Abstract #1522

Adaptive space-filling curve for improved feature selection from fMRI brain activation maps: application to schizophrenia classification

Unal Sakoglu¹, Lohit Ravi Teja Bhupati², Olexandra Petrenko¹, and Vince D Calhoun³

¹Computer Engineering, University of Houston - Clear Lake, Houston, TX, United States, ²Computer Science, University of Houston - Clear Lake, Houston, TX, United States, ³Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, United States

In this work, we develop a 3D to 1D ordering methodology for fMRI data, using a new space filling curve (SFC), which is adaptive to brain's shape based on T1 MRI. We apply this SFC ordering to fMRI activation maps from a schizophrenia study, compress/bin the data, obtain features, and perform classification of schizophrenia vs normal controls. The classification results using SFC ordering are superior to those using linear ordering, the traditional method.

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