Classification of fMRI activation patterns in affective neuroscience Weygandt, M.1,2, Stark, R.1,2, Blecker, C.R.1, & Vaitl, D.1 1) Bender Institute of Neuroimaging, University of Giessen, Germany 2) Department of Clinical and Physiological Psychology, University of Giessen, Germany Methods Background Feature Selection Varying temporal extent of FS Both FS strategies were applied to the first block only, to block one and two, …, up to all five blocks Distribution of activation patterns Patterns belonging to the first half of the fMRI session were used for training of the classifier, patterns belonging to the second half were used for testing Classifier The IRBFN algorithm proposed by Fritzke (1994) corresponds to a two layer ANN. In the learning stage the algorithm adjusts the positions of nodes of layer one in order to map data space in a process of unsupervised learning. During this mapping, the algorithm adjusts connection weights of the nodes of layer one to the nodes of layer two in a supervised fashion. Nodes of layer two correspond to the categories to be classified. In the test stage unknown patterns with coordinates in data space are classified in dependence of distance to nodes of layer one and their connections to nodes of layer two. Applying the approach of classification to fMRI, a classification algorithm tries to make a guess about the condition that generated a given pattern of activation. For example, a classification algorithm may try to infer if a person is lying or telling the truth on the basis of corresponding activation patterns. Recent studies applied the approach to the areas of visual object recognition (e.g Cox & Savoy; 2003), orientation selective processing (e.g. Haynes & Rees, 2005) and attention (e.g. Murao-Miranda et al.; 2005). In the present study, we applied the method to affective neuroscience. In a picture perception paradigm we investigated whether the emotional categories of pictures presented to subjects could be predicted on the basis of corresponding activation patterns. Patterns derived from single fMRI volumes were classified by interconnected artificial neural networks (ANN) called Incremental Radial Basis Function Networks (IRBFNs). Methods Subjects and Experimental Design 24 subjects 12 subjects with and 12 subjects without sadomasochistic preferences (6 male and 6 female subjects in each group) TR = 3 s Pictures from 5 emotional categories (‘Neutral’, ‘Fear’, ‘Disgust’, ‘Erotic’ and ‘Sm’), 40 pictures each, were presented in a block design Within a category each picture was presented for 1.5 s resulting in a total of 60 s per condition/block Each category was repeated five times The order of the conditions during the repetitions and the order of the pictures within a category were pseudo-randomized Data Preprocessing Spatial realignment of fMRI volumes (SPM2) Correction of voxel-timeseries for covariate effects of motion (General Linear Model) Exclusion of initial three fMRI volumes within one block due to the delay of the BOLD-Response Block baseline correction of voxel intensities Feature Selection (FS; = Voxel Selection) Two different strategies: A) Correlative FS: voxels whose timeseries correlated maximally with model functions of conditions were selected B) Stepwise Linear Discriminant Analysis (SLDA): Linear Discriminant Analysis corresponds to a rotation of the coordinate system of the original data space. The coordinate system is rotated according to the criteria of maximization of the ratio of inter-class to intra-class variance of intensities projected on the rotated coordinate system. The rotated coordinate axes are called discriminant functions. SLDA FS selects a subset of voxels which contribute optimally to the maximization of the criteria. Varying numbers of selected voxels In both FS strategies five to fifty voxels were selected Results First of all the analysis indicated that even within the least beneficial setting, i.e. when classification was based on just a few voxels based on few initial fMRI-volumes, the average classification accuracy across the 24 subjects was above chance (P < 0.01; binomial probability). HRF FS Block 1 HRF FS Blocks 1-2 HRF FS Blocks 1-3 HRF FS Blocks 1-4 HRF FS Blocks 1-5 SLDA FS Block 1 SLDA FS Blocks 1-2 SLDA FS Blocks 1-3 SLDA FS Blocks 1-4 SLDA FS Blocks 1-5 Further analysis indicated that classification accuracy increased with the number of voxels underlying classification and with the temporal extent of FS. Finally, the SLDA FS was superior to the correlative FS strategy. The effect of the temporal extent of FS was especially pronounced in the case of the SLDA based FS. For SLDA based FS covering the whole fMRI-session, classification accuracy was highly above chance for each of the subjects. Voxels underlying classification classification accuracy (%) Average (N=24) Discussion Classification of fMRI activation patterns related to emotion seems to be possible, especially in the case of selection of proper spatial features. As indicated by very high classification accuracies in the case of SLDA FS, there exist voxels/discriminant functions which differentiate well between categories across a fMRI session of 25 min duration. It has to be clarified whether those features exhibit characteristics detectable in early stages of a fMRI session. References Cox, D.D., Savoy, R.L., 2003. Functional magnetic resonance imaging (fMRI) ‘‘brain reading’’: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19, 261–270 Fritzke, B., 1994. Fast Learning with incremental RBF networks. Neural Processing Letters, 1, 2-5. Haynes, J.D., & Rees, G., 2005. Predicting the orientation of invisible stimulis from activity in human primary visual cortex. Nature Neuroscience, 8, 686-691. Mourao–Miranda, J., Bokde, A.L.W., Born, C., Hampel, H., & Stetter, M. (2005). Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. NeuroImage, 28, 980–995. Email: Martin.Weygandt@psychol.uni-giessen.de Web: www.bion.de Address: Martin Weygandt Justus-Liebig-University of Giessen Otto-Behaghel-Str. 10F 35394 Giessen Germany