Table_1_The Olfactory Bulb Facilitates Use of Category Bounds for Classification of Odorants in Different Intensity Groups.pdf

Signal processing of odor inputs to the olfactory bulb (OB) changes through top-down modulation whose shaping of neural rhythms in response to changes in stimulus intensity is not understood. Here we asked whether the representation of a high vs. low intensity odorant in the OB by oscillatory neural activity changed as the animal learned to discriminate odorant concentration ranges in a go-no go task. We trained mice to discriminate between high vs. low concentration odorants by learning to lick to the rewarded group (low or high). We recorded the local field potential (LFP) in the OB of these mice and calculated the theta-referenced beta or gamma oscillation power (theta phase-referenced power, or tPRP). We found that as the mouse learned to differentiate odorant concentrations, tPRP diverged between trials for the rewarded vs. the unrewarded concentration range. For the proficient animal, linear discriminant analysis was able to predict the rewarded odorant group and the performance of this classifier correlated with the percent correct behavior in the odor concentration discrimination task. Interestingly, the behavioral response and decoding accuracy were asymmetric as a function of concentration when the rewarded stimulus was shifted between the high and low odorant concentration ranges. A model for decision making motivated by the statistics of OB activity that uses a single threshold in a logarithmic concentration scale displays this asymmetry. Taken together with previous studies on the intensity criteria for decisions on odorant concentrations, our finding suggests that OB oscillatory events facilitate decision making to classify concentrations using a single intensity criterion.

针对嗅球（olfactory bulb, OB）接收的气味输入信号处理过程，可通过自上而下调控发生动态改变；然而该调控针对刺激强度变化塑造神经节律的具体机制，目前尚未明确。本研究以Go-NoGo任务为范式，探究当动物学会区分气味浓度范围时，振荡神经活动在嗅球中对高、低浓度气味分子的表征是否会发生改变。我们通过训练小鼠舔舐对应奖励组（低浓度或高浓度），使其掌握高、低浓度气味分子的区分任务。随后我们记录了这些小鼠嗅球的局部场电位（local field potential, LFP），并计算了θ相位参考的β或γ振荡功率（theta phase-referenced power，简称tPRP）。结果显示，随着小鼠逐渐学会区分气味浓度，奖励组与未奖励组浓度范围对应的tPRP在不同试次中出现显著分化。对于熟练完成任务的小鼠，线性判别分析可准确预测其对应的奖励气味组，且该分类器的分类性能与小鼠在气味浓度区分任务中的行为正确率呈显著正相关。值得注意的是，当奖励刺激在高、低气味浓度范围之间切换时，小鼠的行为反应与解码精度会随浓度变化呈现不对称性。基于嗅球活动统计特征构建的决策模型——该模型在对数浓度尺度下仅设置单一阈值——即可复现这种不对称性。结合此前关于气味浓度决策强度标准的相关研究，本研究结果表明：嗅球振荡活动可辅助基于单一强度标准完成浓度分类的决策过程。