Li Jiaqi, Segel Ari, Feng Xinyang, Tu Jiaxin Cindy, Eck Andy, King Kelsey T, Adeyemo Babatunde, Karcher Nicole R, Chen Likai, Eggebrecht Adam T, Wheelock Muriah D
Department of Statistics and Data Science, Washington University in St. Louis, MO, USA.
Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA.
Netw Neurosci. 2024 Oct 1;8(3):762-790. doi: 10.1162/netn_a_00383. eCollection 2024.
Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.
机器学习算法越来越多地被用于识别与行为和临床结果相关的脑连接生物标志物。然而，研究往往以牺牲生物学可解释性为代价来优先考虑预测准确性，并且机器学习方法的不一致实施可能会阻碍模型准确性。为了解决这个问题，我们的论文引入了一种网络层面的富集方法，该方法在全脑连接组统计分析的背景下整合脑系统组织，以揭示脑连接与行为之间的网络层面联系。为了证明这种方法的有效性，我们使用线性支持向量回归（LSVR）模型来检验静息态功能连接网络与实际年龄之间的关系。我们将基于原始LSVR权重的网络层面关联与正向和反向模型产生的关联进行了比较。结果表明，不考虑共享家族方差会夸大预测性能，通过皮尔逊相关性进行的k最佳特征选择会降低准确性和可靠性，并且原始LSVR模型权重产生的网络层面关联偏离了正向和反向模型确定的重要脑系统。我们的研究结果为将机器学习应用于神经影像数据提供了关键见解，强调了网络富集对生物学解释的价值。
