Program in Neural Computation, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA; Carnegie Mellon Neuroscience Institute, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA.
Carnegie Mellon Neuroscience Institute, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.
Neuroimage Clin. 2022;35:103134. doi: 10.1016/j.nicl.2022.103134. Epub 2022 Jul 29.
Human neuroimaging evidence suggests that cardiovascular disease (CVD) risk may relate to functional and structural features of the brain. The present study tested whether combining functional and structural (multimodal) brain measures, derived from magnetic resonance imaging (MRI), would yield a multivariate brain biomarker that reliably predicts a subclinical marker of CVD risk, carotid-artery intima-media thickness (CA-IMT).
Neuroimaging, cardiovascular, and demographic data were assessed in 324 midlife and otherwise healthy adults who were free of (a) clinical CVD and (b) use of medications for chronic illnesses (aged 30-51 years, 49% female). We implemented a prediction stacking algorithm that combined multimodal brain imaging measures and Framingham Risk Scores (FRS) to predict CA-IMT. We included imaging measures that could be easily obtained in clinical settings: resting state functional connectivity and structural morphology measures from T1-weighted images.
Our models reliably predicted CA-IMT using FRS, as well as for several individual MRI measures; however, none of the individual MRI measures outperformed FRS. Moreover, stacking functional and structural brain measures with FRS did not boost prediction accuracy above that of FRS alone.
Combining multimodal functional and structural brain measures through a stacking algorithm does not appear to yield a reliable brain biomarker of subclinical CVD, as reflected by CA-IMT.
人体神经影像学的证据表明,心血管疾病(CVD)的风险可能与大脑的功能和结构特征有关。本研究测试了是否可以将磁共振成像(MRI)得出的功能和结构(多模态)脑测量结果相结合,从而产生一种可靠的多变量脑生物标志物,可预测心血管疾病风险的亚临床标志物颈动脉内-中膜厚度(CA-IMT)。
对 324 名中年且无(a)临床 CVD 和(b)慢性疾病药物治疗史的健康成年人进行了神经影像学、心血管和人口统计学数据评估(年龄 30-51 岁,49%为女性)。我们实施了一种预测堆叠算法,该算法结合了多模态脑影像学测量和 Framingham 风险评分(FRS)来预测 CA-IMT。我们纳入了在临床环境中可以轻松获得的影像学测量:静息状态功能连接和 T1 加权图像的结构形态学测量。
我们的模型使用 FRS 以及几个单独的 MRI 测量值可靠地预测了 CA-IMT,但没有任何一个单独的 MRI 测量值优于 FRS。此外,通过堆叠功能和结构脑测量值与 FRS 并不能提高预测准确性,超过 FRS 单独的预测准确性。
通过堆叠算法结合多模态功能和结构脑测量值似乎无法产生亚临床 CVD 的可靠脑生物标志物,如 CA-IMT 所反映的那样。