Brain Functional Connectivity Changes in Rapid Eye Movement Sleep Behavior Disorder

Article information

Chronobiol Med. 2024;6(1):15-18
Publication date (electronic) : 2024 March 29
doi :
Department of Neurology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
Corresponding author: Yoonha Hwang, MD, MS, Department of Neurology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbudaero, Paldal-gu, Suwon 16247, Korea. Tel: 82-31-249-8887, E-mail:
Received 2024 March 3; Accepted 2024 March 11.


The idiopathic rapid eye movement sleep behavior disorder (iRBD) has been reported as prodromal biomarker for neurodegenerative synucleinopathies. To early detect neurodegeneration in preclinical stage, pathophysiological changes of iRBD have been studied in many ways including structural magnetic resonance imaging (MRI), functional MRI (fMRI), and connectivity analyses. This review summarizes functional connectivity studies using resting-state fMRI in iRBD patients focused on motor-related network, compensatory mechanism of synucleinopathies, cognitive dysfunction, and non-motor symptoms.


Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by dream-enacting behaviors associated with loss of normal muscle atonia during REM sleep [1]. The idiopathic RBD (iRBD) has been reported as prodromal biomarker for neurodegenerative synucleinopathies, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy [2]. To early detect neurodegeneration in preclinical stage, pathophysiological changes of iRBD have been studied for two decades. Brain structural studies using magnetic resonance imaging (MRI) in iRBD patients suggested alterations in cortical and subcortical volume [3-8].

The brain works as a network of different brain regions that each have their own function. The anatomical connectivity is the physical connections or interactions between two anatomical area and can be studied with structural imaging in collaboration with diffusion tensor tractography methods [9]. In the past few years, novel neuroimaging techniques and analysis methods have enabled the examination of brain functional connectivity (FC) that defined as the temporal dependency of neuronal activation patterns of anatomically separated brain regions [10-12].

The resting-state functional MRI (fMRI) was first reported by Biswal et al. [13], showing brain is not silent even during the resting or relaxing state [11,13]. They showed presence of synchronous low-frequency fluctuations of signal intensities from the resting human brain that have a high degree of temporal correlation both within and across the sensorimotor cortex. And they suggested blood oxygenation level dependent signals play a dominant role in the mechanism that gives rise to FC in the resting human brain. Since then, several similar studies have shown other FC in primary visual network, auditory network, and cognitive networks [14-18]. This review summarizes the existing research on the FC studies in iRBD patients focused on resting-state fMRI.


We searched in the PubMed database for literature using resting-state fMRI in patients with iRBD. Keywords were “(resting-state functional magnetic resonance imaging OR resting-state fMRI) AND (rapid eye movement sleep behavior disorder OR REM sleep behavior disorder OR RBD).” A total number of 39 studies were searched, and we excluded studies that were not written in English; reviews; not related to our main topic. After removing, selected articles were 13 (Table 1).

Summary of 13 studies about functional connectivity using resting-state fMRI in RBD patients

Methodologically, voxel-based connectivity analyses were applied in 11 reviewed studies [19-29] and node-based connectivity analyses were applied in 2 studies [30,31]. The voxel-based analyses models estimate FC values between specific regions of interest and all the other voxels in the whole brain, performing the spatial organization of large-scale resting-state networks. The node-base analyses are graph-based connectivity models represented by two concepts: node (i.e., different brain areas) and edges (i.e., connections between considered regions) [32].

Early two studies, using a voxel-based analysis, showed FC change in networks involving substantia nigra [19] and basal ganglia [20]. These results served as evidence that resting-state fMRI studies could be used as an early biomarker for synucleinopathies, but it was unclear whether they were a result of damaged connectivity by synucleinopathy pathology. There were follow-up studies that also showed abnormalities in motor-related networks using another analysis method like regional homogeneity (ReHo) and independent component analyses (ICA) [22,24,25].

In the preclinical stage of synucleinopathies like PD, compensatory mechanisms may take place to delay the clinical onset of motor symptoms by counteracting nigrostriatal dysfunction. Basal ganglia, cortical premotor areas, thalamus, and cerebellum had been suggested as key regions in those mechanisms [28,33-35]. Recently, some studies showed that there were FC changes related in the PD compensatory mechanisms. Byun et al. [21] showed increased FC between the left thalamus and occipital regions and Chen et al. [28] reported higher amplitude of low-frequency fluctuations (ALFF) values in right parahippocampal gyrus that was already known association with iRBD in positron emission tomography FC study [36].

iRBD is often accompanied by cognitive decline involving executive, visuospatial, attention, and memory functions [37]. A pioneering study by Rolinski et al. [20] showed that FC change did not correlate with changes in cognitive function. But many recent studies using other analyses methods showed FC change in cognitive related brain regions including thalamo-fusiform FC [21], striatal-prefrontal FC [25], striatal-cortical FC [26], and nucleus basalis of Meynert with the left lateral occipital cortex and lingual gyrus [27]. And these results were correlated with neurocognitive results.

The pathophysiology of PD non-motor symptoms is not yet clear. Recently, several studies showed FC changes related in nonmotor symptoms such as olfactory and autonomic dysfunction. Chen et al. [28] reported negative correlation between olfactory function and ALFF in right superior occipital gyrus. Woo et al. [29] showed hypoconnectivities with the left olfactory cortex and left amygdala, increased functional connectivity with the left gyrus rectus. FC change related to autonomic dysfunction was reported by Li et al. [24]. They showed reduced functional connectivity between the brainstem and the cerebellum posterior lobe, temporal lobe and anterior cingulate. And these FC findings were negatively correlated with the Scales for Outcomes in Parkinson’s Disease-Autonomic scores.


As the aging population increases, the prevention and treatment of neurodegenerative diseases is important. Identifying brain changes in iRBD patients, known as a prodromal biomarker of synucleinopathies, gives us much important evidence for early treatment in preclinical stage. Because the brain works in a network system, structural and functional connectivity studies are important in neuropsychological disease. In future study, longitudinal designs are needed to know the temporal and causal relations between FC changes and conversion to synucleinopathies.


The author has no potential conflicts of interest to disclose.

Availability of Data and Material

Data sharing not applicable to this article as no datasets were generated or analyzed during the study.

Funding Statement





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Table 1.

Summary of 13 studies about functional connectivity using resting-state fMRI in RBD patients

Study (author, year) Number of subject (patient/control) Age (yr) of patient/control Number of male subject (patient/control) Imaging modality Covariates
Ellmore et al. (2013) [19] 10/11 57.0±2.7/57.0±2.4 6/4 Voxel (seed-to-seed) -
Rolinski et al. (2016) [20] 26/23 67.0±7.7/60.5±8.9 22/5 Voxel (ICA) -
Campabadal et al. (2020) [30] 20/27 71 (10.0)/66.5 (13.0) median (interquartile range) 14/13 Node (graph theory) Age, sex, head motion
Li et al. (2020) [22] 33/32 64.91±7.74/63.33±5.25 19/14 Voxel (Seed-to-seed) Age, sex, FD
Li et al. (2020) [23] 15/15 64.27±1.87/64.80±1.83 9/9 Voxel (ReHo/ALFF) Age, sex, years of education
Li et al. (2021) [24] 33/38 63.76±7.97/63.03±5.52 21/18 Voxel (ICA) Age, sex
Byun et al. (2020) [21] 37/15 67.7±7.1/68.3±3.3 25/9 Voxel (Seed-to-seed) Age, sex, years of education
Wakasugi et al. (2021) [25] 50/70 69.4±5.6/68.7±6.2 35/38 Voxel (ICA) Age, sex, medication for RBD
Zhang et al. (2021) [26] 21/22 64 (9.5)/60 (8.0) median (interquartile range) 14/9 Voxel (Seed / ReHo) Age, sex, education
Byun et al. (2022) [27] 20/20 70.6±3.6/68.1±3.4 12/11 Voxel (seed to voxel) Age, sex, education level
Chen et al. (2022) [28] 27/33 65.89±8.54/68.25±7.80 19/20 Voxel (ALFF) Age, sex
Geng et al. (2022) [31] 21/22 61.00±10.68/60.27±7.60 14/9 Node (graph theory) Age, sex, education
Woo et al. (2023) [29] 51/39 70.24±5.95/69.51±5.12 29/11 Voxel (seed to voxel) Age, sex

fMRI, functional magnetic resonance imaging; RBD, rapid eye movement sleep behavior disorder; FC, functional connectivity; ICA, independent component analyses; FD, framewise displacement; ReHo, regional homogeneity; ALFF, amplitude of low-frequency fluctuations