Krämer Camilla, Stumme Johanna, da Costa Campos Lucas, Rubbert Christian, Caspers Julian, Caspers Svenja, Jockwitz Christiane
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Netw Neurosci. 2023 Jan 1;7(1):122-147. doi: 10.1162/netn_a_00275. eCollection 2023.
Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (s ≥ 0.75) and low to none explained variance ( ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.
健康的老年人中与年龄相关的认知衰退差异很大，这可能部分归因于脑网络功能结构的差异。静息态功能连接（RSFC）衍生的网络参数作为描述这种结构的广泛使用的标志物，甚至已成功用于支持神经退行性疾病的诊断。当前的研究旨在通过使用机器学习（ML）来检验这些参数是否也有助于对正常衰老大脑中的认知表现差异进行分类和预测。在来自1000BRAINS研究的健康老年人（年龄范围：55 - 85岁）中，研究了从节点和网络层面的RSFC强度测量来区分和预测整体及特定领域认知表现差异的可分类性和可预测性。在一个稳健的交叉验证方案中，系统地评估了不同分析选择下的ML性能。在这些分析中，对于整体和特定领域的认知，分类性能的准确率未超过60%。对于不同的认知目标、特征集和流程配置，预测性能同样很低，平均绝对误差较高（s≥0.75），解释方差较低或几乎没有（≤0.07）。当前结果凸显了功能网络参数作为认知衰老唯一生物标志物的潜力有限，并强调从功能网络模式预测认知可能具有挑战性。
