Demeaned images for SV2A ICA analysis

Citation Author(s):
Daniele
Bertoglio
University of Antwerp
Jordy
Akkermans
University of Antwerp
Submitted by:
Daniele Bertoglio
Last updated:
Fri, 10/28/2022 - 04:28
DOI:
10.21227/8jda-j668
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Abstract 

Synaptic vesicle glycoprotein 2A (SV2A) is the most widely distributed transmembrane glycoprotein present on secretory vesicles in the pre-synaptic terminal of neurons throughout the central nervous system (Bajjalieh et al., 1994).  SV2A can be used as a marker to visualize pre-synaptic density distribution in vivo using positron emission tomography (PET) imaging thanks to the SV2A radioligands available, including [11C]UCB-J (Nabulsi et al., 2016). Given the brain-wide distribution of SV2A, regional analysis of SV2A PET data may be limiting the amount of information that can be obtained. In this context, data-driven approaches such as independent component analysis (ICA) (Bell and Sejnowski, 1995), a blind source separation technique, can separate the brain signal into distinct components, without having any knowledge beforehand about the source signals. This dataset was applied to study SV2A ICA in the preclinical domain.

 

Instructions: 

Nifty files of each subject derived from the parametric maps are included as described in the readme.doc file.