ICA vs. SPM for Similarity Retrieval of fMRI Images

Overview of Sara’s and Rosalia’s Method

Similarity between Components ICA (Sara’s Method) Extract Spatial Feature Vectors fastICA Similarity between Components fMRI raw Data 108 components IC maps Each component associates with time-course

SPM (Rosalia’s Method) SPM Z Score with FDR=0.05 Get SPM clusters (connected Component analysis) Compute Feature Vectors Similarity Between Two Brains

How to Compare these two methods? Similarity between components => similarity between brains? ? Bai etc. (2007) Maximum Weight Bipartite Matching used fMRI brain image retrieval based on ICA components.

Optimal Assignment and Bipartite Matching Given two sets s1 and s2, is defined as the cost between each element i in S1 and each element j in S2. An optimal assignment is a permutation p = (p1, . . . , pn) of the integers (1, . . . , n) that minimizes S1 S2

Example of brains with two components 0.7 1 1 =0.3 =0.2 0.8 2 2 Brain2 Brain1 Minimum Cost = C12+C21 = 0.2+0.3 = 0.5 Similarity Score =0.5

Apply Optimal Assignment Step 1. Convert Similarity Matrix to a cost Matrix Step 2. Find the minimum cost between two brains using Bipartite Matching.

The comparison SPM data: FaceUpVsFixation ICA data: raw fMRI data of 108 scans Run Rosalia’s method to get a similarity Matrix M1 Run Sara’s method and bipartite matching to get similarity Matrix M2

The results Correlation between M1 and M2: 0.7132??? Not a very strong correlation

Possible Reasons Convert Similarity Matrix to Cost Matrix? Raw fMRI data has more than two cognitive tasks, while SPM contrast map has only two cognitive tasks. Is Bipartite Matching a good measure for similarity between two brains? C2 Fix FaceUp HouseUp C1

Reference B. Bai, P. Kantor, A. Shokoufandeh, D. Silver. fMRI brain image retrieval based on ICA components. enc, pp. 10-17,  Eighth Mexican International Conference on Current Trends in Computer Science (ENC 2007),  2007 Y. Cheng, V. Wu, R. Collins, A. Hanson, and E. Riseman. Maximum-weight bipartite matching technique and its application in image feature matching. In Proc. SPIE Visual Comm. And Image Processing, Orlando, FL, 1996.

Component Similarity Measure Each feature is weighted according to its mean and std: if < threshold Similarity Score:

SPM (Rosalia’s Method) Preprocess t-contrast maps of particular cognitive tasks to get the activated voxels. Cluster the resulting voxels to distinct regions. Similarity Measure between brains Q-to-T Score = T-to-Q Score = Similarity Score =

Example The cost between S1 and S2 can be represented by a nxn matrix 1 2 3 C11+C22+C33 1 3 2 C11+C23+C32 3 2 1 C13+C22+C31 2 3 1 C12+C23+C31 2 1 3 C12+C21+C33 3 1 2 C13+C21+C32 S2 C11 C12 C13 C21 C22 C23 C31 C32 C33 S1

Apply Optimal Assignment Step 1. Convert Similarity Matrix (sM)to a cost Matrix (cM) Replace infinite value in sM to the maximum value of all the finite values. Take top N percent of the similarity scores, set others to be zero. Set the non-zero scores to be 1-SM/maximum. set the zeros to be infinite value. Step 2. Using Kuhn-Munkres Algorithm (Hungarian method) to find the minimum cost between two brains.