1. Current work at UCL & KCL

2. Project aim: find the network of regions associated with pleasant and unpleasant stimuli and use this information to classify new stimuli (i.e. is the activation pattern to a new product closest to the pleasant, unpleasant or neutral pattern) We used fMRI data from 16 healthy subjects viewing unpleasant, pleasant and neutral pictures. Application 2

3. Data Description Number of subjects: 16 Tasks: Viewing unpleasant and pleasant pictures (6 blocks of 7 scans) Pre-Processing Procedures Realignment, normalization to standard space, spatial filter. Mask to select voxels inside the brain. Leave one-out-test Training: 15 subjects Test: 1 subject This procedure was repeated 16 times and the results (error rate) were averaged. Training Examples Mean volume per block

4. ? fMRI scanner Machine Learning Method: Support Vector Machine The subject was viewing a pleasant stimuli Test Subject fMRI scanner Brain looking at a pleasant stimulus Brain looking at an unpleasant stimulus fMRI scanner Brain looking at a pleasant stimulus Brain looking at an unpleasant stimulus Training Subjects

5. 1.00 0.66 0.33 0.05 -0.05 -0.33 -0.66 unpleasant pleasant z=-18z=-6z=6z=18z=30z=42 Spatial weight vector Results N=16 subjects Mourao-Miranda et al 2006

6. Application 3 Project aim: discriminate depressed patients from healthy controls using their pattern of brain activation in response to emotional stimuli We used fMRI data from 19 free medication depressed patients vs. 19 healthy controls; The fMRI paradigm consisted of affective processing of sad facial stimuli with modulation of the intensity of the emotional expression (low, medium, and high intensity).

7. Results using brain activation by high emotional intensity Accuracy=76% Fu et al 2008

8. Results using brain activation by medium emotional intensity Accuracy=73.5% Fu et al 2008

9. Results using brain activation by low emotional intensity Accuracy=86.5% Fu et al 2008

10. Wellcome Trust Grant Project title: A machine learning approach to the analysis of psychiatric neuroimaging data Aim: Develop mathematical models and tools for the application of novel machine learning techniques to the automated analysis of brain imaging data. Duration: 07/2009-06/2014

11. Developments Application to structural images Application to fMRI Categorical Classification SVM Probabilistic Classification GP Multimodal Classification Correlation of different sources of information: KCCA Application to genetic and other data Outliers detection OCSVM Applications Data Representation & Feature Selections Temporal based classification PROBID TOOLBOX

12. PROBID Toolbox Pattern Recognition of Brain Imaging Data

13. Development Team Dr. Janaina Mourao-Miranda –Algorithm development Andre Marquand –Graphical interface and algorithm development Dr. Jane Rondina –Graphical interface and algorithm development Dr. Vincent Giampietro –Algorithm development

14. Sponsors & Collaborators Professor John Shawe-Taylor, CSML, UCL Professor Steve Williams, IOP, KCL Professor Mick Brammer, IOP, KCL Professor Gareth Barker, IOP, KCL

15. Aim Matlab toolbox optimized for group comparison and clinical research studies. It provides: (1) an accessible interface to categorical (SVM) and probabilistic (Gaussian Process) pattern recognition algorithms; (2) a processing pipeline for most common neuroimaging data modalities (fMRI, sMRI, diffusion- and perfusion MRI and a text input module); (3) leave-one-subject (LOO) out cross-validation framework; (4) a permutation testing framework for robust significance testing.

16. Prediction y = {+1, -1} p(y = 1|X,θ) MRI images... Class 1 (e.g. patients) Class 2 (e.g. Controls) Compute Kernel Matrix Train Classifier on train subset Multivariate representation of the discriminating pattern Test Classifier on test subset Pre- Processing Module Partition Kernel Matrix Preprocessed Data... Repeat for Each subject Pair LOO Cross-Validation Cross-validation Accuracy Train using all Subject data Σ Weighted Sum of brain images Pre- Processing Module Pre- Processing Module Pre- Processing Module Pre- Processing Module Modality specific Preprocessing modules General Framework Analyze/Nifti images pre- processed in SPM or FSL

17. Probid Toolbox

18. Specify functional

19. Specify structural

20. Pre-processing

21. fMRI: –Detrend voxel time series; –Select parts of the time series correspondent to each experimental condition (accounting for the HRF delay). –Apply a mask to select voxels (whole brain or ROI) –Create pattern: Single volumes Mean volume Spatiotemporal pattern

22. Pre-processing Structural or GLM coefficients (fMRI) –Apply a mask to select voxels (whole brain or ROI) –Create pattern: Each volume represents one pattern Perfusion –Apply a mask –Mean-centering data volumes within each subject to accommodate inter-subject differences in baseline signal.

23. Compute Kernel -Compute kernel matrices for pairwise comparisons: Task comparison: group1 task1 vs. group1 task2 Group comparison: group1 task1 vs. group2 task1

24. Kernel Matrix and Cross- validation procedure Pattern: –x 1 =[x 1 … x v ], v=number of features or voxels Data matrix: –D m,v = [x 1 … x m ], m=number of examples Linear kernel matrix: –K=DD T For each LOO cross-validation iteration –Ktrain = K[ index of training examples, index of training examples ] –Ktest = K[ index of test examples, index of training examples ]

25. Pattern Recognition

26. Classifiers Implemented Support Vector Machine Classifier –LIBSVM toolbox –Linear Kernel –Parameter: C=1 Gaussian Process Classifier –GPML toolbox –Linear Covariance function –Parameters: Bias (b) and regularization (l) ( set automatically by the GPC framework using an empirical Bayesian approach, Marquand et al, in press)

27. Pattern Recognition Maps SVM weight vector w svm = Σα i y i x i, α i ≠0 only for the support vector examples GP weight (MAP estimate of the weight vector) w gp = 1/l 2 Σα i x i =1/l 2 X T a a = K -1  GP latent function map g = Σ  i x i =X T   i is the mean of the latent function evaluated at the i-the training sample Spatial representation of the boundary Measure of the distribution of the two classes with respect to one another Marquand et al 2009

28. On-going & Future Work Outlier Detection: One-class SVM Dynamic System models for classification Multi-modal fusion Power analysis for Pattern Recognition Classification of Resting State fMRI