Abstract #1156

Filtering FMRI Using a SOCK

Kaushik Bhaganagarapu^1,2, Graeme D. Jackson^1,3, David F. Abbott^1,2

¹Brain Research Institute, Florey Neuroscience Institutes (Austin), Melbourne, Victoria, Australia; ²Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia; ³Departments of Medicine and Radiology, The University of Melbourne, Melbourne, Victoria, Australia

BOLD fMRI is restricted by low signal to noise and various artifacts varying from motion to physiological noise. Independent components analysis (ICA) is a data-driven analysis approach that is being used to filter fMRI of such noise. However, one of the problems with ICA remains the interpretation of the results. Recently, we developed an automatic classifier (Spatially Organised Component Klassifikator - SOCK), which uses spatial criteria to help distinguish plausible biological phenomena from noise. We utilize SOCK to automatically filter a conventional fMRI block-design language study and successfully show the significance of activation obtained increases as a result of SOCK.