Model selection analysis.

We performed a model selection analysis using both Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Both AIC and BIC consider how well a model fits the data while penalizing the number of free parameters. For both analyses, a lower score indicates a better model fit given the total number of free parameters. Experiments 1 & 2 were fit simultaneously and given a single combined score for both AIC and BIC. For Experiment 3, the Parkinson’s disease and age-matched control groups were fit separately and each group was given their own AIC and BIC scores. The table shows the sum of all AIC and BIC scores calculated for Experiments 1, 2, & 3. Both analyses would suggest Model 4 as the best-fit model across all experiments.本研究采用赤池信息准则（Akaike Information Criteria, AIC）与贝叶斯信息准则（Bayesian Information Criteria, BIC）开展模型选择分析。两类准则均会考量模型对数据的拟合程度，同时对自由参数的数量施加惩罚项。在两种分析框架下，得分越低即代表在给定自由参数总数量的前提下，模型的拟合效果越佳。实验1与实验2采用联合拟合方式，并为二者生成单一的AIC与BIC综合得分。针对实验3，帕金森病组与年龄匹配对照组分别进行拟合，每组均生成专属的AIC与BIC得分。本表格展示了针对实验1、2与3计算得到的全部AIC与BIC得分之和。两类准则分析均表明，模型4为所有实验中拟合效果最优的模型。