A framework for leveraging multi-rater data in brain decoding analysis: Prediction of evaluation drawn from population data using sparse probit regression

Introduction: Using stimuli (e.g., images, videos, products) labeled by a number of raters has recently become common in brain decoding analysis, where subjective emotion/impression for stimuli felt by the population is predicted from brain responses. However, there remains no established method for constructing a decoder using such multi-rater labels. In previous studies, the variability across multiple raters was assumed to reflect noise, and the answers for a binary judgment were averaged across raters. Then, the average scores (i.e., empirical probabilities) for individual stimuli were predicted using standard regression methods. While this procedure is a simple and popular approach, it is not appropriate because most of these regression methods ignore the fact that probability is the variable to be predicted. To address this in an appropriate manner, we present a new framework in this study.Methods: Here, we assume that individual answers for a binary judgment about a stimulus follow a Bernoulli distribution. We then predicted the probability of positive answers from the human functional magnetic resonance imaging (fMRI) response to the stimulus using probit regression. We also introduced sparse regularization into probit regression (sparse probit regression) to prevent overfitting.Results: In both simulation and real fMRI data analysis, sparse probit regression more accurately predicted the probabilities of positive answers for individual stimuli than probit regression without sparse regularization, indicating that sparseness results in better decoding performance. Sparse probit regression also outperformed linear regression using the same type of sparse regularization, reflecting the advantage of our appropriate treatment of probability.Discussion & Conclusion: Our results suggest that our framework using sparse probit regression provides an effective method for the population prediction of emotion/impression assessment based on brain activity.第5回ヒト脳イメージング研究

Combined effect of salinomycin and feeding on whole body glucose kinetics in sheep fed a high-concentrate diet

The aim of this study was to investigate the effects of salinomycin (SL) and feeding on whole body glucose kinetics in sheep fed a high-concentrate diet (25% orchardgrass hay and 75% commercial concentrate). Four adult sheep were fed the diet with or without 20 mg$\cdot$kg$^{-1}$ diet of SL once daily for each 3 wk. The rates of glucose entry and utilization were determined before and during 3 h after feeding using a [ $^{13}$C$_{6}$] glucose dilution approach. Ruminal characteristics and concentrations of blood volatile fatty acids (VFA) and plasma glucose and insulin were also measured. Metabolizable energy intake was unaffected ($P = 0$.22) with SL. Salinomycin decreased ($P = 0$.06) the ratio of acetate to propionate in rumen fluid. Salinomycin increased ($P = 0$.01) both rates of entry and utilization of glucose, but did not affect ($P > 0$.10) concentrations of blood VFA or plasma glucose or insulin. Feeding caused gradual increases in concentrations of blood acetate ($P < 0$.01) and propionate ($P = 0$.01), a transient increase in plasma insulin concentration ($P = 0.05$), a transient decrease in plasma glucose concentration ($P < 0$.01), and persistent increases in both rates of glucose entry ($P < 0$.01) and utilization ($P < 0$.01). No SL $\times$ feeding interaction was observed ($P > 0$.10) on any measurements. We conclude that SL and feeding would have an additive effect on both rates of glucose entry and utilization without modifications with SL to feeding responses of peripheral concentrations of blood VFA, plasma glucose and insulin