Validation of Critical Ages in MRI Aging Data
Description of this data
Various forms of nonlinear maturation likely reflect different biological mechanisms such that theoretical distinctions between maturation patterns ought to be considered. Code is provided to simulate data with known maturation patterns to establish the level of reliability and validity of a nonparametric fitting method, the smoothing spline. Three categories of maturation patterns are explored: U-shaped, with a change in direction across the life span; sigmoidal, with a period of change preceded and followed by no change in volume; accelerating, with changes in amplitude but not direction. As noise is a limiting factor in curve fitting, smoothing splines were fit to data with idealized low noise levels and higher, more realistic noise levels. Using the included analysis code, the smoothing spline can be shown to contain the relevant information to extract the critical ages of all maturation patterns in the form of derivative zero points, but each derivative zero point is only information for certain maturation patterns. Therefore, an additional classification step was included to first determine the category of maturation pattern. The code can be adapted to analyze actual biological data if it is put into the proper format. Run the demo code to see how the code works.
Experiment data files
Cite this dataset
Nichols, David (2020), “Validation of Critical Ages in MRI Aging Data”, Mendeley Data, v1 http://dx.doi.org/10.17632/2pkhbdgjys.1
The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.