Kaushik Bhaganagarapu1,2, Graeme D. Jackson1,3,
David F. Abbott1,2
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.
