0273 Baseline Excessive Daytime Sleepiness Associated With an Increase in Brain Metabolism in Non-Demented Elderly: A longitudinal FDG-Pet Study

Carvalho, D Z; Louis, EK St.; Boeve, B. F.; Knopman, DS; Vj, Lowe; Roberts, RO; Mielke, MM; Przybelski, S A; Rc, Petersen; Jack, C. R.; Vemuri, Prashanthi

doi:10.1093/sleepj/zsx050.272

Cited by 1 publication

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“…These areas are known to be vulnerable to daytime sleepiness and are associated with increasing amyloid accumulation. 18 Besides these areas, ESS was related to right limbic system and bilateral prefrontal areas-related connectivity in the non-OSA group, but to right DMN and left limbic system-related connectivity in the OSA group. Affected brain connections were more widely distributed in the OSA group.…”

Section: Discussionmentioning

confidence: 92%

“… 37 DMN areas are vulnerable to increased amyloid accumulation in older adults who experience daytime sleepiness. 18 These findings can be interpreted as one of the mechanisms for cognitive impairment in subjects with daytime sleepiness, especially in the elderly population. 17 …”

Section: Discussionmentioning

confidence: 95%

“… 17 An increased rate of β-amyloid accumulation particularly in the DMN areas is associated with sleepiness. 18 Residual sleepiness in treated OSA patients may be partially explained by white matter abnormality on DTI. 19 , 20 However, it is still unclear which clusters of brain connectivity (ie, pairwise connections between brain regions) specifically correlate with the degree of OSA and daytime sleepiness.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning–Based Assessment of Brain Connectivity Related to Obstructive Sleep Apnea and Daytime Sleepiness

Lee¹,

Lee²,

Thomas³

et al. 2021

NSS

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Purpose Obstructive sleep apnea (OSA) is associated with altered pairwise connections between brain regions, which might explain cognitive impairment and daytime sleepiness. By adopting a deep learning method, we investigated brain connectivity related to the severity of OSA and daytime sleepiness. Patients and Methods A cross-sectional design applied a deep learning model on structural brain networks obtained from 553 subjects (age, 59.2 ± 7.4 years; men, 35.6%). The model performance was evaluated with the Pearson’s correlation coefficient ( R ) and probability of absolute error less than standard deviation (P AE

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Section: Discussionmentioning

confidence: 92%