Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study
Aras Kayvanrad,
Stephen R. Arnott,
Nathan Churchill,
Stefanie Hassel,
Aditi Chemparathy,
Fan Dong,
Mojdeh Zamyadi,
Tom Gee,
Robert Bartha,
Sandra E. Black,
Jane M. Lawrence-Dewar,
Christopher J.M. Scott,
Sean Symons,
Andrew D. Davis,
Geoffrey B. Hall,
Jacqueline Harris,
Nancy J. Lobaugh,
Glenda MacQueen,
Cindy Woo,
Stephen Strother
Affiliations
Aras Kayvanrad
Rotman Research Institute, University of Toronto, Toronto, ON, Canada; Corresponding author.
Stephen R. Arnott
Rotman Research Institute, University of Toronto, Toronto, ON, Canada
Nathan Churchill
Neuroscience Research Program, St. Michael's Hospital, Toronto, ON, Canada
Stefanie Hassel
The Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
Aditi Chemparathy
Rotman Research Institute, University of Toronto, Toronto, ON, Canada
Fan Dong
Indoc Research, Toronto, ON, Canada
Mojdeh Zamyadi
Rotman Research Institute, University of Toronto, Toronto, ON, Canada
Tom Gee
Indoc Research, Toronto, ON, Canada
Robert Bartha
Robarts Research Institute, Western University, London, ON, Canada; Medical Biophysics, Western University, London, ON, Canada
Sandra E. Black
Sunnybrook Research Institute, Toronto, ON, Canada
Jane M. Lawrence-Dewar
Thunder Bay Regional Health Research Institute, Thunder Bay, ON, Canada
Christopher J.M. Scott
Sunnybrook Research Institute, Toronto, ON, Canada
Sean Symons
Sunnybrook Research Institute, Toronto, ON, Canada
Andrew D. Davis
Rotman Research Institute, University of Toronto, Toronto, ON, Canada; Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
Geoffrey B. Hall
Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
Jacqueline Harris
Depatment of Computing Science, University of Alberta, Edmonton, AB, Canada
Nancy J. Lobaugh
Center for Addiction and Mental Health, Toronto, ON, Canada
Glenda MacQueen
The Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
Cindy Woo
Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
Stephen Strother
Rotman Research Institute, University of Toronto, Toronto, ON, Canada; Medical Biophysics, University of Toronto, Toronto, ON, Canada
Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network's (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies.
