Aging brain undergoes multiple structural and functional changes. These
may contribute to an increased risk of neurodegenerative disease (NDD)
and other age-related diseases, highlighting the importance of assessing
deviations from healthy brain aging trajectory. In this human brain
study, 50 healthy adults were investigated by functional near-infrared
spectroscopy (fNIRS). A resting state single channel multiwavelength
fNIRS was measured from the forehead in a supine position. The subjects
were divided into four age groups. A machine learning approach was
utilized for age group classification by using support vector machine
and random forest learners with nested cross-validation. The results
suggest brain aging effects being more distinct in the oldest age group
and a difference in the brain aging for the subjects of the in-between
groups. Our study shows high potential for the use of fNIRS in the
analysis of brain aging.