 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.

10. Title: PRoNTo (Pattern Recognition for Neuroimaging Toolbox) Coordinator: Dr. JanainaMourao-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 (EcolePolytechniqueFédérale 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 Website:http://www.mlnl.cs.ucl.ac.uk/pronto/

11. 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

12. For users User Interface Easy to use Click buttons Matlab Batch Very efficient Can be saved and copied Compatible with SPM Functions User-specific analysis More programming skills required

13. User point of view User Interface Matlab Batch 13

14. For developers User Interface Specific GUI Batch system Script Machine learning Features Kernel Model Training Validation Machines Classification (SVM, GPC, RF) Regression (KRR,RVR) Wrapper

15. 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) prt_machine_krr Structure containing: -Data/Kernel -Labels -Etc Structure containing: -Predictions -Coefficients/Weights prt_machine_gpml prt_machine_svm_bin 15

17. Future developments • improved fMRI data handling (detrending & hrf estimation) • feature selection (GP based, RFE,…) • more machines (provided by Machine Learning community) • etc

18. Download Available here: http://www.mlnl.cs.ucl.ac.uk/pronto/

19. 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 RechercheScientifique-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.

20. PRoNTo a team work!