Variability of Independent Components in functional Magnetic Resonance Imaging

1. Variability of Independent Componentsin functional Magnetic Resonance Imaging Jarkko Ylipaavalniemi

2. Introduction • Background • Functional Brain Imaging Becoming Mainstream • Standard Analysis Method Very Limited • Great Results with Independent Component Analysis • But Is ICA Reliable? • Bootstrapping, Icasso and Other Methods • Previous Work with Biomedical Data • Purpose of the Work • Method for Consistent ICA of fMRI Data • Tools for Processing and Visualizing fMRI Data Jarkko Ylipaavalniemi

3. Brain Imaging (fMRI) • Hemodynamic Responses • Blood Oxygenation Dependent Signal • Complex and Delayed Relation to Electrical Activity • Standard Analysis of fMRI Sequences • Realign, Smooth and Normalize Volumes • Statistical Parametric Mapping (SPM) Jarkko Ylipaavalniemi

4. Independent Component Analysis (ICA) • Application to fMRI Data • Independence Seems Natural • Blind Method • Spatial Sequence • Temporal Information Important Jarkko Ylipaavalniemi

5. Independent Component Analysis (ICA) • FastICA Algorithm • Fast and Robust • Handles the Huge Data • Estimation Errors • Initial Conditions • Stochastic Iteration and Optimizations • Data Matrix Jarkko Ylipaavalniemi

6. Variability of Independent Components • Sources of Variability • Estimation Errors • Assumption of Strict Statistical Independence • Weak Signal-to-Noise Ratio • Exploiting with Multiple Runs • Randomizing the Initial Conditions • Resampling the Data Matrix • Clustering the Estimated Components Jarkko Ylipaavalniemi

7. Visualization and Interpretation • Lots of Multi-Modal Information • Structural MRI • Spatiotemporal Activation Patterns • Clustering Statistics • Temporal Variability • Sometimes Even Spatial Variability • Superimposing All Together • Interactive User Interface • Allows Easy Interpretation • Interpretations from Variability • Detect Consistent Components • Constant Noise or Structured Nature • Further Analyze the Structure Jarkko Ylipaavalniemi

8. Illustrative Experiments • Real fMRI Data • Reveals True Performance • Involved 14 Subjects • Auditory Stimulation Pattern • Resulting Sequences 80x95x79x69 • Results • About 20 Clusters x 14 Subjects • All Very Interesting Jarkko Ylipaavalniemi

9. Result Highlights • Components Related to Stimulus • Components Revealing Artifacts Jarkko Ylipaavalniemi

10. Result Highlights • Components with Strong Variability • Other Interesting Components Jarkko Ylipaavalniemi

11. Result Highlights • Spatial Variability • Demanding Computation • More Natural in Some Cases • Spatial Relations Revealed • May Identify Subspaces • Related to Physical Phenomenon? Jarkko Ylipaavalniemi

12. Conclusions • The Method Works • Comparable to Other Methods • Much Faster and Easier to Use • Medical Relevance • Allows Reliable ICA in Real Setups • Detection of Previously Unknown Phenomena • Future Research • Incorporate Knowledge into ICA Estimation • Application of Denoising Source Separation (DSS) • Analyzing More Challenging Data Jarkko Ylipaavalniemi

13. Questions Jarkko Ylipaavalniemi