Current work at UCL & KCL

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

Data Description

Number of subjects: 16Tasks: 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

?fM

RI scanner

Machine Learning Method:Support Vector Machine

The subject was viewing a pleasant stimuli

Test Subject

fMRI scanner

fMRI scanner

Brain looking at a pleasant stimulus

Brain looking at an unpleasant stimulus

fMRI scanner

fMRI scanner

Brain looking at a pleasant stimulus

Brain looking at an unpleasant stimulus

Training Subjects

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Spatial weight vector

Results

N=16 subjectsMourao-Miranda et al 2006

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).

Results using brain activation by high emotional intensity

Accuracy=76%

Fu et al 2008

Results using brain activation by medium emotional intensity

Accuracy=73.5%

Fu et al 2008

Results using brain activation by low emotional intensity

Accuracy=86.5%

Fu et al 2008

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

Developments

Application to structural images

Application to fMRI

Categorical ClassificationSVM

Probabilistic ClassificationGP

Multimodal Classification

Correlation of different sources of information:

KCCA

Application to genetic and other data

Outliers detectionOCSVM

Applications

Data Representation& Feature Selections

Temporal based classification

PROBID TOOLBOX

PROBID Toolbox

Pattern Recognition of Brain Imaging Data

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

Sponsors & Collaborators

• Professor John Shawe-Taylor, CSML, UCL• Professor Steve Williams, IOP, KCL• Professor Mick Brammer, IOP, KCL• Professor Gareth Barker, IOP, KCL

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.

Prediction

y = {+1, -1}p(y = 1|X,θ)

MRI images

...

...

Class 1 (e.g. patients)

Class 2 (e.g. Controls)

ComputeKernelMatrix

Train Classifier on train subset

Multivariate representation of the discriminating pattern

Test Classifier on test subset

Pre- Processing

Module

PartitionKernel Matrix

PreprocessedData

...

...

Repeat for Each subject

Pair

LOO Cross-Validation

Cross-validationAccuracy

Train using allSubject

dataΣWeighted Sumof brain images

Pre- Processing

Module

Pre- Processing

ModulePre-

ProcessingModule

Pre- Processing

Module

Modality specificPreprocessing modules

General Framework

Analyze/Niftiimages pre-processed in SPM or FSL

Probid Toolbox

Specify functional

Specify structural

Pre-processing

Pre-processing

• 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

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.

Compute Kernel

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

Kernel Matrix and Cross-validation procedure

• Pattern: – x1=[x1 … xv], v=number of features or voxels

• Data matrix: – Dm,v = [x1 … xm], m=number of examples

• Linear kernel matrix: – K=DDT

• 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]

Pattern Recognition

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)

Pattern Recognition Maps

• SVM weight vectorwsvm = Σαiyixi,

αi≠0 only for the support vector examples

• GP weight (MAP estimate of the weight vector)wgp = 1/l2Σαixi=1/l2XTaa = K-1

• GP latent function mapg = Σixi =XTi is the mean of the latent function evaluated at the i-the training sample

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Marquand et al 2009

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