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) Noninvasive Spatial resolution: mm Time resolution: about 1 second

Concepts  Data of fMRI Field of view: 64*64 Slices: 32 acquisition time: 2s images: hundreds  Type Rest Task

Concepts  Experimental design Block-design Event-related design

Concepts  fMRI data analysis Data-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-task  Capitalize 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!