Altered network efficiency in isolated REM sleep behavior disorder: A multicentric study

Christina Tremblay; Alexandre Pastor‐Bernier; François Rheault; Véronique Daneault; Violette Ayral; Marie Filiatrault; Liane Desaulniers; Andrew Vo; Jean‐François Gagnon; Ronald B Postuma; Petr Dusek; Stanislav Marecek; Zsoka Varga; Johannes C Klein; Michele T Hu; Stéphane Lehéricy; Isabelle Arnulf; Pauline Dodet; Marie Vidailhet; Jean‐Christophe Corvol; for the ICEBERG Study Group; Shady Rahayel

doi:10.1002/alz.70574

. 2025 Aug 12;21(8):e70574. doi: 10.1002/alz.70574

Altered network efficiency in isolated REM sleep behavior disorder: A multicentric study

Christina Tremblay ^1,^✉, Alexandre Pastor‐Bernier ¹, François Rheault ², Véronique Daneault ¹, Violette Ayral ^1,³, Marie Filiatrault ^1,³, Liane Desaulniers ¹, Andrew Vo ⁴, Jean‐François Gagnon ^1,^5,⁶, Ronald B Postuma ^1,⁷, Petr Dusek ⁸, Stanislav Marecek ⁸, Zsoka Varga ⁸, Johannes C Klein ⁹, Michele T Hu ⁹, Stéphane Lehéricy ¹⁰, Isabelle Arnulf ¹⁰, Pauline Dodet ¹⁰, Marie Vidailhet ¹⁰, Jean‐Christophe Corvol ¹⁰; for the ICEBERG Study Group, Shady Rahayel ^1,^11,^✉

PMCID: PMC12340432 PMID: 40791089

Abstract

INTRODUCTION

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD), characterized by abnormal movements during REM sleep, is a prodromal stage of dementia with Lewy bodies (DLB) and Parkinson's disease (PD). While iRBD shows emerging brain changes, their impact on structural connectivity and network efficiency, and their predictive value, remain poorly characterized.

METHODS

In this international prospective study, 198 polysomnography‐confirmed iRBD patients and 174 controls underwent diffusion magnetic resonance imaging and were analyzed. Cutting‐edge diffusion tractography and network‐based statistics were applied to reconstruct individual connectomes and assess network properties predicting DLB or PD.

RESULTS

Structural architecture was already disrupted in iRBD, with both reduced and compensatory increased connections. Global efficiency was decreased. Local efficiency in motor regions was altered and associated with early clinical symptoms. Altered local efficiency in the supramarginal gyrus predicted DLB only.

DISCUSSION

Early disruption of brain architecture in iRBD predicts progression to synucleinopathy‐related dementia, offering a novel potential prognostic biomarker.

Highlights

Isolated rapid eye movement sleep behavior disorder (iRBD) patients show significant alterations in inter‐regional structural connectivity.
Global efficiency is reduced in iRBD compared to controls.
Areas with increased local efficiency contribute to decreased global efficiency.
Altered network efficiency is associated with emerging Parkinsonian features.
Higher supramarginal efficiency predicts dementia with Lewy bodies in iRBD.

Keywords: dementia with Lewy bodies, diffusion magnetic resonance imaging, graph theory, parasomnias, sleep, structural connectivity, synucleinopathies

1. BACKGROUND

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a parasomnia characterized by abnormal movements during REM sleep.⁠⁠ More than 80% of individuals with iRBD develop a synucleinopathy within 10 to 15 years, most commonly Parkinson's disease (PD) or dementia with Lewy bodies (DLB).⁠ This makes iRBD the strongest known prodromal predictor for these conditions,⁠ offering a critical window to investigate the early structural brain changes preceding synucleinopathies and potentially identify therapeutic targets.

White matter damage and altered structural connectivity, detectable via diffusion‐weighted imaging (DWI), are recognized features in PD⁠ and DLB. ¹ , ² Meta‐analyses confirm widespread white matter alterations primarily affecting subcortical, limbic, and cortical regions, even in early PD stages. ³ , ⁴ , ⁵ These disruptions impact network organization, leading to poorer integration and segregation, characterized by decreased clustering coefficient (regions less closely interconnected in local clusters) and lower global efficiency (less efficient information transfer).⁠⁠⁠⁠ Similarly, structural connectivity alterations are reported in DLB, though findings vary regarding their extent.⁠ Some studies reported widespread white matter disruptions⁠, while others found disruptions confined to parietal and occipital regions. ⁶ Several studies identified increased network efficiency and connectivity in DLB, ⁷ , ⁸ , ⁹ interpreted as a compensatory mechanism that may become burdensome as pathology progresses. ¹⁰ How and when connectivity changes occur remain to be understood in both PD and DLB.

Alterations in white matter and structural connectivity in iRBD have gained attention as potential indicators for prodromal PD or DLB. Initial work with 20 prodromal PD participants (with iRBD and/or hyposmia) found increased connectivity in the supplementary motor area, parahippocampal gyrus, putamen, and cerebellum, potentially reflecting compensatory mechanisms. ¹¹ Others found decreased global and local network efficiency, especially in the frontal cortex, lower degree (lower number of connections), and increased eigenvector centrality (stronger influence of regions within the network) in the caudate and frontal cortex in 10 patients with iRBD. ¹² These findings suggest network alterations in those with iRBD, but larger cohorts are needed to more reliably identify the early structural connectivity changes.

Therefore, this study leverages a large, international, multicentric DWI dataset from polysomnography‐confirmed iRBD participants (n = 198) and healthy controls (n = 174). We aimed to characterize structural connectivity alterations using network‐based statistics (NBS) for connection density and graph theory to assess global and local network efficiency. We further examined relationships between local efficiency and other network metrics (clustering coefficient, connection strength, degree, and eigenvector centrality) to define network reorganization. Finally, we investigated whether efficiency changes predict phenoconversion toward DLB versus PD in the 177 iRBD patients followed longitudinally. We hypothesized that iRBD patients would show localized reduced connection density along with lower global and local network efficiency compared to controls.

2. METHODS

2.1. Participants

Clinical and neuroimaging data, including T1‐weighted magnetic resonance imaging (MRI) and DWI, from individuals with iRBD and healthy controls were acquired by different international centers, including Canada (Montreal: two scanners), Czech Republic (Prague: one scanner), United Kingdom (Oxford: one scanner), and France (Paris: two scanners; see Table S1 in supporting information). Baseline data from iRBD participants and controls in the Parkinson's Progression Markers Initiative (PPMI, http://www.ppmi‐info.org), a longitudinal multicenter study with standardized acquisition protocols, were added to the dataset. ¹³ Finally, control data from the Quebec Parkinson Network were included. ¹⁴ All iRBD patients received a video polysomnography–confirmed diagnosis based on the International Classification of Sleep Disorders, Third Edition (see supporting information for criteria). ¹⁵ The absence of concomitant DLB, PD, and multiple system atrophy (MSA) was confirmed at the neurological evaluation closest in time to the neuroimaging acquisition. The exclusion criteria for all participants were: (1) the presence of Parkinsonism or dementia, (2) a diagnosis of epilepsy or epileptiform abnormalities on electroencephalogram, (3) antidepressant‐induced RBD, and (4) the presence of sleep disorders mimicking RBD (sleepwalking, night terrors, uncontrolled sleep apnea). Most participants completed the Montreal Cognitive Assessment (MoCA) ¹⁶ to evaluate global cognition; the Unified Parkinson's Disease Rating Scale revised by the Movement Disorder Society (MDS‐UPDRS‐III) scores, ¹⁷ to evaluate severity of parkinsonian motor features; and an olfactory identification task (either the 12‐ or 40‐item University of Pennsylvania Smell Identification Test or the 12‐ or 16‐item Sniffin’ Sticks). All participating centers received approval from their respective local research ethics committees, and the multicentric project was approved by the research ethics board of the CIUSSS du Nord‐de‐l’Île‐de‐Montréal and the McGill University Health Centre.

RESEARCH IN CONTEXT

Systematic review: Literature on brain structural connectivity in dementia with Lewy bodies (DLB), Parkinson's disease (PD), and isolated rapid eye movement sleep behavior disorder (iRBD) was reviewed using Google Scholar and PubMed. While structural connectivity alterations have been well documented in DLB and PD, the presence and clinical relevance of such disruptions in iRBD remained unclear. Studies with small samples (n < 25) showed inconsistent findings on whether iRBD shows compensatory or degenerative network changes. References included the relevant studies.
Interpretations: This study demonstrates that iRBD is associated with both reduced global efficiency and region‐specific alterations in local efficiency, especially in subcortical and motor regions. Increased local efficiency in the supramarginal gyrus predicted specific phenoconversion to DLB, supporting its potential as a prognostic biomarker of differential phenoconversion in iRBD.
Future directions: These results offer a novel prognostic marker in iRBD, which may allow for improving risk stratification of iRBD patients for clinical trials in DLB. Future work should integrate the derivation of this marker into a semi‐automated, easy‐to‐use processing pipeline to allow accessible derivation of abnormalities.

2.2. Structural connectivity

Tractography and connectomic pipelines were applied to the DWI data to create structural connectivity matrices for each subject in the groups (Figure 1).

Main steps of the processing pipelines. The diffusion‐weighted images were first acquired from different cohorts with the T1w images. The T1w processed with FreeSurfer v.7.1.1 and DWI were used in TractoFlow‐ABS v.2.4.4 to build a tractogram for each subject in the iRBD and healthy control groups. The Cammoun atlas with 448 cortical and 14 subcortical regions was registered in the T1w native space (ANTs registration) and quality control (dMRIQcpy) was performed for each main step of TractoFlow‐ABS. Subsequently, the Connectoflow pipeline (v.1.1.0) was used with COMMIT2 to build a connectome for each subject (representing the number of streamlines, adjusted for the connection length, between each region of the atlas). The iRBD group average and standard deviation is shown for visualization. DWI, diffusion‐weighted imaging; iRBD, isolated rapid eye movement sleep behavior disorder; T1w, T1‐weighted.

2.2.1. DWI processing

To generate a structural connectivity matrix for each participant, we analyzed 592 DWI scans from all cohorts (289 iRBD and 303 controls). DWI scans were acquired between May 2012 and January 2023 using an echo‐planar imaging sequence on a SIEMENS 3T scanner at each center. Acquisition parameters for DWI and T1w are described in the supporting information. The TractoFlow Atlas‐Based Segmentation pipeline (TractoFlow‐ABS v.2.4.4: github.com/scilus/tractoflow), ¹⁸ , ¹⁹ with the connectomics profile, was used to create tractograms from the raw DWI scans and T1w images resampled with FreeSurfer v.7.1.1 (processing is detailed in supporting information). ²⁰ Using dMRIQCpy, we next produced quality control files for each main step of the tractography process to identify and remove DWI and T1w scans with artifacts. ²¹ Thirteen subjects with iRBD and four controls (2.9% of scans) failed the TractoFlow‐ABS processing. Additionally, 78 DWI scans from the iRBD group and 72 from the control group (26% of scans) were excluded after quality control due to motion artifacts, incomplete cortical or subcortical structures, inadequate correction of image distortions, inaccurate registration, or incomplete tractogram data. This yielded a total of 425 scans for connectome reconstruction (Figure 2).

Flowchart showing the number of subjects in the iRBD and HC groups at each main step of the method and exclusion criteria. DWI, diffusion‐weighted imaging; HC, healthy control; iRBD, isolated rapid eye movement sleep behavior disorder.

2.2.2. Structural connectome derivation and age matching

We used Connectoflow v.1.1.0 (github.com/scilus/connectoflow) to reconstruct a structural connectome for each participant. ²² , ²³ , ²⁴ The connectome was built using the Cammoun atlas to obtain the number of streamlines (length adjusted) between 448 cortical and 14 subcortical regions. ²⁵ First, the atlas was registered in the native space of each subject's T1w MRI using ANTs registration (github.com/ANTsX/ANTs/). ²⁶ Tractograms generated by TractoFlow‐ABS were processed by Connectoflow to generate the whole‐brain connectome. The COMMIT2 tool was applied to assign streamline weights and remove false positive connections. ²⁷ Group similarity matrices were built to exclude connections with extreme values in each subject's connectivity matrix (with dissimilarity scores > 12). Only intra‐hemispheric connections were retained for analysis to remove potential artifacts as inter‐hemispheric connections frequently involve complex fiber crossings that are inaccurately resolved using tractography. ²⁸ Connectivity values were transformed using the reciprocal of log10 to improve normality and interpretability. ²⁹ As a result, structural connectivity matrices were obtained for 198 iRBD patients and 227 controls (no subjects failed Connectoflow).

To minimize age‐related confounding effects, younger controls (< 55 years old) were excluded, aligning with the typical age of iRBD onset. ³⁰ This resulted in similar mean ages between the groups (iRBD mean [standard deviation (SD)]: 66.5 years [7.0]; control: 66.2 years [6.9]; P value = 0.64). Reflecting the demographic trends in this population, the iRBD group had a higher men proportion (174 men/24 women) compared to the control group (99/75). ³¹ The limited number of women, particularly in the iRBD group, prevented stratified analyses to assess sex effects, as such analyses would lack sufficient statistical power. To minimize potential effects, sex was controlled for in all analyses. In total, the structural connectivity matrices from 198 iRBD and 174 controls were available for analysis (Figure 2).

2.3. NBS

The Network‐Based Statistic toolbox v.1.2 (www.nitrc.org/projects/nbs/) was next used to compare the structural connectivity matrices between the iRBD and control groups, and to identify specific connections significantly affected in iRBD. NBS is a widely recognized method for group‐level statistical analysis of structural and functional connectomes. ³² This approach increases statistical power by considering interconnected sets of regions forming networks. To increase sensitivity to focal effects and avoid using arbitrary primary test statistic thresholds, we applied the false discovery rate (FDR) method (P value_FDR < 0.05, 10,000 permutations). ³³ Group was used as the independent variable, with age, sex, and scanner included as covariates in the design matrix.

2.4. Graph theory metrics

We next determined whether network properties differed between iRBD and control connectomes. We used graph theory metrics, such as global and local efficiency, as these metrics have been particularly useful to identify early changes in brain networks due to pathology. We also investigated how local efficiency alterations relate to other topological features, namely clustering coefficient, connection strength, degree, and eigenvector centrality. ³⁴ Clustering coefficient measures how strongly regions in a network are connected to the nearby regions. Connection strength quantifies the density of connections between regions, degree refers to the total number of connections a region has, and eigenvector centrality measures a region's influence based on both the quality (strength) and quantity (degree) of its connections. Table S2 in supporting information illustrates clustering coefficient and local efficiency patterns.

Specifically, global and local efficiency of the structural connectomes were calculated using the Brain Connectivity Toolbox (https://sites.google.com/site/bctnet) for each connectome (global efficiency) and each region of the Cammoun atlas (local efficiency). ³⁴ To account for inter‐scanner variability, ComBat (github.com/Jfortin1/ComBatHarmonization), widely used in neuroimaging (including diffusion MRI), ³⁵ , ³⁶ , ³⁷ was applied on global and local efficiency measurements. Group, age, and sex variance were preserved in ComBat. Regional W scores were next calculated for each iRBD patient to regress out age and sex effects using the control group as reference. ³⁸ , ³⁹ Linear statistical methods, specifically one‐sample t tests with FDR correction, were applied to the W scores for global and local efficiency measures to identify significant differences between groups (P value_FDR < 0.05). A stepwise regression analysis was also conducted to identify which regions, in terms of local efficiency W scores, were retained as significant predictors of global efficiency W scores in iRBD patients.

Additional metrics potentially influencing local efficiency, such as clustering coefficient, connection strength, degree, and eigenvector centrality, were computed using the Brain Connectivity toolbox. ³⁴ These measurements were scanner‐corrected using ComBat ³⁵ and W scored to regress out age and sex effects associated with normal aging. ³⁸ , ³⁹ One‐sample t tests (FDR‐corrected) were applied on the W scores for each metric to identify regions with significant difference between groups (P value_FDR < 0.05). Next, spatial Pearson correlations were calculated between local efficiency and each metric's W score. To account for spatial autocorrelation (i.e., the tendency of anatomically adjacent brain regions to have more similar measurements), ⁴⁰ correlations were compared to spatially constrained null models using the BrainSMASH toolbox ⁴¹ (1000 spins, one‐tailed, FDR‐corrected). Additionally, to assess the differences between regions showing significantly higher and lower local efficiency in iRBD, we grouped these regions into two categories (positive and negative W scores). We then compared their clustering coefficient, connection strength, degree, and eigenvector centrality using paired samples t tests with FDR correction.

2.5. Association with clinical features

The association between local efficiency and clinical features in iRBD was examined using the MoCA, ¹⁶ MDS‐UPDRS‐III, ¹⁷ and olfactory identification scores. Because every site has its own assessment scale, olfactory scores were converted into the 16‐item Sniffin’ Sticks following the Lawton calibration method. ⁴² ⁠ Partial least squares (PLS) regression ⁴³ with 10 components was applied to identify latent variables maximizing the covariance between the local efficiency of the 462 brain regions (absolute W scores representing deviations from the control group) used as dependent variables, and either the MoCA or MDS‐UPDRS‐III scores (Z scores normalized to controls means) or converted 16‐item Sniffin’ Sticks scores (Z scores normalized to iRBD mean, no control data) used as independent variables. The significance of each PLS latent variable was assessed by comparing it to a null distribution generated from 1000 random permutations. For each significant latent variable, the contribution of each region's local efficiency to the latent variable was quantified, and bootstrapping (1000 iterations) was used to evaluate the significance of these contributions. Regions with a bootstrapping ratio > 3.5 or < ‐3.5 were considered to robustly contribute to the latent variable (P value < 0.0005). Only participants with iRBD and MoCA (N = 186), MDS‐UPDRS‐III scores (N = 175), or converted 16‐item Sniffin’ Sticks scores (N = 176) were included in the PLS regression.

2.6. Relationship between local efficiency and phenoconversion status

Finally, we examined whether local efficiency (W scores) was associated with phenoconversion in 177 iRBD patients who underwent longitudinal follow‐up. Among these patients, 43 (24.3%) had converted at the latest clinical visit: 28 (65.1%) converted to PD, 11 (25.6%) to DLB, and 4 (9.3%) to MSA. The remaining 134 individuals (75.7%) had not yet converted (non‐converters). Patients who converted to MSA were excluded due to their low number. The non‐converters and PD and DLB converters had similar age (F = 0.67, P value = 0.51, Figure S1 in supporting information), sex proportion (Fisher exact test P value = 0.07), and time to phenoconversion (for converters, t = −1.23, P value = 0.23, Figure S2 in supporting information). However, the non‐converters had a longer follow‐up time duration since MRI (6.9 years, SD = 3.0) compared to PD (t = 5.26, P value < 0.0001) and DLB converters (t = 2.65, P value = 0.02, Figure S2).

The predictive value of local efficiency (W scores) for phenoconversion to PD or DLB was assessed across 462 regions. First, linear regression models (FDR‐corrected) compared local efficiency between converters (to PD or DLB) and non‐converters (remaining disease‐free), adjusting for age, sex, and follow‐up duration since MRI. Next, Cox proportional hazards regression analyses were conducted on regions with significant differences, controlling for age and sex, to examine associations between local efficiency and time to phenoconversion. Time zero was defined as the MRI date. Survival time was the interval between MRI and the date of clinical visit confirming disease diagnosis (converters) or the last follow‐up (non‐converters). Hazard ratios with 95% confidence intervals were reported. Kaplan–Meier survival curves were generated to compare the proportions of non‐converters and converters between patients with positive versus negative local efficiency W scores in significant regions.

3. RESULTS

3.1. Participants

The iRBD group had worse global cognition and Parkinsonian motor features compared to the control group, as indicated by their MoCA and MDS‐UPDRS‐III scores, respectively (Table 1).

TABLE 1.

Demographics, clinical variables, and graph theory measures of participants with iRBD compared to controls.

	iRBD (n = 198)	Controls (n = 174)	Statistical test
Variables	mean (SD)	mean (SD)	p value
Sex (men/women) ^a	174/24	99/75	<0.001
Age (years) ^b	66.5 (7.0)	66.2 (6.9)	0.64
MoCA ^b	25.6 (3.0)	27.0 (2.4)	<0.001
MDS‐UPDRS‐III ^b	6.4 (5.6)	3.9 (7.1)	0.001
Global efficiency (W score) ^c	−0.31 (1.04)	NA	<0.00001
Local efficiency (W score) ^c	−0.28 (0.08)	NA	<0.00001
Low‐efficiency regions		NA
Local efficiency (W score) ^c	0.27 (0.04)	NA	<0.00001
High‐efficiency regions		NA
Clustering coefficient (W score) ^c	−0.30 (0.09)	NA	<0.00001
Low‐efficiency regions		NA
Clustering coefficient (W score) ^c	0.26 (0.06)	NA	<0.00001
High‐efficiency regions		NA
Connection strength (W score) ^c	0.06 (0.13)	NA	0.06
Low‐efficiency regions		NA
Connection strength (W score) ^c	−0.30 (0.11)	NA	0.00004
High‐efficiency regions		NA
Degree (W score) ^c	0.06 (0.13)	NA	0.04
Low‐efficiency regions		NA
Degree (W score) ^c	−0.31 (0.10)	NA	0.00002
High‐efficiency regions		NA
Eigenvector centrality (W score) ^c	0.01 (0.11)	NA	0.71
Low‐efficiency regions		NA
Eigenvector centrality (W score) ^c	−0.06 (0.11)	NA	0.19
High‐efficiency regions		NA

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Note: Regions with significant alterations in local efficiency were separated into low‐ (negative W score) versus high‐efficiency regions (positive W score).

Abbreviations: iRBD, isolated rapid eye movement sleep behavior disorder; MDS, Movement Disorders Society; MoCA, Montreal Cognitive Assessment; NA, not applicable; SD, standard deviation; UPDRS‐III, Unified Parkinson's Disease Rating Scale part 3 (motor scale).

^{^a}

chi‐squared test.

^{^b}

Two‐sample t test (Student t test).

^{^c}

One‐sample t test.

3.2. Structural connectivity is altered in iRBD

Structural connectivity maps were analyzed to assess significant regional changes in connectivity density in iRBD patients compared to controls (Figure 3A and B). NBS revealed reduced structural connectivity in iRBD relative to controls in 14 connections across 27 different regions (P value_FDR < 0.05; Table S3 in supporting information). Reduced connectivity in the left hemisphere was observed in frontal (precentral, paracentral, pars orbitalis, rostral middle frontal, superior frontal, lateral orbitofrontal, and rostral and caudal anterior cingulate cortices) and postcentral regions. In the right hemisphere, reductions were predominantly found in the frontal (medial orbitofrontal, superior frontal, and precentral gyrus), temporal, and posterior regions, including the fusiform, lingual, inferior parietal, posterior cingulate, and lateral occipital cortices. Increased connectivity was also found in three connections between the right inferior and superior parietal cortex, and in one connection between the left medial orbitofrontal and visual cortex (P value_FDR < 0.05). The number of regions with statistically significant changes did not differ between the two hemispheres: right (21/231 = 9% of regions) compared to the left (12/231 = 5% of regions) hemisphere (chi‐square = 2.64; P value = 0.10).

Structural connectivity changes in patients with iRBD. A, Circular plot showing the structural connections with significantly lower (red) and higher (blue) connection density in iRBD compared to controls (P value_FDR < 0.05 using NBS with age, sex, and scanner as covariates). Subregions of the Cammoun atlas have been combined into 78 regions for visualization. B, Brain map representations of the 27 subregions showing significantly lower connectivity (red areas) and the 6 subregions with stronger connectivity (blue areas) in iRBD compared to controls. FDR, false discovery rate; iRBD, isolated rapid eye movement sleep behavior disorder; LH, left hemisphere; NBS, network based statistics; RH, right hemisphere.

3.3. Structural network efficiency is altered in iRBD

To assess whether connectivity changes impacted network efficiency at global and local scales, we quantified the global efficiency of each subject's connectome as well as the local efficiency of each region. This analysis provided insights into how connectivity changes influence network efficiency. To isolate disease‐specific effects from those related to aging and sex, global and local efficiency W scores were used. A significant decrease in global efficiency was observed in iRBD (W score = –0.31, P value < 0.001; Figure S3 in supporting information) compared to controls. Furthermore, local efficiency was reduced in 18 cortical regions and 3 subcortical structures (left putamen and bilateral thalamus) and increased in 11 cortical regions after FDR correction (Figure 4A, Table S4 in supporting information). Regions showing the largest reduction in local efficiency were the postcentral cortex (W score = −0.52), putamen (−0.41), inferior parietal cortex (−0.41), thalamus (−0.37), and precentral cortex (−0.33), all involved in motor functioning. Interestingly, no regions showed changes in local efficiency and connection density simultaneously. Taken together, these findings suggest that both specific connections and efficiency changes occur in iRBD and might contribute to reduced brain global efficiency.

Local efficiency alterations in iRBD. A, Cortical and subcortical regions showing significant decreases (red) and increases (blue) in local efficiency in the iRBD group compared to controls (P value_FDR < 0.05). B, Decreases and increases in local efficiency in specific cortical and subcortical regions are associated with the reduced global efficiency observed in iRBD. FDR, false discovery rate; iRBD, isolated rapid eye movement sleep behavior disorder; LH, left hemisphere; RH, right hemisphere.

3.4. Subcortical and cortical regions differentially contribute to impaired global efficiency

To identify the regions contributing to the reduction in global efficiency observed in iRBD, a stepwise regression analysis was performed. The local efficiency W scores of the 32 regions with significant changes in iRBD were used as predictors. Eight regions were identified as significant contributors to global efficiency changes in iRBD. Four regions, namely the left putamen, left and right thalamus, and left lateral orbitofrontal gyrus, were positive contributors. This indicates that lower local efficiency in these regions was associated with lower global efficiency. In contrast, four other regions, the left inferior and superior parietal cortex, left insula and right orbitofrontal cortex, were negative contributors, indicating that higher local efficiency in these regions was associated with lower global efficiency (Figure 4B and Table S5 in supporting information). These findings indicate that decreased local efficiency, notably in subcortical regions, contributes to the reduction in global network efficiency. Conversely, increased local efficiency in specific cortical regions contributes to the decrease in global efficiency, suggesting that these increases occur at the expense of global efficiency in iRBD.

3.5. Regions with altered efficiency have specific topological network characteristics

To further understand how changes in network organization impact local efficiency in iRBD, we analyzed the relationships between local efficiency and other graph measures, namely clustering coefficient, connection strength, degree, and eigenvector centrality. W scores were used to quantify the deviation in iRBD patients from controls. One‐sample t tests with FDR correction were applied to the W scores for clustering coefficient, connection strength, degree, and eigenvector centrality to identify significant deviations. A significant reduction was observed in 48 regions in clustering coefficient, while 9 regions showed increased clustering (Table S6 in supporting information, and Figure 5A). Connection strength was significantly decreased in 102 regions while it was increased in 8 regions, predominantly in the frontal lobe as well as in the fusiform, lingual, postcentral, and inferior parietal cortex (Table S7 in supporting information, and Figure 5A). In addition, a lower degree (number of connections) was found in iRBD in 105 regions, while 7 regions had a higher degree, each also showing increased connection strength (Table S8 in supporting information, and Figure 5A). There was no significant difference in eigenvector centrality compared to controls.

Relationships between local efficiency alterations in the iRBD group and graph theory metrics. A, Subcortical and cortical brain maps illustrating significant regional differences in iRBD compared to controls (W scores) for clustering coefficient, connection strength, and degree (number of connections). B, Significant positive spatial correlations were found between local efficiency alterations in iRBD and the clustering coefficients, while significant negative correlations were observed between local efficiency and both connection strength and degree in iRBD. All correlations (Pearson r) were compared against null coefficient distributions using a model that preserves spatial autocorrelation between regions, with FDR correction (significance: P value_spin‐FDR < 0.05). FDR, false discovery rate; iRBD, isolated rapid eye movement sleep behavior disorder.

Regions with the largest reduction in clustering were the postcentral gyrus (W score = −0.57), inferior parietal cortex (−0.42), thalamus (−0.42), putamen (−0.39), supramarginal (−0.34), and precentral cortex (−0.33). Among the 48 regions with decreased clustering, 21 (44%) also showed reduced efficiency. Of the nine regions with increased clustering, six regions (67%) also exhibited increased efficiency. Interestingly, all regions with reduced local efficiency in iRBD had decreased clustering coefficient. This suggests that reduced clustering might be a precursor or primary condition for the alterations in local efficiency in iRBD. Spatial Pearson correlations revealed a positive correlation between local efficiency in each of the 462 regions and clustering coefficient (r = 0.98, P value_spin‐FDR = 0.001) and negative correlations with connection strength (r = −0.63, P value_spin‐FDR = 0.001) and degree (r = −0.66, P value_spin _‐ _FDR = 0.001; Figure 5B). No significant correlation was observed with eigenvector centrality (r = −0.02, P value_spin‐FDR = 0.38).

Next, we investigated whether regions with higher (significant positive) and lower (significant negative) local efficiency W scores in iRBD differed based on the graph theory metrics. Consistent with the correlation results, independent samples t tests indicated significant differences between regions with higher and lower local efficiency in terms of clustering coefficient (t[28] = −17.51, P value_FDR < 0.00001), connection strength (t[28] = 7.07, P value_FDR < 0.00001), and degree (t[28] = 7.40, P value_FDR < 0.00001). However, no difference was observed in eigenvector centrality (t[28] = 1.42, P value_FDR = 0.17). It is noteworthy that there were no significant correlations in regions with lower local efficiency (N = 21) between local efficiency and degree (r = −0.18, P value_spin = 0.20) or connection strength (r = −0.18, P value_spin = 0.21), but a positive correlation was observed with the clustering coefficient (r = 0.90, P value_spin = 0.001). These results indicate that higher efficiency regions in iRBD are more clustered with neighboring regions despite showing fewer and weaker connections, potentially reflecting greater tissue damage, and low‐efficiency regions are less clustered with neighboring regions.

3.6. Local efficiency is associated with motor features and olfaction

We also investigated whether local efficiency related to cognitive and motor features in iRBD patients using PLS regression between local efficiency (absolute W scores) and the MoCA and MDS‐UPDRS‐III scores. A total of 186 iRBD patients with a MoCA score and 175 iRBD patients with an MDS‐UPDRS‐III score were included in these analyses. Of the 10 latent variables tested, we identified one significant latent variable in which local efficiency was associated with MDS‐UPDRS‐III scores (P value = 0.03). This latent variable accounted for 19.5% of the covariance between local efficiency and Parkinsonian motor features (Figure S4 in supporting information). Bootstrapping identified eight regions robustly associated with this latent variable, with ratios ranging from 4.0 to 5.5 (P value < 0.0001), namely the right superior parietal cortex (two subregions); insula; inferior parietal and medial orbitofrontal cortex; as well as the left lateral occipital, lateral orbitofrontal, and inferior parietal cortex. In three of these regions, higher changes in local efficiency were correlated with increased MDS‐UPDRS‐III scores (right insula: r = −0.24, P value = 0.001, right medial orbitofrontal cortex: r = −0.20, P value = 0.007, and left lateral occipital cortex: r = −0.16, P value = 0.04; Figure S5 in supporting information). In contrast, no significant latent variables were identified in relation to the MoCA. These findings suggest that emerging Parkinsonian motor features in iRBD primarily covary with efficiency changes in insular, orbitofrontal, and occipital regions.

To address phenotypic heterogeneity beyond motor and global cognition, we conducted an additional PLS regression between local efficiency (absolute W scores) and olfactory identification performance, converted into the 16‐item Sniffin’ Sticks scale using Lawton calibration (N = 176 iRBD). ⁴² ⁠ Among 10 latent variables tested, we identified one significant variable (P value = 0.02), which explained 61% of the covariance between local efficiency and olfaction scores (Figure S6 in supporting information). Bootstrap resampling revealed nine regions robustly associated with this latent variable and lower olfaction scores (ratios ranging from −4.1 to −6.2, P value < 0.0001). Notably, two regions, the right lateral orbitofrontal and left entorhinal cortex, are part of the olfactory system and have been consistently associated with olfactory impairment in iRBD and early PD. ⁴⁴ , ⁴⁵ ⁠ The remaining regions, including the right supramarginal gyrus (two subregions), inferior temportal, superior parietal and precentral cortices, as well as the left precuneus and superior frontal gyrus, are not part of primary olfactory networks but are frequently implicated in higher order associative processing and dementia‐related network disruption. ⁴⁶ , ⁴⁷ ⁠

3.7. Supramarginal gyrus local efficiency predicts DLB but not PD

Linear regression analyses controlling for age, sex, and follow‐up time were conducted to assess whether there are local efficiency (W scores) differences between individuals who converted to PD or DLB, and those who remained disease free (non‐converters). Among the 462 brain regions assessed, only the right supramarginal gyrus (region 105) showed a significant increase in local efficiency in DLB converters compared to non‐converters (mean W score: 1.35 ± 1.45 vs. −0.10 ± 1.09; estimate = 1.88, t = 4.63, P value_FDR = 0.005). No regions were significantly different in PD converters.

Time‐to‐event analyses found that the local efficiency of the right supramarginal gyrus was a strong and significant predictor of DLB conversion over time (β = 0.90, z = 3.66, P value = 0.0002), with a hazard ratio (HR) of 2.45 (95% confidence interval [CI]: 1.52–3.97). This indicates that each 1 SD increase in local efficiency of the right supramarginal gyrus was associated with a 2.5‐fold increase in the hazard of converting to DLB, independent of age and sex. Neither age (HR = 1.08, P value = 0.18) nor sex (HR = 0.26, P value = 0.14) reached statistical significance in the presence of local efficiency. In contrast, the W score local efficiency of the right supramarginal gyrus was not a significant predictor of PD conversion (β = 0.24, z = 1.56, P value = 0.12), with a HR of 1.27 (95% CI: 0.94–1.72). Neither age (HR = 1.02, P value = 0.63) nor sex (HR = 7.56 × 10⁷, P value = 0.998) was significant in the PD model. Kaplan–Meier survival curves were used to compare survival rates between iRBD patients showing positive versus negative W score local efficiency in the right supramarginal gyrus. They demonstrated that almost all iRBD patients with a negative local efficiency did not convert to DLB during follow‐up, in contrast to those with positive local efficiency (Figure 6). These results support the clinical relevance of local efficiency for the prognosis of DLB in iRBD.

Kaplan–Meier survival curves showing the association between local efficiency in the right supramarginal gyrus (W score) and phenoconversion in iRBD. A, Time to phenoconversion in DLB. Individuals with iRBD with a positive W score (blue line) in the right supramarginal gyrus showed a significantly lower survival probability (i.e., higher risk of conversion), compared to those with a negative W score (pink line); B, Time to phenoconversion in PD. No significant difference in survival was observed between individuals with iRBD with positive (blue) and negative (pink) W scores. P value (p) indicates statistical significance from the log‐rank test. DLB, dementia with Lewy bodies; iRBD, isolated rapid eye movement sleep behavior disorder; MRI, magnetic resonance imaging; PD, Parkinson's disease.

4. DISCUSSION

Leveraging one of the largest iRBD diffusion MRI datasets, we identified different structural connectivity alterations and their impact on network efficiency. Our findings reveal distinct structural disconnection patterns in iRBD, with the most pronounced disruptions in frontal and posterior brain regions. Both global and local network efficiency were significantly reduced, especially in subcortical and sensorimotor areas, suggesting widespread alterations consistent with early synucleinopathy. Regions with higher local efficiency were more clustered but less strongly connected. Importantly, efficiency changes correlated with motor and olfactory features and predicted DLB conversion, demonstrating ongoing network reorganization related to disease progression.

NBS identified reduced connectivity in 14 bilateral connections involving all major lobes, particularly frontal, temporal, and posterior regions, including the insula. These findings align with previous studies reporting widespread structural alterations in iRBD. ³⁶ , ⁴⁴ , ⁴⁸ Widespread atrophy was reported across the frontal, inferior temporal, parietal, and occipital cortices. ³⁶ Disrupted connectivity in the insula and middle frontal cortex has also been observed in iRBD and PD. ⁴⁹ In our study, reduced connectivity density was similarly found in these regions, along with additional frontal and posterior areas. Differences with previous studies may reflect the use of a higher resolution parcellation, larger sample, and the use of Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT2) for tractography to minimize false positive connections. ²⁷ ⁠

The iRBD group showed decreased global efficiency and reduced local efficiency in 18 cortical and three subcortical regions: the left putamen and bilateral thalamus, critical for motor, cognitive, and sensory functions (Figure 7A). ⁵⁰ , ⁵¹ , ⁵² Reduced efficiency in the putamen aligns with the dopaminergic input loss, associated with motor features in DLB and PD, ⁵³ as well as iRBD studies showing decreased putamen volume. ⁵⁴ , ⁵⁵ Similarly, atrophy in the thalamus has been reported in iRBD patients with mild cognitive impairment. ⁵⁶ Conversely, increased local efficiency was found in nine cortical regions across the frontal, temporal, parietal, and occipital lobes (Figure 7B). These regions had higher clustering coefficients but fewer and weaker connections. In this study, the clustering coefficient was reduced in 48 regions and increased in 9 regions. Of these 48 regions, 21 regions only had reduced local efficiency, suggesting that changes in clustering may precede or drive local efficiency alterations in iRBD. Regions with increased efficiency may be undergoing reorganization, potentially pruning weak connections to preserve localized function. ⁵⁷ , ⁵⁸ This process, while possibly beneficial in the short term, may impair global communication efficiency.

Schematic representation of potential network alterations in iRBD. A, Example of network disruption in iRBD compared to controls, where neighboring regions (A, B, E) lose connections (black lines) between them, resulting in reduced local efficiency and clustering coefficient in the central region. B, Example of network reorganization leading to increased local efficiency and clustering coefficient along with lower degree (number of direct connections) and average connection strength (thinner red lines) in a region. Increased strength (or density) in specific connection (thicker red line) can happen among more isolated neighbor regions (G and F) in the context of loss of connections and inputs, due to potential competition reduction. iRBD, isolated rapid eye movement sleep behavior disorder.

Importantly, changes in network topology were clinically relevant. Local efficiency in the insula, orbitofrontal, and occipital cortices was associated with Parkinsonian motor features (MDS‐UPDRS‐III). While these regions are not primary motor areas, they are affected early in synucleinopathies ⁵⁹ , ⁶⁰ and are associated with symptoms such as olfactory loss, ⁶⁰ , ⁶¹ cognitive decline, ⁶² color discrimination, and autonomic dysfunctions. ⁵⁶ , ⁵⁹ A similar observation was made for the association with lower olfactory identification performance, which related to changes in efficiency in the lateral orbitofrontal and entorhinal cortices but also extended to regions part of broader networks. Our findings support the view that correlates of motor features and hyposmia in iRBD may reflect broader disease severity rather than isolated impairment.

We also identified increased connectivity between the right superior and inferior parietal cortices, and between the left medial orbitofrontal and visual cortex. These patterns may represent compensatory hyperconnectivity in response to early degeneration, consistent with prior reports in prodromal PD and DLB. ⁹ , ¹¹ While such increases may initially help maintain function, chronic over‐recruitment of affected networks could become inefficient over time and contribute to functional decline. ⁶³ Notably, increased local efficiency in the right supramarginal gyrus (area 105) predicted conversion to DLB but not PD. While this region did not differ between iRBD and controls in group‐level efficiency, other subregions of the supramarginal gyrus showed reduced connectivity strength (area 104), clustering (area 109), and number of connections (area 104). This suggests that the increase in local efficiency may reflect disrupted intra‐regional connectivity. Given the known involvement of parietal regions in DLB, including white matter damage ⁶⁴ , ⁶⁵ and posterior hypoperfusion, ⁶⁶ local efficiency in the right supramarginal gyrus may serve as a potential marker of risk for DLB.

Moreover, the absence of associations with the MoCA, combined with the predictive value of local efficiency in the supramarginal gyrus for DLB conversion, seems to support the hypothesis that specific structural connectivity alterations may be more sensitive to early neurodegenerative processes than global cognitive screening tools. This raises the possibility that connectomic metrics could offer greater specificity for prodromal DLB detection, even before measurable cognitive decline emerges. Interestingly, despite the higher number of PD converters, no specific connectivity metric predicted PD conversion. This indicates a brain distinction between prodromal pathways leading to DLB versus PD. DLB is known to involve early cortical alterations, particularly in posterior regions, whereas PD may initially involve more restricted subcortical circuits. The fact that a cortical network property like local efficiency predicts DLB but not PD conversion supports this dissociation. Indeed, recent evidence shows that iRBD patients at higher risk of developing a dementia‐first phenotype exhibit early cortical changes that precede subcortical atrophy, suggesting that cortical reorganization may be an early feature of DLB‐related progression. ⁶⁷ ⁠ Moreover, structural connectivity alterations in PD converters may be more variable across individuals. ³ , ⁴ ⁠

Beyond these mechanistic insights, our findings may also carry important clinical implications. The identification of a DLB‐specific structural connectivity marker suggests that graph‐based MRI measures could support individualized stratification in iRBD, particularly for predicting DLB conversion. Since this metric is temporally stable and derivable from a single scan, it could help identify high‐risk individuals before cognitive features emerge, enabling earlier monitoring and enrolment in preventive trials. It may also improve clinical trial design by selecting patients more likely to convert to DLB, thereby increasing the likelihood of detecting disease‐specific therapeutic effects. Finally, this marker can be extracted within standard computational pipelines and could be integrated into multimodal prognostic models to support personalized care.

This study has several strengths. It includes a large sample of polysomnography‐confirmed iRBD patients and controls with harmonized acquisition, improving generalizability and enabling detection of more subtle differences compared to previous studies. This study also used COMMIT2 for tractogram construction, which improves accuracy by filtering out false‐positive streamlines that commonly inflate connectivity estimates. We also used TractoFlow‐ABS, which incorporates atlas‐based segmentation, FreeSurfer priors, and accounts for atrophy to further improve tract accuracy. Finally, we choose a high‐resolution atlas (462 regions), to enable detection of a more detailed set of brain connections compared to standard atlases like Desikan–Killiany and Desikan–Killiany–Tourville atlases.

However, some limitations remain. First, our analyses focused exclusively on structural connectivity. While incorporating resting‐state functional MRI measures could help clarify whether these structural disruptions lead to functional impairments, we chose to focus on structural connectivity due to its higher consistency ⁶⁸ ⁠ and lower susceptibility to transient factors such as circadian variation, transient illnesses, caffeine consumption, arousal fluctuations, or fatigue. ⁶⁹ , ⁷⁰ , ⁷¹ , ⁷² ⁠ Including resting‐state functional MRI data could therefore introduce additional heterogeneity and be less stable across multi‐site datasets. Moreover, functional MRI data were not consistently acquired across the contributing sites. Similarly, inclusion of polysomnography‐derived parameters was not performed due to current limitations regarding data sharing and challenges due to harmonization of hardware setups and lab environments across centers. ⁷³ Future studies should aim at implementing harmonized polysomnography and neuroimaging protocols across centers to elucidate the relationship between sleep architecture and brain network alterations in iRBD.

Second, clinical assessments for the whole dataset were limited to the MDS‐UPDRS‐III and MoCA, which do not fully capture the phenotypic heterogeneity of iRBD. This limitation is common in retrospective multicenter studies, in which broader harmonized clinical data are rarely available. To partially address this limitation, we enriched the dataset with olfactory identification scores obtained in every site, which were calibrated for comparisons across sites, providing an additional perspective on non‐motor dysfunctions. Third, although sex was adjusted for, the iRBD sample had a higher predominance of men, limiting sex‐specific interpretations. Future studies should incorporate more women and further explore how sex interacts with network alterations. Fourth, while the present study included both PD and DLB converters, the number of converters, particularly in the DLB group, was relatively low. However, the emergence of a significant predictor for DLB conversion despite the sample size suggests that the observed effect (local efficiency in the supramarginal gyrus) is substantial. Future studies should validate this marker in larger longitudinal cohorts of prodromal and manifest DLB patients. As this study includes the largest diffusion MRI dataset of iRBD patients with confirmed converters and non‐converters, further follow‐up of this cohort is expected to yield additional converters, which will allow assessment of robustness and generalizability.

In conclusion, our results provide evidence for early structural network reorganization in iRBD, marked by reduced global efficiency and region‐specific alterations in local efficiency. Notably, subcortical and sensorimotor regions showed marked reductions in local efficiency, contributing to lower global efficiency, consistent with synucleinopathy‐related neurodegeneration. Clinically, local efficiency alterations were associated with motor features and predicted DLB conversion. These findings identify candidate imaging markers for prodromal synucleinopathies and underscore the value of connectivity approaches to track disease progression and stratify risk in iRBD.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest. Author disclosures are available in the supporting information.

CONSENT STATEMENT

All procedures adhered to research ethics guidelines and all human subjects provided informed consent in accordance with the Declaration of Helsinki.

Supporting information

Supporting Information

ALZ-21-e70574-s001.pdf^{(1.9MB, pdf)}

Supporting Information

ALZ-21-e70574-s002.pdf^{(2.4MB, pdf)}

ACKNOWLEDGMENTS

We would like to thank Paul Cuciureanu for his help with the DWI data processing. This study was supported by grants to S. Rahayel from Alzheimer Society of Canada (0000000082) and Parkinson Canada (PPG‐2023‐0000000122). This work was also supported by a donation in memoriam of Gaetan Boulianne and Pierre‐Claude Durivage to the Foundation of Hopital du Sacré‐Coeur de Montréal, in support of Parkinson's disease research at the Center for Advanced Research in Sleep Medicine. The work performed by the investigators from the Montreal iRBD cohort was supported by the Canadian Institutes of Health Research (CIHR), the Fonds de recherche du Québec ‐ Santé (FRQS), and the W. Garfield Weston Foundation. Specifically, S. Rahayel reports grant support and travel reimbursement from the Michael J. Fox Foundation. J.F. Gagnon reports grants from the Fonds de recherche du Québec – Santé, the Canadian Institutes of Health Research, the W. Garfield Weston Foundation, the Michael J. Fox Foundation for Parkinson's Research, and the National Institutes of Health, and holds a Canada Research Chair in Cognitive Decline in Pathological Aging. R. B. Postuma received grants from the CIHR, Michael J. Fox Foundation, NIH, Roche Diagnostics, and the Weston Foundation. He received consulting fees from Novartis, Eisai, Merck, Vaxxinity, BMS, Ventus, Korro, Vanqua, Roche, Regeneron, Helicon, Epic, and Clinilabs. He holds leadership roles with Parkinson Canada, the Michael J. Fox Foundation, MDS, Movement Disorders journal, and the RBD Study Group. The work performed by the Prague iRBD cohort was supported by the Czech Health Research Council (grant NU21‐04‐00535) and The National Institute for Neurological Research (Project No. LX22NPO5107), funded by the European Union – Next Generation EU, all paid to their institution. The work performed by the investigators from the Oxford iRBD cohort was supported by Parkinson's UK (J‐2101) and the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). Specifically, J. C. Klein acknowledges salary support from the National Institute for Health and Care Research (NIHR) Oxford Health Clinical Research Facility and the NIHR Oxford Biomedical Research Centre. M. T. Hu acknowledges support from Parkinson's UK, the Oxford BRC, CPT, EPND, and the Michael J. Fox Foundation. The work performed by the investigators from the Paris iRBD cohort was supported by the Programme d'investissements d'avenir (ANR‐10‐IAIHU‐06), the Paris Institute of Neurosciences – IHU (IAIHU‐06), Agence Nationale de la Recherche (ANR‐11‐INBS‐0006), Électricité de France (Fondation d'Entreprise EDF), Control‐PD (Joint Programme‐Neurodegenerative Disease Research ‐JPND‐ Cognitive Propagation in Prodromal Parkinson's Disease), the Fondation Thérèse et René Planiol, the Fonds Saint‐Michel, unrestricted support for research on Parkinson's disease from Énergipole (M. Mallart) and Société Française de Médecine Esthétique (M. Legrand), a grant from the Institut de France to I. Arnulf (for the ALICE Study), with payments made to institution. The Parkinson's Progression Markers Initiative (PPMI)—a public–private partnership—is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including 4D Pharma, AbbVie Inc., AcureX Therapeutics, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson's (ASAP), Avid Radiopharmaceuticals, Bial Biotech, Biogen, BioLegend, Bristol Myers Squibb, Calico Life Sciences LLC, Celgene Corporation, DaCapo Brainscience, Denali Therapeutics, The Edmond J. Safra Foundation, Eli Lilly and Company, GE Healthcare, GlaxoSmithKline, Golub Capital, Handl Therapeutics, Insitro, Janssen Pharmaceuticals, Lundbeck, Merck & Co., Inc., Meso Scale Diagnostics, LLC, Neurocrine Biosciences, Pfizer Inc., Piramal Imaging, Prevail Therapeutics, F. Hoffmann‐La Roche Ltd and its affiliated company Genentech Inc., Sanofi Genzyme, Servier, Takeda Pharmaceutical Company, Teva Neuroscience, Inc., UCB, Vanqua Bio, Verily Life Sciences, Voyager Therapeutics, Inc., and Yumanity Therapeutics, Inc. For up‐to‐date information on the study, visit www.ppmi‐info.org. This work was also supported by a donation in memoriam of Gaëtan Boulianne and Pierre‐Claude Durivage to the Foundation of Hôpital du Sacré‐Cœur de Montréal, in support of Parkinson's disease research at the Centre for Advanced Research in Sleep Medicine.

APPENDIX A.

A.1.

List of the contributors involved in the ICEBERG Study Group:

Steering committee: Marie Vidailhet, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, principal investigator of ICEBERG); Jean‐Christophe Corvol, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, scientific lead); Isabelle Arnulf, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, member of the steering committee); Stéphane Lehericy, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, member of the steering committee).

Clinical data: Marie Vidailhet, MD, PhD, (Pitié‐Salpêtrière Hospital, Paris, coordination); Graziella Mangone, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, co‐coordination); Jean‐Christophe Corvol, MD, PhD (Pitié‐Salpêtrière Hospital, Paris); Isabelle Arnulf, MD, PhD (Pitié‐Salpêtrière Hospital, Paris); Smaranda Leu MD (Pitié‐Salpêtrière Hospital, Paris); Sara Sambin, MD (Pitié‐Salpêtrière Hospital, Paris); Jonas Ihle, MD (Pitié‐Salpêtrière Hospital, Paris); Caroline Weill, MD, (Pitié‐Salpêtrière Hospital, Paris); Poornima Menon, MD (Pitié‐Salpêtrière Hospital, Paris); David Grabli, MD, PhD (Pitié‐Salpêtrière Hospital, Paris); Florence Cormier‐Dequaire, MD (Pitié‐Salpêtrière Hospital, Paris); Louise Laure Mariani, MD, PhD (Pitié‐Salpêtrière Hospital, Paris); Emmanuel Roze, MD, PhD (Pitié‐Salpêtrière Hospital, Paris); Cécile Delorme, MD (Pitié‐Salpêtrière Hospital, Paris); Elodie Hainque, MD, PhD (Pitié‐Salpêtrière Hospital, Paris); Aurelie Méneret, MD, PhD (Pitié‐Salpêtrière Hospital, Paris); Bertrand Degos, MD, PhD (Avicenne Hospital, Bobigny).

Neuropsychological data: Richard Levy, MD (Pitié‐Salpêtrière Hospital, Paris, coordination); Fanny Pineau, MS (Pitié‐Salpêtrière Hospital, Paris, neuropsychologist); Julie Socha, MS (Pitié‐Salpêtrière Hospital, Paris, neuropsychologist); Eve Benchetrit, MS (La Timone Hospital, Marseille, neuropsychologist); Virginie Czernecki, MS (Pitié‐Salpêtrière Hospital, Paris, neuropsychologist); Marie‐Alexandrine Glachant, MS (Pitié‐Salpêtrière Hospital, Paris, neuropsychologist).

Eye movement: Sophie Rivaud‐Pechoux, PhD (ICM, Paris, coordination); Elodie Hainque, MD, PhD (Pitié‐Salpêtrière Hospital, Paris).

Sleep assessment: Isabelle Arnulf, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, coordination); Smaranda Leu Semenescu, MD (Pitié‐Salpêtrière Hospital, Paris); Pauline Dodet, MD (Pitié‐Salpêtrière Hospital, Paris).

Genetic data: Jean‐Christophe Corvol, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, coordination); Graziella Mangone, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, co‐coordination); Samir Bekadar, MS (Pitié‐Salpêtrière Hospital, Paris, biostatistician); Alexis Brice, MD (ICM, Pitié‐Salpêtrière Hospital, Paris); Suzanne Lesage, PhD (INSERM, ICM, Paris, genetic analyses).

Metabolomics: Fanny Mochel, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, coordination); Farid Ichou, PhD (ICAN, Pitié‐Salpêtrière Hospital, Paris); Vincent Perlbarg, PhD (Pierre and Marie Curie University); Benoit Colsch, PhD (CEA, Saclay); Arthur Tenenhaus, PhD (Supelec, Gif‐sur‐Yvette, data integration).

Brain MRI data: Stéphane Lehericy, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, coordination); Rahul Gaurav, MS (Pitié‐Salpêtrière Hospital, Paris, data analysis); Nadya Pyatigorskaya, MD, PhD (Pitié‐Salpêtrière Hospital, Paris, data analysis); Lydia Yahia‐Cherif, PhD (ICM, Paris, Biostatistics); Romain Valabregue, PhD (ICM, Paris, data analysis); Cécile Galléa, PhD (ICM, Paris).

DaTscan imaging data: Marie‐Odile Habert, MCU‐PH (Pitié‐Salpêtrière Hospital, Paris, coordination).

Voice recording: Dijana Petrovska, PhD (Telecom Sud Paris, Evry, coordination); Laetitia Jeancolas, MS (Telecom Sud Paris, Evry).

Study management: Vanessa Brochard (Pitié‐Salpêtrière Hospital, Paris, coordination); Alizé Chalançon (Pitié‐Salpêtrière Hospital, Paris, project manager); Carole Dongmo‐Kenfack (Pitié‐Salpêtrière Hospital, Paris, clinical research assistant); Christelle Laganot (Pitié‐Salpêtrière Hospital, Paris, clinical research assistant); Valentine Maheo (Pitié‐Salpêtrière Hospital, Paris, clinical research assistant); Manon Gomes (Pitié‐Salpêtrière Hospital, Paris, clinical research assistant).

Tremblay C, Pastor‐Bernier A, Rheault F, et al. Altered network efficiency in isolated REM sleep behavior disorder: A multicentric study. Alzheimer's Dement. 2025;21:e70574. 10.1002/alz.70574

List of the contributors involved in the ICEBERG Study Group.

Contributor Information

Christina Tremblay, Email: Christina.TREMBLAY.cnmtl@ssss.gouv.qc.ca.

Shady Rahayel, Email: shady.rahayel@umontreal.ca.

DATA AVAILABILITY STATEMENT

The data used in this study were obtained from multiple collaborating centers, each of which retains ownership of their respective datasets. The principal investigator had authorized access to all data necessary for the analyses performed in this study. However, the accessibility and sharing of data are subject to the local policies and restriction criteria of each center involved. As such, data availability is restricted, and requests for access should be directed to the respective institutions, pending their specific data access and sharing guidelines. DWI data from the PPMI are publicly available at www.ppmi‐info.org. The average structural connectivity matrices for the iRBD and control groups are available from the authors upon reasonable request. The software used can be accessed from the sources cited in Section 2.

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Associated Data

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Supplementary Materials

Supporting Information

ALZ-21-e70574-s001.pdf^{(1.9MB, pdf)}

Supporting Information

ALZ-21-e70574-s002.pdf^{(2.4MB, pdf)}

Data Availability Statement

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PERMALINK

Altered network efficiency in isolated REM sleep behavior disorder: A multicentric study

Christina Tremblay

Alexandre Pastor‐Bernier

François Rheault

Véronique Daneault

Violette Ayral

Marie Filiatrault

Liane Desaulniers

Andrew Vo

Jean‐François Gagnon

Ronald B Postuma

Petr Dusek

Stanislav Marecek

Zsoka Varga

Johannes C Klein

Michele T Hu

Stéphane Lehéricy

Isabelle Arnulf

Pauline Dodet

Marie Vidailhet

Jean‐Christophe Corvol

Shady Rahayel

Abstract

INTRODUCTION

METHODS

RESULTS

DISCUSSION

Highlights

1. BACKGROUND

2. METHODS

2.1. Participants

RESEARCH IN CONTEXT

2.2. Structural connectivity

FIGURE 1.

2.2.1. DWI processing

FIGURE 2.

2.2.2. Structural connectome derivation and age matching

2.3. NBS

2.4. Graph theory metrics

2.5. Association with clinical features

2.6. Relationship between local efficiency and phenoconversion status

3. RESULTS

3.1. Participants

TABLE 1.

3.2. Structural connectivity is altered in iRBD

FIGURE 3.

3.3. Structural network efficiency is altered in iRBD

FIGURE 4.

3.4. Subcortical and cortical regions differentially contribute to impaired global efficiency

3.5. Regions with altered efficiency have specific topological network characteristics

FIGURE 5.

3.6. Local efficiency is associated with motor features and olfaction

3.7. Supramarginal gyrus local efficiency predicts DLB but not PD

FIGURE 6.

4. DISCUSSION

FIGURE 7.

CONFLICT OF INTEREST STATEMENT

CONSENT STATEMENT

Supporting information

ACKNOWLEDGMENTS

APPENDIX A.

A.1.

Contributor Information

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases