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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here