Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders.
Soonhyun Yook,
Hea Ree Park,
Claire Park,
Gilsoon Park,
Diane C. Lim,
Jinyoung Kim,
Eun Yeon Joo,
Hosung Kim
Affiliations
Soonhyun Yook
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., Los Angeles, CA 90033, USA
Hea Ree Park
Department of Neurology, Inje University College of Medicine, Ilsan Paik Hospital, Goyang 10380, Korea
Claire Park
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., Los Angeles, CA 90033, USA; School of Medicine, California University of Science and Medicine, Colton, CA 92324, USA
Gilsoon Park
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., Los Angeles, CA 90033, USA
Diane C. Lim
Division of Pulmonary, Critical Care, Sleep, University of Miami, Miami, FL 33125, USA
Jinyoung Kim
School of Nursing, University of Nevada, Las Vegas, NV 89154, USA
Eun Yeon Joo
Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul 06351, Korea; Corresponding authors.
Hosung Kim
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., Los Angeles, CA 90033, USA; Corresponding authors.
Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in β and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.
