Abstract:Carotid plaque is an aggregate marker of exposure to vascular risk factors, which are linked to structural brain changes. We investigated prestroke global and regional changes in brain volume in a carotid plaque population of cognitively healthy individuals and the association between carotid plaque characteristics and these changes.A total of 76 participants were divided into healthy control (HC, n = 28), vulnerable plaque (n = 27) and stable plaque groups (n = 21). All subjects underwent carotid ultrasound a… Show more
“…Secondly, our methodological approach replicates that of Liem et al, 2017 in a new sample, validating stacked learning as a useful tool for predicting individual differences from MRI-based measures. Finally, our results confirm some of the findings in the neuroimaging literature, namely that individually, cortical thickness and brain volumes are associated with CA-IMT ( Cardenas et al, 2012 , Muller et al, 2011 , Tuo et al, 2018 ). However, these associations are weak in comparison to that of FRS and do not add to that model’s predictive power.…”
Section: Discussionsupporting
confidence: 90%
“…Separately from functional neuroimaging studies, there is structural brain imaging evidence indicating that CA-IMT is inversely associated with total brain tissue volume, as well as cortical tissue volume more specifically ( Muller et al, 2011 , Tuo et al, 2018 ). In parallel, however, other lines of evidence suggest no association between CA-IMT and total brain tissue volume or gray matter tissue volumes ( Cermakova et al, 2020 ).…”
“…Secondly, our methodological approach replicates that of Liem et al, 2017 in a new sample, validating stacked learning as a useful tool for predicting individual differences from MRI-based measures. Finally, our results confirm some of the findings in the neuroimaging literature, namely that individually, cortical thickness and brain volumes are associated with CA-IMT ( Cardenas et al, 2012 , Muller et al, 2011 , Tuo et al, 2018 ). However, these associations are weak in comparison to that of FRS and do not add to that model’s predictive power.…”
Section: Discussionsupporting
confidence: 90%
“…Separately from functional neuroimaging studies, there is structural brain imaging evidence indicating that CA-IMT is inversely associated with total brain tissue volume, as well as cortical tissue volume more specifically ( Muller et al, 2011 , Tuo et al, 2018 ). In parallel, however, other lines of evidence suggest no association between CA-IMT and total brain tissue volume or gray matter tissue volumes ( Cermakova et al, 2020 ).…”
“…Secondly, our methodological approach replicates that of Liem et al, 2016 20 in a new sample, validating stacked learning as a useful tool for predicting individual differences from MRI-based measures. Finally, our results confirm some of the findings in the neuroimaging literature, namely that individually, cortical thickness and brain volumes are associated with CA-IMT [14][15][16] . However, these associations are weak in comparison to that of FRS and do not add to that model's predictive power.…”
Section: Discussionsupporting
confidence: 90%
“…Other findings indicate that CA-IMT associates with lower cerebral blood flow in gray matter and across the entire brain 13 . Separately from functional neuroimaging studies, there is structural brain imaging evidence indicating that CA-IMT is inversely associated with total brain tissue volume, as well as cortical tissue volume more specifically 14,15 . In parallel, however, other lines of evidence suggest no association between CA-IMT and total brain tissue volume or gray matter tissue volumes 13 .…”
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 illness (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.
“…It has been reported that up to 5% of asymptomatic women and 12% of asymptomatic men over 80 years of age have a moderate carotid plaque (stenotic diameter of 50–70%) [1] . Apart from the risk of ischemic stroke, that in this population is relatively low with a reported annual incidence between 0.35 and 1.30% [2] , the presence of increased common carotid intima-media thickness (CC-IMT) and carotid artery stenosis have been associated to cognitive decline [3] , and reduced brain volumes [4] .…”
Highlights
Age and fibrocalcific carotid plaques are associated with lower total brain volume.
Fibrocalcific carotid plaques are associated with lower gray matter volumes.
White matter volumes are associated with the extent of carotid atherosclerosis.
Grey and white matter likely have differential susceptibility to processes.
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