Statistical parametric mapping on physiological maps of human hands with arthritic joints

Statistical parametric mapping (SPM) refers to the construction of spatially extended statistical processes to test the hypothesis of regional effects. It is commonly used to identify regionally specific effects in neuroimaging data to characterize brain activity and disease-related changes.

In our study, three different variations of SPM were applied on physiological maps of arthritic and healthy human hands obtained from RGB images. We tested if there is a significant difference between the means of healthy and diseased groups on each joint using Student’s t-test, which could be related to arthritis. The second variation of the SPM considered rheumatologist disease level scores (1-3) and weighted them accordingly. Finally, the randomization approach was used as it does not require an assumption of the normal sample distribution.

Statistical maps showed that blood volume fraction at joints with severe arthritis is increased for all three SPM approaches. T- values for the first two SPM variations were 7 ± 1, while the SoV metric in the third SPM variation reached the mean value of 0.96 ± 0.03.

Results of this study show some promise for optical imaging and SPM for early arthritis detection. However, further research is warranted to fulfill this promise.