HELSINKI UNIVERSITY OF TECHNOLOGY LABORATORY OF COMPUTER AND INFORMATION SCIENCE NEURAL NETWORKS RESEACH CENTRE Variability of Independent Components in 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

3Jarkko Ylipaavalniemi 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)

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

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

6Jarkko Ylipaavalniemi 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

7Jarkko Ylipaavalniemi 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

8Jarkko Ylipaavalniemi 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

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

Continues 10Jarkko Ylipaavalniemi Result Highlights Components with Strong Variability Other Interesting Components

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

12Jarkko Ylipaavalniemi 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

13Jarkko Ylipaavalniemi Questions