Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives

It is well established that neural activity is stochastically modulated over time. Therefore, direct comparisons across experimental conditions and determination of change points or maximum firing rates are not straightforward. This study sought to compare temporal firing probability curves that may vary across groups defined by different experimental conditions. Odds-ratio (OR) curves were used as a measure of comparison, and the main goal was to provide a global test to detect significant differences of such curves through the study of their derivatives. An algorithm is proposed that enables ORs based on generalized additive models, including factor-by-curve-type interactions to be flexibly estimated. Bootstrap methods were used to draw inferences from the derivatives curves, and binning techniques were applied to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to study premotor ventral cortex neural activity associated with decision making. The proposed statistical procedures proved very useful in revealing the neural activity correlates of decision-making in a visual discrimination task.

keywords: bootstrap, derivatives, electrophysiology, generalized additive models, interactions, kernel smoothing, neural activity