Cholinergic Nucleus 4 Degeneration and Cognitive Impairment in isolated REM Sleep Behavior Disorder (2024)

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Mov Disord. Author manuscript; available in PMC 2024 Mar 1.

Published in final edited form as:

Mov Disord. 2023 Mar; 38(3): 474–479.

Published online 2023 Jan 4. doi:10.1002/mds.29306

PMCID: PMC10033349

NIHMSID: NIHMS1859226

PMID: 36598142

Christopher Tan,¹ Huma Nawaz, MBBS,² Sarah K. Lageman, PhD,² Leslie J. Cloud, MD, MSc,² Amy W. Amara, MD, PhD,³ Benjamin T. Newman, PhD,⁴ T. Jason Druzgal, MD, PhD,⁴ Brian D. Berman, MD, MSc,² Nitai Mukhopadhyay, PhD,⁵ and Matthew J. Barrett, MD, MSc²

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The publisher's final edited version of this article is available at Mov Disord

Associated Data

Supplementary Materials

Abstract

Background:

Cholinergic nucleus 4 (Ch4) degeneration is associated with cognitive impairment in Parkinson disease and dementia with Lewy bodies, but it is unknown if Ch4 degeneration is also present in isolated REM sleep behavior disorder (iRBD).

Objective:

To determine if there is evidence of Ch4 degeneration in patients with iRBD and if it is associated with cognitive impairment.

Methods:

We analyzed clinical and neuropsychological data for 35 iRBD patients and 35 age- and sex-matched healthy controls. Regional grey matter density (GMD) was calculated for Ch4 using probabilistic maps applied to brain MRIs.

Results:

Ch4 GMD was significantly lower in the iRBD group compared to controls (0.417 vs. 0.441; p=0.02). Ch4 GMD was also found to be a significant predictor of Letter Number Sequencing (β-coefficient=58.31, p=0.026, 95% CI [7.47, 109.15]), a measure of working memory.

Conclusions:

iRBD is associated with Ch4 degeneration, and Ch4 degeneration in iRBD is associated with impairment in working memory.

Keywords: Cholinergic basal forebrain, cognitive impairment, REM sleep behavior disorder, working memory, MRI, Voxel-based morphometry

Introduction

REM sleep behavior disorder (RBD) is characterized by loss of muscle atonia during REM sleep, which results in dream enactment behavior. Isolated RBD (iRBD) is associated with alpha-synuclein pathology and is recognized as a precursor to other alpha-synucleinopathies, especially Lewy body diseases, with 45% progressing to Parkinson disease (PD) and 45% progressing to dementia with Lewy bodies (DLB) after 15 years of follow-up.¹

Consistent with PD and DLB, individuals with iRBD also have cognitive impairment. Compared to controls, studies report impairments in memory, visuo-spatial function, attention, and executive function.^2–4 In one of these studies, half of iRBD patients met criteria for mild cognitive impairment (MCI),³ and individuals with iRBD and MCI are at increased risk of developing dementia.⁵ One of the major sources of cognitive impairment in PD and DLB is degeneration of the cholinergic basal forebrain, especially the nucleus basalis of Meynert, the major constituent of cholinergic nucleus 4 (Ch4).^6,7 Measurement of cholinergic basal forebrain nuclei volumes using probabilistic cytoarchitectonic maps applied to MRI has demonstrated reduced Ch4 volumes in DLB and in PD with cognitive impairment,^8,9 but it is unknown if Ch4 degeneration is also present in iRBD.

The objective of this study was to determine if there is evidence of cholinergic basal forebrain degeneration, i.e., reduced Ch4 volume on MRI, in patients with iRBD compared to healthy controls (HC) and whether Ch4 degeneration is associated with cognitive impairment in iRBD. Determining whether Ch4 degeneration is present in iRBD and associated with cognitive impairment has implications for development of therapies for cognitive impairment in this population. We also examined the volumes of cholinergic nuclei 1, 2, and 3 (Ch123) as a comparison region to Ch4. As these cholinergic nuclei project to hippocampi and the olfactory bulbs, we hypothesized that this region would be less likely to be associated with cognitive impairment in iRBD.

Methods

Study Participants

All participant data was obtained from the Michael J. Fox Foundation’s Parkinson’s Progression Markers Initiative (PPMI). The PPMI is a prospective, longitudinal, observational, multicenter study, which aims to verify biomarkers of PD progression by studying those with newly diagnosed PD, prodromal PD, and HC. For this study, we included participants in the prodromal cohort diagnosed with iRBD. Inclusion criteria for participants with iRBD in the PPMI included age ≥ 60 years old and confirmation of RBD based on polysomnography (PSG). Key exclusion criteria were a clinical diagnosis of dementia or PD at screening as determined by the investigator. For the iRBD participants who had completed brain MRIs at the baseline visit, we selected age- and sex-matched HC who had also completed brain MRIs at their baseline visit. Of note, HC with MoCA score ≤26 during screening were excluded. Further details regarding PPMI methodology are available online (https://www.ppmi-info.org/study-design/) and data are available through the PPMI website. The institutional review board at each PPMI site approved the study and participants provided informed consent.

Clinical Assessments

The following assessments were performed at the baseline visit: 1) Montreal Cognitive Assessment (cognitive screener), 2) Hopkins Verbal Learning Test – Revised (verbal memory), 3) Letter Number Sequencing (LNS, working memory), 4) Semantic Fluency Test – animals (SFT-animals, executive function), 5) Symbol Digit Modalities Test (processing speed/attention), 6) Benton Judgement of Line orientation (JLO, visuospatial skills), 7) Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), and 8) REM sleep behavior disorder questionnaire (RBDSQ). In the iRBD and control cohorts an RBDSQ score ≥ 5 was used as the cutoff consistent with the diagnosis of RBD. In the iRBD cohort, PD-MCI Level 1 criteria were applied to determine the percentage of participants who met criteria for MCI.¹⁰

MRI Acquisition and Analysis

Depending on the site, MRI scans were performed on Siemens, Philips or GE scanners with 3.0 or 1.5 Tesla magnets. We analyzed T1-weighted, sagittal 3D volumetric sequences (e.g., MP-RAGE for Siemens and IR-FSPGR for Philips and GE) with slice thickness of 1.5 mm or less. MRI acquisition protocols can be found here: https://www.ppmi-info.org/study-design/research-documents-and-sops. Regional grey matter density (GMD) was calculated for all subjects according to a longitudinal processing pipeline according to previously published methods.¹¹ Processing took place in two steps: 1) preprocessing via voxel-based morphometry and 2) application of cytoarchitectonic probabilistic maps to determine GMD values. Voxel-based morphometry^12,13 was applied to all time points using the CAT12 toolbox (http://www.neuro.uni-jena.de/cat/) within SPM12 (Wellcome Department of Imaging Neuroscience Group, London, UK: http://www.fil.ion.ucl.ac.uk/spm) using previously described methods.¹¹ Briefly, before processing, the origin of each image was manually reoriented to the anterior commissure in SPM12. Within the CAT12 toolbox, images were denoised according to spatial-adaptive non-local Means (SANLM) denoising and Markov Random Field approaches.^14,15 Images were bias corrected, spatially normalized to standard stereotactic space with an affine registration, and a local intensity transformation was performed. Normalized images were segmented into grey matter, white matter, and cerebrospinal fluid according to the Adaptive Maximum A Posterior (AMAP) technique.¹⁴ Tissue priors were used for spatial normalization, skull-stripping, and initial segmentation estimation within the AMAP segmentation.¹⁶ Partial volume estimation was performed to account for voxels which may contain more than one tissue type.¹⁷ The Diffeomorphic Anatomic Registration Through Exponentiated Lie (DARTEL)¹⁸ algorithm as well as Geodesic Shooting¹⁹ were used to register segmented images into standard Montreal Neurological Institute (MNI) space. The longitudinal processing option, which performs spatial normalization for the mean image of each of the time points for each subject and applies the same normalization to all images, was selected within CAT12. Segmented images were modulated by the amount of volume changes from the spatial registration to preserve the total amount of grey matter.

Total intracranial volume (TIV) was determined with CAT12 for every participant’s MRI. Basal forebrain GMD was measured using probabilistic maps of Ch4 and Ch123 for the reference MNI single subject brain that were derived from 3D reconstruction of histological sections from 10 post-mortem human brains.²⁰ These probabilistic tissue maps were moved from the anatomical space of the single subject MNI template into standard MNI template space by an affine translation along the y and z axes of 4 and 5 mm.²¹ Quality of the overlay was confirmed by visual inspection. Relative bilateral Ch4 and Ch123 GMD were calculated with a MATLAB script which multiplied the GMD value for each voxel by the weighting contained within the probabilistic map (Supplementary Figure 1).⁸ To produce standardized GMD values, each relative GMD was divided by the total weighting contained within the probabilistic map. TIV-normalized GMD values were calculated by dividing the GMD value by TIV and multiplying by 1000.

Statistical Analysis

Statistical analysis was performed using Stata IC 14 (StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP.). We compared demographics, cognitive assessment scores and GMD values between iRBD and HC using two-sample t-tests or Mann-Whitney U tests when appropriate. We examined Q-Q plots to determine whether data were normally distributed. Sex distribution was compared between groups using chi-square test. For comparisons of cognitive test scores between iRBD and HC groups, p-values were corrected for the false discovery rate using the Benjamini-Hochberg procedure. To adjust for the effects of scanner type we used partial correlation analysis to evaluate the relationship between group (iRBD vs. HC) and TIV-normalized cholinergic basal forebrain GMD, Ch4 and Ch123. We did not need to adjust for the effects of age and sex in these analyses because the groups were well-matched (Table 1).

Table 1.

Clinical characteristics of healthy controls and isolated REM sleep behavior disorder participants

	Healthy controls (n=35)	iRBD (n=35)	p-value
Age	69.1 ± 5.8	69.2 ± 5.7	0.9535
Sex, women (% total)	5 (14.3%)	5 (14.3%)	1.000
REM sleep behavior disorder questionnaire, median score (IQR)	2 (1–4)	10 (7–12)	<0.0001
REM sleep behavior disorder questionnaire ≥ 5, n(%)	8 (22.9%)	33 (94.3%)	<0.001
MDS-UPDRS total score, median score (IQR)	2 (1–8) (n=31)	14 (8–18) (n=31)	<0.0001
MDS-UPDRS part 1 total score, median score (IQR)	2 (1–5) (n=31)	8 (4–10) (n=31)	<0.0001
MDS-UPDRS part 2 total score, median score (IQR)	0 (0–0) (n=31)	1 (0–4) (n=31)	0.0003
MDS-UPDRS part 3 total score, median score (IQR)	0 (0–1) (n=31)	3 (2–7) (n=31)	<0.0001
Total Intracranial Volume, cm³	1578.1 ± 156.0	1569.3 ± 126.9	0.7974
Ch4 GMD	0.441 ± 0.039	0.417 ± 0.046	0.0233
TIV-normalized Ch4 GMD	0.281 ± 0.028	0.266 ± 0.020	0.0126
Ch123 GMD	0.4567 ± 0.044	0.4546 ± 0.046	0.8503
TIV-normalized Ch123 GMD	0.291 ± 0.024	0.290 ± 0.026	0.9777

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Unless otherwise specified, data are reported as mean values ± SD. Abbreviations: Ch4 = cholinergic nucleus 4; Ch123 = cholinergic nucleus 1, 2, and 3; GMD = grey matter density; IQR = interquartile range; iRBD = isolated REM sleep behavior disorder; MDS-UPDRS = Movement Disorders Society Unified Parkinson’s Disease Rating Scale; REM sleep = rapid eye movement sleep; SD = standard deviation.

In secondary analyses we used a backward selection method to investigate the relationship between basal forebrain GMD (independent variable) and cognitive test scores (dependent variable) adjusted for group (HC vs. iRBD), scanner type, and TIV. We did not adjust for age or sex because both were well-matched between the groups. Predictors at p<0.1 were included in the final models. TIV was included in all models that retained basal forebrain regions. Final models were stratified by group. Statistical significance for the final models was set at p < 0.05. hom*ogeneity of variance was tested using the Breusch–Pagan test.

Results

Of 47 iRBD participants enrolled in the PPMI, only 35 had sagittal T1 MRI sequences at baseline. There were no significant differences between the 35 iRBD participants included in this study and the 12 iRBD participants without MRI scans in regards to age, sex, RBDSQ score, MoCA score, and MDS-UPDRS total score (all p>0.05, Supplementary Table 1). For this group we identified 35 age- and sex-matched HC. Clinical characteristics for each group are presented in Table 1. Among the iRBD participants, 33 MRIs were performed using a Siemens scanner and 2 were performed using a Philips scanner. Among the HC, 25 MRIs were performed with a Siemens scanner, 5 were performed with a Philips scanner, and 5 were performed with a GE scanner. As expected, individuals in the iRBD cohort had higher mean scores on the RBDSQ compared to HC. This difference remained statistically significant when the question regarding presence of nervous system disease was excluded. MDS-UPDRS total score was significantly higher in the iRBD group compared to HC. Similarly, individual MDS-UPDRS Part I, Part II and Part III scores were significantly higher in the iRBD cohort compared to HC (all p-value≤0.0005).

Ch4 GMD was significantly lower in iRBD participants compared to HC(5.3% lower in RBD, t-statistic value = −2.56), while there was no significant difference in Ch123 between the two groups (Figure 1). When adjusted for scanner type, Group (iRBD (coded as 1) vs HC (coded as 0)) was significantly correlated with TIV-normalized Ch4 GMD (r_p= −0.3479, p= 0.0037). For the categorical variable Group, negative correlations indicate that the iRBD group had reduced GMD measures. When adjusted for scanner type, group (iRBD vs HC) was not significantly correlated with TIV-normalized Ch123 GMD (r_p= −0.0777, p= 0.53). These partial correlations were not significantly altered by adjustment for age.

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Figure 1.

Cholinergic forebrain nuclei grey matter density in REM sleep behavior disorder and healthy controls.

Horizontal lines indicate mean values of cholinergic nuclei grey matter density and error bars indicate 95% confidence interval for (A) Ch4 and (B) Ch123 for both 35 iRBD participants and 35 age- and sex-matched healthy controls. Abbreviations: Ch4 = cholinergic nucleus 4; Ch123 = cholinergic nucleus 1, 2, and 3; GMD = grey matter density; HC = healthy controls; iRBD = isolated REM sleep behavior disorder.

Compared to HC, iRBD performed significantly worse on all cognitive assessments except SFT-animals (Supplementary Table 2). There were 17 iRBD participants (48.6%) who met criteria for mild cognitive impairment. The results of the secondary analyses are provided in Supplementary Table 3. Ch4 GMD (β-coefficient=65.26, p=0.008, 95% CI [18.47, 112.05]) and Ch123 GMD (β-coefficient=−48.34, p=0.012, 95% CI [−85.22, −11.45]) were both significant predictors of LNS in iRBD. When adjusted for age, sex, scanner type and TIV, only Ch4 GMD was a significant predictor of LNS (β-coefficient=58.31, p=0.026, 95% CI [7.47, 109.15]) in iRBD. When using TIV-normalized GMD as a predictor and removing TIV as a covariate, TIV-normalized Ch4 GMD remained a significant predictor of LNS (β-coefficient=84.89, p=0.041, 95% CI [3.70, 166.07]). The results were not altered when only RBD participants whose MRIs were completed on a Siemens scanner were included (n=33). Ch123 GMD (β-coefficient=43.04, p=0.014, 95% CI [9.5, 76.5]) was a significant predictor of JLO in HC; however, when adjusted for age, sex, scanner type and TIV Ch123 GMD was not a significant predictor of JLO scaled score (p>0.05).

Discussion

We found that Ch4 GMD is reduced in iRBD participants compared to HC, while Ch123 GMD was not significantly different between the iRBD and HC groups. Our findings are consistent with prior cholinesterase PET studies. In one previous [¹¹C]-donepezil PET study, iRBD participants had reduced neocortical binding compared to controls,²² consistent with neocortical denervation from Ch4 cholinergic neurons. Another cholinesterase PET study in PD found that RBD was associated with cortical, limbic, and thalamic cholinergic denervation but not dopaminergic or serotonergic denervation compared to participants with PD without RBD.²³ It is possible that reduced Ch4 GMD in iRBD is part of greater regional atrophy as a previous VBM study found reduced volumes of the anterior cerebellum, tegmental pons, and left parahippocampal gyrus in iRBD;²⁴ however, we did not find a difference in Ch123 GMD between the iRBD group and HC. This supports the presence of preferential or at least earlier Ch4 degeneration in iRBD. This would be consistent with a prior longitudinal MRI study in PD which showed that Ch4 atrophy preceded Ch1/Ch2 atrophy.²⁵ In an exploratory analysis, a recent VBM study found reduced left Ch4 GMD in iRBD with MCI compared to controls but did not detect a difference for the right Ch4 or for the iRBD group without MCI.²⁶ Our study suggests that bilateral reduction in Ch4 GMD is a feature of iRBD regardless of cognitive status, which is consistent with the previous PET studies reviewed above.

We found that participants with iRBD performed worse on all cognitive assessments compared to age- and sex-matched HC except for semantic fluency. Previous studies also reported cognitive impairment in iRBD.^2,27 One study reported worse attention, executive function, and verbal memory in iRBD patients even when adjusted for education and age.²⁷ A longitudinal prospective study found that patients with iRBD performed worse on cognitive assessments involving memory and executive function at baseline and their performance worsened over the subsequent two years.² Ch4 degeneration among our iRBD patients was correlated with impairment in tasks of executive function (SFT-animals) and attention and working memory (LNS). When adjusted for age, sex, scanner type, and TIV in a linear regression model, Ch4 GMD was only found to be a significant predictor of performance on letter number sequencing, a task assessing working memory. Prior studies have shown that cholinergic pathways projecting to the cortex play an important role in cognitive function, especially in working memory.^28,29 Moreover, Ch4 GMD has previously been shown to be correlated with performance on LNS task in PD.⁹

This study examined bilateral cholinergic basal forebrain GMD in iRBD and is the first to find a relationship between Ch4 GMD and working memory in iRBD. The lack of an association between Ch4 GMD and the other cognitive test scores in iRBD may be because not all iRBD participants had cognitive impairment or this study was underpowered to detect these relationships. Limitations could arise from the use and selection of PPMI subjects. One limitation of the study is that HC in PPMI were required to have MoCA ≥ 26 which may have created a larger difference in cognitive function between iRBD and HC than a randomly selected and matched HC group without a minimum MoCA requirement. However, a HC group with a more similar cognitive performance to iRBD would potentially be affected by other age-related pathologies. For this reason, we chose to use the HC group as our comparison group. While efforts were made to account or adjust for the heterogeneity of scanner types and specifications, this is a limitation of this dataset. Another limitation is that we did not adjust for multiple comparisons in our secondary analyses, so these results require future replication. In summary, our study found greater Ch4 degeneration in iRBD compared to controls, and Ch4 GMD was associated with performance on a task assessing working memory. These findings support the idea that Ch4 degeneration is associated with cognitive impairment in iRBD. Future longitudinal studies are needed to determine whether iRBD patients with reduced Ch4 volume are at increased risk of future cognitive decline.

Supplementary Material

supinfo1

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fS1

Supplementary Figure 1. Probabilistic map of cholinergic nuclei 1–4 in the basal forebrain.

Volumetric rendering of cholinergic nuclei 1, 2, 3 (Ch123, red), and 4 (Ch4, blue) overlaid on a sectioned Colin-27 brain (A). For additional context, coronal views show Ch123 (B) and Ch4 (C) shown at their greatest volumes in the Y dimension overlaid on the Colin-27 brain. In between (B) and (C), coronal views of Ch123 and Ch4, surveyed at different y-coordinates in MNI atlas space are also provided. The color scales indicate the probability (range 0.001–1) that a voxel corresponds to Ch4 (blue) or Ch123 (red).

Click here to view.^{(6.9M, tiff)}

Acknowledgement

Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/access-data-specimens/download-data). For up-to-date information on the study, visit ppmi-info.org. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/about-ppmi/who-we-are/study-sponsors].

Financial disclosures of all authors for the previous 12 months.

Mr. Tan, Dr. Nawaz, and Dr. Lageman have no disclosures. Dr. Cloud received funding from MJFF and NIH (1R01NS120560-01); is site investigator for studies sponsored by Bukwang, Cerevel, Amneal, and Neuro-point alliance; served on the NIH CDE committee for PD in 2022 and the PALTOWN medical and scientific advisory board; received honoraria from Medlink neurology, HMP global, Colontown, M3 global research team, and Qessential research; and has interests in the following inventions: Gastrointestinal Symptoms in Neurodegenerative Disease (GIND) scale, VCU Office of Technology Transfer # CLO-11-067 and Recognize and Deploy Vibration for Mitigation of Freezing of Gait, VCU Office of Technology Transfer #PRE-21-137F (322203-8130) with a provisional patent application filed 2022. Dr. Amara receives grant funding from the National Institutes of Health (K23NS080912 and R01HD100670) and the McKnight Brain Institute and serves as site investigator for studies sponsored by the Michael J Fox Foundation for Parkinson’s Research, NeuroNEXT, Eli Lilly and Company, Biogen Idec, and Jazz Pharmaceuticals. Dr. Druzgal receives funding support as site PI for NIH R01NS107513, Co-I for NIH K23NS116225, and PI for UVA Brain Institute Alzheimer’s seed grant. Dr. Berman has received research grant support from the Dystonia Coalition (receives the majority of its support through NIH grant NS065701 from the Office of Rare Diseases Research in the National Center for Advancing Translational Science and National Institute of Neurological Disorders and Stroke), the Parkinson’s Foundation, the VCU School of Medicine, the Administration for Community Living, and the Dystonia Medical Research Foundation. He has received consultancy payments from AbbVie Inc., and has received honoraria from the MedLink Corporation and the International Parkinson and Movement Disorder Society and serves on the medical advisory board of the Benign Essential Blepharospasm Research Foundation and the National Spasmodic Torticollis Association. Dr. Mukhopadhyay received funding from the National Institutes of Health (1R21AG077469). Dr. Barrett received research funding from the VCU Center for Clinical and Translational Research, the National Institutes of Health (1R21AG077469, R21AG074368 (PI:Wyman-Chick)) and Kyowa Kirin, Inc. He serves as site PI for clinical trials sponsored by CHDI Foundation, University of Rochester, uniQure, Parkinson’s Foundation, and Prilenia Therapeutics.

Footnotes

Financial Disclosure/Conflict of Interest: None.

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