  The field of neuroimaging has truly become data rich, and novel analytical
methods capable of gleaning meaningful information from large stores of imaging
data are in high demand. Those methods that might also be applicable on the
level of individual subjects, and thus potentially useful clinically, are of
special interest. In the present study, we introduce just such a method, called
nonlinear functional mapping (NFM), and demonstrate its application in the
analysis of resting state fMRI from a 242-subject subset of the IMAGEN project,
a European study of adolescents that includes longitudinal phenotypic,
behavioral, genetic, and neuroimaging data. NFM employs a computational
technique inspired by biological evolution to discover and mathematically
characterize interactions among ROI (regions of interest), without making
linear or univariate assumptions. We show that statistics of the resulting
interaction relationships comport with recent independent work, constituting a
preliminary cross-validation. Furthermore, nonlinear terms are ubiquitous in
the models generated by NFM, suggesting that some of the interactions
characterized here are not discoverable by standard linear methods of analysis.
We discuss one such nonlinear interaction in the context of a direct comparison
with a procedure involving pairwise correlation, designed to be an analogous
linear version of functional mapping. We find another such interaction that
suggests a novel distinction in brain function between drinking and
non-drinking adolescents: a tighter coupling of ROI associated with emotion,
reward, and interoceptive processes such as thirst, among drinkers. Finally, we
outline many improvements and extensions of the methodology to reduce
computational expense, complement other analytical tools like graph-theoretic
analysis, and allow for voxel level NFM to eliminate the necessity of ROI
selection.
