Abstract #1916

Comparison of ComBat harmonization methods for longitudinal magnetic resonance imaging data in a travelling subject cohort

Sophie Richter¹, Stefan Winzeck^1,2, Marta M Correia³, Evgenios N Kornaropoulos⁴, Anne Manktelow¹, Joanne Outtrim¹, Doris Chatfield¹, Jussi Posti⁵, Olli Tenovuo⁵, Guy B Williams⁶, David K Menon¹, and Virginia F J Newcombe¹

¹Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom, ²BioMedIA Group, Department of Computing, Imperial College, London, United Kingdom, ³MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, ⁴Diagnostic Radiology, Lund University, Lund, Sweden, ⁵Department of Neurosurgery and Turku Brain Injury Centre, Turku University Hospital and University of Turku, Turku, Finland, ⁶Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom

Synopsis

The trend in neuroimaging towards multi-site studies requires validated harmonization approaches to eradicate scanner differences which mask the biological effect of interest. Here, the harmonization algorithm ComBat and its modification for longitudinal data (LongComBat) were compared on a large travelling subject sample (n=23 for structural MRI and n=31 for diffusion tensor MRI).

In structural data scanner difference are not apparent in unharmonized data but can be created by harmonization. For DTI data, scanner differences in unharmonized data are large and, both ComBat and LongComBat successfully diminished those in most regions of interest, with LongComBat achieving slightly lower false positive rates.

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