Louis, Erik K. St.

AbstractBackgroundQuantitative rapid eye movement (REM) sleep without atonia (RSWA) recorded during polysomnography (PSG) represents loss of normal REM sleep atonia. RSWA is the neurophysiologic signature of REM sleep behavior disorder (RBD). Visually scored RSWA is a candidate biomarker for synucleinopathies. However, visual RSWA scoring requires expert scoring, limiting widespread application. We comparatively analyzed RSWA in DLB and iRBD using the automated Ferri REM Atonia Index (RAI).MethodWe quantitatively analyzed the RAI in 12 DLB and 20 iRBD patients, compared to 20 controls using Hypnolab Software for Sleep Analysis (SWS Soft, Inc, Colognola ai Colli, Italy). PSG data were converted to European Data Format (.EDF), and the submentalis surface electromyogram (EMG) signal was notch‐filtered at 60 Hertz and rectified. Automatically calculated RAI scores were comparatively analyzed across groups (DLB, iRBD, controls) using Kruskal Wallis H tests for group comparisons, and regression analyses determined relationships between diagnosis, age, and sex. Logistic regression ROC determined optimal RAI for distinguishing iRBD from DLB. When interpreting RAI, lower values represent greater amounts of RSWA, and higher values represent more normal REM atonia.ResultMean age was older in DLB than in iRBD or controls (72.6 vs. 68.7 vs. 66.5 years). All 12 DLB participants were men, the iRBD group had 18 men and 2 women, and controls were comprised of 16 men and 4 women. Mean RAI was lowest in DLB (0.46), intermediate in iRBD (0.71) and highest in controls (0.96), indicating a gradient in RSWA amounts from DLB patients (highest) to controls (lowest) (Figure). Regression demonstrated diagnosis independently predicted RAI, controlling for age and sex (adjusted R2 = 0.49, p = 0.0001). The RAI that optimally distinguished iRBD from DLB was 0.46 (specificity 85%, sensitivity 58%, AUC = 0.75).ConclusionThe RAI demonstrated a clear gradient throughout the Lewy body spectrum; highest (indicating lowest RSWA amounts) in controls, intermediate in iRBD, and lowest (indicating greatest RSWA) in DLB. The RAI is a promising, time efficient and broadly applicable candidate biomarker for DLB and iRBD. Automated quantitative RSWA could be a valuable tool for deep phenotyping throughout the Lewy body disease spectrum.

AbstractBackgroundDementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) are challenging to differentiate. Mobile health devices that measure motor and sleep domains between clinic visits may improve differential diagnosis.MethodParticipants with probable AD and probable DLB wore ActiGraph triaxial wGT3X‐BT actigraphy devices on their nondominant wrists for 2 weeks and multichannel Sleep ProfilerX8TM EEG devices on their foreheads for 2 consecutive nights.3 Actigraphy data were processed and scored using GGIR and activity counts in R. The Sleep Profiler characterizes abnormal slow‐wave sleep and non‐REM hypertonia (NRH).ResultAt baseline, participants with DLB (n = 9) had higher UPDRS scores and a higher likelihood of psychosis than participants with AD (n = 8; see Table 1). Using univariate Wilcoxon Rank‐Sum tests, we found that at baseline participants with DLB had lower daily activity counts, higher % time in NRH, and lower % time in REM sleep than participants with AD (unadjusted p‐values < 0.05; See Figure 1).In a recent 6‐month followup, activity levels were lower on average than baseline activity levels for both DLB (n = 5) and AD (n = 5). at 6‐months, the differences in % sleep in NRH increased between DLB participants (n = 5) and AD participants (n = 3) as average % NRH increase 6% in DLB and decreased 1% in AD (see Table 2). These 6‐month differences are not statistically significant, but data collection are ongoing.An exploration of methods for processing and scoring actigraphy data revealed that processing can result in wide range of findings. As a result, we have focused on actigraphy outcomes which are more suitable to older participants with dementia.ConclusionParticipants with DLB demonstrated lower baseline physical activity and higher rates of baseline sleep disturbances than participants with AD; sleep differences between DLB and AD appeared to increase over 6 months.Actigraphy and sleep‐monitoring devices are well tolerated in participants with dementia and they can reliably collect valid, useful activity and sleep data in between clinic visits.

AbstractBackgroundEEG slowing, a biomarker of cognitive decline in Alzheimer’s disease (AD), has been reported in wake or sleep EEG [1,2,3]. However, the relation between wake and sleep EEG and their comparative sensitivity to early stage of cognitive decline is less understood. In this study we compared sleep and wake EEG biomarkers using a common set of endpoints.MethodsParticipants included individuals with mild cognitive impairment (MCI, n = 30, ages 53‐88), Alzheimer’s disease dementia (ADem, n = 15, ages 60‐84), and controls with normal cognition (HC, n = 55, ages 50‐84). Twenty‐channel resting‐state EEG with eyes‐closed was collected during 5‐minutes of wakefulness (STAT X24). Sleep EEG recordings from AF7‐AF8 were collected in‐home and staged using Sleep ProfilerTM. Power spectral densities (PSD) and the derived Theta‐Alpha ratios were computed for wake EEG sites (counterpart bipolar montage), with the wake FP1‐FP2 compared by stage to the sleep EEG at AF7‐AF8. Significant differences in Theta‐Alpha ratios between MCI or ADem vs. HC groups were identified using independent t‐test (p<0.05) with the normalized effect size measured using Hedges‐g (E.S.).ResultsCorrelation coefficients between wake Alpha, Theta power and non‐REM sleep were: r = 0.83, 0.75 for stage N1, r = 0.71, 0.66 for stage N2, and r = 0.55, 0.50 for stage N3. Based on the frontal Theta‐Alpha ratios, the ADem group showed significant EEG slowing during both wake (E.S. = 0.73) and sleep (E.S. = 1.65, 1.31, 0.61, 1.71 for N1, N2, N3, and REM) as compared to the HC, while MCI group only exhibited EEG slowing during sleep (E.S. = 0.56, 0.77, 0.51 for N1, N2 and REM). Temporal (T5‐T6) wake EEG manifested significant slowing in both ADem (E.S. = 1.21) and MCI (E.S. = 0.56) groups. Compared to the HC, ADem patients exhibited significantly less sleep time (E.S. = 0.91, p = 0.002) and less stage REM (E.S. = 1.0, p = 0.001).ConclusionsCognitive decline in the AD spectrum is associated with common signatures in both sleep and wake EEG. However, the magnitude of these effects and their spatial distribution may be different in sleep versus wakefulness. These findings support a multi modal approach to neurophysiological assessment using both sleep and wakefulness data.

Abstract Background Dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) are challenging to differentiate. Mobile health devices that measure motor and sleep domains between clinic visits may improve differential diagnosis. Method Participants with probable AD and probable DLB wore ActiGraph triaxial wGT3X‐BT actigraphy devices on their nondominant wrists for 2 weeks and multichannel Sleep ProfilerX8TM EEG devices on their foreheads for 2 consecutive nights.3 Actigraphy data were processed and scored using GGIR and activity counts in R. The Sleep Profiler characterizes abnormal slow‐wave sleep and non‐REM hypertonia (NRH). Result At baseline, participants with DLB (n = 9) had higher UPDRS scores and a higher likelihood of psychosis than participants with AD (n = 8; see Table 1). Using univariate Wilcoxon Rank‐Sum tests, we found that at baseline participants with DLB had lower daily activity counts, higher % time in NRH, and lower % time in REM sleep than participants with AD (unadjusted p‐values < 0.05; See Figure 1). In a recent 6‐month followup, activity levels were lower on average than baseline activity levels for both DLB (n = 5) and AD (n = 5). at 6‐months, the differences in % sleep in NRH increased between DLB participants (n = 5) and AD participants (n = 3) as average % NRH increase 6% in DLB and decreased 1% in AD (see Table 2). These 6‐month differences are not statistically significant, but data collection are ongoing. An exploration of methods for processing and scoring actigraphy data revealed that processing can result in wide range of findings. As a result, we have focused on actigraphy outcomes which are more suitable to older participants with dementia. Conclusion Participants with DLB demonstrated lower baseline physical activity and higher rates of baseline sleep disturbances than participants with AD; sleep differences between DLB and AD appeared to increase over 6 months. Actigraphy and sleep‐monitoring devices are well tolerated in participants with dementia and they can reliably collect valid, useful activity and sleep data in between clinic visits.

AbstractBackgroundPatients with dementia with Lewy bodies (DLB) may have Alzheimer´s disease (AD) pathology. Plasma biomarkers of beta‐amyloid (Aß), phosphorylated tau (pTau), and neurodegeneration are sensitive to AD neuropathologic changes in AD dementia. While these plasma biomarkers are well tested in the AD continuum, their performance for concomitant AD pathology in the DLB continuum is still unclear.MethodWe included patients with isolated REM sleep behavior disorder (iRBD; n = 15), with mild cognitive impairment with Lewy bodies (MCI‐LB; n = 37), and with DLB (n = 70) from the Mayo Clinic Alzheimer’s Disease Research Center. Cognitively unimpaired individuals (CU; n = 100) included from the Mayo Clinic Study of Aging were balanced on age and sex. A panel of plasma biomarkers of Aß40, Aß42, p‐tau181, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) measures was obtained. We calculated the ratio of plasma Aß40/42. Lower Aß40/42 indicates increased Aß pathology. Plasma Aß40, Aß42, GFAP, and NfL were measured using the Simoa® Neurology 4‐Plex E Advantage kit and pTau‐181 was measured with the Simoa® pTau‐181 Advantage V2 kit. Both were run on a Quanterix HD‐X analyzer. AD imaging biomarkers were assessed on PET using Pittsburgh compound B (PiB) for Aß and 18F‐Flortaucipir for tau.ResultHigher levels of plasma GFAP were observed in the MCI‐LB (p = 0.014) and DLB (p<0.001) groups compared to CU (Figure). DLB patients also showed higher levels of plasma pTau181 (p<0.001) and NfL (p<0.001). Higher amyloid and tau PET standardized uptake value ratio (SUVr) in the DLB continuum were associated with higher levels of plasma p‐tau181, NfL, and GFAP (p<0.001). After accounting for age, plasma Aß40/42 did not correlate with amyloid or tau PET SUVr.ConclusionIn the DLB continuum, abnormal levels of GFAP in plasma can be detected as early as the prodromal stages of the disease. Plasma pTau181 reached abnormal levels in the DLB stage. Higher plasma GFAP, NfL, and p‐tau181 are associated with higher amyloid and tau PET SUVr in the DLB continuum. Plasma biomarkers have the potential to contribute to screening and early diagnosis in the DLB continuum, which has implications in designing new treatments.

AbstractBackgroundCo‐morbid Alzheimer’s disease (AD) pathology in the form of amyloid‐ß plaques and tau neurofibrillary tangles are frequently observed in people with probable dementia with Lewy bodies (DLB). Neurite Orientation Dispersion and Density Imaging (NODDI) is an emerging technique to characterize white matter (WM) tissue microstructure, although NODDI alterations and associations with AD biomarkers remain undefined in DLB. The objectives of this study are to compare NODDI and DTI parameters between DLB and controls and (ii) evaluate the pattern of DTI and NODDI changes associated with amyloid and tau deposition.MethodConsecutive patients within the DLB spectrum (n = 45), including probable DLB (n = 32) and probable Mild Cognitive Impairment with Lewy bodies (MCI‐LB) (n = 13) from the Mayo Clinic ADRC underwent multi‐shell diffusion MRI, [18F]‐Flortaucipir and [11C]‐PiB PET. A comparison group of 45 age‐ and gender‐matched clinically unimpaired controls (CU) was included. Voxelwise non‐parametric permutation models were used to determine pair‐wise group differences and assess the associations of AD biomarkers with fractional anisotropy (FA), mean diffusivity (MD), tissue‐weighted neurite density index (tNDI; NDI x (1 – Free water fraction)) and orientation dispersion index (ODI), controlling for age. Complementary analyses were performed using regions‐of‐interest (ROI) obtained from the JHU atlas.ResultCompared to CU, the DLB spectrum group exhibited widespread decreases in FA and tNDI and increases in MD and ODI, with the most pronounced effects in the cingulum bundles, uncinate, inferior and superior longitudinal fasciculi (Fig. 1). Greater [18F]‐Flortaucipir uptake was also associated with lower FA and tNDI, and higher MD and ODI in the DLB spectrum compared to CU, independently of age and [11C]‐PiB uptake (Fig. 2). In contrast, no associations were found between [11C]‐PiB uptake and any of the DTI or NODDI parameters. Complementary ROI analyses corroborated these findings.ConclusionWidespread loss of WM integrity was observed on DTI and NODDI in probable DLB and prodromal DLB. These findings were associated with greater cortical tau, but not with amyloid‐ß deposition. Although alpha‐synuclein pathology underlies the loss of WM integrity in DLB, amyloid‐independent tau pathology may also be an important influence on white matter integrity within the DLB spectrum.

Abstract Study Objectives The body-first Parkinson’s disease (PD) hypothesis suggests initial gut Lewy body pathology initially propagates to the pons before reaching the substantia nigra, and subsequently progresses to the diencephalic and cortical levels, a disease course presumed to likely occur in PD with rapid eye movement sleep behavior disorder (RBD). We aimed to explore the potential association between colonic phosphorylated alpha-synuclein histopathology (PASH) and diencephalic or cortical dysfunction evidenced by non-rapid eye movement (NREM) sleep and wakefulness polysomnographic markers. Methods In a study involving 43 patients with PD who underwent clinical examination, rectosigmoidoscopy, and polysomnography, we detected PASH on colonic biopsies using whole-mount immunostaining. We performed a visual semi-quantitative analysis of NREM sleep and wake electroencephalography (EEG), confirmed it with automated quantification of spindle and slow wave features of NREM sleep, and the wake dominant frequency, and then determined probable Arizona PD stage classifications based on sleep and wake EEG features. Results The visual analysis aligned with the automated quantified spindle characteristics and the wake dominant frequency. Altered NREM sleep and wake parameters correlated with markers of PD severity, colonic PASH, and RBD diagnosis. Colonic PASH frequency also increased in parallel to probable Arizona PD stage classifications. Conclusions Colonic PASH is strongly associated with widespread brain sleep and wake dysfunction, suggesting an extensive diffusion of the pathologic process in PD. Visual and automated analyses of polysomnography signals provide useful markers to gauge covert brain dysfunction in PD. Clinical Trial Name: SYNAPark, URL: https://clinicaltrials.gov/study/NCT01748409, registration: NCT01748409