Uncertainty and Information Integration in Biomedical Applications Claudia Plant Research Group for Bioimaging TU München

Uncertainty and Information Integration in Biomedical Applications Outline 1)Motivation: massive increase of data 2) Integration and Uncertainty Neurosciences: fMRI and EEG data. Proteomics: Peptide Profiling. 3) Conclusion

Uncertainty and Information Integration in Biomedical Applications Motivation: Data Explosion in Medicine and Life Sciences The amount of scientific data doubles each year. Szalay et Grey, Nature 2006

Uncertainty and Information Integration in Biomedical Applications BMBF Project: Understanding Resting-state Brain Aktivity Metabolism of the brain is not significantly reduced in comparison to task. Other regions become active during rest, so-called resting state networks. Goal of this project: Understand function of Resting state networks, compare healthy persons And subjects with functional brain disorders. Methods: fMRI, EEG Challenge for data mining: Massive data sets, uncertainty, information integration

Uncertainty and Information Integration in Biomedical Applications fMRI Imaging: Principle and Setup

Uncertainty and Information Integration in Biomedical Applications Slice Thickness e.g., 6 mm Number of Slices e.g., 10 SAGITTAL SLICE IN-PLANE SLICE Field of View (FOV) e.g., 19.2 cm VOXEL (Volumetric Pixel) 3 mm 6 mm fMRI Imaging: Spatial Aspect Matrix Size e.g., 64 x 64 In-plane resolution e.g., 192 mm / 64 = 3 mm

Uncertainty and Information Integration in Biomedical Applications fMRI Imaging: Temporal Aspect With spatial resolution 3x3x6 mm approximately 80,000 voxels the brain. Temporal resolution: up to some hundreds of timepoints. 3 mm 6 mm

Uncertainty and Information Integration in Biomedical Applications EEG/MEG Low spatial but high temporal resolution (milliseconds). Can we combine the benefits of the two modalites? fMRI: high spatial, low temporal resolution EEG/MEG: high temporal, low spatial resolution

Uncertainty and Information Integration in Biomedical Applications The Cocktail Party Problem brain process electrode/ voxel Space: (x +/-  y +/-  1, z +/-  1) Time: t +/-  Space: (x +/-  y +/-  3, z +/-  3) Time: t +/-  With  And 

Uncertainty and Information Integration in Biomedical Applications For Single Type of Microphone: ICA brain process Successfully applied for spatial and temporal de-mixing of fMRI and EEG data. V. D. Calhoun, T. Adali, M. Stevens, K. A. Kiehl, and J. J. Pekar, Semi-Blind ICA of FMRI: A Method for Utilizing Hypothesis-Derived Time Courses in a Spatial ICA Analysis, NeuroImage, vol. 25, pp , 2005.Semi-Blind ICA of FMRI: A Method for Utilizing Hypothesis-Derived Time Courses in a Spatial ICA Analysis, NeuroImage V. D. Calhoun, J. J. Pekar, and G. D. Pearlson, Alcohol Intoxication Effects on Simulated Driving: Exploring Alcohol-Dose Effects on Brain Activation Using Functional MRI, Neuropsychopharmacology, vol. 29, pp , 2004.

Uncertainty and Information Integration in Biomedical Applications Temporal ICA with FastICA Example temporal ICA u = u 1, …, u n v = v 1, …, v n 1) Centering and Whitening De-correlate and standardizise u w =  -1/2 * V T * (u-  ) 2) Fix Point Iteration: w i = E{u w (g(w i T -u w )} – E{u w (g‘(w i T -u w )} 3) Konvergence M = V *  -1/2 * W, S = X * M -1

Uncertainty and Information Integration in Biomedical Applications Results of Spatial ICA on Task-fMRI IC1: visual cortex IC2: basal regions Experiment: Subject hits buttom as soon she sees a red light. X M S = Spatial ICA ICTime series The red time series of IC1 preceeds the green of IC2.

Uncertainty and Information Integration in Biomedical Applications Existing Approches to Joint ICA V. D. Calhoun., T. Adali, N. R. Giuliani, J. J. Pekar, K. A. Kiehl and G. D. Pearlson, Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data, HBM, vol. 27, pp , 2006 EEG fMRI 1)Scale to common resolution and perform usual ICA Problem: Information Loss!

Uncertainty and Information Integration in Biomedical Applications Existing Approaches to Joint ICA EEG fMRI 2) Perform ICA on each modality separately Problem: How to interpret the result? 3) Parallel ICA: Change the objective function of ICA to find similar components in both modalities Problem: Objective function has now two different goals. How to weight them? Parametrization difficult. Perhaps use concepts of Information Theory for this? -> Later

Uncertainty and Information Integration in Biomedical Applications Our Idea: Probabilistic ICA Represent each object (x,y,z,t) as PDF and perform Joint ICA. How to represent ? As PDF

Uncertainty and Information Integration in Biomedical Applications Probabilistic ICA combined with Information-theoretic Clustering Classical ICA model assumes a global mixing matrix A. This is not always the case, especially for data from different modalites. Do not force integration by parameters, let the data decide. Combine ICA with Clustering!

Uncertainty and Information Integration in Biomedical Applications OCI: Outlier-robust Clustering using Independent Components (Sigmod 2008) Non-Gaussian Clusters noise Parameter- free clustering …so far only for certain data.

Uncertainty and Information Integration in Biomedical Applications Relationship between PDFs and Data Compression Suppose we know the mixing Matrix and have two candidate PDFs for coordinate z i Information Theory: We want to transmit the data and sender and receiver know the correct PDF. The minimum description length is: We do not know the correct PDF. Try both! ?? good fit too many bits too few bits

Uncertainty and Information Integration in Biomedical Applications ICA and Data Compression ICA yields mixing matrix with directions of minimal entropy -> Efficient coding. Apply FastICA algorithm at a cluster level. Centering Whitening After 1 iteration After 4 iterations ICA minimizes Entropy -> reduces uncertainty -> reduces compression cost x before x after

Uncertainty and Information Integration in Biomedical Applications Data Integration and Information Theory Concepts of Information Theory provide means to measure how different Information of different sources is. If information is similar, it can be compressed effectively together. Therefore, information-theoretic clustering is a parameter-free approch to data Integration.

Uncertainty and Information Integration in Biomedical Applications Conclusion Integrative mining of uncertain data is a challenging task of emerging importance in many applications, We discussed an example from Neurosciences and some ideas for possible but there are many, many others.. (applications and ideas) This is a very interesting problem specification for basic research in data mining. Have fun!