RESULTS REFERENCES Geiser, E., Walker, K. M., & Bendor, D. (2014). Global timing: a conceptual framework to investigate the neural basis of rhythm perception in humans and non-human species. Front Psychol, 5. Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform, 5(3). Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., ... & Pollmann, S. (2015). PyMVPA: a unifying approach to the analysis of neuroscientific data. Python in Neuroscience, 157. Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proc Natl Acad Sci USA, 103(10), 3863-3868. Stelzer, J., Chen, Y., & Turner, R. (2013). Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): random permutations and cluster size control. Neuroimage, 65, 69-82. INTRODUCTION • Humans are able to recognize a familiar rhythm, for example the rhythm of a song, independently of the tempo at which it is played. This is exemplary of the fact that auditory experiences comprise percepts of auditory Gestalt based on the rhythmic structure of sounds. • The pattern resulting from the relative temporal relationship between acoustic events is the basis for the perceptual emergence of temporal Gestalt. This Gestalt percept is invariant to the absolute temporal relationship between constituent acoustic events (Hulse et al. 1992). • It is unknown, where in the brain percepts of temporal Gestalt are generated. • A linear support vector machine (SVM) classifier was trained to differentiate activation patterns in functional Magnetic Resonance Imaging (fMRI) data to two different percepts, across different sensory constituents. • A searchlight analysis localized activation patterns specific to the temporal Gestalt. CONCLUSIONS →Based on brain activation patterns, a linear SVM classifier identified which relative temporal pattern had been presented. The classifier was trained across absolute temporal patterns (tempi) of sequences. -> The classifier had identified the neural activation pattern associated with the Gestalt percept. →Based on a searchlight analysis, activation patterns in the temporo-parietal junction (TPJ) (left and right hemisphere) and the inferior frontal gyrus (right hemisphere) discriminated between the two different temporally grouped percepts of temporal Gestalt. →Our findings reveal for the first time that both sensory and higher cortical areas encode temporal Gestalt. →The study provides an example of how we can tackle the neural basis of perceptual invariance using neuroimaging. METHODS EXPERIMENTAL SETUP • 19 volunteers underwent functional neuroimaging (fMRI). Where the rhythm plays: Machine learning decodes rhythm-sensitive cortices Michael P. Notter1,2, Michael Hanke3, Micah M. Murray1,2,4,5, Eveline Geiser1,6 1The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology and Department of Clinical Neurosciences, Vaudois University Hospital Center and University of Lausanne, Lausanne, Switzerland 2Electroencephalography Brain Mapping Core, Center for Biomedical Imaging of Lausanne and Geneva, Switzerland 3Psychoinformatics lab, Institute of Psychology II, Otto-von-Guericke-University and the Center for Behavioral Brain Sciences, Magdeburg, Germany 4Department of Ophthalmology, University of Lausanne, Jules-Gonin Eye Hospital, Lausanne, Switzerland 5Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, USA 6McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, USA SCANNER SETUP • 3T Siemens scanner; 64-channel head coil (coverage shown on the right) • TR of 2s; 365 volumes x 5 sessions; 33 slices (ascending order) PREPROCESSING • Pre-processing was done with Nipype (Gorgolewski et al., 2011): despiking (A