FIGURE 4.

Multinodal distributed degree properties predict pain intensity numeric pain rating (NRS) in osteoarthritis (OA) patients. (a) Graphical representation of the linear regression model using Elastic net regularization and variable selection with penalty weight (α) of .5 and regularization parameter (λ) choice via a 10‐fold cross validation. Brain nodes depicted correspond to regions predicting pain intensity; node size reflects the weight (B‐coefficients) in the regression model. This is also illustrated in (b) the majority of nodes has regression coefficients set to zero, indicating that the corresponding variables are not contributing to the model. Nonzero regression coefficients identify the predictive features and indicate the weight and direction of degree change in relation to the response variable: that is, higher levels of pain (NRS) relate with lower degree in parahipoccampal gyrus, putamen, and superior temporal gyrus and higher degree in paracingulate cortex and inferior temporal gyrus. (c) Features selected in the elastic net regression predicted the magnitude of response and (d) validate in the hold out knee OA (KOA) and (e) hip OA samples: high correlation value between predicted and actual NRS scores in the KOA discovery group (Pearson's r = .84, p < .001) KOA holdout testing group (r = .57, p < .001) and HOA holdout testing group (r = .92, p < .001)