Electroencephalographic slowing during REM sleep is a marker of cholinergic dysfunction in Lewy body disorders

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Progressive cholinergic dysfunction is known to be a key contributor to cognitive decline in LBDs, yet accessible biomarkers of cholinergic impairment remain limited. Because cholinergic neurons play a central role in REM sleep and cortical activation, alterations in REM electroencephalographic activity (EEG) may provide a functional marker of cholinergic network integrity in these disorders. Twenty-four participants with DLB, 36 with PD, and 44 controls underwent neuropsychological testing and overnight polysomnography, with a subset completing structural and resting-state MRI. EEG spectral power across δ, θ, α, σ and β frequency bands was quantified during REM sleep and the REM EEG slowing ratio (δ + θ)/(α + σ + β) was derived for frontal, central and occipital regions. Nucleus basalis of Meynert (NBM) volumes were derived from T1-weighted images, and functional connectivity was examined between the NBM and large-scale networks, and between the pedunculopontine nucleus (PPN) and thalamus. DLB participants had greater occipital REM EEG slowing compared with PD and controls. Across LBDs, REM slowing was associated with aberrant nucleus basalis of Meynert connectivity to visual, ventral attentional, default mode, and frontoparietal networks, as well as altered pedunculopontine-thalamic connectivity. Greater REM EEG slowing correlated with worse global cognition and executive function deficits. These findings demonstrate that REM EEG slowing reflects cholinergic network dysfunction and clinically relevant cognitive impairment in LBDs. REM slowing may represent a non-invasive and transdiagnostic biomarker for disease monitoring, prognosis, and stratification in clinical trials. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Neurology Biological sciences/Neuroscience Rapid Eye Movement Sleep Parkinson’s Dementia with Lewy bodies Lewy Body Disorders Neuroimaging EEG Cholinergic System Cognition Figures Figure 1 Figure 2 Figure 3 Introduction Lewy body disorders (LBDs), including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB), are characterized pathologically by the aggregation of intraneuronal α-synuclein and represent the second most common cause of neurodegeneration in older adults. In addition to their neuropathology, these LBDs share overlapping clinical features of cognitive, neuropsychiatric, sleep, motor and autonomic symptoms 1 , 2 . Although motor deficits initially dominate the clinical picture of PD, most patients will exhibit progressive cognitive impairment throughout the disease course, with approximately 80% ultimately progressing to dementia. 1 , 3 Similarly, sleep-wake disturbances are a highly prevalent non-motor feature of the LBDs, presenting in prodromal 4 , 5 and early stages 6 , 7 with increasing frequency as the disease progresses 6 , 8 – 10 . Both cognitive dysfunction and sleep-wake disturbances severely impact patient and caregiver quality of life 11 – 13 . However, there is significant heterogeneity across LBD disease progression with some patients manifesting a more aggressive course. 14 Therefore, understanding the biological basis for disease progression and developing non-invasive, clinically accessible markers that can predict disease trajectory in an individual, are critically important. Neuropathological and recent neuroimaging studies have highlighted dysfunction in cholinergic circuitry as being central to the development of cognitive impairment in LBDs. 15 Prior work has predominantly focused on the basal forebrain cholinergic system, in particular the nucleus basalis of Meynert (NBM), which provides the majority of cholinergic input to the cortex through its widespread projections. 16 Early degeneration of the NBM and its cortical projections occur in LBDs 17 , 18 and are linked to cognitive decline. 19 , 20 Additionally, the pedunculopontine nucleus (PPN), located in the dorsal pons, contains cholinergic neurons that project primarily to the basal ganglia, thalamus, lower brainstem and spinal cord. 21 , 22 Lewy pathology and neuronal loss occurs within the PPN in LBDs, 23,24 and are associated with deficits in motor control 25 and cognition. 26 , 27 Molecular imaging markers have revealed cortical posterior cholinergic dysfunction in LBDs which are linked to the presence of dementia 28 . Very recently changes in these cholinergic nuclei have also been shown to explain metabolic deficits in LBDs, especially in posterior regions which are a supportive marker of DLB. 1 , 29 Cholinergic circuits also play a central role in the regulation of sleep and wakefulness. Previous studies have identified that cholinergic activity is highest during REM sleep, when most other neurotransmitter systems are relatively quiescent. 30 – 32 Experimental work in animal models indicates that REM sleep generation and maintenance depend on ascending cholinergic inputs from the PPN, with cortical desynchronization driven by projections from the basal forebrain. 33 – 35 Therefore, dysfunction in cholinergic circuitry observed in LBDs is likely to produce electrophysiological changes in brain activity during REM sleep. At the sleep macro-architectural level, a lower percentage of REM sleep time has been shown in DLB, 36 and a recent meta-analysis demonstrated both decreased REM sleep and increased REM latency in PD compared to controls. 37 Moreover, electroencephalographic (EEG) slowing in REM sleep predicts subsequent dementia in people with PD. 38 Collectively, these findings suggest that EEG activity during REM sleep may serve as a unique window to investigate cholinergic integrity in LBDs. In this study, we sought to characterize REM sleep EEG alterations in PD and DLB and investigate their associations with neuroimaging markers of the cholinergic system, along with cognitive function. First, we explored differences in REM EEG spectral power between DLB, PD and cognitively unimpaired controls. Next, we examined the association of REM EEG alterations with structural and functional MRI measures of the NBM and PPN, and with cognitive performance across LBDs. We hypothesized that REM EEG slowing particularly in posterior regions, would be most pronounced in DLB, followed by PD compared to controls; and REM slowing would correlate with cholinergic hub dysfunction and poorer cognitive performance across LBDs. Results Participant characteristics One hundred and four participants were included in this study, including 36 diagnosed with PD 39 , 24 diagnosed with DLB 1 and 44 control participants without objective cognitive impairment. Participant demographics, clinical and questionnaire data are summarised in Table 1 . On average, the DLB participants were 10 years older than the PD and 7 years older than the control groups (p < 0.001). Compared to controls, the DLB participants had a higher proportion of males (79 vs 48%, p = 0.040), has 2 years less education (12 ± 4 vs 14 ± 3 years p = 0.029) and reported higher score on the Epworth Sleepiness Scale (10 ± 6 vs 6 ± 4, p = 0.005) and SCOPA Sleep Day (p = 0.003). DLB participants also scored higher on all sections of the MDS Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) compared to PD (p < 0.001). Five patients in the both the PD and DLB group were receiving antidepressants, while 9 DLB patients and 1 PD patient were taking cholinesterase inhibitors. Detailed neuropsychological testing was also performed, with group comparisons presented in Supplementary Table 1. As expected, DLB participants performed poorer in global cognition and across all domains in neuropsychological testing compared to PD and control participants. Table 1 Group Differences in Demographics, Clinical Data and Self-report Questionnaires Clinical Variables Controls ( n = 44) PD (n = 36) DLB ( n = 24) p value Age (years) 66.7 ± 7.5 63.6 ± 9.5 74.4 ± 6.6 < 0.001 b,c Education (years) 14.2 ± 3.0 13.9 ± 3.1 12.0 ± 3.9 0.029 b Male (n,%) 21 (47.8%) 22 (61.1%) 19 (79.2%) 0.040 b MMSE (/30) 28.9 ± 1.7 28.9 ± 1.4 21.8 ± 5.4 < 0.001 b,c Questionnaires Epworth Sleepiness Scale (/24) 6.1 ± 3.8 7.1 ± 4.2 9.9 ± 5.9 0.005 b,c SCOPA Sleep Night - 4.9 ± 3.7 2.8 ± 4.5 0.061 SCOPA Sleep Day - 3.5 ± 2.8 6.3 ± 4.6 0.003 RBD Screening Questionnaire - 5.1 ± 3.4 7.1 ± 3.7 0.073 MDS-UPDRS MDS-UPDRS I - 7.8 ± 4.3 16.7 ± 7.7 < 0.001 MDS-UPDRS II - 8.2 ± 6.4 16.5 ± 9.4 0.001 MDS-UPDRS III - 22.0 ± 11.2 35.9 ± 17.5 0.001 Medications LEDD total (mg/d) - 473 ± 439 274 ± 455 0.101 Benzodiazepines (% on) 2 (4.6%) 4 (11.1%) 2 (8.3%) 0.543 Antidepressants (% on) 0 (0.0%) 5 (13.9%) 5 (20.8%) 0.012 a,b,c Cholinesterase Inhibitors (% on) 0 (0.0%) 1 (2.8%) 9 (37.5%) < 0.001 b,c Demographic, clinical and questionnaire data Values are displayed as number (proportion as a percentage) or mean ± standard deviation. P values were calculated using one-way ANOVAs and chi-squared tests where applicable. DLB, Dementia with Lewy Bodies; HC, Healthy Controls; LEDD, Levodopa Equivalent Daily Dose; MDS-UPDRS I, Unified Parkinson’s Disease Rating Scale Section I; MDS-UPDRS II, Unified Parkinson’s Disease Rating Scale Section II; MDS-UPDRS III, Unified Parkinson’s Disease Rating Scale Section III; MMSE; The Mini Mental State Examination; PD, Parkinson’s Disease; RBD, REM Sleep Behaviour Disorder; SCOPA, Scales for Outcomes in Parkinson’s disease. a Pairwise comparisons: significant differences between PD and HC b Pairwise comparisons: significant differences between DLB and HC c Pairwise comparisons: significant differences between DLB and PD Sleep macroarchitecture To characterise sleep stages and timings within and between groups, sleep macroarchitecture measures are reported in Table 2 . DLB and PD groups had shorter sleep latency and a reduced proportion of REM sleep compared to controls. Total NREM sleep duration did not differ between groups, although some differences were observed in individual NREM sleep stages. There were no significant group differences in respiratory measures. Table 2 Group Differences in Sleep Macro-architecture Controls ( n = 44) PD (n = 36) DLB ( n = 24) pFDR Time in bed, min 486.2 ± 65.2 457.1 ± 45.1 450.2 ± 44.2 0.075 Total sleep time, min 351.2 ± 67.2 365.8 ± 65.7 328.0 ± 113.1 0.901 WASO, min 89.0 ± 56.8 85.3 ± 46.8 113.3 ± 84.8 0.960 Sleep efficiency, % 72.7 ± 14.7 79.5 ± 11.6 71.4 ± 22.4 0.589 Sleep latency, min 48.8 ± 65.5 10.6 ± 8.7 19.6 ± 31.6 0.024 a,b REM onset latency, min 129.4 ± 82.1 171.8 ± 90.3 179.2 ± 116.3 0.112 Sleep Stages N1% TST 12.3 ± 9.5 9.3 ± 6.6 18.4 ± 14.6 0.033 c N2% TST 44.8 ± 11.4 50.7 ± 12.4 42.0 ± 13.2 0.020 a,c N3% TST 22.8 ± 11.1 24.7 ± 7.9 28.6 ± 13.2 0.056 REM % TST 20.2 ± 7.8 15.3 ± 7.1 11.0 ± 7.3 < 0.001 a,b NREM sleep duration, min 278.8 ± 53.7 308.5 ± 61.4 289.3 ± 97.5 0.289 Respiratory AHI, events/hr 13.1 ± 12.3 9.7 ± 13.5 14.6 ± 14.2 0.663 RDI, events/hr 13.6 ± 12.3 10.0 ± 13.5 15.0 ± 14.3 0.663 Minimum SpO2 during sleep, % 85.9 ± 6.6 89.4 ± 4.0 87.3 ± 7.1 0.105 Arousals Total arousal index, arousals/hr 22.9 ± 11.3 16.9 ± 7.2 21.5 ± 10.5 0.037 a PLM arousal index, arousals/hr 2.3 ± 2.5 1.4 ± 2.1 3.3 ± 2.8 0.037 c Sleep macro-architecture data. Values are displayed as mean ± standard deviation. P values were calculated using non-parametric permutation testing. AHI, Apnea Hypopnea Index; DLB, Dementia with Lewy Bodies; NREM, Non-rapid Eye Movement Sleep; N1, Sleep stage 1; N2, Sleep stage 2; N3, Sleep Stage 3; RDI, Respiratory Disturbance Index; PLM, Periodic Limb Movement; RERA, Respiratory Effort-Related Arousal; REM, Rapid Eye Movement Sleep; PD, Parkinson’s Disease; WASO, Wake After Sleep Onset. a Pairwise comparisons: significant differences between PD and HC b Pairwise comparisons: significant differences between DLB and HC c Pairwise comparisons: significant differences between DLB and PD Greater REM EEG slowing in posterior regions in DLB compared to PD and controls After adjusting for age and sex, there were significant overall group differences in REM EEG slowing in occipital regions (pFDR < 0.001) with trends in the same direction for frontal and central regions (both pFDR = 0.057). Post-hoc pairwise tests showed greater occipital REM slowing in DLB than in both PD and controls (Fig. 1 ). At the frequency-band level, the occipital EEG slowing pattern was driven primarily by increased absolute delta power in DLB (pFDR = 0.030; DLB > PD and DLB > HC), with numerically higher theta that did not survive correction. Frontal beta power during REM was higher in both PD and DLB relative to controls (pFDR = 0.030). This beta elevation was also present in NREM, where frontal beta was higher in PD and DLB versus controls and DLB > PD (pFDR < 0.001) (see state specificity sensitivity analysis details below). Group comparisons for REM EEG power and slowing ratios are detailed in Supplementary Table 2. To test state specificity, we examined NREM EEG slowing ratios. PD showed reduced occipital NREM slowing relative to controls (pFDR = 0.015), whereas DLB did not differ from controls and DLB did not differ from PD (Supplementary Table 3). To account for any potential effects of cholinergic medication, a sensitivity analysis was performed including cholinesterase inhibitor use as a covariate alongside age and sex. Occipital REM EEG slowing remained significantly greater in DLB than PD and controls (pFDR = 0.003, see Supplementary Table 4). Occipital REM EEG slowing is associated with motor impairment, lower global cognition and poorer performance in executive function We next examined whether occipital REM EEG slowing was related to demographic, clinical, and medication variables across LBD participants, PD and DLB combined. Higher occipital REM slowing was significantly associated with greater motor impairment as indexed by the MDS-UPDRS III (pFDR = 0.034) and lower performance in global cognition (MoCA, pFDR = 0.002; MMSE, pFDR = 0.001), whereas no significant associations were observed with age, sex, years of education, or medication use (Table 3 , Fig. 2 ). Table 3 Correlations Between Occipital REM EEG Slowing and Clinical, Demographics and Medication use Across LBDs Beta R squared pFDR Clinical Age 0.188 0.043 0.197 Sex 0.508 0.056 0.179 Education (years) -0.054 0.003 0.830 MDS-UPDRS III 0.349 0.120 0.034 Medications LEDD total (mg/d) 0.010 0.000 0.946 Cholinesterase Inhibitors (% on) 0.532 0.041 0.197 Global Cognition MMSE -0.434 0.313 0.001 MoCA -0.278 0.270 0.002 P values were calculated using linear regression models. LEDD, Levodopa Equivalent Daily Dose; MMSE; The Mini Mental State Examination; MoCA; Montreal Cognitive Assessment. We next examined whether occipital REM EEG slowing was related to performance of specific cognitive domains within all LBD participants. After controlling for age and sex, occipital REM slowing was associated with poorer performance in the executive function domain, associated with poorer performance on both the Stroop Inhibition/Switching task (pFDR = 0.007) and Trail Making Test Part B (pFDR = 0.001). Occipital REM slowing was also associated with deficits in visuospatial performance (pFDR = 0.009) during the Clock Drawing task. No other cognitive domains showed significant associations with occipital REM slowing in LBDs after correction for multiple comparisons (Table 4 , Fig. 2 ). Table 4 Correlations Between Occipital REM EEG Slowing and Cognition Across LBDs Beta R squared pFDR Learning and Memory Logical Memory I -0.273 0.165 0.193 Logical Memory II -0.338 0.201 0.088 RAVLT A1-A5 -0.508 0.339 0.056 Executive Function Trail Making Test Part B 0.682 0.417 0.001 Stroop Inhibition 0.255 0.223 0.159 Stroop Inhibition/Switching 0.292 0.343 0.009 Visuospatial Clock Drawing -0.757 0.420 0.009 Attention and Working Memory Digit Span Forwards 0.045 0.119 0.837 Digit Span Backwards -0.097 0.125 0.659 Language Verbal Fluency – Letters -0.006 0.366 0.982 Verbal Fluency – Animals -0.172 0.375 0.659 P values were calculated using linear regression models. RAVLT, Rey Auditory Verbal Learning Test. NBM volume is not associated with occipital REM EEG slowing in LBDs Within the LBD group, we next examined whether structural changes in the basal forebrain were related to REM slowing. A subset of 34 LBD (16 DLB and 18 PD) completed a T1-weighted MRI scan within 6 months of their PSG. After adjusting for estimated total intracranial volume, NBM volume was significantly less in DLB (229 ± 29 mm³) compared to PD (266 ± 27 mm³; p < 0.001). However, NBM volume was not significantly associated with occipital REM EEG slowing (β=-0.006, p = 0.51) across LBDs. Occipital REM EEG slowing is associated with aberrant functional connectivity in key cholinergic hubs We then investigated whether occipital REM slowing was related to functional connectivity of cholinergic nuclei within the subset of 31 LBDs (15 DLB and 16 PD) of whom resting state functional MRI (rsfMRI) was conducted (Fig. 3 ). Across LBDs, occipital REM slowing was significantly associated with NBM functional connectivity to the visual (pFDR = 0.034), ventral attention (pFDR = 0.034), frontoparietal (pFDR = 0.038) and default mode networks (pFDR = 0.038). These findings were unique to occipital REM slowing, with no significant associations found between frontal or central REM slowing and NBM functional connectivity patterns. Additionally, occipital REM slowing was associated with PPN-thalamic functional connectivity (β = 1.75, p = 0.043) across LBDs. Unlike the NBM findings, this functional measure was also significantly associated with frontal (β = 6.10, p = 0.010) and central (β = 9.19, p = 0.048) REM EEG slowing. Correlations of REM slowing and functional imaging in LBDs are presented in Fig. 3 and Table 5 . To verify the specificity of the findings to cholinergic pathways, we examined the relationship between REM slowing and thalamic to whole brain network resting-state functional connectivity. We found no significant associations between REM slowing and general thalamic functional connectivity (Supplementary Table 5). Table 5 Correlations between REM EEG slowing and both NBM functional connectivity with resting-state networks and PPN functional connectivity with the thalamus Frontal REM Slowing NBM-VIS NBM-SMN NBM-DAN NBM-VAN NBM-LIM NBM-FPN NBM-DMN PPN-thal Beta 14.779 5.376 7.096 21.884 4.385 15.995 13.388 16.098 R squared 0.220 0.051 0.068 0.275 0.070 0.159 0.154 0.267 pFDR 0.091 0.534 0.422 0.066 0.422 0.148 0.148 0.010 Central REM Slowing NBM-VIS NBM-SMN NBM-DAN NBM-VAN NBM-LIM NBM-FPN NBM-DMN PPN-thal Beta 11.038 2.326 4.137 13.398 4.871 8.683 9.538 9.192 R squared 0.216 0.038 0.053 0.192 0.097 0.101 0.137 0.181 pFDR 0.159 0.722 0.555 0.159 0.273 0.273 0.204 0.048 Occipital REM Slowing NBM-VIS NBM-SMN NBM-DAN NBM-VAN NBM-LIM NBM-FPN NBM-DMN PPN-thal Beta 16.200 11.251 12.553 19.758 7.514 18.212 15.971 11.751 R squared 0.353 0.199 0.231 0.326 0.232 0.292 0.299 0.262 pFDR 0.034 0.159 0.092 0.034 0.092 0.038 0.038 0.043 Linear regressions with age and sex as covariates. DAN, Dorsal attentional network; DMN, Default mode network; FC, Functional connectivity; FPN, Fronto-parietal network; LIM, Limbic network; NBM. Nucleus Basalis of Meynert; PPN, Pedunculopontine Nucleus; SMN, Somatomotor network; VAN, Ventral attentional network; VIS, Visual network. Discussion In this study, we demonstrate that occipital REM sleep EEG slowing is more pronounced in DLB compared to PD and cognitively unimpaired controls. We show this electrophysiological marker is sleep state-dependent, implicating mechanisms specific to REM sleep physiology. Importantly, we provide evidence that REM slowing is associated with aberrant functional connectivity in key cholinergic hubs, as well as both global and domain-specific cognitive deficits across LBDs. Together, these findings support existing models of the cholinergic contributions to REM EEG characteristics and indicate that REM sleep EEG slowing may serve as a potential non-invasive marker of cholinergic dysfunction in LBDs. REM EEG slowing across Lewy body disorders To our knowledge, this is the first study investigating REM microarchitectural changes in DLB. Our findings converge with prior evidence of posterior EEG abnormalities in DLB during wakefulness, reinforcing the concept that disrupted posterior cortical network function is a core feature of DLB. 1 EEG is a low-cost, non-invasive tool that provides a functional index of neuronal and synaptic integrity. In DLB, conventional wake EEG studies report reduced background reactivity and prominent slow-wave or paroxysmal activity. 40 – 42 Quantitative EEG further shows increased theta and delta power, reduced and variable dominant frequency 43 , 44 , and disrupted functional connectivity relative to AD and controls. 45 , 46 Our work demonstrated that this slowing of posterior EEG is mirrored in REM, but not NREM sleep. Like wakefulness, REM sleep is characterised by desynchronised EEG patterns. In contrast to wakefulness, where cortical activation arises from the integrated activity of multiple neurotransmitter systems (including cholinergic, noradrenergic, serotonergic, and orexinergic pathways) 47 , 48 , the generation of REM sleep relies predominantly on cholinergic drive, as other neurotransmitter systems are largely quiescent during this state. 31 , 47 – 49 By focusing on REM sleep we highlight a state-specific manifestation of this EEG slowing, consistent with the strong dependence of REM sleep on cholinergic neurotransmission. REM EEG slowing as a marker of cholinergic dysfunction Converging animal studies demonstrate that pharmacological, optogenetic, and lesion-based disruption of cholinergic neurons diminishes REM sleep while selectively enhancing EEG delta activity. 50 – 52 Given cholinergic dysfunction and degeneration play a significant role in LBDs 15 , 53 – 56 our results suggest that REM EEG slowing in LBDs is associated with functional, but not structural changes in the NBM and PPN. While structural changes in the NBM have been associated with a lower proportion of REM sleep in amnestic MCI 57 , our findings underscore that disruptions of cholinergic activity may occur even in the absence of overt atrophy and aligns with evidence suggesting that functional dysregulation of neural networks may precede irreversible cell loss. 58 This is consistent with previous studies showing that slowing of REM may occur in people with isolated REM sleep behaviour disorder and can predict neurodegeneration in this cohort. 59 Together, our findings suggest that REM sleep EEG slowing may provide an early and sensitive functional biomarker of progressive cholinergic impairment in Lewy body disorders, with potential to distinguish subtypes where cholinergic deficits are most prominent. REM EEG slowing and cognition We also found that REM EEG slowing correlates with performance across multiple neuropsychological domains, suggesting that it may index clinically meaningful cognitive dysfunction. Prior work has associated REM sleep abnormalities, specifically the presence of RBD, with impaired attention, memory, and executive function in LBDs. 60 , 61 Greater REM slowing in parietal and occipital regions has also been shown to be associated with non-amnestic MCI 62 , which is a risk factor for the future development of DLB. 63 Our findings extend this literature by linking REM EEG spectral slowing specifically to cognition in LBDs, strengthening the case that altered REM physiology is associated with cognitive decline. Whether these findings linking REM sleep changes and cognitive performance in LBD are associative or causative is unknown. The cholinergic system which supports attention, working memory and executive function 64 , 65 , may underlie the associations we observed between these domains and occipital REM slowing. This may argue for the REM findings simply acting as a surrogate for more generalised impairments in the cholinergic circuitry. However, REM sleep itself has been shown to be critical for supporting cognition, with roles implicated in memory consolidation, synaptic plasticity, and integration of learning. 66 , 67 Therefore, REM sleep alterations in LBDs can also conceivably disrupt these processes, thereby exacerbating cognitive symptoms. This raises the possibility that REM EEG slowing is not only a biomarker of underlying cholinergic impairment but could be a candidate mechanism through which sleep disruption directly contributes to cognitive decline in Lewy body disease. Strengths, limitations and future directions A key strength of the study is the use of a well-characterized LBD cohort with diagnoses supported by detailed clinical evaluation and polysomnography. The integration of multimodal data, including cognitive testing and MRI, allowed us to explore both structural and functional substrates of REM EEG slowing. Several limitations should also be acknowledged. First, our sample size, was modest, which may limit generalizability and precluded more detailed stratification (e.g., by cognitive stage in the fMRI data or medication subgroups). Second, while we interpret REM slowing as a marker of cholinergic dysfunction, direct measures of cholinergic activity (e.g., PET imaging with cholinergic tracers or pharmacological challenge paradigms) were not available in this cohort. Third, although MRI and EEG were acquired within six months, the cross-sectional design limits inferences about temporal relationships or progression. Future work should aim to validate these findings in larger, longitudinal cohorts to establish whether REM slowing predicts clinical outcomes, such as cognitive decline or treatment response. Combining EEG measures with molecular imaging of cholinergic systems and structural MRI could help to disentangle functional from structural contributions. While our scalp EEG methods captured broad spatial differences, future work using high-density EEG may provide improved spatial resolution to delineate regional patterns of EEG abnormalities during REM sleep. Finally, intervention studies testing whether cholinesterase inhibitors normalize REM EEG slowing in LBDs would be particularly informative, offering direct evidence for its utility as a treatment-sensitive biomarker. Conclusion In summary, we show that REM sleep EEG slowing in posterior regions is a feature in DLB and linked to both cholinergic dysfunction and cognitive impairment across LBDs. By highlighting a state-dependent signature of Lewy body pathology, our findings suggest that REM slowing may serve as a transdiagnostic biomarker of cholinergic dysfunction, offering both mechanistic insight and potential clinical utility for early detection, prognosis, and treatment monitoring in Lewy body disorders generally. Methods Participants Participants with a clinical diagnosis of DLB or PD were recruited from the Parkinson’s Disease Research Clinic, University of Sydney, along with age-matched controls having provided informed written consent in accordance with the Declaration of Helsinki and the University of Sydney Human Research Ethics committee approved this research study (HREC number 2013/HE000945). As this study was not conducted as a clinical trial, no clinical trial registration number is applicable. All participants were assessed by a specialist neurologist who applied current clinical criteria 1 , 39 to confirm the diagnosis of PD or DLB. Control participants had no evidence of cognitive impairment based on formal neuropsychological testing and were screened to exclude sleep, psychiatric, or neurological disorders. None of the control participants were receiving serotonergic or noradrenergic reuptake inhibitors or antipsychotic medications at the time of assessment. All assessments, including polysomnography (PSG) and MRI scans, were conducted while participants remained on their usual anti-parkinsonian medications. Dopaminergic dose equivalents were calculated following standard conversion formulas (mg/day). 68 Polysomnographic data Overnight, in-laboratory PSG was conducted at the Woolcock Institute of Medical Research, Sydney. EEG was recorded using the international 10–20 system, including referential derivations at F3, F4, C3, C4, O1, and O2, each referenced to the contralateral mastoid (M1 or M2). This data was collected using one of three clinical acquisition systems, with sampling rates of 512 Hz (Sandman Elite), 256 Hz (Compumedics Profusion 4), or 200 Hz (Respironics Alice-5), depending on the platform in use at the time of recording. In addition to EEG, standard PSG channels included bilateral electro-oculography (EOG), submental electromyography (chin EMG), electrocardiography (ECG), nasal pressure transducer for airflow, thoracic and abdominal inductance belts to assess respiratory effort, body position sensors, finger pulse oximetry to monitor oxygen saturation, and bilateral anterior tibialis electromyography for limb movement detection. Sleep stages were manually scored in 30-second epochs according to the criteria established by the American Academy of Sleep Medicine (AASM). 69 EEG pre-processing and power spectral analysis To monitor the EEG signals, the hardware filters were set at 0.3 Hz (high-pass filter) and 35 Hz (low-pass filter). A 50-Hz notch filter was used for power line noise reduction. Polysomnographic EEG recordings were then exported in European Data Format (EDF) for standardization and further analysis. Sleep stage annotations were synchronized and formatted facilitating alignment of raw EEG data with scored sleep stages. Each full-night PSG recording underwent automated EEG artifact detection followed by visual inspection to ensure signal integrity. Artifacts were identified using a validated algorithm that flagged contaminated EEG segments at a 5-second epoch resolution, based on established amplitude and frequency threshold parameters. 70 Identified epochs with significant artifacts were excluded from all subsequent analyses. Quantitative EEG analysis was performed on artifact-free segments using a conventional fast Fourier transform (FFT) approach. Power spectral density was calculated for each non-overlapping 5-second epoch across the following standard frequency bands: delta (0.5–4.5 Hz), theta (4.5–8 Hz), alpha (8–12 Hz), sigma (12–15 Hz), and beta (15–32 Hz). Absolute spectral power was computed for each frequency band and aggregated within 30-second epochs, aligned to sleep staging by averaging up to six corresponding 5-second EEG segments. For periodograms, power was computed in 0.5 Hz frequency bins to optimise frequency resolution for figure presentation. Analyses were primarily conducted using derivations from the left hemisphere at frontal (F3-M2), central (C3-M2), and occipital (O1-M2) sites. In cases where these channels contained excessive artifact and did not meet quality criteria, homologous right hemisphere electrodes (F4-M1, C4-M1, O2-M1) were substituted. To quantify cortical activity during REM sleep, weighted mean power across artifact-free REM epochs was calculated for each frequency band. EEG slowing during REM was operationalized using a slowing ratio defined as the sum of delta and theta power divided by the sum of alpha, sigma, and beta power: [(δ + θ) / (α + σ + β)]. 38,62 Clinical and neuropsychological assessments All participants underwent a comprehensive clinical assessment conducted by a specialist neurologist within 12 months of their PSG (mean = 35.86 days, SD = 22.65). The Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) was utilised to assess non-motor experiences of daily living, motor experiences of daily living, and motor symptom severity. 71 A detailed medical history was obtained via a semi-structured clinical interview, which included review of current medications and relevant comorbidities. Subjective sleep quality and daytime sleepiness were assessed using validated questionnaires. The Epworth Sleepiness Scale (ESS) was administered to quantify excessive daytime sleepiness, with higher scores indicating greater sleep propensity. 72 Nocturnal and daytime sleep-related symptoms were further evaluated using the SCales for Outcomes in Parkinson’s Disease – Sleep (SCOPA-Sleep) questionnaire, comprising two subscales: SCOPA-Sleep Nocturnal, which assesses nighttime sleep disturbances, and SCOPA-Sleep Daytime, which captures daytime sleepiness and fatigue. 73 Participants completed a standardized neuropsychological assessment administered in a single session. The battery was selected to capture a broad range of cognitive domains, including processing speed, attention, memory, language, and executive functioning. As part of the assessment, the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were administered to provide a global index of cognitive function. Processing speed was evaluated using the Trail Making Test Part A (completion time in seconds), which involves sequentially connecting numbered targets and where longer times indicate slower performance. Attention and immediate mental manipulation were assessed via the Mental Control subtest from the Wechsler Memory Scale, which includes tasks such as forward and backward counting (maximum score = 24). Verbal episodic memory was evaluated using the Logical Memory subtest (delayed percentage retention after 25–35 minutes, maximum = 100%), while non-verbal memory was assessed with the three-minute delayed recall of the Rey-Osterrieth Complex Figure (maximum score = 36). Executive function was assessed using several measures. Verbal fluency was indexed via the Controlled Oral Word Association Test (COWAT), which requires the generation of as many words as possible beginning with the letters F, A, and S across three one-minute trials. Cognitive flexibility and set-shifting were assessed using the Trail Making Test Part B (seconds to completion), where participants alternate between connecting numbers and letters. Inhibitory control was measured with the Inhibition-Switching subtask of the Delis-Kaplan Executive Function System (D-KEFS) Stroop task, which challenges participants to suppress automatic word-reading responses. Visuospatial ability was assessed using the Clock Drawing Task, which required participants to draw a clock face showing a specific time. Performance was scored out of a maximum of 10 points with higher scores reflecting better visuospatial construction and planning abilities. MRI acquisition All participants underwent a whole brain structural T1-weighted MRI scan and resting state blood oxygen level dependent (BOLD) functional scan. Imaging data was obtained on a 3-Tesla MRI scanner (General Electric). Sagittal 3D T1-weighted structural images were acquired with an echo time (TE) = 2.7 ms, repetition time (TR) = 7.2 ms, an acquisition matrix of 256 x 256, 200 slices and a slice thickness = 1mm. The acquisition parameters for the T2*-weighted echo-planar functional scans included TE = 36 ms, TR = 3000 ms, flip angle = 90°, field of view = 220 mm, slice thickness = 3 mm and raw voxel size = 3.75 × 3.75 × 3 mm. The total duration of resting-state scan was approximately 7 minutes during which participants were instructed to lie awake with their eyes closed. Structural imaging preprocessing and volumetric analysis T1-weighted images were processed using the Computational Anatomy Toolbox (CAT12; http://www.neuro.uni-jena.de/cat/ ) implemented within SPM12 ( http://www.fil.ion.ucl.ac.uk/spm/software/spm12 ) on the MATLAB R2023a platform (MathWorks, Natick, MA). CAT12 was selected for its robust and efficient segmentation capabilities, offering enhanced accuracy in volumetric brain measurements relative to other available tools. Only T1-weighted scans that achieved CAT12 automated QC ≥ B + were included in the analysis. Grey matter volumes of the nucleus basalis of Meynert (NBM) were extracted in each participant’s native space using the standard CAT12 pipeline and delineated using the Julich-Brain cytoarchitectonic atlas. 74 , 75 This region of interest corresponded to the same anatomical maps employed as a seed in the functional connectivity analysis, ensuring consistency between structural and functional measures. Volumes for the NBM were then adjusted for estimated total intracranial volume using the residual correction method. 76 Functional imaging preprocessing and denoising Preprocessing of fMRI data was performed using fMRIprep 21.0.2 77 , a standardised and validated pipeline that integrates functions from established toolboxes. Major preprocessing steps included intensity non-uniformity correction, skull stripping, tissue segmentation, co-registration, normalisation, resampling for spatial alignment across participants and confound estimation. Visual quality control reports were generated for each participant to ensure preprocessing accuracy. The pre-processed functional data were then passed through fMRIDenoise ( https://github.com/compneuro-ncu/fmridenoise ), which involved regressing out motion parameters and signals from the cerebrospinal fluid and white matter. Temporal filtering was applied with a high-pass band filter (0.01 Hz) to remove low-frequency drifts and a low-pass band filter (0.1 Hz) to exclude high-frequency physiological noise. Further details are outlined in Supplementary Martial. Seed-based functional connectivity analysis The NBM and PPN were selected a priori as seeds of interest to determine whether alterations in functional connectivity in these cholinergic hub regions (the NBM and several canonical resting-state networks; the PPN to the thalamus) are associated with REM EEG slowing in LBDs. The mean BOLD signal time series were extracted from resting-state fMRI data using 400 cortical regions using the Schaefer parcellation 78 , 54 subcortical regions from the Tian parcellation 79 , and a probabilistic anatomical map of the NBM derived from microscopic delineations of 10 postmortem human brains. 80 Functional connectivity matrices were computed for each participant by calculating the pairwise Pearson correlation coefficients between the time series of all regions, followed by Fisher z-transformation. The 400 cortical parcels were assigned to one of 17 intrinsic functional networks derived from resting-state fMRI data in approximately 1,000 participants, which represent subdivisions of a lower-resolution 7-network model. 81 For the current analysis, each parcel was grouped into the seven broader resting-state networks. Seed-network connectivity was calculated as the average connectivity between each seed region and all cortical parcels within a network, yielding a single value representing the overall connectivity strength. Functional connectivity between the PPN and the thalamus was also examined to determine whether disrupted coupling between these regions is associated with REM EEG slowing in LBDs. Further details on structural and functional imaging preprocessing and analysis can be found in Supplementary Material. Statistical analysis All statistical analyses were conducted using RStudio (v4.3.1) 82 and Python (v3.10). Demographic comparisons across groups were performed using one-way ANOVA for continuous variables and chi-square tests for categorical variables, where appropriate. To examine group differences in sleep macroarchitecture, REM EEG measures, neuropsychological outcomes and MRI-derived structural brain measures, we conducted generalized linear models with diagnostic group as the fixed factor and age and sex included as covariates. F-statistics for the diagnosis term were extracted to evaluate the overall group effect. To assess the significance of these F-statistics without assuming parametric distributions, permutation testing was applied: the diagnostic group labels were randomly permuted 10,000 times, and F-statistics were recomputed for each permutation. Empirical p-values were calculated as the proportion of permuted F-statistics exceeding the observed statistic. For post hoc comparisons, we applied a residual-based pairwise permutation test. First, residuals were obtained from a linear model that adjusted each variable for age and sex. Differences in residual means between diagnostic pairs (HC vs PD, HC vs DLB, PD vs DLB) were computed. A non-parametric test assessed the significance of each pairwise difference (10,000 permutations) allowing inference on adjusted group differences while avoiding reliance on normality assumptions. To investigate REM slowing associations, we used linear regression models. Separate models were run for each neuroimaging measure (including both volumetric and functional connectivity metrics) and demographic, clinical and cognitive measures. Continuous demographic, clinical and cognitive variables were z-scored prior to regression to allow direct comparison of effect sizes across measures. This normalization expresses each regression coefficient (β) in standard deviation units, providing standardized beta values that indicate the strength and direction of associations between the REM EEG slowing ratio and each predictor variable. Regression models with neuroimaging and cognition as predictors included age and sex as covariates, and standardized beta coefficients were reported. All statistical tests were two-tailed, with significance set at p < 0.05, and multiple comparisons were corrected for using the Benjamini-Hochberg procedure to control for the false discovery rate (FDR) with q < 0.05, where appropriate. Declarations Competing interests All authors declare no financial or non-financial competing interests. Funding JA and AI are recipients of the Australian Postgraduate Award. LC is the recipient of the Bierzonski Burczyk Foundation Postgraduate Research Scholarship. AK is supported by the Australian Rotary Health of Belconnen 50th Anniversary PhD scholarship (SC4968). ALD is supported by an NHMRC fellowship (2008001). SLN is supported by the Dementia Leadership Fellowship (APP1135639). RRG is supported by a National Health and Medical Research Council Leadership Fellowship (1197439). SJGL is supported by a National Health and Medical Research Council Leadership Fellowship (1195830) and has received research funding from the Michael J. Fox Foundation and the Australian Research Council, as well as consulting for Pharmaxis Ltd. EM is supported by a National Health and Medical Research Council Emerging Leadership Fellowship (2008565), the U.S. Department of Defense Congressionally Directed Medical Research Program Early Investigator Grant (PD220061) and the University of Sydney Horizon Fellowship. Author Contribution Author roles included: conception and design of the study (JA, RRG, SJGL, EM); acquisition and analysis of data (JA, LC, AK, AI, GC); and final approval of manuscript and/or figures (JA, LC, AK, AI, GC, AL, ALD, SLN, RRG, SJGL, EM). All authors declare accountability for the work contained in the manuscript. Acknowledgement We would like to acknowledge participants and caregivers for their efforts participating in this study. Data Availability Data supporting the findings of this study are available from the corresponding author, upon reasonable request. References McKeith, I. G. et al. Diagnosis and management of dementia with Lewy bodies. Neurology 89, 88–100 (2017). Emre, M. et al. Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Mov. Disord. 22, 1689–1707 (2007). Hely, M. A., Reid, W. G. J., Adena, M. A., Halliday, G. M. & Morris, J. G. L. 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15:07:43","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194294,"visible":true,"origin":"","legend":"","description":"","filename":"4b6a4acc9f064b6ea8b3ec8d871787f91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8131680/v1/5a5292775a3c9a2718f30d74.xml"},{"id":97272188,"identity":"91566d7e-0ade-4fd9-b885-92e7379e531f","added_by":"auto","created_at":"2025-12-02 15:07:43","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":210589,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8131680/v1/7acc50ad7b2bfa8102c55ad0.html"},{"id":97368598,"identity":"da80499a-6daf-4c36-b8e1-ca376c445206","added_by":"auto","created_at":"2025-12-03 16:22:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116630,"visible":true,"origin":"","legend":"\u003cp\u003eGroup comparisons of occipital REM spectral power differences in DLB, PD and HC. (a) Bar graph of log transformed absolute REM spectral power frequency bands (δ, θ, α, σ, β), error bars represent standard deviation of the mean; (b) bar graph showing the comparison of REM EEG slowing ratios, defined as (δ+θ)/(α+σ+β), between groups, error bars represent standard deviation of the mean, and; (c) visualisation of log transformed absolute REM spectral power differences between groups with absolute REM power calculated at every 0.5Hz across frequencies, shadows represent standard deviation of the mean.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8131680/v1/ba68bc60cc49497262df99cc.png"},{"id":97369375,"identity":"f336d5de-1013-4819-88e1-45745d3407bd","added_by":"auto","created_at":"2025-12-03 16:24:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147903,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Correlations between occipital REM EEG slowing and clinical, demographic and medication variables across LBDs. Standardized beta coefficients from linear regression models are plotted for each variable. (b) Correlations between occipital REM EEG slowing and cognitive sub-domains across LBDs. Standardized beta coefficients from linear regression models are plotted for each cognitive variable, ordered by strength of association with occipital REM slowing. Models were corrected for age and sex. All predictor variables were z-scored prior to analysis to enable comparison across measures; black dots denote associations surviving FDR correction (p\u0026lt;0.05). MMSE, The Mini Mental State Examination; MoCA, Montreal Cognitive Assessment; RAVLT, Rey Auditory Verbal Learning Test.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8131680/v1/9384046f25d5bdf91b63d5c5.png"},{"id":97368421,"identity":"b64aac14-e4d5-48e0-bc95-bd1d4c81cb76","added_by":"auto","created_at":"2025-12-03 16:22:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":231059,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Diagram describing the cholinergic projections in the human brain including the pedunculopontine nucleus to the thalamus and the nucleus basalis of Meynert which serves as the primary source of cholinergic projections to the cortex. (b) \u0026nbsp;Heatmap displaying the beta coefficients of the association between REM slowing and functional connectivity of the NBM to resting-state networks and PPN to the thalamus in LBDs. Only statistically significant correlations after FDR correction (pFDR \u0026lt; 0.05) are shown. DAN, Dorsal attentional network; DMN, Default mode network; FPN, Fronto-parietal network; LIM, Limbic network; NBM. Nucleus Basalis of Meynert; PPN, Pedunculopontine Nucleus; REM, Rapid Eye Movement Sleep; SMN, Somatomotor network; VAN, Ventral attentional network; VIS, Visual network.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8131680/v1/9da92f6ff0115e1f2a0869b9.png"},{"id":97664707,"identity":"ae7cd7b4-fcbd-46f5-87b7-96fac944290c","added_by":"auto","created_at":"2025-12-08 09:13:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1869767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8131680/v1/bbedcc77-ec3d-4732-b822-f21209bc72be.pdf"},{"id":97272176,"identity":"0bb2170f-46c2-435d-b983-43169dfb30a3","added_by":"auto","created_at":"2025-12-02 15:07:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":44565,"visible":true,"origin":"","legend":"","description":"","filename":"REMEEGSlowingAndersonJSUPP.docx","url":"https://assets-eu.researchsquare.com/files/rs-8131680/v1/f04983120763e1ab32d18e3b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Electroencephalographic slowing during REM sleep is a marker of cholinergic dysfunction in Lewy body disorders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLewy body disorders (LBDs), including Parkinson\u0026rsquo;s disease (PD) and dementia with Lewy bodies (DLB), are characterized pathologically by the aggregation of intraneuronal α-synuclein and represent the second most common cause of neurodegeneration in older adults. In addition to their neuropathology, these LBDs share overlapping clinical features of cognitive, neuropsychiatric, sleep, motor and autonomic symptoms\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Although motor deficits initially dominate the clinical picture of PD, most patients will exhibit progressive cognitive impairment throughout the disease course, with approximately 80% ultimately progressing to dementia.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Similarly, sleep-wake disturbances are a highly prevalent non-motor feature of the LBDs, presenting in prodromal\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and early stages\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e with increasing frequency as the disease progresses\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Both cognitive dysfunction and sleep-wake disturbances severely impact patient and caregiver quality of life\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, there is significant heterogeneity across LBD disease progression with some patients manifesting a more aggressive course.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Therefore, understanding the biological basis for disease progression and developing non-invasive, clinically accessible markers that can predict disease trajectory in an individual, are critically important.\u003c/p\u003e\u003cp\u003eNeuropathological and recent neuroimaging studies have highlighted dysfunction in cholinergic circuitry as being central to the development of cognitive impairment in LBDs.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Prior work has predominantly focused on the basal forebrain cholinergic system, in particular the nucleus basalis of Meynert (NBM), which provides the majority of cholinergic input to the cortex through its widespread projections.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Early degeneration of the NBM and its cortical projections occur in LBDs\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and are linked to cognitive decline.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Additionally, the pedunculopontine nucleus (PPN), located in the dorsal pons, contains cholinergic neurons that project primarily to the basal ganglia, thalamus, lower brainstem and spinal cord.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Lewy pathology and neuronal loss occurs within the PPN in LBDs,\u003csup\u003e23,24\u003c/sup\u003e and are associated with deficits in motor control\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and cognition.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Molecular imaging markers have revealed cortical posterior cholinergic dysfunction in LBDs which are linked to the presence of dementia\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Very recently changes in these cholinergic nuclei have also been shown to explain metabolic deficits in LBDs, especially in posterior regions which are a supportive marker of DLB.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eCholinergic circuits also play a central role in the regulation of sleep and wakefulness. Previous studies have identified that cholinergic activity is highest during REM sleep, when most other neurotransmitter systems are relatively quiescent.\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Experimental work in animal models indicates that REM sleep generation and maintenance depend on ascending cholinergic inputs from the PPN, with cortical desynchronization driven by projections from the basal forebrain.\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Therefore, dysfunction in cholinergic circuitry observed in LBDs is likely to produce electrophysiological changes in brain activity during REM sleep. At the sleep macro-architectural level, a lower percentage of REM sleep time has been shown in DLB,\u003csup\u003e36\u003c/sup\u003e and a recent meta-analysis demonstrated both decreased REM sleep and increased REM latency in PD compared to controls.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Moreover, electroencephalographic (EEG) slowing in REM sleep predicts subsequent dementia in people with PD.\u003csup\u003e38\u003c/sup\u003e Collectively, these findings suggest that EEG activity during REM sleep may serve as a unique window to investigate cholinergic integrity in LBDs.\u003c/p\u003e\u003cp\u003eIn this study, we sought to characterize REM sleep EEG alterations in PD and DLB and investigate their associations with neuroimaging markers of the cholinergic system, along with cognitive function. First, we explored differences in REM EEG spectral power between DLB, PD and cognitively unimpaired controls. Next, we examined the association of REM EEG alterations with structural and functional MRI measures of the NBM and PPN, and with cognitive performance across LBDs. We hypothesized that REM EEG slowing particularly in posterior regions, would be most pronounced in DLB, followed by PD compared to controls; and REM slowing would correlate with cholinergic hub dysfunction and poorer cognitive performance across LBDs.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipant characteristics\u003c/h2\u003e\n \u003cp\u003eOne hundred and four participants were included in this study, including 36 diagnosed with PD\u003csup\u003e39\u003c/sup\u003e, 24 diagnosed with DLB\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and 44 control participants without objective cognitive impairment. Participant demographics, clinical and questionnaire data are summarised in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. On average, the DLB participants were 10 years older than the PD and 7 years older than the control groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared to controls, the DLB participants had a higher proportion of males (79 vs 48%, p\u0026thinsp;=\u0026thinsp;0.040), has 2 years less education (12\u0026thinsp;\u0026plusmn;\u0026thinsp;4 vs 14\u0026thinsp;\u0026plusmn;\u0026thinsp;3 years p\u0026thinsp;=\u0026thinsp;0.029) and reported higher score on the Epworth Sleepiness Scale (10\u0026thinsp;\u0026plusmn;\u0026thinsp;6 vs 6\u0026thinsp;\u0026plusmn;\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.005) and SCOPA Sleep Day (p\u0026thinsp;=\u0026thinsp;0.003). DLB participants also scored higher on all sections of the MDS Unified Parkinson\u0026rsquo;s Disease Rating Scale (MDS-UPDRS) compared to PD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Five patients in the both the PD and DLB group were receiving antidepressants, while 9 DLB patients and 1 PD patient were taking cholinesterase inhibitors. Detailed neuropsychological testing was also performed, with group comparisons presented in Supplementary Table\u0026nbsp;1. As expected, DLB participants performed poorer in global cognition and across all domains in neuropsychological testing compared to PD and control participants.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGroup Differences in Demographics, Clinical Data and Self-report Questionnaires\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eClinical Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControls (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDLB (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEducation (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.029\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMale (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (61.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (79.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.040\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMMSE (/30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestionnaires\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEpworth Sleepiness Scale (/24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSCOPA Sleep Night\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSCOPA Sleep Day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRBD Screening Questionnaire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDS-UPDRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMDS-UPDRS I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMDS-UPDRS II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMDS-UPDRS III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLEDD total (mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e473\u0026thinsp;\u0026plusmn;\u0026thinsp;439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274\u0026thinsp;\u0026plusmn;\u0026thinsp;455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBenzodiazepines (% on)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAntidepressants (% on)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.012\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCholinesterase Inhibitors (% on)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003eDemographic, clinical and questionnaire data\u003c/p\u003e\n \u003cp\u003eValues are displayed as number (proportion as a percentage) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/p\u003e\n \u003cp\u003eP values were calculated using one-way ANOVAs and chi-squared tests where applicable. DLB, Dementia with Lewy Bodies; HC, Healthy Controls; LEDD, Levodopa Equivalent Daily Dose; MDS-UPDRS I, Unified Parkinson\u0026rsquo;s Disease Rating Scale Section I; MDS-UPDRS II, Unified Parkinson\u0026rsquo;s Disease Rating Scale Section II; MDS-UPDRS III, Unified Parkinson\u0026rsquo;s Disease Rating Scale Section III; MMSE; The Mini Mental State Examination; PD, Parkinson\u0026rsquo;s Disease; RBD, REM Sleep Behaviour Disorder; SCOPA, Scales for Outcomes in Parkinson\u0026rsquo;s disease.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003ePairwise comparisons: significant differences between PD and HC\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003ePairwise comparisons: significant differences between DLB and HC\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003ePairwise comparisons: significant differences between DLB and PD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSleep macroarchitecture\u003c/h3\u003e\n\u003cp\u003eTo characterise sleep stages and timings within and between groups, sleep macroarchitecture measures are reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. DLB and PD groups had shorter sleep latency and a reduced proportion of REM sleep compared to controls. Total NREM sleep duration did not differ between groups, although some differences were observed in individual NREM sleep stages. There were no significant group differences in respiratory measures.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGroup Differences in Sleep Macro-architecture\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eControls (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDLB (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epFDR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTime in bed, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e486.2\u0026thinsp;\u0026plusmn;\u0026thinsp;65.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e457.1\u0026thinsp;\u0026plusmn;\u0026thinsp;45.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450.2\u0026thinsp;\u0026plusmn;\u0026thinsp;44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal sleep time, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e351.2\u0026thinsp;\u0026plusmn;\u0026thinsp;67.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365.8\u0026thinsp;\u0026plusmn;\u0026thinsp;65.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e328.0\u0026thinsp;\u0026plusmn;\u0026thinsp;113.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWASO, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e89.0\u0026thinsp;\u0026plusmn;\u0026thinsp;56.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.3\u0026thinsp;\u0026plusmn;\u0026thinsp;46.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.3\u0026thinsp;\u0026plusmn;\u0026thinsp;84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSleep efficiency, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e72.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSleep latency, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e48.8\u0026thinsp;\u0026plusmn;\u0026thinsp;65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.6\u0026thinsp;\u0026plusmn;\u0026thinsp;31.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eREM onset latency, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e129.4\u0026thinsp;\u0026plusmn;\u0026thinsp;82.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.8\u0026thinsp;\u0026plusmn;\u0026thinsp;90.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179.2\u0026thinsp;\u0026plusmn;\u0026thinsp;116.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Stages\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eN1% TST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eN2% TST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e44.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eN3% TST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eREM % TST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNREM sleep duration, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e278.8\u0026thinsp;\u0026plusmn;\u0026thinsp;53.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308.5\u0026thinsp;\u0026plusmn;\u0026thinsp;61.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e289.3\u0026thinsp;\u0026plusmn;\u0026thinsp;97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespiratory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAHI, events/hr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRDI, events/hr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMinimum SpO2 during sleep, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e85.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eArousals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal arousal index, arousals/hr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003ePLM arousal index, arousals/hr\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSleep macro-architecture data.\u003c/p\u003e\n\u003cp\u003eValues are displayed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/p\u003e\n\u003cp\u003eP values were calculated using non-parametric permutation testing. AHI, Apnea Hypopnea Index; DLB, Dementia with Lewy Bodies; NREM, Non-rapid Eye Movement Sleep; N1, Sleep stage 1; N2, Sleep stage 2; N3, Sleep Stage 3; RDI, Respiratory Disturbance Index; PLM, Periodic Limb Movement; RERA, Respiratory Effort-Related Arousal; REM, Rapid Eye Movement Sleep; PD, Parkinson\u0026rsquo;s Disease; WASO, Wake After Sleep Onset.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003ePairwise comparisons: significant differences between PD and HC\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003ePairwise comparisons: significant differences between DLB and HC\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003ePairwise comparisons: significant differences between DLB and PD\u003c/p\u003e\n\u003ch3\u003eGreater REM EEG slowing in posterior regions in DLB compared to PD and controls\u003c/h3\u003e\n\u003cp\u003eAfter adjusting for age and sex, there were significant overall group differences in REM EEG slowing in occipital regions (pFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with trends in the same direction for frontal and central regions (both pFDR\u0026thinsp;=\u0026thinsp;0.057). Post-hoc pairwise tests showed greater occipital REM slowing in DLB than in both PD and controls (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). At the frequency-band level, the occipital EEG slowing pattern was driven primarily by increased absolute delta power in DLB (pFDR\u0026thinsp;=\u0026thinsp;0.030; DLB\u0026thinsp;\u0026gt;\u0026thinsp;PD and DLB\u0026thinsp;\u0026gt;\u0026thinsp;HC), with numerically higher theta that did not survive correction.\u003c/p\u003e\n\u003cp\u003eFrontal beta power during REM was higher in both PD and DLB relative to controls (pFDR\u0026thinsp;=\u0026thinsp;0.030). This beta elevation was also present in NREM, where frontal beta was higher in PD and DLB versus controls and DLB\u0026thinsp;\u0026gt;\u0026thinsp;PD (pFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (see state specificity sensitivity analysis details below). Group comparisons for REM EEG power and slowing ratios are detailed in Supplementary Table\u0026nbsp;2.\u003c/p\u003e\n\u003cp\u003eTo test state specificity, we examined NREM EEG slowing ratios. PD showed reduced occipital NREM slowing relative to controls (pFDR\u0026thinsp;=\u0026thinsp;0.015), whereas DLB did not differ from controls and DLB did not differ from PD (Supplementary Table\u0026nbsp;3). To account for any potential effects of cholinergic medication, a sensitivity analysis was performed including cholinesterase inhibitor use as a covariate alongside age and sex. Occipital REM EEG slowing remained significantly greater in DLB than PD and controls (pFDR\u0026thinsp;=\u0026thinsp;0.003, see Supplementary Table\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOccipital REM EEG slowing is associated with motor impairment, lower global cognition and poorer performance in executive function\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe next examined whether occipital REM EEG slowing was related to demographic, clinical, and medication variables across LBD participants, PD and DLB combined. Higher occipital REM slowing was significantly associated with greater motor impairment as indexed by the MDS-UPDRS III (pFDR\u0026thinsp;=\u0026thinsp;0.034) and lower performance in global cognition (MoCA, pFDR\u0026thinsp;=\u0026thinsp;0.002; MMSE, pFDR\u0026thinsp;=\u0026thinsp;0.001), whereas no significant associations were observed with age, sex, years of education, or medication use (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelations Between Occipital REM EEG Slowing and Clinical, Demographics and Medication use Across LBDs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR squared\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epFDR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDS-UPDRS III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEDD total (mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCholinesterase Inhibitors (% on)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal Cognition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eP values were calculated using linear regression models. LEDD, Levodopa Equivalent Daily Dose; MMSE; The Mini Mental State Examination; MoCA; Montreal Cognitive Assessment.\u003c/p\u003e\n\u003cp\u003eWe next examined whether occipital REM EEG slowing was related to performance of specific cognitive domains within all LBD participants. After controlling for age and sex, occipital REM slowing was associated with poorer performance in the executive function domain, associated with poorer performance on both the Stroop Inhibition/Switching task (pFDR\u0026thinsp;=\u0026thinsp;0.007) and Trail Making Test Part B (pFDR\u0026thinsp;=\u0026thinsp;0.001). Occipital REM slowing was also associated with deficits in visuospatial performance (pFDR\u0026thinsp;=\u0026thinsp;0.009) during the Clock Drawing task. No other cognitive domains showed significant associations with occipital REM slowing in LBDs after correction for multiple comparisons (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelations Between Occipital REM EEG Slowing and Cognition Across LBDs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR squared\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epFDR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLearning and Memory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogical Memory I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogical Memory II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT A1-A5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExecutive Function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrail Making Test Part B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroop Inhibition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroop Inhibition/Switching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisuospatial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClock Drawing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttention and Working Memory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigit Span Forwards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigit Span Backwards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLanguage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVerbal Fluency \u0026ndash; Letters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVerbal Fluency \u0026ndash; Animals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eP values were calculated using linear regression models. RAVLT, Rey Auditory Verbal Learning Test.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eNBM volume is not associated with occipital REM EEG slowing in LBDs\u003c/h3\u003e\n\u003cp\u003eWithin the LBD group, we next examined whether structural changes in the basal forebrain were related to REM slowing. A subset of 34 LBD (16 DLB and 18 PD) completed a T1-weighted MRI scan within 6 months of their PSG. After adjusting for estimated total intracranial volume, NBM volume was significantly less in DLB (229\u0026thinsp;\u0026plusmn;\u0026thinsp;29 mm\u0026sup3;) compared to PD (266\u0026thinsp;\u0026plusmn;\u0026thinsp;27 mm\u0026sup3;; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, NBM volume was not significantly associated with occipital REM EEG slowing (\u0026beta;=-0.006, p\u0026thinsp;=\u0026thinsp;0.51) across LBDs.\u003c/p\u003e\n\u003ch3\u003eOccipital REM EEG slowing is associated with aberrant functional connectivity in key cholinergic hubs\u003c/h3\u003e\n\u003cp\u003eWe then investigated whether occipital REM slowing was related to functional connectivity of cholinergic nuclei within the subset of 31 LBDs (15 DLB and 16 PD) of whom resting state functional MRI (rsfMRI) was conducted (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Across LBDs, occipital REM slowing was significantly associated with NBM functional connectivity to the visual (pFDR\u0026thinsp;=\u0026thinsp;0.034), ventral attention (pFDR\u0026thinsp;=\u0026thinsp;0.034), frontoparietal (pFDR\u0026thinsp;=\u0026thinsp;0.038) and default mode networks (pFDR\u0026thinsp;=\u0026thinsp;0.038). These findings were unique to occipital REM slowing, with no significant associations found between frontal or central REM slowing and NBM functional connectivity patterns.\u003c/p\u003e\n\u003cp\u003eAdditionally, occipital REM slowing was associated with PPN-thalamic functional connectivity (\u0026beta;\u0026thinsp;=\u0026thinsp;1.75, p\u0026thinsp;=\u0026thinsp;0.043) across LBDs. Unlike the NBM findings, this functional measure was also significantly associated with frontal (\u0026beta;\u0026thinsp;=\u0026thinsp;6.10, p\u0026thinsp;=\u0026thinsp;0.010) and central (\u0026beta;\u0026thinsp;=\u0026thinsp;9.19, p\u0026thinsp;=\u0026thinsp;0.048) REM EEG slowing. Correlations of REM slowing and functional imaging in LBDs are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTo verify the specificity of the findings to cholinergic pathways, we examined the relationship between REM slowing and thalamic to whole brain network resting-state functional connectivity. We found no significant associations between REM slowing and general thalamic functional connectivity (Supplementary Table\u0026nbsp;5).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelations between REM EEG slowing and both NBM functional connectivity with resting-state networks and PPN functional connectivity with the thalamus\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrontal REM Slowing\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNBM-VIS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNBM-SMN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNBM-DAN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNBM-VAN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNBM-LIM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNBM-FPN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNBM-DMN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPN-thal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentral REM Slowing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-VIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-SMN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-DAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-VAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-LIM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-FPN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-DMN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPN-thal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccipital REM Slowing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-VIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-SMN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-DAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-VAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-LIM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-FPN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBM-DMN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPN-thal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLinear regressions with age and sex as covariates. DAN, Dorsal attentional network; DMN, Default mode network; FC, Functional connectivity; FPN, Fronto-parietal network; LIM, Limbic network; NBM. Nucleus Basalis of Meynert; PPN, Pedunculopontine Nucleus; SMN, Somatomotor network; VAN, Ventral attentional network; VIS, Visual network.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrate that occipital REM sleep EEG slowing is more pronounced in DLB compared to PD and cognitively unimpaired controls. We show this electrophysiological marker is sleep state-dependent, implicating mechanisms specific to REM sleep physiology. Importantly, we provide evidence that REM slowing is associated with aberrant functional connectivity in key cholinergic hubs, as well as both global and domain-specific cognitive deficits across LBDs. Together, these findings support existing models of the cholinergic contributions to REM EEG characteristics and indicate that REM sleep EEG slowing may serve as a potential non-invasive marker of cholinergic dysfunction in LBDs.\u003c/p\u003e\n\u003ch3\u003eREM EEG slowing across Lewy body disorders\u003c/h3\u003e\n\u003cp\u003eTo our knowledge, this is the first study investigating REM microarchitectural changes in DLB. Our findings converge with prior evidence of posterior EEG abnormalities in DLB during wakefulness, reinforcing the concept that disrupted posterior cortical network function is a core feature of DLB.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e EEG is a low-cost, non-invasive tool that provides a functional index of neuronal and synaptic integrity. In DLB, conventional wake EEG studies report reduced background reactivity and prominent slow-wave or paroxysmal activity.\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Quantitative EEG further shows increased theta and delta power, reduced and variable dominant frequency\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, and disrupted functional connectivity relative to AD and controls.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e Our work demonstrated that this slowing of posterior EEG is mirrored in REM, but not NREM sleep. Like wakefulness, REM sleep is characterised by desynchronised EEG patterns. In contrast to wakefulness, where cortical activation arises from the integrated activity of multiple neurotransmitter systems (including cholinergic, noradrenergic, serotonergic, and orexinergic pathways)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, the generation of REM sleep relies predominantly on cholinergic drive, as other neurotransmitter systems are largely quiescent during this state.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e By focusing on REM sleep we highlight a state-specific manifestation of this EEG slowing, consistent with the strong dependence of REM sleep on cholinergic neurotransmission.\u003c/p\u003e\n\u003ch3\u003eREM EEG slowing as a marker of cholinergic dysfunction\u003c/h3\u003e\n\u003cp\u003eConverging animal studies demonstrate that pharmacological, optogenetic, and lesion-based disruption of cholinergic neurons diminishes REM sleep while selectively enhancing EEG delta activity.\u003csup\u003e\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e Given cholinergic dysfunction and degeneration play a significant role in LBDs\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan additionalcitationids=\"CR54 CR55\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e our results suggest that REM EEG slowing in LBDs is associated with functional, but not structural changes in the NBM and PPN. While structural changes in the NBM have been associated with a lower proportion of REM sleep in amnestic MCI\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, our findings underscore that disruptions of cholinergic activity may occur even in the absence of overt atrophy and aligns with evidence suggesting that functional dysregulation of neural networks may precede irreversible cell loss.\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e This is consistent with previous studies showing that slowing of REM may occur in people with isolated REM sleep behaviour disorder and can predict neurodegeneration in this cohort.\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e Together, our findings suggest that REM sleep EEG slowing may provide an early and sensitive functional biomarker of progressive cholinergic impairment in Lewy body disorders, with potential to distinguish subtypes where cholinergic deficits are most prominent.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eREM EEG slowing and cognition\u003c/h2\u003e\u003cp\u003eWe also found that REM EEG slowing correlates with performance across multiple neuropsychological domains, suggesting that it may index clinically meaningful cognitive dysfunction. Prior work has associated REM sleep abnormalities, specifically the presence of RBD, with impaired attention, memory, and executive function in LBDs.\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e Greater REM slowing in parietal and occipital regions has also been shown to be associated with non-amnestic MCI\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, which is a risk factor for the future development of DLB.\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e Our findings extend this literature by linking REM EEG spectral slowing specifically to cognition in LBDs, strengthening the case that altered REM physiology is associated with cognitive decline.\u003c/p\u003e\u003cp\u003eWhether these findings linking REM sleep changes and cognitive performance in LBD are associative or causative is unknown. The cholinergic system which supports attention, working memory and executive function\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, may underlie the associations we observed between these domains and occipital REM slowing. This may argue for the REM findings simply acting as a surrogate for more generalised impairments in the cholinergic circuitry. However, REM sleep itself has been shown to be critical for supporting cognition, with roles implicated in memory consolidation, synaptic plasticity, and integration of learning.\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e Therefore, REM sleep alterations in LBDs can also conceivably disrupt these processes, thereby exacerbating cognitive symptoms. This raises the possibility that REM EEG slowing is not only a biomarker of underlying cholinergic impairment but could be a candidate mechanism through which sleep disruption directly contributes to cognitive decline in Lewy body disease.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStrengths, limitations and future directions\u003c/h2\u003e\u003cp\u003eA key strength of the study is the use of a well-characterized LBD cohort with diagnoses supported by detailed clinical evaluation and polysomnography. The integration of multimodal data, including cognitive testing and MRI, allowed us to explore both structural and functional substrates of REM EEG slowing. Several limitations should also be acknowledged. First, our sample size, was modest, which may limit generalizability and precluded more detailed stratification (e.g., by cognitive stage in the fMRI data or medication subgroups). Second, while we interpret REM slowing as a marker of cholinergic dysfunction, direct measures of cholinergic activity (e.g., PET imaging with cholinergic tracers or pharmacological challenge paradigms) were not available in this cohort. Third, although MRI and EEG were acquired within six months, the cross-sectional design limits inferences about temporal relationships or progression.\u003c/p\u003e\u003cp\u003eFuture work should aim to validate these findings in larger, longitudinal cohorts to establish whether REM slowing predicts clinical outcomes, such as cognitive decline or treatment response. Combining EEG measures with molecular imaging of cholinergic systems and structural MRI could help to disentangle functional from structural contributions. While our scalp EEG methods captured broad spatial differences, future work using high-density EEG may provide improved spatial resolution to delineate regional patterns of EEG abnormalities during REM sleep. Finally, intervention studies testing whether cholinesterase inhibitors normalize REM EEG slowing in LBDs would be particularly informative, offering direct evidence for its utility as a treatment-sensitive biomarker.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we show that REM sleep EEG slowing in posterior regions is a feature in DLB and linked to both cholinergic dysfunction and cognitive impairment across LBDs. By highlighting a state-dependent signature of Lewy body pathology, our findings suggest that REM slowing may serve as a transdiagnostic biomarker of cholinergic dysfunction, offering both mechanistic insight and potential clinical utility for early detection, prognosis, and treatment monitoring in Lewy body disorders generally.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003e Participants with a clinical diagnosis of DLB or PD were recruited from the Parkinson’s Disease Research Clinic, University of Sydney, along with age-matched controls having provided informed written consent in accordance with the Declaration of Helsinki and the University of Sydney Human Research Ethics committee approved this research study (HREC number 2013/HE000945). As this study was not conducted as a clinical trial, no clinical trial registration number is applicable. All participants were assessed by a specialist neurologist who applied current clinical criteria\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e to confirm the diagnosis of PD or DLB. Control participants had no evidence of cognitive impairment based on formal neuropsychological testing and were screened to exclude sleep, psychiatric, or neurological disorders. None of the control participants were receiving serotonergic or noradrenergic reuptake inhibitors or antipsychotic medications at the time of assessment. All assessments, including polysomnography (PSG) and MRI scans, were conducted while participants remained on their usual anti-parkinsonian medications. Dopaminergic dose equivalents were calculated following standard conversion formulas (mg/day).\u003csup\u003e68\u003c/sup\u003e\u003c/p\u003e\u003ch2\u003ePolysomnographic data\u003c/h2\u003e\u003cp\u003eOvernight, in-laboratory PSG was conducted at the Woolcock Institute of Medical Research, Sydney. EEG was recorded using the international 10–20 system, including referential derivations at F3, F4, C3, C4, O1, and O2, each referenced to the contralateral mastoid (M1 or M2). This data was collected using one of three clinical acquisition systems, with sampling rates of 512 Hz (Sandman Elite), 256 Hz (Compumedics Profusion 4), or 200 Hz (Respironics Alice-5), depending on the platform in use at the time of recording.\u003c/p\u003e\u003cp\u003eIn addition to EEG, standard PSG channels included bilateral electro-oculography (EOG), submental electromyography (chin EMG), electrocardiography (ECG), nasal pressure transducer for airflow, thoracic and abdominal inductance belts to assess respiratory effort, body position sensors, finger pulse oximetry to monitor oxygen saturation, and bilateral anterior tibialis electromyography for limb movement detection. Sleep stages were manually scored in 30-second epochs according to the criteria established by the American Academy of Sleep Medicine (AASM).\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003ch2\u003eEEG pre-processing and power spectral analysis\u003c/h2\u003e\u003cp\u003eTo monitor the EEG signals, the hardware filters were set at 0.3 Hz (high-pass filter) and 35 Hz (low-pass filter). A 50-Hz notch filter was used for power line noise reduction. Polysomnographic EEG recordings were then exported in European Data Format (EDF) for standardization and further analysis. Sleep stage annotations were synchronized and formatted facilitating alignment of raw EEG data with scored sleep stages. Each full-night PSG recording underwent automated EEG artifact detection followed by visual inspection to ensure signal integrity. Artifacts were identified using a validated algorithm that flagged contaminated EEG segments at a 5-second epoch resolution, based on established amplitude and frequency threshold parameters.\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e Identified epochs with significant artifacts were excluded from all subsequent analyses.\u003c/p\u003e\u003cp\u003eQuantitative EEG analysis was performed on artifact-free segments using a conventional fast Fourier transform (FFT) approach. Power spectral density was calculated for each non-overlapping 5-second epoch across the following standard frequency bands: delta (0.5–4.5 Hz), theta (4.5–8 Hz), alpha (8–12 Hz), sigma (12–15 Hz), and beta (15–32 Hz). Absolute spectral power was computed for each frequency band and aggregated within 30-second epochs, aligned to sleep staging by averaging up to six corresponding 5-second EEG segments. For periodograms, power was computed in 0.5 Hz frequency bins to optimise frequency resolution for figure presentation. Analyses were primarily conducted using derivations from the left hemisphere at frontal (F3-M2), central (C3-M2), and occipital (O1-M2) sites. In cases where these channels contained excessive artifact and did not meet quality criteria, homologous right hemisphere electrodes (F4-M1, C4-M1, O2-M1) were substituted.\u003c/p\u003e\u003cp\u003eTo quantify cortical activity during REM sleep, weighted mean power across artifact-free REM epochs was calculated for each frequency band. EEG slowing during REM was operationalized using a slowing ratio defined as the sum of delta and theta power divided by the sum of alpha, sigma, and beta power: [(δ + θ) / (α + σ + β)].\u003csup\u003e38,62\u003c/sup\u003e\u003c/p\u003e\u003ch2\u003eClinical and neuropsychological assessments\u003c/h2\u003e\u003cp\u003eAll participants underwent a comprehensive clinical assessment conducted by a specialist neurologist within 12 months of their PSG (mean = 35.86 days, SD = 22.65). The Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) was utilised to assess non-motor experiences of daily living, motor experiences of daily living, and motor symptom severity.\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e A detailed medical history was obtained via a semi-structured clinical interview, which included review of current medications and relevant comorbidities.\u003c/p\u003e\u003cp\u003eSubjective sleep quality and daytime sleepiness were assessed using validated questionnaires. The Epworth Sleepiness Scale (ESS) was administered to quantify excessive daytime sleepiness, with higher scores indicating greater sleep propensity.\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e Nocturnal and daytime sleep-related symptoms were further evaluated using the SCales for Outcomes in Parkinson’s Disease – Sleep (SCOPA-Sleep) questionnaire, comprising two subscales: SCOPA-Sleep Nocturnal, which assesses nighttime sleep disturbances, and SCOPA-Sleep Daytime, which captures daytime sleepiness and fatigue.\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eParticipants completed a standardized neuropsychological assessment administered in a single session. The battery was selected to capture a broad range of cognitive domains, including processing speed, attention, memory, language, and executive functioning.\u003c/p\u003e\u003cp\u003eAs part of the assessment, the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were administered to provide a global index of cognitive function. Processing speed was evaluated using the Trail Making Test Part A (completion time in seconds), which involves sequentially connecting numbered targets and where longer times indicate slower performance. Attention and immediate mental manipulation were assessed via the Mental Control subtest from the Wechsler Memory Scale, which includes tasks such as forward and backward counting (maximum score = 24). Verbal episodic memory was evaluated using the Logical Memory subtest (delayed percentage retention after 25–35 minutes, maximum = 100%), while non-verbal memory was assessed with the three-minute delayed recall of the Rey-Osterrieth Complex Figure (maximum score = 36).\u003c/p\u003e\u003cp\u003eExecutive function was assessed using several measures. Verbal fluency was indexed via the Controlled Oral Word Association Test (COWAT), which requires the generation of as many words as possible beginning with the letters F, A, and S across three one-minute trials. Cognitive flexibility and set-shifting were assessed using the Trail Making Test Part B (seconds to completion), where participants alternate between connecting numbers and letters. Inhibitory control was measured with the Inhibition-Switching subtask of the Delis-Kaplan Executive Function System (D-KEFS) Stroop task, which challenges participants to suppress automatic word-reading responses. Visuospatial ability was assessed using the Clock Drawing Task, which required participants to draw a clock face showing a specific time. Performance was scored out of a maximum of 10 points with higher scores reflecting better visuospatial construction and planning abilities.\u003c/p\u003e\u003ch2\u003eMRI acquisition\u003c/h2\u003e\u003cp\u003eAll participants underwent a whole brain structural T1-weighted MRI scan and resting state blood oxygen level dependent (BOLD) functional scan. Imaging data was obtained on a 3-Tesla MRI scanner (General Electric). Sagittal 3D T1-weighted structural images were acquired with an echo time (TE) = 2.7 ms, repetition time (TR) = 7.2 ms, an acquisition matrix of 256 x 256, 200 slices and a slice thickness = 1mm. The acquisition parameters for the T2*-weighted echo-planar functional scans included TE = 36 ms, TR = 3000 ms, flip angle = 90°, field of view = 220 mm, slice thickness = 3 mm and raw voxel size = 3.75 × 3.75 × 3 mm. The total duration of resting-state scan was approximately 7 minutes during which participants were instructed to lie awake with their eyes closed.\u003c/p\u003e\u003ch2\u003eStructural imaging preprocessing and volumetric analysis\u003c/h2\u003e\u003cp\u003eT1-weighted images were processed using the Computational Anatomy Toolbox (CAT12; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.neuro.uni-jena.de/cat/\u003c/span\u003e\u003cspan address=\"http://www.neuro.uni-jena.de/cat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) implemented within SPM12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk/spm/software/spm12\u003c/span\u003e\u003cspan address=\"http://www.fil.ion.ucl.ac.uk/spm/software/spm12\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on the MATLAB R2023a platform (MathWorks, Natick, MA). CAT12 was selected for its robust and efficient segmentation capabilities, offering enhanced accuracy in volumetric brain measurements relative to other available tools. Only T1-weighted scans that achieved CAT12 automated QC ≥ B + were included in the analysis.\u003c/p\u003e\u003cp\u003eGrey matter volumes of the nucleus basalis of Meynert (NBM) were extracted in each participant’s native space using the standard CAT12 pipeline and delineated using the Julich-Brain cytoarchitectonic atlas.\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e This region of interest corresponded to the same anatomical maps employed as a seed in the functional connectivity analysis, ensuring consistency between structural and functional measures. Volumes for the NBM were then adjusted for estimated total intracranial volume using the residual correction method.\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003ch2\u003eFunctional imaging preprocessing and denoising\u003c/h2\u003e\u003cp\u003ePreprocessing of fMRI data was performed using fMRIprep 21.0.2\u003csup\u003e77\u003c/sup\u003e, a standardised and validated pipeline that integrates functions from established toolboxes. Major preprocessing steps included intensity non-uniformity correction, skull stripping, tissue segmentation, co-registration, normalisation, resampling for spatial alignment across participants and confound estimation. Visual quality control reports were generated for each participant to ensure preprocessing accuracy.\u003c/p\u003e\u003cp\u003eThe pre-processed functional data were then passed through fMRIDenoise (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/compneuro-ncu/fmridenoise\u003c/span\u003e\u003cspan address=\"https://github.com/compneuro-ncu/fmridenoise\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which involved regressing out motion parameters and signals from the cerebrospinal fluid and white matter. Temporal filtering was applied with a high-pass band filter (0.01 Hz) to remove low-frequency drifts and a low-pass band filter (0.1 Hz) to exclude high-frequency physiological noise. Further details are outlined in Supplementary Martial.\u003c/p\u003e\u003ch2\u003eSeed-based functional connectivity analysis\u003c/h2\u003e\u003cp\u003eThe NBM and PPN were selected a priori as seeds of interest to determine whether alterations in functional connectivity in these cholinergic hub regions (the NBM and several canonical resting-state networks; the PPN to the thalamus) are associated with REM EEG slowing in LBDs. The mean BOLD signal time series were extracted from resting-state fMRI data using 400 cortical regions using the Schaefer parcellation\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, 54 subcortical regions from the Tian parcellation\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e, and a probabilistic anatomical map of the NBM derived from microscopic delineations of 10 postmortem human brains.\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e Functional connectivity matrices were computed for each participant by calculating the pairwise Pearson correlation coefficients between the time series of all regions, followed by Fisher z-transformation.\u003c/p\u003e\u003cp\u003eThe 400 cortical parcels were assigned to one of 17 intrinsic functional networks derived from resting-state fMRI data in approximately 1,000 participants, which represent subdivisions of a lower-resolution 7-network model.\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e For the current analysis, each parcel was grouped into the seven broader resting-state networks. Seed-network connectivity was calculated as the average connectivity between each seed region and all cortical parcels within a network, yielding a single value representing the overall connectivity strength. Functional connectivity between the PPN and the thalamus was also examined to determine whether disrupted coupling between these regions is associated with REM EEG slowing in LBDs.\u003c/p\u003e\u003cp\u003eFurther details on structural and functional imaging preprocessing and analysis can be found in Supplementary Material.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using RStudio (v4.3.1)\u003csup\u003e82\u003c/sup\u003e and Python (v3.10). Demographic comparisons across groups were performed using one-way ANOVA for continuous variables and chi-square tests for categorical variables, where appropriate.\u003c/p\u003e\u003cp\u003eTo examine group differences in sleep macroarchitecture, REM EEG measures, neuropsychological outcomes and MRI-derived structural brain measures, we conducted generalized linear models with diagnostic group as the fixed factor and age and sex included as covariates. F-statistics for the diagnosis term were extracted to evaluate the overall group effect. To assess the significance of these F-statistics without assuming parametric distributions, permutation testing was applied: the diagnostic group labels were randomly permuted 10,000 times, and F-statistics were recomputed for each permutation. Empirical p-values were calculated as the proportion of permuted F-statistics exceeding the observed statistic.\u003c/p\u003e\u003cp\u003eFor post hoc comparisons, we applied a residual-based pairwise permutation test. First, residuals were obtained from a linear model that adjusted each variable for age and sex. Differences in residual means between diagnostic pairs (HC vs PD, HC vs DLB, PD vs DLB) were computed. A non-parametric test assessed the significance of each pairwise difference (10,000 permutations) allowing inference on adjusted group differences while avoiding reliance on normality assumptions.\u003c/p\u003e\u003cp\u003eTo investigate REM slowing associations, we used linear regression models. Separate models were run for each neuroimaging measure (including both volumetric and functional connectivity metrics) and demographic, clinical and cognitive measures. Continuous demographic, clinical and cognitive variables were z-scored prior to regression to allow direct comparison of effect sizes across measures. This normalization expresses each regression coefficient (β) in standard deviation units, providing standardized beta values that indicate the strength and direction of associations between the REM EEG slowing ratio and each predictor variable. Regression models with neuroimaging and cognition as predictors included age and sex as covariates, and standardized beta coefficients were reported.\u003c/p\u003e\u003cp\u003eAll statistical tests were two-tailed, with significance set at p \u0026lt; 0.05, and multiple comparisons were corrected for using the Benjamini-Hochberg procedure to control for the false discovery rate (FDR) with q \u0026lt; 0.05, where appropriate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eJA and AI are recipients of the Australian Postgraduate Award. LC is the recipient of the Bierzonski Burczyk Foundation Postgraduate Research Scholarship. AK is supported by the Australian Rotary Health of Belconnen 50th Anniversary PhD scholarship (SC4968). ALD is supported by an NHMRC fellowship (2008001). SLN is supported by the Dementia Leadership Fellowship (APP1135639). RRG is supported by a National Health and Medical Research Council Leadership Fellowship (1197439). SJGL is supported by a National Health and Medical Research Council Leadership Fellowship (1195830) and has received research funding from the Michael J. Fox Foundation and the Australian Research Council, as well as consulting for Pharmaxis Ltd. EM is supported by a National Health and Medical Research Council Emerging Leadership Fellowship (2008565), the U.S. Department of Defense Congressionally Directed Medical Research Program Early Investigator Grant (PD220061) and the University of Sydney Horizon Fellowship.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor roles included: conception and design of the study (JA, RRG, SJGL, EM); acquisition and analysis of data (JA, LC, AK, AI, GC); and final approval of manuscript and/or figures (JA, LC, AK, AI, GC, AL, ALD, SLN, RRG, SJGL, EM). All authors declare accountability for the work contained in the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge participants and caregivers for their efforts participating in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData supporting the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcKeith, I. G. \u003cem\u003eet al.\u003c/em\u003e Diagnosis and management of dementia with Lewy bodies. \u003cem\u003eNeurology\u003c/em\u003e 89, 88\u0026ndash;100 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEmre, M. \u003cem\u003eet al.\u003c/em\u003e Clinical diagnostic criteria for dementia associated with Parkinson\u0026rsquo;s disease. \u003cem\u003eMov. 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R Foundation for Statistical Computing, Vienna, Austria. \u003cem\u003eHttpwwwR-Proj.\u003c/em\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rapid Eye Movement, Sleep, Parkinson’s, Dementia with Lewy bodies, Lewy Body Disorders; Neuroimaging, EEG; Cholinergic System; Cognition","lastPublishedDoi":"10.21203/rs.3.rs-8131680/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8131680/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLewy body disorders (LBDs), including Parkinson\u0026rsquo;s disease (PD) and dementia with Lewy bodies (DLB), are the second most common cause of neurodegeneration in older adults. Progressive cholinergic dysfunction is known to be a key contributor to cognitive decline in LBDs, yet accessible biomarkers of cholinergic impairment remain limited. Because cholinergic neurons play a central role in REM sleep and cortical activation, alterations in REM electroencephalographic activity (EEG) may provide a functional marker of cholinergic network integrity in these disorders.\u003c/p\u003e\u003cp\u003eTwenty-four participants with DLB, 36 with PD, and 44 controls underwent neuropsychological testing and overnight polysomnography, with a subset completing structural and resting-state MRI. EEG spectral power across δ, θ, α, σ and β frequency bands was quantified during REM sleep and the REM EEG slowing ratio (δ\u0026thinsp;+\u0026thinsp;θ)/(α\u0026thinsp;+\u0026thinsp;σ\u0026thinsp;+\u0026thinsp;β) was derived for frontal, central and occipital regions. Nucleus basalis of Meynert (NBM) volumes were derived from T1-weighted images, and functional connectivity was examined between the NBM and large-scale networks, and between the pedunculopontine nucleus (PPN) and thalamus.\u003c/p\u003e\u003cp\u003eDLB participants had greater occipital REM EEG slowing compared with PD and controls. Across LBDs, REM slowing was associated with aberrant nucleus basalis of Meynert connectivity to visual, ventral attentional, default mode, and frontoparietal networks, as well as altered pedunculopontine-thalamic connectivity. Greater REM EEG slowing correlated with worse global cognition and executive function deficits.\u003c/p\u003e\u003cp\u003eThese findings demonstrate that REM EEG slowing reflects cholinergic network dysfunction and clinically relevant cognitive impairment in LBDs. REM slowing may represent a non-invasive and transdiagnostic biomarker for disease monitoring, prognosis, and stratification in clinical trials.\u003c/p\u003e","manuscriptTitle":"Electroencephalographic slowing during REM sleep is a marker of cholinergic dysfunction in Lewy body disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 15:07:39","doi":"10.21203/rs.3.rs-8131680/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T14:43:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T17:14:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168128561589162534872116067582756016376","date":"2026-03-24T07:58:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T06:44:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149471402036687359854189154665715270348","date":"2026-01-15T09:57:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T22:17:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178622135964427857146654644802866968349","date":"2025-12-01T14:41:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-30T23:17:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T21:19:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T18:20:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Parkinson's Disease","date":"2025-11-17T06:12:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa95e9ff-5370-4bcb-a9ca-510cc3d7c434","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58896872,"name":"Health sciences/Biomarkers"},{"id":58896873,"name":"Health sciences/Diseases"},{"id":58896874,"name":"Health sciences/Neurology"},{"id":58896875,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-05-17T02:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 15:07:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8131680","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8131680","identity":"rs-8131680","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}