标题： 比较和缩放fMRI特征用于脑行为预测 显示英文标题

标题： Comparing and Scaling fMRI Features for Brain-Behavior Prediction

摘要： 从磁共振成像（MRI）等神经影像学模式预测行为变量有可能促进精神和神经系统疾病神经影像生物标志物的发展。 为了达到这一目标，关键的处理步骤是提取合适的特征。 这些特征在预测感兴趣的目标方面的能力可能不同，并且这种预测能力会随着样本量和扫描时间的变化而变化。 在这里，我们比较了从静息态功能磁共振成像记录中提取的九种特征子类型，用于行为预测，范围从区域功能活动的测量到功能连接（FC）以及通过图信号处理（GSP）获得的度量，这是一种结构信息引导的功能特征提取的系统方法。 我们研究了来自人类连接组计划青年成年人数据集的979名受试者，预测心理健康、认知、加工速度和物质使用的总结分数，以及年龄和性别。 我们研究了不同样本量和扫描时间组合下的特征的缩放特性。 功能连接（FC）被证明是预测认知、年龄和性别的最佳特征。 图功率谱密度是预测认知和年龄的第二佳特征，而对于性别，基于变异性的特征也显示出潜力。 在预测性别时，低通图滤波耦合FC略微优于简单的FC变体。 其他目标没有被显著预测。 缩放结果表明，表现更好的特征具有更高的性能储备。 它们还表明，在获取预测研究的数据时，平衡样本量和扫描时间非常重要。 结果确认了FC作为行为预测的稳健特征，但也显示了GSP和基于变异性的测量的潜力。 我们讨论了未来预测研究在采集策略和样本组成方面的意义。 显示英文摘要

摘要： Predicting behavioral variables from neuroimaging modalities such as magnetic resonance imaging (MRI) has the potential to allow the development of neuroimaging biomarkers of mental and neurological disorders. A crucial processing step to this aim is the extraction of suitable features. These can differ in how well they predict the target of interest, and how this prediction scales with sample size and scan time. Here, we compare nine feature subtypes extracted from resting-state functional MRI recordings for behavior prediction, ranging from regional measures of functional activity to functional connectivity (FC) and metrics derived with graph signal processing (GSP), a principled approach for the extraction of structure-informed functional features. We study 979 subjects from the Human Connectome Project Young Adult dataset, predicting summary scores for mental health, cognition, processing speed, and substance use, as well as age and sex. The scaling properties of the features are investigated for different combinations of sample size and scan time. FC comes out as the best feature for predicting cognition, age, and sex. Graph power spectral density is the second best for predicting cognition and age, while for sex, variability-based features show potential as well. When predicting sex, the low-pass graph filtered coupled FC slightly outperforms the simple FC variant. None of the other targets were predicted significantly. The scaling results point to higher performance reserves for the better-performing features. They also indicate that it is important to balance sample size and scan time when acquiring data for prediction studies. The results confirm FC as a robust feature for behavior prediction, but also show the potential of GSP and variability-based measures. We discuss the implications for future prediction studies in terms of strategies for acquisition and sample composition.