![]() Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904 Principal Investigator: Michael Weiner) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012 Principal Investigator: Michael Weiner). received subcontract grants to Penn State from Proactive Life LLC (formerly Mobile Sleep technologies) doing business as SleepScape (NSF/STTR #1622766, NIH/NIA SBIR R43- AG056250, R44-AG056250), received honoraria/travel support for lectures from Boston University, Boston College, Tufts School of Dental Medicine, New York University and Allstate, and receives an honorarium for his role as the Editor in Chief of Sleep Health. For up-to-date information, see Funding: This work is supported by funding from the National Institutes of Health Pathway to Independence Award (K99/R00 5R00NS092996-03 to X.L.), the Brain Initiative award (1RF1MH123247-01 to X.L.), the NIH R01 award (1R01NS113889-01A1 to X.L.), and NIA R03 award (1R03AG070812-01 to L.L.). The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. All the code and data used to generate the graphs in the present study are available in S1 Data, and at ( ). The information on how to gain the access to the ADNI dataset can be found at. According to the ADNI Data Sharing and Publication Policy ( ), the supporting data file is available upon request to all interested researchers who have authorized access to the same ADNI dataset ( ). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The multimodal data, including subject characteristics, rsfMRI, Amyloid-PET SUVR, APOE genotypes, and MMSE scores, have been summarized as a supporting data file. Received: AugAccepted: ApPublished: June 1, 2021Ĭopyright: © 2021 Han et al. ![]() PLoS Biol 19(6):Īcademic Editor: Maiken Nedergaard, University of Rochester Medical Center, UNITED STATES (2021) Reduced coupling between cerebrospinal fluid flow and global brain activity is linked to Alzheimer disease–related pathology. These results provide critical initial evidence for involvement of sleep-dependent global brain activity, as well as the associated physiological modulations, in the clearance of AD-related brain waste.Ĭitation: Han F, Chen J, Belkin-Rosen A, Gu Y, Luo L, Buxton OM, et al. By analyzing multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project, here we showed that the coupling between the global fMRI signal and CSF influx is correlated with AD-related pathology, including various risk factors for AD, the severity of AD-related diseases, the cortical Aβ level, and cognitive decline over a 2-year follow-up. Recent studies have reported that widespread, high-amplitude spontaneous brain activations in the drowsy state and during sleep, which are shown as large global signal peaks in resting-state functional magnetic resonance imaging (rsfMRI), are coupled with CSF movements, suggesting their potential link to glymphatic flux and metabolite clearance. Glymphatic clearance, as well as Aβ accumulation, is highly dependent on sleep, but the sleep-dependent driving forces behind cerebrospinal fluid (CSF) movements essential to the glymphatic flux remain largely unclear. The glymphatic system plays an important role in clearing the amyloid-β (Aβ) and tau proteins that are closely linked to Alzheimer disease (AD) pathology.
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