1. Automatic Identification of ROIs (Regions of interest) in fMRI data

2. Data Blood-oxigen levels in high spatial resolution (low temporal resolution). ~160,000 voxels ~25,000 voxels of gray matter 100-500 TRs (time points per signal). The data is highly correlated (mean 0.25 after removing the main effect).

3. Goal To map the functional role of each area. Identify areas that work together in common tasks (referred to as “Functional Networks”). Study dynamic processes within and between such networks. * Many of these functions and roles have already been identified.

4. Handling Noise Standard pre-processing: adjusting for head movements, normalization to a standard space (to allow comparing different subjects). Mask for grey matter only. Removing the main effect: for each time point, reduce the average signal of the brain on that time point.

5. Identifying networks– functional connectivity Perform voxel wise comparison against one/multiple pre-defined regions of interest (ROIs) -> “functional network”. No ROI: Voxel based clustering (mainly ICA).

6. How do we define ROIs? Lu et. Al. 2004 – split-merge based method. Use Kendall’s Coefficient of Concordance (KCC) to measure regional homogeneity. At each step measure KCC, split if required, measure KCC, merge if possible. Add singleton neighbors to each block if correlation>thresh K K1 K4K3 2K2K K1 K4K3 K1 K3

7. How do we define ROIs? Bellec et. Al. 2006 – use mutual nearest neighbor principle as merging rule (size limit). Stop when no more merging is possible. Clustering (Hierarchical, ICA, etc.)+proximity filter.Does not use the regional behavior to reduce complexity.

8. Suggested Method Sample many voxel neighboring pairs (e.g. 10,000) to obtain correlation threshold (C th ). For each voxel (v i ) go over all neighbors, and assign to the group of the first neighbor v j for which corr(v i,G(v j ))> C th. Corr(v i,G(v j )) is the correlation between the signal of v i and the mean signal of G(v J ).

9. Refinements Consult with neighbours in border (search for similarity of v i to the mean signal of the group and to the neighbours of v i that belong to the group). Helps prevents structures that are poorly connected. E.g.

10. Pros & Cons Fast Works with a threshold that considers the entire brain (does not impose grouping). Completely data driven. But… Finding the right correlation threshold. Naïve approach – takes the first “friend”, may not be robust enough. Many results - how do we know what is “real”?

11. Tests Was tested on 5 motor datasets (moving left hand and right hand). In some of the cases, the areas expected to be active* were detected in some cases this did not happen. * Note: assumption, if it is currently used, it should be highly correlated.

12. Matisse Identifies modules using gene expression data and interaction networks. Integrative analysis - can identify weaker signals Identifies a group of genes as well as the connections between them Only variant genes (front nodes) have meaningful similarity values These can be linked by not regulated genes (back nodes) Works even when only a fraction of the genes expression patterns are informative No need to pre-specify the number of modules

13. Attempt to use Matisse Use MATISSE to detect adjacent groups of voxels that are highly correlated. Similarity Matrix was replaced with adjacency list to decrease memory usage. Memory problems – MATISSE underwent optimizations by Dorit & Eyal – needs to be retested.

14. ROI border improvements In most cases the general location of the ROI is known. The exact borders need to be defined Use an arbitrary area – e.g sphere – problematic. Define borders based on the data

15. ROI border improvements Input: an initial pre-defined ROI At each step: 1) Remove marginal voxels that decrease homogeneity. 2) Add neighboring voxels increase homogeneity. Stop when the improvement is below a predefined delta. Output: a functionally homogeneous ROI

16. Tested on a very simple motor dataset. Detected the center of activation using another tool. Generated an artificial area around it (a cube of size 7*7*7). Fed this area into the algorithm. Results: 3 voxels left. Preliminary tests

17. Problems When do we stop? We can keep peeling off voxels until there is only one/few left. Shape of the area