Fig. 4: Receptor profiles shape oscillatory neural dynamics.
We fit a multi-linear regression model that predicts MEG-derived power distributions from receptor distributions. a, Receptor distributions closely correspond to all six MEG-derived power bands (\(0.78\le {R}_{{{{\rm{adj}}}}}^{2}(80)\le 0.94\)). The significance of each model is assessed against a spatial permutation-preserving null model and corrected for multiple comparisons (FDR correction). Asterisks denote significant models (FDR-corrected Pspin < 0.05, one-tailed). Delta \({R}_{{{{\rm{adj}}}}}^{2}(80)=0.89\), Pspin = 0.03; theta \({R}_{{{{\rm{adj}}}}}^{2}(80)=0.94\), Pspin = 0.0006; alpha \({R}_{{{{\rm{adj}}}}}^{2}(80)=0.93\), Pspin = 0.0006; beta \({R}_{{{{\rm{adj}}}}}^{2}(80)=0.84\), Pspin = 0.008; low-gamma \({R}_{{{{\rm{adj}}}}}^{2}(80)=0.83\), Pspin = 0.04; and high-gamma \({R}_{{{{\rm{adj}}}}}^{2}(80)=0.78\), Pspin = 0.16. b, Dominance analysis distributes the fit of the model across input variables such that the contribution of each variable can be assessed and compared to other input variables. The percent contribution of each input variable is defined as the variableʼs dominance normalized by the total fit (\({R}_{{{{\rm{adj}}}}}^{2}\)) of the model. Note that dominance analysis is not applied to the input variables of non-significant models (that is, high-gamma).
