Neurochemical Features of Rem Sleep Behaviour Disorder
Abstract
:1. Introduction
- (1)
- A first study identified a hexanucleotide repeat expansion in the C9orf72-SMCR8 complex subunit (C9orf72) gene (chromosome 9p21.2; gene ID 203228, MIM 614260; this gene encodes a protein with an important role in the regulation of endosomal trafficking), which has been related with familial amyotrophic lateral sclerosis and FTD dementia, in 2 of 344 patients diagnosed with RBD [15].
- (2)
- Missense variations in the glucosylceramidase beta (glucocerebrosidase or GBA) gene (chromosome 1q22; gene ID 2629, MIM 606463), related with PD and DLBD, have been found in 7 of 69 iRBD patients (11.6%) and in 1 of 84 healthy matched controls (1.2%, p = 0.026) [16].
- (3)
- A sequencing study of 25 genes previously identified in Genomic Wide Association Studies (GWAS) of PD involving 1039 iRBD patients and 1852 controls found an association of rare coding heterozygous nonsynonymous variants in the bone marrow stromal cell antigen 1 (BST1; chromosome 4p15.32; gene ID 683, MIM 600387; implicated in facilitation of pre-B-cell growth) and rare noncoding variants in the lysosomal associated membrane protein 3 (LAMP3) genes (chromosome 3q27.1; gene ID 27074; MIM 605883; implicated in the induction of primary T-cell response) [17].
- (4)
- A study involving 347 RBD patients and 347 matched controls showed that, compared with controls, RBD patients were more likely to smoke, to report a previous head injury, and to have worked as farmers, with a borderline increase in welding, to have previously occupational exposure to pesticides, and to have few years of formal schooling, while there were no significant differences in coffee consumption [18].
2. Search Strategy
2.1. Dopaminergic Dysfunction
Method | Authors, Year [Ref] | RBD/ Controls | Main Findings | |
---|---|---|---|---|
Presynaptic DA terminal | 123I-beta-CIT-SPECT | Stiasny-Kolster et al., 2005 [63] | 30 (6 iRBD, 13 symptomatic, 11 subclinical)/30 |
|
123I-beta-CIT-SPECT | Unger et al., 2008 [19] | 5/0 |
| |
123I-beta-CIT-SPECT | Miyamoto et al., 2010 [64] | 1/6 |
| |
123I-beta-CIT-SPECT | Kim et al. [20] | 14/12 (+14 PD patients) |
| |
123I-beta-CIT-SPECT | Iranzo et al., 2010 [21] | 43/18 (149 for transcranial sonogra-phy—TCS-studies) |
| |
123I-beta-CIT-SPECT | Iranzo et al., 2011 [22] | 20/20 (study at baseline and after 1.5 and 3 years) |
| |
123I-beta-CIT-SPECT | Mossa et al., 2012 [23] | 5/5 |
| |
123I-beta-CIT-SPECT | Rupprecht et al., 2013 [24] | 28/0 (18 iRBD patients showed mild motor abnormalities) |
| |
123I-beta-CIT-SPECT | Arnaldi et al., 2015 [25] | 23/23 |
| |
123I-beta-CIT-SPECT | Arnaldi et al., 2015 [26] | 12/0 (+16 patients with PD and 24 with PD + RBD) |
| |
123I-beta-CIT-SPECT | Zoetmulder et al., 2016 [27] | 10/10 (+10 patients with PD and 10 with PD + RBD) |
| |
123I-beta-CIT-SPECT | Rolinski et al., 2017 [28] | 26/23 (+48 PD patients) |
| |
123I-beta-CIT-SPECT | Meles et al., 2017 [29] | 21/19 (+20 patients with PD and 22 with DLB) |
| |
123I-beta-CIT-SPECT | Frosini et al., 2015 [65] | 15/14 (+28 PD patients) |
| |
123I-beta-CIT-SPECT | Iranzo et al., 2017 [30] | 20/20 (follow-up during 5.7 ± 2.2 years) |
| |
123I-beta-CIT-SPECT | Bae et al., 2018 [31] | 18/18 (+18 PD patients) |
| |
123I-beta-CIT-SPECT | Barber et al., 2018 [66] | 43 (18 of them with apathy) |
| |
123I-beta-CIT-SPECT | Chahine et al., 2019 [32] | 75/0 |
| |
123I-beta-CIT-SPECT | Yamada et al., 2019 [33] | 23 (8 with mild motor impairment)/20 |
| |
123I-beta-CIT-SPECT | Dušek et al., 2019 [34] | 74/39 |
| |
123I-beta-CIT-SPECT | Barber et al., 2020 [35] | 46/32 (+28 PD patients) |
| |
123I-beta-CIT-SPECT | Li et al., 2020 [36] | 15/7 |
| |
123I-beta-CIT-SPECT | Arnaldi et al., 2021 [67] | 263/243 |
| |
123I-beta-CIT-SPECT | Mattiolli et al., 2021 [37] | 39 (17 with mild cognitive impairment) |
| |
123I-IPT-SPECT | Eisensehr et al., 2003 [38] | 16 (8 subclinical)/11 (+8 patients with early PD) |
| |
99mTC-TRODAT-1 SPECT | Rizzo 2018 [39] | 6/0 |
| |
18F-FP-CIT PET | Yoon et al., 2019 [45] | 28/24 (+21 PD patients with RBD) |
| |
18F-FP-CIT PET | Shin et al., 2020 [40] | 39/19 (+31 drug-naïve PD patients) |
| |
18Fluoro-L-Dopa (F-DOPA) PET | Wing et al., 2015 [41] | 11 (with comorbid major depressive disorder)/10 (+8 with comorbid major depressive disorder without RBD) |
| |
18Fluoro-L-Dopa (F-DOPA) PET | Stokholm et al., 2018 [42] | 21/9 |
| |
18Fluoro-L-Dopa (F-DOPA) PET | Gersel Stokholm et al., 2018 [68] | 17/9 |
| |
18Fluoro-L-Dopa (F-DOPA) PET | Knudsen et al., 2018 [69] | 17/14 (+8 PD patients) |
| |
[11C]dihydro-tetrabenazine ([11C]DTBZ) PET | Gillman et al., 2003 [70] | 13 (RBD + MSA)/15 |
| |
[11C]dihydro-tetrabenazine ([11C]DTBZ) PET | Kotagal et al., 2012 [46] | 80 PD patients (27 of them with RBD) |
| |
9-18F-fluoropropyl-(+)-dihydrotetra-benazine (18F-AV133) (assessing VMAT2) | Beauchamp et al., 2020 [43] | 14/16 (+20 PD + 10 DLB patients) |
| |
6-[(18)F] fluoro-metatyrosine (FMT) PET | Miyamoto et al., 2012 [44] | 19 (9 with SN hyperechogenicity by TCS) |
| |
6-[(18)F] fluoro-metatyrosine (FMT) PET | Miyamoto et al., 2020 [71] | 24 (follow-up during 1–10 years |
| |
11C-CFT PET | Huang et al., 2020 [72] | 37/15 (+86 PD patients) |
| |
Postsynaptic DA terminal | 123I-IBZM-SPECT (striatal D2 receptors) | Eisensehr et al., 2003 [38] | 16 (8 subclinical)/11 (+8 patients with early PD) |
|
11C-raclopride PET (striatal D2 receptors) | Wing et al., 2015 [41] | 11 (with comorbid major depressive disorder)/10 (+8 with major depressive disorder without RBD) |
| |
Noradrenalin transporter | 11C-methyl-reboxetine (MeNER) PET | Knudsen et al., 2018 [69] | 14/9 (+22 PD patients) |
|
11C-methyl-reboxetine (MeNER) PET | Andersen et al., 2020 [73] | 17/25 (+30 PD patients, 16 of them with RBD) |
| |
Serotonin transporter (SERT) | 123I-beta-CIT-SPECT | Arnaldi et al., 2015 [25] | 23/23 |
|
123I-beta-CIT-SPECT | Barber et al., 2018 [66] | 43 (18 of them with apathy) |
| |
11C3-Amino-4-(2-dimethyl-aminome-thyl-phenylsulfa-ryl)-benzonitrile (DASB) PET | Kotagal et al., 2012 [46] | 80 PD patients (27 of them with RBD) |
| |
Acetylcholine transporter | 123I-iodobenzove-samicol (123I-IBVM) PET | Gillman et al., 2003 [70] | 13 (RBD + MAS)/12 |
|
18F-fluoroethoxy-benzovesamicol (FEOBV) PET | Bedard et al., 2019 [74] | 5/5 |
| |
Postsynaptic acetylcholine terminal | 11Cmethylpiperidylpropionate acetyl-cholinesterase PET | Kotagal et al., 2012 [46] | 80 PD patients (27 of them with RBD) |
|
11C-donepezil PET | Gersel Stokholm et al., 2020 [68] | 17/9 |
| |
11C-donepezil PET | Staer et al., 2020 [75] | 19/27 |
| |
Inflammation markers (microglia activation) | 11C-PK11195 positron emission tomography (PET) | Stokholm et al., 2018 [42] | 21/20 |
|
11C-PK11195 positron emission tomography (PET) | Staer et al., 2020 [75] | 19/27 |
| |
Amyloid | 18F-flutemetamol amyloid PET | Lee et al., 2020 [76] | 23 (4 with and 19 without amyloid deposits) |
|
2.2. Noradrenaline
2.3. Serotonin
2.4. Acetylcholine
2.5. Other Neurotransmitter Systems, Neuropeptides, and Hormones
2.5.1. Aspartate, Glutamate, Gamma-Amino-Hydroxybutyric Acid (GABA), and Glycine
2.5.2. Adenosine
2.5.3. Peptides and Hormones
2.6. Other Substances
2.6.1. Uric Acid
2.6.2. Proinflammatory Substances
2.6.3. Alpha-Synuclein
2.6.4. Lipoprotein and Protein Glycosylation Profile
2.6.5. Nasal and Gut Microbiome
3. Brain Perfusion Studies
4. Brain Glucose Metabolism Studies
5. Structural and Functional Magnetic Resonance Imaging (Mri) Studies
5.1. Studies on Gray Matter Volume (GMV)
5.2. Studies on Cortical Thickness
5.3. Studies Using Diffusion Tensor Imaging (DTI)
5.4. Studies Addressing Dorsolateral Nigral Hyperintensity (DNH)
5.5. Studies with Neuromelanin-Sensitive Structural and Diffusion MRI
5.6. Studies with Functional MRI (fMRI)
- Decreased functional connectivity in the basal ganglia network both in iRBD and in PD patients which differentiated iRBD and PD from controls with high sensitivity and specificity, but did not differentiate RBD from controls [28].
- Reduced cortico-cortical functional connectivity strength in edges located in posterior regions in iRBD patients compared with controls [180].
- Increased functional connectivity between the left thalamus and occipital regions including the right cuneal cortex, left fusiform gyrus, and lingual gyrus in iRBD patients compared with controls [181].
- Decreased functional connectivity between the limbic striatum and temporo-occipital regions in iRBD patients compared with controls, which was associated with the presence of impulse control disorder in iRBD patients [189].
- Decreased functional connectivity between the brainstem and the anterior cingulated, temporal lobe, and the cerebellum posterior lobe in iRBD patients compared with controls [191].
- Decreased striatal-prefrontal (this in executive control) and midbrain-pallidum functional connectivity in the basal ganglia network, decreased motor and somatosensory cortex functional connectivity in the sensorimotor network, and lack of abnormalities in the default mode network compared with controls [190].
- Decreased functional connectivity with two clusters located in the precuneus in iRBD patients compared to the controls using a multivariate pattern analysis [186].
5.7. Measurement of Visible Enlarged Perivascular Space (EPVS)
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors, Year [Ref] | Patients/Controls | Method | Main Findings |
---|---|---|---|
Mazza et al., 2006 [146] | 9/8 | 99mTc-ECD SPECT |
|
Hanyu et al., 2011 [147] | 24/18 | N-isopropyl-p-123I-iodoamphetamine SPECT |
|
Vendette et al., 2011 [148] | 20/20 | 99mTc-ECD SPECT |
|
Vendette et al., 2011 [149] | 20/20 (10 RBD patients had mild cognitive impairment- MCI) | 99mTc-ECD SPECT |
|
Dang-Vu et al., 2012 [150] | 20/0 (10 RBD patients developed PD or LBD after 3 years of follow-up) | 99mTc-ECD SPECT |
|
Sakurai et al., 2014 [151] | 9/0 | N-isopropyl-p-123I-iodoamphetamine SPECT |
|
Mayer et al., 2015 [152] | 1/0 (+1 PD with RBD and 2 narcolepsy with RBD) | 99mTc-ECD SPECT |
|
Chen et al., 2020 [153] | 15/20 | Pseudocontinuous arterial spin-labeled (pASL) perfusion with high resolution T1-weighted images using a 3.0 Tesla MRI unit |
|
Barill et al., 2020 [154] | 37/23 | 99mTc-HMPAO SPECT |
|
Eskildsen et al., 2021 [155] | 20/25 | Dynamic susceptibility contrast MRI using spin echo echo-planar imaging with high resolution T1-weighted images using a 3.0 Tesla MRI unit |
|
Authors, Year [Ref] | Patients/Controls | Method | Main Findings |
---|---|---|---|
Fujishiro et al., 2010 [92] | 9/0 | 18F-FDG PET |
|
Wu et al., 2014 [156] | 21/21 (and 16 moderate PD patients) | 18F-FDG PET |
|
Wing et al., 2015 [41] | 11 (with comorbid major depressive disorder)/10 (+8 with major depressive disorder without RBD) | 18F-FDG PET |
|
Ge et al., 2015 [157] | 20/21 | 18F-FDG PET |
|
Ota et al., 2016 [158] | 11 iRBD non-demented with cognitive decline 4 years later | 18F-FDG PET |
|
Meles et al., 2017 [29] | 21/19 (+20 PD and 22 DLB patients) | 18F-FDG PET and DAT. Analysis of PD-related pattern (PDRP) expression (characterized by relatively decreased cortical glucose metabolism in the supplementary motor area, and in the prefrontal association, posterior and inferior parietal, lateral occipital, and temporal cortices, and a relative increased metabolism in the pons, cerebellum, pallidum, thalamus, limbic association and sensorimotor cortices, left supplementary motor area, and paracentral lobule) |
|
Meles et al., 2017 [159] | 21/44 (+38 de novo PD patients; 24 with probable RBD) | 18F-FDG PET and DAT. Analysis of iRBD related pattern (iRBDRP; relative increased glucose metabolism in the brain stem cerebellum, thalamus, hippocampus, and sensorimotor cortex, and decreased metabolism in the middle cingulated, parietal, temporal, and occipital cortices) which showed a partial overlapping with the PDRP. |
|
Huang et al., 2020 [72] | 37/15 (+86 PD patients) | 18F-FDG PET and 11C-CFT PET. Patients were divided into those with relatively normal (iRBD-RN) or abnormal (iRBD-AB) striatal DAT binding |
|
Arnaldi et al., 2019 [160] | 36/79 (+72 PD patients; 40 with probable RBD) | 18F-FDG PET. Discriminant analysis according to the metabolic pattern |
|
Yoon et al. [45] | 28/24 (+21 patients with PDRBD) | 18F-FDG PET and 18F-FP-CIT PET. Analysis of PDRBDRP |
|
Liguori et al. [161] | 54/35 (+28 patients with PD, 10 with LBD and 55 with Alzheimer disease -AD) | 18F-FDG PET |
|
Mattioli et al., 2021 [37] | 39 (17 with MCI)/42 | 18F-FDG-PET and 123I-FP-CIT-SPECT |
|
Shin et al., 2021 [162] | 30/24 (+28 patients with de novo PD and RBD –dnPDRBD-, and 21 with PD without RBD) | 18F-FDG-PET. Calculation of derived metabolic patterns from the PDRP and dnPDRBDRP scores in iRBD patients. |
|
Authors, Year [Ref] | RBD/ Controls | Method | Main Findings |
---|---|---|---|
Unger et al., 2010 [163] | 12/10 | 1.5 T MRI single-shot echo planar sequence with a twice-refocused spin echo pulse, sequence acquisition, processed with voxel-wise analysis of DTI. Measurement of AD (a potential marker of neuronal loss), RD (a potential marker of glial pathology), and FA (measure of brain-tissue integrity) |
|
Ellmore et al., 2010 [164] | 5/17 (+5 early PD patients) | 3.0 T MRI T1-weighted acquisition and processed with VBM. Quantification of volumes of specific subcortical gray matter nuclei implicated in PD |
|
Scherfler et al., 2011 [165] | 26/14 | 1.5 T MRI T1-weighted acquisition and processed with VBM and DTI. Measurement of MD and FA |
|
Hanyu et al., 2012 [166] | 20/18 | 1.5 T MRI T1-weighted acquisition and processed with VBM. |
|
García-Lorenzo et al., 2013 [167] | 36 PD patients (24 of them with RBD)/19 | 3.0 T MRI T1-weighted acquisition, and processing by using combined NM-sensitive, structural and diffusion MRI approaches |
|
Ellmore et al., 2013 [168] | 10/10 (+11 PD patients) | 3.0 T MRI T1-weighted acquisition. Measurement of correlations of SN time series using resting-state BOLD-fMRI and voxelwise analysis of variance |
|
Ehrminger et al., 2016 [169] | 21/21 | 3.0 T MRI T1-weighted acquisition, and processing by using NM-sensitive imaging |
|
De Marzi et al., 2016 [170] | 15/42 (+104 PD patients) | 3.0 T MRI T1-weighted acquisition. Measurement of DNH with high-field SWI |
|
Boucetta et al., 2016 [171] | 309 PD patients (69 of them with probable RBD)/19 | 3.0 or 1.5 T MRI T1-weighted acquisition and processed with DBM |
|
Rolinski et al., 2016 [28] | 26/23 (+48 early PD patients) | 3.0 T MRI. T1-weighted acquisition, and processing with VBM. Measurement of basal ganglia network dysfunction by using resting-state fMRI |
|
Rahayel et al., 2018 [172] | 41/41 | 3.0 T MRI T1-weighted acquisition and processed with VBM. |
|
Frosini et al., 2015 [65] | 15/14 (+28 PD patients) | 7.0 T MRI T1-weighted acquisition. Processing by using three-dimensional gradient-recalled-echo multi-echo SWI of the SN |
|
Pyatigorskaya et al., 2017 [173] | 19/18 | 3-T MRI, with analysis of DTI, NM-sensitive imaging, and T2* mapping. Regions of interest in the SN area were drawn in NM-sensitive and T2-weighted images |
|
Rahayel et al., 2018 [174] | 52 (17 with MCI)/41 | 3.0 T MRI T1-weighted acquisition and processed with VBM. |
|
Park et al., 2019 [175] | 10/14 | 3.0 T MRI T1-weighted acquisition, calculation of absolute structural volumes using FreeSurfer image analysis software, and structural volume and connectivity analyses performed with Brain Analysis using Graph Theory. |
|
Bourgouin et al., 2019 [176] | 46/31 | 3.0 T MRI T1-weighted acquisition and processed with VBM. |
|
Campabadal et al., 2019 [177] | 20/27 | 3.0 T MRI T1-weighted acquisition; FreeSurfer was used to estimate cortical thickness, subcortical volumetry, and hippocampal subfields segmentation; and FIRST, FSL’s model-based segmentation/registra-tion tool to perform hippocampal shape analysis |
|
Pereira et al., 2019 [178] | 27/31 (+ 151 de novo PD patients) | 3.0 T MRI T1-weighted acquisition. Cortical thickness and volumes of subcortical gray matter structures (hippocampus, amygdala, thalamus, caudate, putamen, pallidum, accumbens) processed with Freesurfer39 in addition to the estimated total intracranial volume (TIV) |
|
Yamada et al., 2019 [33] | 23 (8 with mild motor impairment)/20 | 3.0 T MRI T1-weighted acquisition resting state whole-brain fMRI acquired with single-shot gradient echo planar imaging (GE-EPI). |
|
Ohlhauser et al., 2019 [179] | 17 (all converted further to PD)/21 (+20 prodromal PD patients, 14 with RBD and 6 with hyposmia) | 3.0 T MRI T1-weighted acquisition, and processed with DTI. Measurement of MD and FA by using tract-based spatial statistics |
|
Barber et al., 2020 [35] | 46/32 (+28 PD patients) | 3.0 T MRI T2*-weighted images T1-weighted acquisition and T1-weighted structural MRI acquisition; assessment of DNH using SWI MRI. |
|
Campabadal et al., 2020 [180] | 20/27 | 3.0 T MRI T1-weighted acquisition; study of brain functional connectivity using resting-state fMRI |
|
Byun et al., 2020 [181] | 37/15 | 3.0 T MRI T1-weighted acquisition; resting-state fMRI and seed-to voxel analysis were used to study thalamo-cortical functional connectivity |
|
Chen et al., 2020 [182] | 27/33 | 3.0 T MRI T1-weighted acquisition; VBM was used to assess gray matter alterations, with VBM; resting-state fMRI to study thalamo-cortical functional connectivity, and calculation ALFF to measure differences in spontaneous brain activity |
|
Ellmore et al., 2020 [183] | 32/11 (+23 PD patients, 16 with RBD) | 3.0 T MRI T1-weighted acquisition; study of the whole brain SN functional connectivity using resting-state fMRI and voxelwise analysis; correlation with serum uric acid levels |
|
Li et al., 2020 [36] | 15/20 | 3.0 T MRI T1-weighted acquisition; measurement of spontaneous neuronal activity of the striatum ReHo and ALFF analysis |
|
Campabadal et al., 2020 [184] | 14/18 | 3.0 T MRI T1-weighted acquisition; estimation of cortical thickness and subcortical volumetry with FreeSurfer |
|
Si et al., 2020 [185] | 33/35 (+82 PD patients, 39 with RBD) | 3.0 T MRI T1-weighted acquisition; assessment of visible EPVS in CSO, BG, SN, and BS |
|
Byun et al., 2020 [186] | 50/20 | 3.0 T MRI T1-weighted acquisition; use of resting-state fMRI and MVPA to study whole-brain functional connectivity |
|
Yoon et al., 2021 [187] | 78 PD patients (18 with probable RBD) | 3.0 T MRI T1-weighted acquisition; comparison between PD patients with RBD and without RBD of both cross-sectional and longitudinal cortical thickness and subcortical volume changes, |
|
Jiang et al., 2021 [188] | 50 PD patients (26 with probable RBD)/26 | 3.0 T MRI T1-weighted acquisition; assessment of gray matter alterations with VBM; study of seed-based functional connectivity using resting-state fMRI |
|
Marques et al., 2021 [189] | 17/14 | 3.0 T MRI T1-weighted acquisition; assessment of grey matter alterations with VBM; study of the whole brain functional connectivity using seed-based analysis of resting-state fMRI |
|
Wakasugi et al., 2021 [190] | 50/70 | 3.0 T MRI T1-weighted acquisition; study of the whole brain functional connectivity using resting-state fMRI with GE-EPI |
|
Li et al., 2021 [191] | 32/33 | 3.0 T MRI T1-weighted acquisition; assessment of gray matter alterations with VBM; study of the whole brain functional connectivity using seed-based analysis of resting-state fMRI |
|
Gan et al., 2021 [192] | 126 PD patients (45 with probable RBD)/37 | 3.0 T MRI T1-weighted acquisition; study of DFC using resting-state fMRI and a sliding-window analysis, |
|
Zhou et al., 2021 [193] | 34/32 (+38 PD patients) | 3.0 T MRI T1-weighted acquisition, and processed with DTI; calculation of free-water maps with a bi-tensor model based on the diffusion measurements; assessment of DAT binding with 18F-FP-CIT PET |
|
Zhang et al., 2021 [194] | 29/28 | 3.0 T MRI T1-weighted acquisition, processing with SWI and quantitative susceptibility mapping for evaluation of the nigrosome-1 (N1) sign in the SN, global and regional high-iron content, and volume of subcortical nuclei. |
|
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Jiménez-Jiménez, F.J.; Alonso-Navarro, H.; García-Martín, E.; Agúndez, J.A.G. Neurochemical Features of Rem Sleep Behaviour Disorder. J. Pers. Med. 2021, 11, 880. https://doi.org/10.3390/jpm11090880
Jiménez-Jiménez FJ, Alonso-Navarro H, García-Martín E, Agúndez JAG. Neurochemical Features of Rem Sleep Behaviour Disorder. Journal of Personalized Medicine. 2021; 11(9):880. https://doi.org/10.3390/jpm11090880
Chicago/Turabian StyleJiménez-Jiménez, Félix Javier, Hortensia Alonso-Navarro, Elena García-Martín, and José A. G. Agúndez. 2021. "Neurochemical Features of Rem Sleep Behaviour Disorder" Journal of Personalized Medicine 11, no. 9: 880. https://doi.org/10.3390/jpm11090880
APA StyleJiménez-Jiménez, F. J., Alonso-Navarro, H., García-Martín, E., & Agúndez, J. A. G. (2021). Neurochemical Features of Rem Sleep Behaviour Disorder. Journal of Personalized Medicine, 11(9), 880. https://doi.org/10.3390/jpm11090880