Abstract #2473

Age and sex contributions to variance in resting state fMRI (rs-fMRI) temporal complexity analysis

Nicholas Maurice Simard^1,2, Dinesh A Kumbhare^3,4, Stephan Ulmer^5,6, and Michael D Noseworthy^1,2,7,8

¹Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, ²St. Joseph's Healthcare Hamilton, Imaging Research Centre, Hamilton, ON, Canada, ³Toronto Rehabilitation Institute, Toronto, ON, Canada, ⁴Department of Medicine, University of Toronto, Toronto, ON, Canada, ⁵neurorad.ch, Zurich, Switzerland, ⁶Department of Radiology and Neuroradiology, University hospital of Schleswig-Holstein, Kiel, Germany, ⁷School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada, ⁸Radiology, McMaster University, Hamilton, ON, Canada

Synopsis

Understanding the process of aging and the differences in sex with regards to large data repositories can help improve the implementation of machine learning and artificial intelligence paradigms in neuroimaging. The following research presents data that identifies a relationship between aging and sex in resting state functional magnetic resonance imaging (rs-fMRI) data. Using over 10,000 age and sex matched healthy controls and performing a homebuilt processing pipeline for rs-fMRI data, significant relationships between aging and reduced temporal complexity (TC) was found (p=0.03058), along with women having a higher TC than men (p=0.000623).

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