Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an
important role on cognitive performance in elderly individuals, both those who are asymptomatic
and those who suffer from symptoms of neurodegenerative disorders. Findings
from studies applying neuroimaging methods have increasingly reinforced such notion.
Studies addressing the impact of CVRF on brain anatomy changes have gained increasing
importance, as recent papers have reported gray matter loss predominantly in regions
traditionally affected in Alzheimer’s disease (AD) and vascular dementia in the presence
of a high degree of cardiovascular risk. In the present paper, we explore the association
between CVRF and brain changes using pattern recognition techniques applied to structural
MRI and the Framingham score (a composite measure of cardiovascular risk largely used
in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer
the following questions: is it possible to decode (i.e., to learn information regarding cardiovascular
risk from structural brain images) enabling individual predictions? Among clinical
measures comprising the Framingham score, are there particular risk factors that stand
as more predictable from patterns of brain changes? Our main findings are threefold: (i)
we verified that structural changes in spatially distributed patterns in the brain enable statistically
significant prediction of Framingham scores. This result is still significant when
controlling for the presence of the APOE 4 allele (an important genetic risk factor for both
AD and cardiovascular disease). (ii) When considering each risk factor singly, we found
different levels of correlation between real and predicted factors; however, single factors
were not significantly predictable from brain images when considering APOE4 allele presence
as covariate. (iii) We found important gender differences, and the possible causes of
that finding are discussed