Joint Sparse Representation of Brain Activity Patterns in Multi-Task fMRI

Data

2015/03/21

Concepts

fMRI(functional magnetic resonance imaging)BOLD(blood oxygenation-level dependent) NoninvasiveSpatial resolution: mmTime resolution: about 1 second

Concepts

Data of fMRIField of view: 64*64Slices: 32acquisition time: 2s images: hundreds

TypeRestTask

Concepts

Experimental designBlock-design

Event-related design

Concepts

fMRI data analysisData-driven

PCA、 ICA、 CA

Model-driven

GLM (SPM)

GLM

Model

i.e.

where Y is observations, X is the design matrix.

GLM

Steps for SPM

1. Slice timing

2. Realignment

3. coregister

4. Segment

5. normalise

6. Smooth

7. Specify 1st-level

ICA

Suppose the signal has the model

The question is to find a matrix to estimate

Sparse representation

Problem

Multi-taskCapitalize on the joint information that may

exist among tasks.The joint information is not usually directly

examined.

Data fusion

Multivariate methods in fusion

Idea

This would result in a set of dictionaries and sparse coefficients. To obtain the joint relation of the results we would need to combine the sparse coefficients.

Framework

Feature: an activation map for each task and each individual.

use SPM

Model

JSRA

Algorithm

• OMP(orthogonal matching pursuit)

• SVD(singular value decomposition)

Simulation

A total of 20 simulated datasets that represent two groups of subjects, each with 10 datasets.

Simulation

FP, FN, TP, TN

Simulation

Experiment

Conditions

Experiment

K=4

Experiment

K=8

Experiment

K=12

Thank you!