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Testing the Structural Equivalence of Pre-processed Chronic Pain MRI Data

The use of functional magnetic resonance imaging (fMRI) to investigate the brain correlates of cognition has grown more important in recent years. fMRI is a non-invasive way of detecting patterns in brain activity induced by tasks done during scanning. Despite the technique’s lengthy history, the distinct properties of each dataset have forced the adoption of custom preprocessing techniques for each sample. This approach is inefficient, prone to errors, and unfit for combining data from many sources. Preprocessing the functional magnetic resonance imaging (fMRI) data requires various processes to clean and standardise the data before any further research. The fMRIPrep (http://fmriprep.org) specification describes a thorough approach for preprocessing human fMRI data for data analysis, which has been standardised as a pipeline. The Chronic Pain data was preprocessed using this fMRIPrep. We compare the chronic pain MRI data to the healthy control data after preprocessing the anatomical and functional data received from the MRI. The structural similarity index (SSIN) is used to find structural differences between chronic pain patients and healthy controls. The findings show that the structures of healthy and chronic pain regulation are quite similar.