Dear experts,
I am looking for a way how to effectively run a regression model diagnostic in context of surface-based analysis.
My example case is the regression model where response variable is cortical thickness and explanatory variable is continuous (disease duration). The statistical linear modelling uses several principial approaches to find model which best describes the studied relation in the data, namely:
1. model simplification by elimination of insignificant effects 2. transformation of dependent variable to improve normality of residuals 3. transformation of explanatory variable to improve model fit or use of linear combination of various terms in explanatory variable (for example apart from linear term to include also quadratic and higher-order polynomial terms)
My question is, how to effectively exploit methods 2 and 3 in context of vertex-wise surface-based analysis? Could you please comment on what tools inside FreeSurfer framework can be used for this purpose? What is your common aproach to find regression model which best approximates the relation present in the data?
Thank you in advance for pointing me to right direction.
Regards,
Antonin Skoch