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Abstract #2473

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

Nicholas Maurice Simard1,2, Dinesh A Kumbhare3,4, Stephan Ulmer5,6, and Michael D Noseworthy1,2,7,8
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,, 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


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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