Integrating multiple brain imaging modalities does not boost prediction of subclinical atherosclerosis in midlife adults

Amy Isabella Sentis; Javier Rasero; Peter J Gianaros; Timothy D Verstynen

doi:10.1016/j.nicl.2022.103134

Integrating multiple brain imaging modalities does not boost prediction of subclinical atherosclerosis in midlife adults

Neuroimage Clin. 2022:35:103134. doi: 10.1016/j.nicl.2022.103134. Epub 2022 Jul 29.

Authors

Amy Isabella Sentis¹, Javier Rasero², Peter J Gianaros³, Timothy D Verstynen⁴

Affiliations

¹ 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.
² Carnegie Mellon Neuroscience Institute, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.
³ Carnegie Mellon Neuroscience Institute, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA; Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
⁴ Carnegie Mellon Neuroscience Institute, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA; Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. Electronic address: timothyv@andrew.cmu.edu.

Abstract

Background: 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).

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Brain imaging; Cardiovascular disease; Intima-media thickness; Prediction stacking.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Adult
Atherosclerosis* / diagnostic imaging
Biomarkers
Brain / diagnostic imaging
Carotid Intima-Media Thickness*
Female
Humans
Male
Neuroimaging
Predictive Value of Tests
Risk Factors

Substances

Biomarkers

Abstract

Publication types

MeSH terms

Substances

Grants and funding