Data_Sheet_1_Cerebral Metabolism Related to Cognitive Impairments in Multiple System Atrophy.doc
Objective: We aimed to characterize the cognitive profiles in multiple system atrophy (MSA) and explore the cerebral metabolism related to the cognitive decline in MSA using 18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET).
Methods: In this study, 105 MSA patients were included for cognitive assessment and 84 of them were enrolled for 18F-FDG PET analysis. The comprehensive neuropsychological tests covered five main domains including execution, attention, memory, language, and visuospatial function. The cognitive statuses were classified to MSA with normal cognition (MSA-NC) and MSA with cognitive impairment (MSA-CI), including dementia (MSA-D), and mild cognitive impairment (MSA-MCI). With 18F-FDG PET imaging, the cerebral metabolism differences among different cognitive statuses were analyzed using statistical parametric mapping and post-hoc analysis.
Results: Among 84 MSA patients, 52 patients were found with MSA-CI, including 36 patients as MSA-MCI and 16 patients as MSA-D. In detail, the cognitive impairments were observed in all the five domains, primarily in attention, executive function and memory. In 18F-FDG PET imaging, MSA-D and MSA-MCI patients exhibited hypometabolism in left middle and superior frontal lobe compared with MSA-NC (p < 0.001). The normalized regional cerebral metabolic rate of glucose (rCMRglc) in left middle frontal lobe showed relative accuracy in discriminating MSA-CI and MSA-NC [areas under the curve (AUC) = 0.750; 95%CI = 0.6391–0.8609].
Conclusions: Cognitive impairments were not rare in MSA, and the hypometabolism in frontal lobe may contribute to such impairments.
History
References
- https://doi.org//10.1002/mds.870120203
- https://doi.org//10.1016/S1474-4422(09)70288-1
- https://doi.org//10.1212/01.wnl.0000324625.00404.15
- https://doi.org//10.1002/mds.25880
- https://doi.org//10.1371/journal.pone.0139773
- https://doi.org//10.1093/brain/awq158
- https://doi.org//10.1016/j.parkreldis.2013.02.012
- https://doi.org//10.1093/brain/awm328
- https://doi.org//10.1002/mds.27085
- https://doi.org//10.1002/mds.27701
- https://doi.org//10.1002/mds.24893
- https://doi.org//10.1002/mds.21507
- https://doi.org//10.1016/0022-510X(89)90219-0
- https://doi.org//10.1093/brain/117.2.235
- https://doi.org//10.1093/cercor/bhv228
- https://doi.org//10.1111/neup.12289
- https://doi.org//10.1002/mds.26874
- https://doi.org//10.1007/s00702-016-1636-0
- https://doi.org//10.1159/000430953
- https://doi.org//10.1111/j.1468-1331.2009.02661.x
- https://doi.org//10.1007/s00415-018-8923-7
- https://doi.org//10.2967/jnumed.116.186403
- https://doi.org//10.1016/j.parkreldis.2020.10.039
- https://doi.org//10.1016/j.parkreldis.2019.09.005
- https://doi.org//10.1080/13854040590945210
- https://doi.org//10.1371/journal.pone.0057333
- https://doi.org//10.1016/j.acn.2005.07.003
- https://doi.org//10.1097/WAD.0b013e3181999e92
- https://doi.org//10.1007/s100720200003
- https://doi.org//10.1080/13854040590945337
- https://doi.org//10.1002/hbm.24311
- https://doi.org//10.1038/jcbfm.2015.208
- https://doi.org//10.1007/s12021-014-9235-4
- https://doi.org//10.1016/j.neuroimage.2005.03.012
- https://doi.org//10.1016/j.nicl.2020.102416
- https://doi.org//10.1212/01.wnl.0000310413.04462.6a
- https://doi.org//10.1016/S0022-510X(98)00018-5
- https://doi.org//10.1007/s004010051008
- https://doi.org//10.1093/brain/awv114
CC BY 4.0