Diffuse optical assessment of cerebral-autoregulation in older adults stratified by cerebrovascular risk
Ahmed A. Bahrani
Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky, USA
Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Search for more papers by this authorWeikai Kong
Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorYu Shang
Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China, Shanxi, China
Search for more papers by this authorChong Huang
Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorCharles D. Smith
Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
Magnetic Resonance Imaging and Spectroscopy Center (MRISC), University of Kentucky, Lexington, Kentucky, USA
Department of Neurology, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorDavid K. Powell
Magnetic Resonance Imaging and Spectroscopy Center (MRISC), University of Kentucky, Lexington, Kentucky, USA
Neuroscience Department, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorYang Jiang
Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
Magnetic Resonance Imaging and Spectroscopy Center (MRISC), University of Kentucky, Lexington, Kentucky, USA
Department of Behavioral Science, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorAbner O. Rayapati
Department of Psychiatry, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorGregory A. Jicha
Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
Magnetic Resonance Imaging and Spectroscopy Center (MRISC), University of Kentucky, Lexington, Kentucky, USA
Department of Neurology, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorCorresponding Author
Guoqiang Yu
Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky, USA
Correspondence
Guoqiang Yu, Department of Biomedical Engineering, University of Kentucky, Lexington, KY 40506.
Email: [email protected]
Search for more papers by this authorAhmed A. Bahrani
Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky, USA
Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Search for more papers by this authorWeikai Kong
Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorYu Shang
Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China, Shanxi, China
Search for more papers by this authorChong Huang
Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorCharles D. Smith
Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
Magnetic Resonance Imaging and Spectroscopy Center (MRISC), University of Kentucky, Lexington, Kentucky, USA
Department of Neurology, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorDavid K. Powell
Magnetic Resonance Imaging and Spectroscopy Center (MRISC), University of Kentucky, Lexington, Kentucky, USA
Neuroscience Department, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorYang Jiang
Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
Magnetic Resonance Imaging and Spectroscopy Center (MRISC), University of Kentucky, Lexington, Kentucky, USA
Department of Behavioral Science, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorAbner O. Rayapati
Department of Psychiatry, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorGregory A. Jicha
Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
Magnetic Resonance Imaging and Spectroscopy Center (MRISC), University of Kentucky, Lexington, Kentucky, USA
Department of Neurology, University of Kentucky, Lexington, Kentucky, USA
Search for more papers by this authorCorresponding Author
Guoqiang Yu
Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky, USA
Correspondence
Guoqiang Yu, Department of Biomedical Engineering, University of Kentucky, Lexington, KY 40506.
Email: [email protected]
Search for more papers by this authorFunding information: American Heart Association, Grant/Award Number: 16GIA30820006; Higher Committee for Education Development in Iraq, Grant/Award Number: Supporting the primary author scholarship; National Institutes of Health, Grant/Award Numbers: 1R01AG062480, 5P30AG028383, R01-HD101508, R21-HD091118; National Science Foundation (NSF), Grant/Award Number: EPSCoR1539068
Abstract
Diagnosis of cerebrovascular disease (CVD) at early stages is essential for preventing sequential complications. CVD is often associated with abnormal cerebral microvasculature, which may impact cerebral-autoregulation (CA). A novel hybrid near-infrared diffuse optical instrument and a finger plethysmograph were used to simultaneously detect low-frequency oscillations (LFOs) of cerebral blood flow (CBF), oxy-hemoglobin concentration ([HbO2]), deoxy-hemoglobin concentration ([Hb]) and mean arterial pressure (MAP) in older adults before, during and after 70° head-up-tilting (HUT). The participants with valid data were divided based on Framingham risk score (FRS, 1-30 points) into low-risk (FRS ≤15, n = 13) and high-risk (FRS >15, n = 11) groups for developing CVD. The LFO gains were determined by transfer function analyses with MAP as the input, and CBF, [HbO2] and [Hb] as the outputs (CA ∝ 1/Gain). At resting-baseline, LFO gains in the high-risk group were relatively lower compared to the low-risk group. The lower baseline gains in the high-risk group may attribute to compensatory mechanisms to maintain stronger steady-state CAs. However, HUT resulted in smaller gain reductions in the high-risk group compared to the low-risk group, suggesting weaker dynamic CAs. LFO gains are potentially valuable biomarkers for early detection of CVD based on associations with CAs.
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