Spinal cord involvement and cardiovascular autonomic dysfunction in Parkinson’s disease

Spinal cord involvement and cardiovascular autonomic dysfunction in Parkinson’s disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spinal cord involvement and cardiovascular autonomic dysfunction in Parkinson’s disease Lydia Chougar, François-Xavier Lejeune, Julien Cohen-Adad, Caroline Landelle, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7704958/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background : Patients with Parkinson’s disease (PD) frequently present autonomic cardiovascular dysfunction. This study investigated the involvement of autonomic centers in the upper thoracic spinal cord in cardiovascular dysfunction in patients with PD using multimodal MRI and markers of orthostatic hypotension. Methods : We recruited 26 patients with PD, stratified based on the presence (PD RBD(+) , n=11) or absence (PD RBD(-) , n=15) of rapid-eye movement sleep behavior disorder (RBD), and 22 matched healthy controls (HC). Participants underwent multimodal MRI of the cervical and upper thoracic spinal cord. Quantitative metrics, including T1 relaxation times, diffusion metrics, and magnetization transfer ratio (MTR) values, were extracted from gray and white matter spinal cord regions. MRI metrics were compared across groups and examined for associations with blood pressure drops, both cross-sectionally and longitudinally, as indicators of orthostatic hypotension. Results : No significant differences in MRI metrics were found between patients with PD and HCs, nor between PD subgroups. A multivariate analysis pooling all MRI metrics together allowed for the separation of HCs and PD subgroups. In the PD RBD(+) subgroup, positive correlations were found between systolic blood pressure drop and T1 relaxation times as well as mean diffusivity values at the cervicothoracic junction. Longitudinal changes in blood pressure drops were associated with MRI measurements after adjusting for baseline blood pressure, age, and sex, suggesting that these metrics may serve as potential markers of future blood pressure changes. Conclusions : Spinal cord quantitative MRI measurements at the cervicothoracic junction may be associated with orthostatic hypotension and its progression over time in PD RBD(+) patients. Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Parkinson’s disease Spinal cord Quantitative MRI Autonomic Dysfunction Orthostatic Hypotension Figures Figure 1 Figure 2 Figure 3 Introduction Parkinson’s disease (PD) is a neurodegenerative disorder that involves the autonomic nervous system, 1,2 contributing to the frequent dysautonomic symptoms observed in patients. Notably, the cardiovascular system is affected, resulting in symptoms such as neurogenic orthostatic hypotension and cardiac rhythm disturbances. 3,4 Cardiac innervation is provided by the parasympathetic and sympathetic systems. Preganglionic neurons of the parasympathetic system are located in several nuclei of the brainstem and in the sacral parasympathetic nuclei of the spinal cord segments S2 to S4. The vagus nerve, which originates in the medulla oblongata, innervates the sinoatrial and atrioventricular nodes of the heart. 5 In PD, neurodegeneration affects the dorsal motor nucleus of the vagus nerve in the medulla oblongata. 1,2 Involvement of this nucleus, as seen on multimodal magnetic resonance imaging (MRI), was shown to correlate with cardiac rhythm abnormalities during sleep. 6 On the other hand, preganglionic neurons of the sympathetic system targeting the heart are located in the intermediolateral cell column (IML) of the upper thoracic spinal cord. 7 The presence of pathological α-synuclein inclusions in the spinal cord, particularly in the IML, is almost constant in patients with PD. 2,8,9 Furthermore, two main models of pathology propagation have been proposed based on histological evidence by Braak et al. 1 and, more recently, in vivo imaging studies. 10–13 In the first model, the initial pathology originates in the enteric nervous system and propagates in an ascending manner to the central nervous system via the peripheral system. In the second model, the pathology arises in the olfactory bulb or amygdala and spreads in a descending fashion. 10–13 Previous studies suggested that the ascending model may be associated with the presence of rapid-eye movement sleep behavior disorder (RBD) and characterized by early autonomic damage preceding involvement of the nigrostriatal dopaminergic system. 10–13 Conversely, the descending model has been observed in RBD-negative PD patients. 10,11,13 Although these models remains debated, 11 spinal cord damage might be more severe in patients with RBD compared to those without RBD. In the current study, we aimed to investigate spinal cord pathology in patients with PD in comparison with healthy controls (HC), and to assess its relationship with clinical markers of autonomic dysfunction, such as orthostatic hypotension, cross-sectionally and then longitudinally, using multimodal spinal cord MRI. We hypothesized that PD patients would exhibit structural alterations in the upper portion of the thoracic spinal cord, that these alterations would be more prominent in patients with RBD compared to those without RBD, and that such changes would be associated with cardiac autonomic dysfunction observed in PD. Methods and Materials Participants This prospective study was conducted as part of the ICEBERG study, a five-year longitudinal project aimed at identifying and validating markers to predict and monitor the progression of dopaminergic and non-dopaminergic lesions in early and prodromal PD. Patients with PD, along with age- and sex-matched HCs, were recruited at the Paris Brain Institute (ICM) between 2020 and 2023. The diagnosis of PD was established according to the MDS clinical diagnostic criteria for PD. 3 Polysomnography was used to assess the presence or absence of rapid-eye movement sleep behavior disorder (RBD) in our sample of PD patients, who were then stratified based on the presence (PD RBD (+) ) or absence (PD RBD (−) ) of RBD. After visual inspection, participants were excluded in cases of poor image quality related to motion and/or significant susceptibility artifacts, marked cervical lordosis, disk protrusion compressing the spinal cord, or any spinal cord signal abnormality. The study was performed in accordance with the Declaration of Helsinki and was approved by the institutional ethical standard committee (CPP Paris VI/RCB: 2014-A00725-42). All participants gave written informed consent. Clinical data The following clinical scores were collected in PD patients under the “off” condition at the time of MRI: disease duration, Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) 14 part III, Hoehn and Yahr stage, 15 REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), 16 and SCales for Outcomes in PArkinson’s disease - Autonomic Dysfunction (SCOPA-AUT) scores including the cardiovascular subscore. 17 Participants were assessed for the presence or absence of orthostatic hypotension, defined as a drop in systolic blood pressure ≥ 20 mmHg or diastolic blood pressure ≥ 10 mmHg within 3 or 5 minutes of standing compared to baseline blood pressure (defined as the mean of two measurements on the upper right arm with the participant in the supine position after 5 min of rest). 18 In addition, systolic and diastolic blood pressure drops were also recorded at baseline and five-year follow-up visits. MRI acquisition Spinal MRI scans were performed at either the first- or the third-year follow-up visit. Participants were scanned using a 3 Tesla Siemens PRISMA scanner (Siemens Healthcare, Erlangen, Germany) using a 32-channel posterior spine coil, an 18-channel flex body coil placed anteriorly and covering the neck area, and a 64-channel head-neck coil. The field of view covered the cervical and upper thoracic spinal cord from C2 to T5 vertebral levels, thus including the cardiovascular autonomic control centers located in the upper thoracic region. The MRI protocol comprised (see supplementary Table S1 for details about the acquisition parameters; supplementary Figure S1 ): 3D turbo spin echo T2-weighted acquisition, voxel size: 0.8-mm isotropic. Diffusion-weighted imaging (DWI) echo planar imaging (EPI) with cardiac gating; b-value = 1000 s/mm²; 64 diffusion encoding directions; voxel size = 1.3 × 1.3 × 5 mm³; three adjacent axial slabs covering the spinal cord from C2 to T5 vertebral levels. 3D gradient echo images acquired with (MT on) and without (MT off) magnetization transfer saturation pulse; voxel size = 0.9-mm isotropic. 3D T1 Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) sequence with T1 relaxometry map; voxel size = 1-mm isotropic. MRI analysis MRI images were processed using the Spinal Toolbox (SCT) version 6.3. 19 All acquired data were transformed into NIfTI file format and organized using the Brain Imaging Data Structure (BIDS) standard. For all MRI contrasts (T2w, MTon, MToff, DWI, and T1 MP2RAGE), the spinal cord was segmented using the contrast-agnostic model ( sct_deepseg ) 20 to extract a mask for subsequent registration to a template. Vertebral discs were manually labeled on the T2w image, and these labels were used as landmarks for template registration. After virtual straightening of the spine, the T2w image was registered to the PAM50 template using affine and non-rigid registration steps. 21–23 In addition, the MT off image was registered to the MT on image, and the ratio between the two co-registered images (( MT off – MT on) / MT off ) was calculated for each voxel as the magnetization transfer ratio (MTR, sct_compute_mtr ). Diffusion processing involved several steps. For each slab, diffusion images were first averaged across the time dimension to obtain a 3D volume; the spinal cord was segmented on the resulting mean diffusion volume. The obtained mask was used for motion correction of the diffusion volume. 24 The diffusion tensor model was fitted, and quantitative maps of fractional anisotropy (FA) and mean diffusivity (MD) were computed ( sct_dmri_compute_dti ). Finally, diffusion slabs were merged, and metrics were extracted in the regions of interest. T1 relaxometry maps, calculated as a default output of the T1 MP2RAGE acquisition, were used to calculate regional T1 longitudinal relaxation times. First, shape-based analysis was performed on T2-weighted images, in individual space, to compute spinal cord cross-sectional area (CSA) at each slice across the rostro-caudal axis. Next, MTR, diffusion and T1 relaxation metric extractions were conducted in the participant space using the distinct gray matter and white matter probabilistic atlases available in the SCT. Specifically, four regions of interest were selected, including two white matter tracts (ascending and descending tracts) and two gray matter regions (intermediolateral zone, which encompasses the intermediolateral columns, and ventral horns; see supplementary Table S2 for details about the regions). The analysis was restricted to these regions to mitigate the decrease in statistical power due to a relatively small sample size and a large number of variables. Metrics were extracted slice-by-slice and averaged across each vertebral level (from C2 to T5), and within each region. For each region, values from the right or left atlas were averaged. All raw MRI images and processing outputs were visually inspected for quality control. 25 Statistical analyses Clinical and demographic data Statistical analyses were performed using R version 4.3.2 (R Development Core Team, 2023). Continuous data were reported as mean ± standard deviation, and categorical variables as counts and percentages. All tests were two-sided, with significance set at p < 0.05 or false discovery rate (FDR)-adjusted p < 0.05. Clinical and demographic data were compared across the three groups (HCs, PD RBD(+) , and PD RBD(−) patients) using Kruskal-Wallis tests followed by Dunn’s post hoc test for multiple comparisons for continuous variables, or Fisher’s exact tests for categorical variables. Group comparisons Between-group differences in MRI metrics were evaluated using linear models (LMs), with one model per region (ascending tracts, descending tracts, intermediolateral zone, ventral horns) and per MRI metric (CSA, T1, FA, MD, MTR), with ‘Group’ as the main factor and age and sex as covariates of no interest. Group effects were tested using Type II analysis of variance (ANOVA) F-tests, performed with the ‘car’ R package (v3.1-2). P-values from the F-tests were corrected for multiple comparisons across regions using FDR correction, with each MRI metric type treated separately. To assess group differences, we tested both two-group and three-group models: the first compared HCs and all PD patients; the second compared HCs, PD RBD(+) , and PD RBD(–) patients. For the three-group model, when a significant group effect was detected, post hoc pairwise comparisons were performed using Tukey’s method with the ‘emmeans’ R package (v1.8.9). For each model, assumptions and model fit were checked afterwards by visually inspecting residual distribution plots using the ‘ggResidpanel’ R package (v0.3.0). MRI metrics were first averaged across all vertebral levels from C2 to T5 to assess overall spinal cord differences, and then specifically analyzed at the C6–T1 vertebral levels, which correspond to C7-T2 spinal cord segments that encompass key centers for cardiovascular autonomic control. 7 Compared to vertebral levels, spinal segments provide a more accurate representation of the spinal cord’s functional organization into distinct rootlets. 26,27 Lower levels were excluded due to their higher susceptibility to motion artifacts from cardiac and respiratory activity. Multivariate analysis A multivariate analysis was conducted to investigate group separation using candidate imaging features preselected from the four spinal cord regions, across all individual vertebral levels (C2 to T5) and for all MRI metrics. Variables were included in the model if the p-value from Kruskal–Wallis tests comparing HCs and PD patients was < 0.15. The discriminative ability of these features was then assessed using partial least squares discriminant analysis (PLS-DA), as implemented in the mixOmics package (v6.26.0). PLS-DA is a supervised machine learning method that enables dimensionality reduction, feature selection, and multiclass classification. The PLS-DA model consists of a small number of orthogonal components, each calculated as a weighted sum of the original imaging variables to maximize covariance with the group labels. The weight values (or loadings) of the resulting components indicate the contribution of each feature to group discrimination across different dimensions. Model performance was evaluated on the training dataset using a receiver operating characteristic (ROC) analysis with two components, with the area under the ROC curve (AUC) serving as the evaluation metric. Individual-level classification accuracy was determined based on the predicted class assigned to each participant for each component using the maximum distance criterion. Associations with clinical features Spearman’s rank partial correlation analysis, controlling for age and sex, was performed to investigate relationships between imaging metrics and clinical features at the time of the MRI visit, including disease duration, systolic and diastolic blood pressure drops at 3 minutes, and the cardiovascular subscore of the SCOPA-AUT. P-values were adjusted for multiple correlation tests using the FDR method. Correlations were analyzed across all PD patients as well as within each PD subgroup (PD RBD(+) and PD RBD(−) ). Next, associations between longitudinal changes in systolic and diastolic blood pressure drops (3-min values at 5 years minus baseline) and MRI metrics were assessed using linear regression models, adjusted for age, sex, and baseline blood pressure drops. Associations were reported as standardized regression coefficients (β) with 95% confidence intervals, and p-values from the models were corrected for multiple comparisons across all MRI metric types. Results Participants The study population included 34 patients with PD and 30 HCs. We excluded 8 HCs (7 did not pass quality check, one had spinal cord signal abnormalities) and 8 PD patients (7 did not pass quality check, one had spinal cord signal abnormalities related to cervical spondylotic myelopathy). This resulted in 22 HCs and 26 patients with PD, subdivided into PD RBD(+) (n = 11) and PD RBD(+) (n = 15) subgroups. There were no significant differences in age or sex distribution between PD patients and HCs, nor within the PD subgroups. As expected, PD patients had higher MDS-UPDRS III (p < 0.0001) than HCs. RBDSQ scores were significantly higher in the PD RBD(+) subgroup compared to the PD RBD(−) one (p < 0.001). There were no significant differences between PD subgroups in disease duration, MDS-UPDRS III, SCOPA-AUT scores, cardiovascular SCOPA-AUT subscore, presence of orthostatic hypotension, or systolic and diastolic drops at 3 or 5 minutes (all p > 0.05), although values tended to be higher in the PD RBD(+) subgroup (Table 1 ). Table 1 Participants’ demographical and clinical characteristics HC PD Global tests Post hoc tests All PD PD RBD(+) PD RBD(−) n 22 26 11 15 Age (years) 67.9 ± 8.7 [53–83] 68.2 ± 9.0 [47–82] 71.4 ± 9.4 [47–82] 65.8 ± 8.3 [53–78] 0.21 ns Sex (female), n (%) 13 (59.1%) 15 (57.7%) 7 (63.6%) 8 (53.3%) 0.87 ns Disease duration (months) 45.7 ± 13.9 [20.2–80.5] 47.6 ± 16.9 [20.2–80.5] 44.2 ± 11.7 [21.7–61.1] 0.57 ns MDS-UPDRS III (off) 4.1 ± 3.3 [0–10] 28.6 ± 11.5 [9–67] 32.1 ± 14.1 [14–67] 26.0 ± 8.8 [9–39] HC***, PD RBD(−) > HC*** Hoehn and Yahr scale 1.8 ± 0.6 [ 1 – 3 ] 2.0 ± 0.4 [ 1 – 3 ] 1.6 ± 0.6 [ 1 – 3 ] ns ns RBDSQ 7.75 ± 3.79 [ 2 – 14 ] 18.19 ± 18.21 [0–74] 32.6 ± 19.4 [12–74] 7.8 ± 6.6 [0–17] HC**, PD RBD(+) > PD RBD(−) ** Orthostatic hypotension 1 (8.3%) 5 (19.2%) 4 (36.4%) 1 (6.7%) 0.12 ns Systolic blood pressure drop at 3min (mmHg) 0.8 ± 8.9 [-12-13] 3.3 ± 12.1 [-20-33] 4.8 ± 16.1 [-20-33] 2.2 ± 8.5 [-13-23] 0.64 ns at 5min (mmHg) 2.2 ± 11.1 [-19-25] 3.2 ± 13.9 [-25-35] 4.3 ± 18.6 [-25-35] 2.47 ± 9.8 [-15-22] 0.88 ns Diastolic blood pressure drop at 3min (mmHg) 2.9 ± 8.7 [-22-7] -0.8 ± 7.0 [-11-13] 1.2 ± 7.6 [-10-13] -2.2 ± 6.4 [-11-9] 0.46 ns at 5min (mmHg) -4.3 ± 6.4 [-16-7] 0.4 ± 7.5 [-13-21] 3.2 ± 7.6 [-5-21] -1.7 ± 6.9 [-13-10] 0.07 ns SCOPA-AUT 8.9 ± 7.0 [ 1 – 25 ] 15.0 ± 6.9 [ 3 – 26 ] 16.7 ± 7.2 [ 4 – 25 ] 13.7 ± 6.6 [ 3 – 26 ] HC*, PD RBD(−) > HC° SCOPA cardiovascular subscore 0.3 ± 0.5 [0–1] 0.7 ± 0.9 [0–3] 1.0 ± 1.1 [0–3] 0.4 ± 0.5 [0–1] 0.15 ns MoCA 27.3 ± 2.47 [ 22 – 30 ] 27.5 ± 2.0 [ 24 – 30 ] 27.7 ± 2.1 [ 24 – 30 ] 27.3 ± 2.0 [ 24 – 30 ] 0.770 ns Quantitative variables are summarized as mean ± standard deviation [min-max] and categorical variables as counts and percentages. Clinical data other than the MDS-UPDRS III were only available in 12 HCs. Statistically significant effects for global comparisons with Kruskal-Wallis or Fisher’s exact tests are shown in bold. Asterisks indicate the significance level of the post hoc comparisons: adjusted p < 0.05 (*), adjusted p < 0.01 (**), adjusted p < 0.001 (***). Abbreviations: F: female; HC, healthy controls; M: male; MDS-UPDRS part III, Movement Disorder Society Unified Parkinson’s Disease Rating Scale part III; ns, non-significant; PD; Parkinson’s disease; PD RBD(+) , PD with RBD; PD RBD(−) , PD without RBD; RBDSQ, REM Sleep Behavior Disorder Screening Questionnaire; RBD, Rapid-Eye Movement Sleep Disorder; SCOPA-AUT SCales for Outcomes in PArkinson’s disease - Autonomic Dysfunction. Group comparisons All F-values are reported as F df1,df2 , where df1 and df2 represent numerator and denominator degrees of freedom. First, our analysis, which covered the spinal cord from C2 to T5 vertebrae, revealed no significant group differences between HCs and all PD patients for any structural MRI metric taken individually. However, there was a trend for higher MTR values in the ventral horn of PD patients compared with HCs at the cervicothoracic junction (C6-T1), although it did not survive after FDR correction (F 1,44 =6.54, p = 0.06; Fig. 1 , supplementary Table S3 ). When comparing HCs and the two PD subgroups, this trend remained (F 2,43 =4.11, p = 0.09) with PD RBD(+) patients showing higher MTR values at C6-T1 compared to HCs (p = 0.02). No other significant differences were seen after FDR correction for any other MRI metric (Fig. 1 , supplementary Table S3 ). Multivariate analysis Overall, the PLS-DA model visualization with two components achieved group separation using 26 preselected variables that contributed most to the discrimination (supplementary Table S4). Component 1 separated PD RBD(−) patients (12/15, 80%, correctly classified) from HCs (17/22, 77.3%) and PD RBD(+) patients (all misclassified as HCs). Component 2 separated HCs (19/22 correctly predicted, 86.4%) from PD RBD(−) (12/15, 80%) and PD RBD(+) (8/11, 72.7%) subgroups. Area under the ROC curve (AUC) values on the training dataset exceeded 0.90 for the discrimination of each group against the others (supplementary Figure S2 ). Component 1 was negatively correlated with FA values in the descending tracts (C4, C6, and C7) and with MTR values in the intermediate zone (T3), while showing positive correlations in the descending tracts with T1 relaxation values at C3 and C4, and MD values at C4. Component 2 exhibited negative correlations with MTR values in the ventral horns (C2, C6, and C7) and in the descending tracts (C5 and C6), and positive correlations MD values in the descending tracts (C7) (supplementary Table S5, Fig. 2 ). Associations with clinical features Analyses were performed at the cervicothoracic junction (C6–T1), where group differences showed trends. For the PD RBD(+) subgroup, we found significant positive correlations between systolic drop at 3 minutes and T1 relaxation values in the ascending (ρ = 0.78, p = 0.03) and descending tracts (ρ = 0.81, p = 0.03). There were also positive correlations between systolic drop at 3 minutes and MD values in the ascending tracts (ρ = 0.77, p = 0.04) and ventral horns (ρ = 0.75, p = 0.04), with a trend in the descending tracts (ρ = 0.62, p = 0.01) and intermediate zone (ρ = 0.64, p < 0.10). MD values in the descending tracts were significantly correlated with the cardiovascular subscore of the SCOPA-AUT (ρ = 0.75, p = 0.04). No significant correlation was found with disease duration, though MD values in the intermediate zone showed a positive trend (ρ = 0.62, p < 0.10) (Fig. 3 ). No significant correlations were observed in the entire PD group, nor in the PD RBD(−) subgroup. Linear models using MRI metrics and baseline blood pressure drop, and age and sex as covariates in the PD RBD(+) subgroup showed that MD values in the descending (standardized β = 0.94, 95% CI [0.42. 1.45], p = 0.02) and ascending (standardized β = 1.21, 95% CI [0.40, 2.02], p = 0.02) tracts were significantly associated with 5-year changes in systolic blood pressure drop. Diastolic changes were also significantly associated with MD values in the ascending (standardized β = 1.08, 95% CI [0.37, 1.79], p = 0.04), with trends observed for MD (standardized β = 0.78, 95% CI [0.11, 1.45], p = 0.06) and T1 relaxation (standardized β = 0.73, 95% CI [0.17, 1.29], p = 0.07) values in the descending tracts (supplementary Table S6). Together, these associations suggest that microstructural alterations in the descending and ascending tracts may serve as potential progression markers of systolic and diastolic blood pressure changes. Discussion In this study, we used multimodal MRI to investigate spinal cord pathology in patients with PD in comparison with HCs, and to explore its association cross-sectionally and longitudinally with clinical features of autonomic dysfunction. While group comparisons between PD and HCs, and between PD subgroups, showed no significant differences in MRI metrics, multivariate analysis combining white and gray matter measurements discriminated between PD subgroups and HCs. Within the PD RBD(+) subgroup, we found that blood pressure drops were positively correlated with T1 relaxation and MD values in the ascending and descending tracts, as well as with MD values in the ventral horns and intermediate zone at the cervicothoracic junction, indicating that longer T1 relaxation and MD values were associated with more severe orthostatic hypotension. SCOPA-AUT cardiovascular subscores were positively correlated with MD values in the descending tracts. In addition, longitudinal changes in systolic and diastolic blood pressure drops from baseline to the five-year follow-up were associated with MRI metrics. These findings suggest that subtle microstructural changes in the examined regions may contribute to cardiovascular dysautonomia in PD and are associated with its progression over time. Our findings align with neuropathological evidence showing that PD involves not only the brain but the entire nervous system, including the spinal cord and the peripheral nervous system. 2,8,9 Spinal cord involvement contributes to the occurrence of motor and non-motor symptoms in PD such as autonomic symptoms, constipation, and pain. 3,4 The presence of α-synuclein inclusions has consistently been reported in the thoracic intermediolateral column and the sacral dorsal horns in PD. 2,8,9 In line with our hypothesis, the associations we observed between MRI metrics and clinical features of cardiovascular dysautonomia within the PD RBD(+) subgroup were localized to the cervicothoracic junction, specifically the C6-T1 vertebral levels, corresponding to the C7-T2 spinal segments. 26,27 These spinal segments contain key cardiovascular autonomic centers involved in dysautonomia in PD, 7 thereby reinforcing the relevance of our associations between MRI metrics and PD pathology. Furthermore, in our study, no correlations were observed in the entire PD group or in the PD RBD(-) subgroup. This lack of association may align with the hypothesis that spinal cord damage is more prominent in PD RBD(+) patients. Indeed, PD RBD(+) patients are expected to follow an ascending model of disease propagation, characterized by earlier and more severe autonomic dysfunction, 10–13 which may explain why MRI changes in the spinal cord were specifically associated with cardiovascular dysautonomia in this subgroup. Regarding disease propagation, a recent study reported that spinal pathology was only observed in patients already exhibiting Lewy pathology in the brain, with a strong correlation between the amount of spinal cord Lewy pathology and the severity of brain lesions. 9 Using unsupervised K-means analysis, the authors identified two cluster types of spinal and brain Lewy pathology: a caudo-rostral pattern (consistent with an ascending model of disease propagation) and an amygdala-based pattern (i.e, descending model) Lewy pathology types. Interestingly, the spinal cord Lewy pathology type was more strongly associated with the caudo-rostral-based type than the amygdala-based type, further supporting the hypothesis of two distinct propagation patterns of Lewy pathology. 9 To our knowledge, only one study has investigated spinal cord structural abnormalities at the cervical level (C2-C5) in a cohort of PD patients (n=68), stratified into early (n=23), moderate (n=22) and advanced (n=23) stages, using diffusion, MTR and T2* metrics. Subtle but significant differences were observed between HC and the advanced PD group for FA in the white matter, as well as between HC and the moderate PD group for radial diffusivity in the white matter, based on average values across C2-C5. 28 No significant associations were observed with UPDRS III scores. Unlike our study, the authors did not stratify PD patients based on the presence of absence of RBD and restricted the field of view to the C2-C5 vertebral levels, which might not have captured alterations expected to occur preferentially in the upper thoracic cord and sacral regions.[2,8,9] A resting-state functional MRI (fMRI) study 29 conducted on the same cohort of PD patients as in 28 showed a decrease in functional connectivity in the cervical spinal cord, which was associated with upper limb motor symptoms severity between C4 and C6 spinal levels. However, these functional changes did not correlate with microstructural measures. 28 Similarly, a study on transgenic M83 murine models of PD overexpressing the mutated A53T α-synuclein form (n=22) did not reveal any structural spinal cord abnormalities in comparison with non-transgenic mice (n=13) while oxygen saturation levels in the spinal cord measured with in vivo spiral volumetric optoacoustic tomography were shown to be reduced. 30 Several factors may account for the absence of significant differences in MRI metrics between PD patients and HCs in our study. First, we may have lacked statistical power given the relatively small sample size of PD patients, further reduced after stratification. Second, spinal cord imaging is highly prone to motion artifacts from heart and respiratory activity or swallowing, despite the use of cardiac gating for diffusion imaging, as well as to susceptibility artefacts, particularly affecting the upper thoracic portion. As a result, almost one-quarter (23.3%) of HCs and one-fifth (20.6%) of PD patients were excluded due to insufficient image quality. Third, the effect size of potential spinal cord alterations was small, with subtle microstructural changes that were hard to capture in PD in comparison with other conditions such as amyotrophic lateral sclerosis 31 or spinal-muscular amyotrophy. 32 Stratifying PD patients based on the presence of orthostatic hypotension would have been interesting. However, the sample size of patients with this feature was too small (5/26). To conclude, our exploratory findings suggest region-specific associations between structural metrics and cardiovascular dysautonomic features in PDpatients with RBD. Correlations at the C6–T1 vertebral levels support the potential role for cervicothoracic spinal cord alterations in the pathophysiology of autonomic failure in PD despite the lack of significant group-level structural differences. Future studies should include individuals with isolated RBD and incorporate additional measures of cardiovascular function, such as the RR interval. Recent advances in analysis methods, notably rootlet-based instead of vertebral-based analyses, 26,33 might provide more sensitivity to the MRI metrics. Technological advances such as ultra–high-field MRI may improve spatial resolution and signal-to-noise ratio, enabling more sensitive detection of subtle spinal cord changes in PD. Furthermore, resting-state fMRI can reveal network-level alterations, with connectivity changes reflecting PD-related pathology. Combining structural and functional measures may enable the detection of subtle microstructural alterations associated with functional changes, offering a more comprehensive understanding of brain changes in PD. Abbreviations HC healthy controls VH ventral horns IZ intermediate zone MTR magnetization transfer ratio PD Parkinson’s disease PD RBD(+) PD with RBD PD RBD(−) PD without RBD RBD Rapid-Eye Movement Sleep Behavior Disorder. Declarations Author contributions L.C. designed and conceptualized the study, collected and analyzed the data, drafted the manuscript for intellectual content. F.X.L conceptualized the study, analyzed the data, and revised the manuscript for intellectual content. J.C.A., M.G.P., J.N., J.V., K.W., analyzed the data and revised the manuscript. C.L., J.D., and A.D., helped with the methodology and revised the manuscript for intellectual content. E.B., C.J., N.P., S.B. G.M., J.C.C, I.A., collected the data, and revised the manuscript for intellectual content. M.V. and S.L. designed and conceptualized the study, collected the data, and revised the manuscript for intellectual content. Declaration of Competing Interests Nothing related to this work. J.C.C. has served in advisory boards for Alzprotect, Bayer, Ferrer, iRegene, Servier, UCB, Roche, and received grants from AXA and the ICM Foundation outside of this work. Funding/Acknowledgments The study was funded by grants from Agence Nationale de la Recherche (ANRMNP 2009, Nucleipark), DHOS-Inserm (2010, Nucleipark), France Parkinson, École des NeuroSciences de Paris (ENP), Fondation pour la Recherche Médicale (FRM), and the Investissements d'Avenir, IAIHU-06 (Paris Institute of Neurosciences – IHU), ANR-11-INBS-0006, Fondation d’Entreprise EDF, Biogen Inc., Fondation Thérèse and René Planiol, Unrestricted support for Research on Parkinson's disease from Energipole and Société Française de Médecine Esthétique. L.C. received funding from the Société Française de Radiologie (SFR), the Collège des Enseignants en Radiologie de France (CERF), and the Société Française de Neuroradiologie (SFNR). J.V. received funding from the European Union’s Horizon Europe research and innovation program under the Marie Skłodowska-Curie grant (no. 101107932). E.B. received fellowship funding from Association France Parkinson, Biogen Inc., and the European Union’s Horizon Europe research and innovation program under the Marie Skłodowska-Curie Actions (no. 101066055, acronym HERMES). Data Availability Statement The data obtained in this research are available from the corresponding author upon reasonable request. Code Availability Statement The codes used for the analyses are available at https://github.com/sct-pipeline/spine-park. References Braak, H. et al. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiology of Aging 24 , 197–211 (2003). Del Tredici, K. & Braak, H. Spinal cord lesions in sporadic Parkinson’s disease. Acta Neuropathol 124 , 643–664 (2012). Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov. 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Borghammer, P. & Van Den Berge, N. Brain-First versus Gut-First Parkinson’s Disease: A Hypothesis. J Parkinsons Dis 9 , S281–S295 (2019). Borghammer, P. et al. A postmortem study suggests a revision of the dual-hit hypothesis of Parkinson’s disease. NPJ Parkinsons Dis 8 , 166 (2022). Horsager, J. & Borghammer, P. Brain-first vs. body-first Parkinson’s disease: An update on recent evidence. Parkinsonism & Related Disorders 122 , 106101 (2024). Passaretti, M. et al. Clinical progression and genetic pathways in body-first and brain-first Parkinson’s disease. Molecular Neurodegeneration 20 , 74 (2025). Goetz, C. G. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results. Movement Disorders 23 , 2129–2170 (2008). Hoehn, M. M. & Yahr, M. D. Parkinsonism: onset, progression and mortality. Neurology 17 , 427–442 (1967). Stiasny-Kolster, K. et al. The REM sleep behavior disorder screening questionnaire--a new diagnostic instrument. Mov Disord 22 , 2386–2393 (2007). Visser, M., Marinus, J., Stiggelbout, A. M. & Van Hilten, J. J. Assessment of autonomic dysfunction in Parkinson’s disease: the SCOPA-AUT. Mov Disord 19 , 1306–1312 (2004). Wieling, W. et al. Diagnosis and treatment of orthostatic hypotension. Lancet Neurol 21 , 735–746 (2022). De Leener, B. et al. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. NeuroImage 145 , 24–43 (2017). Bédard, S. et al. Towards contrast-agnostic soft segmentation of the spinal cord. Med Image Anal 101 , 103473 (2025). De Leener, B. et al. PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space. Neuroimage 165 , 170–179 (2018). Fonov, V. S. et al. Framework for integrated MRI average of the spinal cord white and gray matter: the MNI-Poly-AMU template. Neuroimage 102 Pt 2 , 817–827 (2014). Lévy, S. et al. White matter atlas of the human spinal cord with estimation of partial volume effect. Neuroimage 119 , 262–271 (2015). Xu, J. et al. Improved in vivo diffusion tensor imaging of human cervical spinal cord. Neuroimage 67 , 64–76 (2013). Valošek, J. & Cohen-Adad, J. Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox. Magnetic Resonance in Medical Sciences 23 , 307–315 (2024). Valošek, J., Mathieu, T., Schlienger, R., Kowalczyk, O. S. & Cohen-Adad, J. Automatic segmentation of the spinal cord nerve rootlets. Imaging Neurosci (Camb) 2 , imag–2–00218 (2024). Frostell, A., Hakim, R., Thelin, E. P., Mattsson, P. & Svensson, M. A Review of the Segmental Diameter of the Healthy Human Spinal Cord. Front Neurol 7 , 238 (2016). St-Onge, S. et al. Parkinson’s disease in the spinal cord: an exploratory study to establish T2*w, MTR and diffusion-weighted imaging metric values. NeuroLibre Reproducible Preprints 39 (2025) doi:10.55458/neurolibre.00039. Landelle, C. et al. Altered Spinal Cord Functional Connectivity Associated with Parkinson’s Disease Progression. Movement Disorders 38 , 636–645 (2023). Combes, B. F. et al. Spiral volumetric optoacoustic tomography of reduced oxygen saturation in the spinal cord of M83 mouse model of Parkinson’s disease. Eur J Nucl Med Mol Imaging 52 , 427–443 (2025). Querin, G. et al. Spinal cord multi-parametric magnetic resonance imaging for survival prediction in amyotrophic lateral sclerosis. Eur J Neurol 24 , 1040–1046 (2017). Querin, G. et al. The spinal and cerebral profile of adult spinal-muscular atrophy: A multimodal imaging study. Neuroimage Clin 21 , 101618 (2019). Bédard, S. & Cohen-Adad, J. Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. Front Neuroimaging 1 , 1031253 (2022). Table Table 1 is available in the supplementary files section Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx FigS1.png FigS2.tiff Table.docx Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 16 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Editor invited by journal 07 Oct, 2025 Submission checks completed at journal 02 Oct, 2025 First submitted to journal 02 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":1168429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMTR values across vertebral levels in HCs and PD patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean values with shaded areas representing the 95% confidence intervals of Locally Estimated Scatterplot Smoothing (LOESS)-smoothed MTR values (y-axis) across vertebral levels (x-axis) for different regions of interest are shown for HCs, all PD patients, and PD subgroups. The gray shaded rectangle indicates the C6–T1 junction, where group differences in average MRI values were assessed using an F-test adjusted for age and sex.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: HC, healthy controls; VH, ventral horns; IZ, intermediate zone; MTR, magnetization transfer ratio; PD; Parkinson’s disease; PD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(+)\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, PD with RBD;\u003c/em\u003e\u003csub\u003e\u003cem\u003e \u003c/em\u003e\u003c/sub\u003e\u003cem\u003ePD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(-)\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, PD without RBD; RBD, Rapid-Eye Movement Sleep Behavior Disorder.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7704958/v1/825ca9ae68f7fd0da8a20c2a.png"},{"id":94731691,"identity":"ff5a66ec-1d21-48f1-8148-739a8d6b2041","added_by":"auto","created_at":"2025-10-30 07:08:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":610012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividual (left) and variable (right) plots illustrate the two-component PLS-DA model based on 26 preselected FA, MD, MTR, and T1 relaxation variables across vertebral levels from C2 to T5. The individual plot shows fair separation among the groups of HCs, PD\u003csub\u003eRBD(+)\u003c/sub\u003e, and PD\u003csub\u003eRBD(-)\u003c/sub\u003e. Percentages on the axes represent the variance explained by each component. Colors in the variable plot correspond to variable types: FA (red), MD (blue), MTR (green), and T1 relaxation (purple).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: HC, healthy controls; VH: ventral horns; IZ, intermediate zone; MTR, magnetization transfer ratio; PD; Parkinson’s disease; PD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(+)\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, PD with RBD;\u003c/em\u003e\u003csub\u003e\u003cem\u003e \u003c/em\u003e\u003c/sub\u003e\u003cem\u003ePD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(-)\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, PD without RBD ; RBD, Rapid-Eye Movement Sleep Behavior Disorder.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7704958/v1/a26aa58df5d9dfa8b2989ae8.png"},{"id":94731423,"identity":"6b113f2b-b05c-4532-b380-565115f23f9c","added_by":"auto","created_at":"2025-10-30 07:08:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1412163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between clinical variables and MRI measurements at the cervicothoracic junction of the spinal cord in the PD\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRBD(+)\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e subgroup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, C) Spearman’s rank partial correlation matrices (rho coefficients) between clinical variables and averaged T1 relaxation (A) and MD (C) values across the C6-T1 vertebral levels in the PD\u003csub\u003eRBD(+)\u003c/sub\u003e subgroup. Asterisks indicate correlations that were significant before correction for multiple comparisons, while symbols in parentheses indicate those that remain significant after FDR correction. Circles specifically denote non-significant trends (p \u0026lt; 0.10). Significance levels are indicated as follows: p \u0026lt; 0.10 (°), p \u0026lt; 0.05 (*), p \u0026lt; 0.01 (**), and p \u0026lt; 0.001 (***). (B, D) Scatterplots with linear regression lines showing significant correlations (after FDR correction) between systolic blood pressure drop values at 3 minutes (adjusted for age and sex) and T1 relaxation values in the descending tracts (B), as well as MD values in the ascending tracts (D), also adjusted for age and sex, in the PD\u003csub\u003eRBD(+)\u003c/sub\u003e subgroup. These correlations were not significant in the PD\u003csub\u003eRBD(-) \u003c/sub\u003esubgroup. The shaded area around each regression line represents the 95% confidence interval of the fitted model. Marginal density plots along the top and right margins illustrate the distributions of systolic blood pressure drops at 3 minutes, T1 relaxation values in the descending tracts (B), and MD values in the ascending tracts (D) for each subgroup, respectively. Raw Spearman’s rho coefficients and p-values without correction are indicated on each plot.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: FDR, false discovery rate; MD, mean diffusivity; PD; Parkinson’s disease; PD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(+)\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, PD with RBD;\u003c/em\u003e\u003csub\u003e\u003cem\u003e \u003c/em\u003e\u003c/sub\u003e\u003cem\u003eRBD, Rapid-Eye Movement Sleep Behavior Disorder; SCOPA-AUT, SCales for Outcomes in PArkinson’s disease - Autonomic Dysfunction; T1, longitudinal relaxation time.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7704958/v1/d687bcb38caf7e26ee756f39.png"},{"id":105224060,"identity":"e0b97863-0ef3-4d3d-9dec-b4fb99a5fe81","added_by":"auto","created_at":"2026-03-23 16:12:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3930087,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7704958/v1/d496aa17-ec8f-420f-bfb7-b1812e7cc395.pdf"},{"id":94731694,"identity":"f07b8a2a-fc6f-420b-85d1-434092b1bfbc","added_by":"auto","created_at":"2025-10-30 07:08:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46448,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7704958/v1/f49acd92af7d2f22f052b9a6.docx"},{"id":94731569,"identity":"ccf14893-5d10-4829-a438-ba0532128c21","added_by":"auto","created_at":"2025-10-30 07:08:39","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":960121,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.png","url":"https://assets-eu.researchsquare.com/files/rs-7704958/v1/15ca37d8c8a6f94ac2e83381.png"},{"id":94731568,"identity":"bd035eaa-4eb1-4643-be45-6d56f91e77aa","added_by":"auto","created_at":"2025-10-30 07:08:39","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5610192,"visible":true,"origin":"","legend":"","description":"","filename":"FigS2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7704958/v1/482c9c387aa9574ae228c1e0.tiff"},{"id":94731554,"identity":"ae2f7bcc-5213-412c-8b05-c3c5f41c8888","added_by":"auto","created_at":"2025-10-30 07:08:39","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18767,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-7704958/v1/20373b45f40eb33c3165f4aa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spinal cord involvement and cardiovascular autonomic dysfunction in Parkinson’s disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson’s disease (PD) is a neurodegenerative disorder that involves the autonomic nervous system,\u003csup\u003e1,2\u003c/sup\u003e contributing to the frequent dysautonomic symptoms observed in patients. Notably, the cardiovascular system is affected, resulting in symptoms such as neurogenic orthostatic hypotension and cardiac rhythm disturbances.\u003csup\u003e3,4\u003c/sup\u003e Cardiac innervation is provided by the parasympathetic and sympathetic systems. Preganglionic neurons of the parasympathetic system are located in several nuclei of the brainstem and in the sacral parasympathetic nuclei of the spinal cord segments S2 to S4. The vagus nerve, which originates in the medulla oblongata, innervates the sinoatrial and atrioventricular nodes of the heart.\u003csup\u003e5\u003c/sup\u003e In PD, neurodegeneration affects the dorsal motor nucleus of the vagus nerve in the medulla oblongata.\u003csup\u003e1,2\u003c/sup\u003e Involvement of this nucleus, as seen on multimodal magnetic resonance imaging (MRI), was shown to correlate with cardiac rhythm abnormalities during sleep.\u003csup\u003e6\u003c/sup\u003e On the other hand, preganglionic neurons of the sympathetic system targeting the heart are located in the intermediolateral cell column (IML) of the upper thoracic spinal cord.\u003csup\u003e7\u003c/sup\u003e The presence of pathological α-synuclein inclusions in the spinal cord, particularly in the IML, is almost constant in patients with PD.\u003csup\u003e2,8,9\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eFurthermore, two main models of pathology propagation have been proposed based on histological evidence by Braak et al. \u003csup\u003e1\u003c/sup\u003e and, more recently, \u003cem\u003ein vivo\u003c/em\u003e imaging studies.\u003csup\u003e10–13\u003c/sup\u003e In the first model, the initial pathology originates in the enteric nervous system and propagates in an ascending manner to the central nervous system via the peripheral system. In the second model, the pathology arises in the olfactory bulb or amygdala and spreads in a descending fashion.\u003csup\u003e10–13\u003c/sup\u003e Previous studies suggested that the ascending model may be associated with the presence of rapid-eye movement sleep behavior disorder (RBD) and characterized by early autonomic damage preceding involvement of the nigrostriatal dopaminergic system.\u003csup\u003e10–13\u003c/sup\u003e Conversely, the descending model has been observed in RBD-negative PD patients.\u003csup\u003e10,11,13\u003c/sup\u003e Although these models remains debated,\u003csup\u003e11\u003c/sup\u003e spinal cord damage might be more severe in patients with RBD compared to those without RBD.\u003c/p\u003e\u003cp\u003eIn the current study, we aimed to investigate spinal cord pathology in patients with PD in comparison with healthy controls (HC), and to assess its relationship with clinical markers of autonomic dysfunction, such as orthostatic hypotension, cross-sectionally and then longitudinally, using multimodal spinal cord MRI. We hypothesized that PD patients would exhibit structural alterations in the upper portion of the thoracic spinal cord, that these alterations would be more prominent in patients with RBD compared to those without RBD, and that such changes would be associated with cardiac autonomic dysfunction observed in PD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\n\n\n\n\n"},{"header":"Methods and Materials","content":"\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eThis prospective study was conducted as part of the ICEBERG study, a five-year longitudinal project aimed at identifying and validating markers to predict and monitor the progression of dopaminergic and non-dopaminergic lesions in early and prodromal PD.\u003c/p\u003e\u003cp\u003ePatients with PD, along with age- and sex-matched HCs, were recruited at the Paris Brain Institute (ICM) between 2020 and 2023. The diagnosis of PD was established according to the MDS clinical diagnostic criteria for PD.\u003csup\u003e3\u003c/sup\u003e Polysomnography was used to assess the presence or absence of rapid-eye movement sleep behavior disorder (RBD) in our sample of PD patients, who were then stratified based on the presence (PD\u003csub\u003eRBD (+)\u003c/sub\u003e) or absence (PD\u003csub\u003eRBD (−)\u003c/sub\u003e) of RBD.\u003c/p\u003e\u003cp\u003eAfter visual inspection, participants were excluded in cases of poor image quality related to motion and/or significant susceptibility artifacts, marked cervical lordosis, disk protrusion compressing the spinal cord, or any spinal cord signal abnormality.\u003c/p\u003e\u003cp\u003e The study was performed in accordance with the Declaration of Helsinki and was approved by the institutional ethical standard committee (CPP Paris VI/RCB: 2014-A00725-42). All participants gave written informed consent.\u003c/p\u003e\u003cp\u003eClinical data\u003c/p\u003e\u003cp\u003eThe following clinical scores were collected in PD patients under the “off” condition at the time of MRI: disease duration, Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) \u003csup\u003e14\u003c/sup\u003e part III, Hoehn and Yahr stage,\u003csup\u003e15\u003c/sup\u003e REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ),\u003csup\u003e16\u003c/sup\u003e and SCales for Outcomes in PArkinson’s disease - Autonomic Dysfunction (SCOPA-AUT) scores including the cardiovascular subscore.\u003csup\u003e17\u003c/sup\u003e Participants were assessed for the presence or absence of orthostatic hypotension, defined as a drop in systolic blood pressure ≥ 20 mmHg or diastolic blood pressure ≥ 10 mmHg within 3 or 5 minutes of standing compared to baseline blood pressure (defined as the mean of two measurements on the upper right arm with the participant in the supine position after 5 min of rest).\u003csup\u003e18\u003c/sup\u003e In addition, systolic and diastolic blood pressure drops were also recorded at baseline and five-year follow-up visits.\u003c/p\u003e\u003cp\u003eMRI acquisition\u003c/p\u003e\u003cp\u003eSpinal MRI scans were performed at either the first- or the third-year follow-up visit. Participants were scanned using a 3 Tesla Siemens PRISMA scanner (Siemens Healthcare, Erlangen, Germany) using a 32-channel posterior spine coil, an 18-channel flex body coil placed anteriorly and covering the neck area, and a 64-channel head-neck coil. The field of view covered the cervical and upper thoracic spinal cord from C2 to T5 vertebral levels, thus including the cardiovascular autonomic control centers located in the upper thoracic region. The MRI protocol comprised (see supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for details about the acquisition parameters; supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e):\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e3D turbo spin echo T2-weighted acquisition, voxel size: 0.8-mm isotropic.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDiffusion-weighted imaging (DWI) echo planar imaging (EPI) with cardiac gating; b-value = 1000 s/mm²; 64 diffusion encoding directions; voxel size = 1.3 × 1.3 × 5 mm³; three adjacent axial slabs covering the spinal cord from C2 to T5 vertebral levels.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e3D gradient echo images acquired with (MT on) and without (MT off) magnetization transfer saturation pulse; voxel size = 0.9-mm isotropic.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e3D T1 Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) sequence with T1 relaxometry map; voxel size = 1-mm isotropic.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eMRI analysis\u003c/p\u003e\u003cp\u003eMRI images were processed using the Spinal Toolbox (SCT) version 6.3.\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAll acquired data were transformed into NIfTI file format and organized using the Brain Imaging Data Structure (BIDS) standard. For all MRI contrasts (T2w, MTon, MToff, DWI, and T1 MP2RAGE), the spinal cord was segmented using the contrast-agnostic model (\u003cem\u003esct_deepseg\u003c/em\u003e) \u003csup\u003e20\u003c/sup\u003e to extract a mask for subsequent registration to a template. Vertebral discs were manually labeled on the T2w image, and these labels were used as landmarks for template registration. After virtual straightening of the spine, the T2w image was registered to the PAM50 template using affine and non-rigid registration steps.\u003csup\u003e21–23\u003c/sup\u003e In addition, the MT off image was registered to the MT on image, and the ratio between the two co-registered images ((\u003cem\u003eMT off – MT on) / MT off\u003c/em\u003e) was calculated for each voxel as the magnetization transfer ratio (MTR, \u003cem\u003esct_compute_mtr\u003c/em\u003e). Diffusion processing involved several steps. For each slab, diffusion images were first averaged across the time dimension to obtain a 3D volume; the spinal cord was segmented on the resulting mean diffusion volume. The obtained mask was used for motion correction of the diffusion volume.\u003csup\u003e24\u003c/sup\u003e The diffusion tensor model was fitted, and quantitative maps of fractional anisotropy (FA) and mean diffusivity (MD) were computed (\u003cem\u003esct_dmri_compute_dti\u003c/em\u003e). Finally, diffusion slabs were merged, and metrics were extracted in the regions of interest. T1 relaxometry maps, calculated as a default output of the T1 MP2RAGE acquisition, were used to calculate regional T1 longitudinal relaxation times.\u003c/p\u003e\u003cp\u003eFirst, shape-based analysis was performed on T2-weighted images, in individual space, to compute spinal cord cross-sectional area (CSA) at each slice across the rostro-caudal axis. Next, MTR, diffusion and T1 relaxation metric extractions were conducted in the participant space using the distinct gray matter and white matter probabilistic atlases available in the SCT. Specifically, four regions of interest were selected, including two white matter tracts (ascending and descending tracts) and two gray matter regions (intermediolateral zone, which encompasses the intermediolateral columns, and ventral horns; see supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e for details about the regions). The analysis was restricted to these regions to mitigate the decrease in statistical power due to a relatively small sample size and a large number of variables. Metrics were extracted slice-by-slice and averaged across each vertebral level (from C2 to T5), and within each region. For each region, values from the right or left atlas were averaged.\u003c/p\u003e\u003cp\u003eAll raw MRI images and processing outputs were visually inspected for quality control.\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses\u003c/p\u003e\u003ch3\u003eClinical and demographic data\u003c/h3\u003e\u003cp\u003eStatistical analyses were performed using R version 4.3.2 (R Development Core Team, 2023). Continuous data were reported as mean ± standard deviation, and categorical variables as counts and percentages. All tests were two-sided, with significance set at p \u0026lt; 0.05 or false discovery rate (FDR)-adjusted p \u0026lt; 0.05. Clinical and demographic data were compared across the three groups (HCs, PD\u003csub\u003eRBD(+)\u003c/sub\u003e, and PD\u003csub\u003eRBD(−)\u003c/sub\u003e patients) using Kruskal-Wallis tests followed by Dunn’s post hoc test for multiple comparisons for continuous variables, or Fisher’s exact tests for categorical variables.\u003c/p\u003e\u003ch2\u003eGroup comparisons\u003c/h2\u003e\u003cp\u003eBetween-group differences in MRI metrics were evaluated using linear models (LMs), with one model per region (ascending tracts, descending tracts, intermediolateral zone, ventral horns) and per MRI metric (CSA, T1, FA, MD, MTR), with ‘Group’ as the main factor and age and sex as covariates of no interest. Group effects were tested using Type II analysis of variance (ANOVA) F-tests, performed with the ‘car’ R package (v3.1-2). P-values from the F-tests were corrected for multiple comparisons across regions using FDR correction, with each MRI metric type treated separately. To assess group differences, we tested both two-group and three-group models: the first compared HCs and all PD patients; the second compared HCs, PD\u003csub\u003eRBD(+)\u003c/sub\u003e, and PD\u003csub\u003eRBD(–)\u003c/sub\u003e patients. For the three-group model, when a significant group effect was detected, post hoc pairwise comparisons were performed using Tukey’s method with the ‘emmeans’ R package (v1.8.9). For each model, assumptions and model fit were checked afterwards by visually inspecting residual distribution plots using the ‘ggResidpanel’ R package (v0.3.0).\u003c/p\u003e\u003cp\u003eMRI metrics were first averaged across all vertebral levels from C2 to T5 to assess overall spinal cord differences, and then specifically analyzed at the C6–T1 vertebral levels, which correspond to C7-T2 spinal cord segments that encompass key centers for cardiovascular autonomic control.\u003csup\u003e7\u003c/sup\u003e Compared to vertebral levels, spinal segments provide a more accurate representation of the spinal cord’s functional organization into distinct rootlets.\u003csup\u003e26,27\u003c/sup\u003e Lower levels were excluded due to their higher susceptibility to motion artifacts from cardiac and respiratory activity.\u003c/p\u003e\u003ch3\u003eMultivariate analysis\u003c/h3\u003e\u003cp\u003eA multivariate analysis was conducted to investigate group separation using candidate imaging features preselected from the four spinal cord regions, across all individual vertebral levels (C2 to T5) and for all MRI metrics. Variables were included in the model if the p-value from Kruskal–Wallis tests comparing HCs and PD patients was \u0026lt; 0.15. The discriminative ability of these features was then assessed using partial least squares discriminant analysis (PLS-DA), as implemented in the mixOmics package (v6.26.0). PLS-DA is a supervised machine learning method that enables dimensionality reduction, feature selection, and multiclass classification. The PLS-DA model consists of a small number of orthogonal components, each calculated as a weighted sum of the original imaging variables to maximize covariance with the group labels. The weight values (or loadings) of the resulting components indicate the contribution of each feature to group discrimination across different dimensions. Model performance was evaluated on the training dataset using a receiver operating characteristic (ROC) analysis with two components, with the area under the ROC curve (AUC) serving as the evaluation metric. Individual-level classification accuracy was determined based on the predicted class assigned to each participant for each component using the maximum distance criterion.\u003c/p\u003e\u003ch3\u003eAssociations with clinical features\u003c/h3\u003e\u003cp\u003eSpearman’s rank partial correlation analysis, controlling for age and sex, was performed to investigate relationships between imaging metrics and clinical features at the time of the MRI visit, including disease duration, systolic and diastolic blood pressure drops at 3 minutes, and the cardiovascular subscore of the SCOPA-AUT. P-values were adjusted for multiple correlation tests using the FDR method. Correlations were analyzed across all PD patients as well as within each PD subgroup (PD\u003csub\u003eRBD(+)\u003c/sub\u003e and PD\u003csub\u003eRBD(−)\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003eNext, associations between longitudinal changes in systolic and diastolic blood pressure drops (3-min values at 5 years minus baseline) and MRI metrics were assessed using linear regression models, adjusted for age, sex, and baseline blood pressure drops. Associations were reported as standardized regression coefficients (β) with 95% confidence intervals, and p-values from the models were corrected for multiple comparisons across all MRI metric types.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eThe study population included 34 patients with PD and 30 HCs. We excluded 8 HCs (7 did not pass quality check, one had spinal cord signal abnormalities) and 8 PD patients (7 did not pass quality check, one had spinal cord signal abnormalities related to cervical spondylotic myelopathy). This resulted in 22 HCs and 26 patients with PD, subdivided into PD\u003csub\u003eRBD(+)\u003c/sub\u003e (n\u0026thinsp;=\u0026thinsp;11) and PD\u003csub\u003eRBD(+)\u003c/sub\u003e (n\u0026thinsp;=\u0026thinsp;15) subgroups.\u003c/p\u003e\u003cp\u003eThere were no significant differences in age or sex distribution between PD patients and HCs, nor within the PD subgroups. As expected, PD patients had higher MDS-UPDRS III (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) than HCs. RBDSQ scores were significantly higher in the PD\u003csub\u003eRBD(+)\u003c/sub\u003e subgroup compared to the PD\u003csub\u003eRBD(\u0026minus;)\u003c/sub\u003e one (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no significant differences between PD subgroups in disease duration, MDS-UPDRS III, SCOPA-AUT scores, cardiovascular SCOPA-AUT subscore, presence of orthostatic hypotension, or systolic and diastolic drops at 3 or 5 minutes (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), although values tended to be higher in the PD\u003csub\u003eRBD(+)\u003c/sub\u003e subgroup (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipants\u0026rsquo; demographical and clinical characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003ePD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGlobal tests\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePost hoc tests\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAll PD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(+)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(\u0026minus;)\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 [53\u0026ndash;83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0 [47\u0026ndash;82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4 [47\u0026ndash;82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 [53\u0026ndash;78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (female), n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (59.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (57.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (63.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDisease duration (months)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9 [20.2\u0026ndash;80.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9 [20.2\u0026ndash;80.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 [21.7\u0026ndash;61.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMDS-UPDRS III (off)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 [0\u0026ndash;10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5 [9\u0026ndash;67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1 [14\u0026ndash;67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8 [9\u0026ndash;39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePD\u003csub\u003eRBD(+)\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;HC***, PD\u003csub\u003eRBD(\u0026minus;)\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;HC***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHoehn and Yahr scale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRBDSQ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79 [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.19\u0026thinsp;\u0026plusmn;\u0026thinsp;18.21 [0\u0026ndash;74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4 [12\u0026ndash;74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6 [0\u0026ndash;17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePD\u003csub\u003eRBD(+)\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;HC**, PD\u003csub\u003eRBD(+)\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;PD\u003csub\u003eRBD(\u0026minus;)\u003c/sub\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOrthostatic hypotension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (19.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSystolic blood pressure drop at 3min (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9 [-12-13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 [-20-33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1 [-20-33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5 [-13-23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eat 5min (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 [-19-25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9 [-25-35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6 [-25-35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 [-15-22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiastolic blood pressure drop at 3min (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 [-22-7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0 [-11-13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6 [-10-13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 [-11-9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eat 5min (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 [-16-7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5 [-13-21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6 [-5-21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9 [-13-10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSCOPA-AUT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0 [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9 [\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6 [\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePD\u003csub\u003eRBD(+)\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;HC*, PD\u003csub\u003eRBD(\u0026minus;)\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;HC\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSCOPA cardiovascular subscore\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 [0\u0026ndash;1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 [0\u0026ndash;3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 [0\u0026ndash;3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 [0\u0026ndash;1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMoCA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47 [\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eQuantitative variables are summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation [min-max] and categorical variables as counts and percentages. Clinical data other than the MDS-UPDRS III were only available in 12 HCs. Statistically significant effects for global comparisons with Kruskal-Wallis or Fisher\u0026rsquo;s exact tests are shown in bold. Asterisks indicate the significance level of the post hoc comparisons: adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*), adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (**), adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (***).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eAbbreviations: F: female; HC, healthy controls; M: male; MDS-UPDRS part III, Movement Disorder Society Unified Parkinson\u0026rsquo;s Disease Rating Scale part III; ns, non-significant; PD; Parkinson\u0026rsquo;s disease; PD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(+)\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ePD with RBD; PD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(\u0026minus;)\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ePD without RBD; RBDSQ, REM Sleep Behavior Disorder Screening Questionnaire; RBD, Rapid-Eye Movement Sleep Disorder; SCOPA-AUT SCales for Outcomes in PArkinson\u0026rsquo;s disease - Autonomic Dysfunction.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGroup comparisons\u003c/p\u003e\u003cp\u003eAll F-values are reported as F\u003csub\u003edf1,df2\u003c/sub\u003e, where df1 and df2 represent numerator and denominator degrees of freedom. First, our analysis, which covered the spinal cord from C2 to T5 vertebrae, revealed no significant group differences between HCs and all PD patients for any structural MRI metric taken individually. However, there was a trend for higher MTR values in the ventral horn of PD patients compared with HCs at the cervicothoracic junction (C6-T1), although it did not survive after FDR correction (F\u003csub\u003e1,44\u003c/sub\u003e=6.54, p\u0026thinsp;=\u0026thinsp;0.06; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). When comparing HCs and the two PD subgroups, this trend remained (F\u003csub\u003e2,43\u003c/sub\u003e=4.11, p\u0026thinsp;=\u0026thinsp;0.09) with PD\u003csub\u003eRBD(+)\u003c/sub\u003e patients showing higher MTR values at C6-T1 compared to HCs (p\u0026thinsp;=\u0026thinsp;0.02). No other significant differences were seen after FDR correction for any other MRI metric (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003cp\u003eOverall, the PLS-DA model visualization with two components achieved group separation using 26 preselected variables that contributed most to the discrimination (supplementary Table S4).\u003c/p\u003e\u003cp\u003eComponent 1 separated PD\u003csub\u003eRBD(\u0026minus;)\u003c/sub\u003e patients (12/15, 80%, correctly classified) from HCs (17/22, 77.3%) and PD\u003csub\u003eRBD(+)\u003c/sub\u003e patients (all misclassified as HCs). Component 2 separated HCs (19/22 correctly predicted, 86.4%) from PD\u003csub\u003eRBD(\u0026minus;)\u003c/sub\u003e (12/15, 80%) and PD\u003csub\u003eRBD(+)\u003c/sub\u003e (8/11, 72.7%) subgroups. Area under the ROC curve (AUC) values on the training dataset exceeded 0.90 for the discrimination of each group against the others (supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eComponent 1 was negatively correlated with FA values in the descending tracts (C4, C6, and C7) and with MTR values in the intermediate zone (T3), while showing positive correlations in the descending tracts with T1 relaxation values at C3 and C4, and MD values at C4. Component 2 exhibited negative correlations with MTR values in the ventral horns (C2, C6, and C7) and in the descending tracts (C5 and C6), and positive correlations MD values in the descending tracts (C7) (supplementary Table S5, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAssociations with clinical features\u003c/p\u003e\u003cp\u003eAnalyses were performed at the cervicothoracic junction (C6\u0026ndash;T1), where group differences showed trends. For the PD\u003csub\u003eRBD(+)\u003c/sub\u003e subgroup, we found significant positive correlations between systolic drop at 3 minutes and T1 relaxation values in the ascending (ρ\u0026thinsp;=\u0026thinsp;0.78, p\u0026thinsp;=\u0026thinsp;0.03) and descending tracts (ρ\u0026thinsp;=\u0026thinsp;0.81, p\u0026thinsp;=\u0026thinsp;0.03). There were also positive correlations between systolic drop at 3 minutes and MD values in the ascending tracts (ρ\u0026thinsp;=\u0026thinsp;0.77, p\u0026thinsp;=\u0026thinsp;0.04) and ventral horns (ρ\u0026thinsp;=\u0026thinsp;0.75, p\u0026thinsp;=\u0026thinsp;0.04), with a trend in the descending tracts (ρ\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;=\u0026thinsp;0.01) and intermediate zone (ρ\u0026thinsp;=\u0026thinsp;0.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). MD values in the descending tracts were significantly correlated with the cardiovascular subscore of the SCOPA-AUT (ρ\u0026thinsp;=\u0026thinsp;0.75, p\u0026thinsp;=\u0026thinsp;0.04). No significant correlation was found with disease duration, though MD values in the intermediate zone showed a positive trend (ρ\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No significant correlations were observed in the entire PD group, nor in the PD\u003csub\u003eRBD(\u0026minus;)\u003c/sub\u003e subgroup.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLinear models using MRI metrics and baseline blood pressure drop, and age and sex as covariates in the PD\u003csub\u003eRBD(+)\u003c/sub\u003e subgroup showed that MD values in the descending (standardized β\u0026thinsp;=\u0026thinsp;0.94, 95% CI [0.42. 1.45], p\u0026thinsp;=\u0026thinsp;0.02) and ascending (standardized β\u0026thinsp;=\u0026thinsp;1.21, 95% CI [0.40, 2.02], p\u0026thinsp;=\u0026thinsp;0.02) tracts were significantly associated with 5-year changes in systolic blood pressure drop. Diastolic changes were also significantly associated with MD values in the ascending (standardized β\u0026thinsp;=\u0026thinsp;1.08, 95% CI [0.37, 1.79], p\u0026thinsp;=\u0026thinsp;0.04), with trends observed for MD (standardized β\u0026thinsp;=\u0026thinsp;0.78, 95% CI [0.11, 1.45], p\u0026thinsp;=\u0026thinsp;0.06) and T1 relaxation (standardized β\u0026thinsp;=\u0026thinsp;0.73, 95% CI [0.17, 1.29], p\u0026thinsp;=\u0026thinsp;0.07) values in the descending tracts (supplementary Table S6). Together, these associations suggest that microstructural alterations in the descending and ascending tracts may serve as potential progression markers of systolic and diastolic blood pressure changes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we used multimodal MRI to investigate spinal cord pathology in patients with PD in comparison with HCs, and to explore its association cross-sectionally and longitudinally with clinical features of autonomic dysfunction. While group comparisons between PD and HCs, and between PD subgroups, showed no significant differences in MRI metrics, multivariate analysis combining white and gray matter measurements discriminated between PD subgroups and HCs. Within the PD\u003csub\u003eRBD(+)\u003c/sub\u003e subgroup, we found that blood pressure drops were positively correlated with T1 relaxation and MD values in the ascending and descending tracts, as well as with MD values in the ventral horns and intermediate zone at the cervicothoracic junction, indicating that longer T1 relaxation and MD values were associated with more severe orthostatic hypotension. SCOPA-AUT cardiovascular subscores were positively correlated with MD values in the descending tracts. In addition, longitudinal changes in systolic and diastolic blood pressure drops from baseline to the five-year follow-up were associated with MRI metrics. These findings suggest that subtle microstructural changes in the examined regions may contribute to cardiovascular dysautonomia in PD and are associated with its progression over time.\u003c/p\u003e\n\u003cp\u003eOur findings align with neuropathological evidence showing that PD involves not only the brain but the entire nervous system, including the spinal cord and the peripheral nervous system.\u003csup\u003e2,8,9\u003c/sup\u003e Spinal cord involvement contributes to the occurrence of motor and non-motor symptoms in PD such as autonomic symptoms, constipation, and pain.\u003csup\u003e3,4\u003c/sup\u003e The presence of α-synuclein inclusions has consistently been reported in the thoracic intermediolateral column and the sacral dorsal horns in PD.\u003csup\u003e2,8,9\u003c/sup\u003eIn line with our hypothesis, the associations we observed between MRI metrics and clinical features of cardiovascular dysautonomia within the PD\u003csub\u003eRBD(+)\u003c/sub\u003e subgroup were localized to the cervicothoracic junction, specifically the C6-T1 vertebral levels, corresponding to the C7-T2 spinal segments.\u003csup\u003e26,27\u003c/sup\u003e These spinal segments contain key cardiovascular autonomic centers involved in dysautonomia in PD,\u003csup\u003e7\u003c/sup\u003e thereby reinforcing the relevance of our associations between MRI metrics and PD pathology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, in our study, no correlations were observed in the entire PD group or in the PD\u003csub\u003eRBD(-)\u003c/sub\u003e subgroup. This lack of association may align with the hypothesis that spinal cord damage is more prominent in PD\u003csub\u003eRBD(+)\u0026nbsp;\u003c/sub\u003epatients. Indeed, PD\u003csub\u003eRBD(+)\u0026nbsp;\u003c/sub\u003epatients are expected to follow an ascending model of disease propagation, characterized by earlier and more severe autonomic dysfunction,\u003csup\u003e10–13\u003c/sup\u003e which may explain why MRI changes in the spinal cord were specifically associated with cardiovascular dysautonomia in this subgroup. Regarding disease propagation, a recent study reported that spinal pathology was only observed in patients already exhibiting Lewy pathology in the brain, with a strong correlation between the amount of spinal cord Lewy pathology and the severity of brain lesions.\u003csup\u003e9\u003c/sup\u003e Using unsupervised K-means analysis, the authors identified two cluster types of spinal and brain Lewy pathology: a caudo-rostral pattern (consistent with an ascending model of disease propagation) and an amygdala-based pattern (i.e, descending model) Lewy pathology types. Interestingly, the spinal cord Lewy pathology type was more strongly associated with the caudo-rostral-based type than the amygdala-based type, further supporting the hypothesis of two distinct propagation patterns of Lewy pathology.\u003csup\u003e9\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo our knowledge, only one study has investigated spinal cord structural abnormalities at the cervical level (C2-C5) in a cohort of PD patients (n=68), stratified into early (n=23), moderate (n=22) and advanced (n=23) stages, using diffusion, MTR and T2* metrics. Subtle but significant differences were observed between HC and the advanced PD group for FA in the white matter, as well as between HC and the moderate PD group for radial diffusivity in the white matter, based on average values across C2-C5.\u003csup\u003e28\u003c/sup\u003e No significant associations were observed with UPDRS III scores. Unlike our study, the authors did not stratify PD patients based on the presence of absence of RBD and restricted the field of view to the C2-C5 vertebral levels, which might not have captured alterations expected to occur preferentially in the upper thoracic cord and sacral regions.[2,8,9] A resting-state functional MRI (fMRI) study\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e conducted on the same cohort of PD patients as in\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e showed a decrease in functional connectivity in the cervical spinal cord, which was associated with upper limb motor symptoms severity between C4 and C6 spinal levels. However, these functional changes did not correlate with microstructural measures.\u003csup\u003e28\u003c/sup\u003e Similarly, a study on transgenic M83 murine models of PD overexpressing the mutated A53T α-synuclein form (n=22) did not reveal any structural spinal cord abnormalities in comparison with non-transgenic mice (n=13) while oxygen saturation levels in the spinal cord measured with \u003cem\u003ein vivo\u003c/em\u003e spiral volumetric optoacoustic tomography were shown to be reduced.\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSeveral factors may account for the absence of significant differences in MRI metrics between PD patients and HCs in our study. First, we may have lacked statistical power given the relatively small sample size of PD patients, further reduced after stratification. Second, spinal cord imaging is highly prone to motion artifacts from heart and respiratory activity or swallowing, despite the use of cardiac gating for diffusion imaging, as well as to susceptibility artefacts, particularly affecting the upper thoracic portion. As a result, almost one-quarter (23.3%) of HCs and one-fifth (20.6%) of PD patients were excluded due to insufficient image quality. Third, the effect size of potential spinal cord alterations was small, with subtle microstructural changes that were hard to capture in PD in comparison with other conditions such as amyotrophic lateral sclerosis\u0026nbsp;\u003csup\u003e31\u003c/sup\u003e or spinal-muscular amyotrophy.\u003csup\u003e32\u003c/sup\u003e Stratifying PD patients based on the presence of orthostatic hypotension would have been interesting. However, the sample size of patients with this feature was too small (5/26).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo conclude, our exploratory findings suggest region-specific associations between structural metrics and cardiovascular dysautonomic features in PDpatients with RBD. Correlations at the C6–T1 vertebral levels support the potential role for cervicothoracic spinal cord alterations in the pathophysiology of autonomic failure in PD despite the lack of significant group-level structural differences. Future studies should include individuals with isolated RBD and incorporate additional measures of cardiovascular function, such as the RR interval. Recent advances in analysis methods, notably rootlet-based instead of vertebral-based analyses,\u003csup\u003e26,33\u003c/sup\u003e might provide more sensitivity to the MRI metrics. Technological advances such as ultra–high-field MRI may improve spatial resolution and signal-to-noise ratio, enabling more sensitive detection of subtle spinal cord changes in PD. Furthermore, resting-state fMRI can reveal network-level alterations, with connectivity changes reflecting PD-related pathology. Combining structural and functional measures may enable the detection of subtle microstructural alterations associated with functional changes, offering a more comprehensive understanding of brain changes in PD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eHC\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ehealthy controls\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eVH\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eventral horns\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eIZ\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eintermediate zone\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eMTR\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003emagnetization transfer ratio\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003ePD\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eParkinson\u0026rsquo;s disease\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003ePD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(+)\u003c/em\u003e\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ePD with RBD\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003ePD\u003c/em\u003e\u003csub\u003e\u003cem\u003eRBD(\u0026minus;)\u003c/em\u003e\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ePD without RBD\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eRBD\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eRapid-Eye Movement Sleep Behavior Disorder.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.C. designed and conceptualized the study, collected and analyzed the data, drafted the manuscript for intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eF.X.L conceptualized the study, analyzed the data, and revised the manuscript for intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJ.C.A., M.G.P., J.N., J.V., K.W., analyzed the data and revised the manuscript.\u003c/p\u003e\n\u003cp\u003eC.L., J.D., and A.D., helped with the methodology and revised the manuscript for intellectual content.\u003c/p\u003e\n\u003cp\u003eE.B., C.J., N.P., S.B. G.M., J.C.C, I.A., collected the data, and revised the manuscript for intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eM.V. and S.L. designed and conceptualized the study, collected the data, and revised the manuscript for intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNothing related to this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJ.C.C. has served in advisory boards for Alzprotect, Bayer, Ferrer, iRegene, Servier, UCB, Roche, and received grants from AXA and the ICM Foundation outside of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding/Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by grants from Agence Nationale de la Recherche (ANRMNP 2009, Nucleipark), DHOS-Inserm (2010, Nucleipark), France Parkinson, \u0026Eacute;cole des NeuroSciences de Paris (ENP), Fondation pour la Recherche M\u0026eacute;dicale (FRM), and the Investissements d\u0026apos;Avenir, IAIHU-06 (Paris Institute of Neurosciences \u0026ndash; IHU), ANR-11-INBS-0006, Fondation d\u0026rsquo;Entreprise EDF, Biogen Inc., Fondation Th\u0026eacute;r\u0026egrave;se and Ren\u0026eacute; Planiol, Unrestricted support for Research on Parkinson\u0026apos;s disease from Energipole and Soci\u0026eacute;t\u0026eacute; Fran\u0026ccedil;aise de M\u0026eacute;decine Esth\u0026eacute;tique.\u003c/p\u003e\n\u003cp\u003eL.C.\u0026nbsp;received funding from the Soci\u0026eacute;t\u0026eacute; Fran\u0026ccedil;aise de Radiologie (SFR), the Coll\u0026egrave;ge des Enseignants en Radiologie de France (CERF), and the Soci\u0026eacute;t\u0026eacute; Fran\u0026ccedil;aise de Neuroradiologie (SFNR).\u003c/p\u003e\n\u003cp\u003eJ.V. received funding from the European Union\u0026rsquo;s Horizon Europe research and innovation program under the Marie Skłodowska-Curie grant (no. 101107932).\u003c/p\u003e\n\u003cp\u003eE.B. received fellowship funding from Association France Parkinson, Biogen Inc., and the European Union\u0026rsquo;s Horizon Europe research and innovation program under the Marie Skłodowska-Curie Actions (no. 101066055, acronym HERMES).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data obtained in this research are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe codes used for the analyses are available at https://github.com/sct-pipeline/spine-park.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBraak, H. \u003cem\u003eet al.\u003c/em\u003e Staging of brain pathology related to sporadic Parkinson\u0026rsquo;s disease. \u003cem\u003eNeurobiology of Aging\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 197\u0026ndash;211 (2003).\u003c/li\u003e\n\u003cli\u003eDel Tredici, K. \u0026amp; Braak, H. 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A Review of the Segmental Diameter of the Healthy Human Spinal Cord. \u003cem\u003eFront Neurol\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 238 (2016).\u003c/li\u003e\n\u003cli\u003eSt-Onge, S. \u003cem\u003eet al.\u003c/em\u003e Parkinson\u0026rsquo;s disease in the spinal cord: an exploratory study to establish T2*w, MTR and diffusion-weighted imaging metric values. \u003cem\u003eNeuroLibre Reproducible Preprints\u003c/em\u003e 39 (2025) doi:10.55458/neurolibre.00039.\u003c/li\u003e\n\u003cli\u003eLandelle, C. \u003cem\u003eet al.\u003c/em\u003e Altered Spinal Cord Functional Connectivity Associated with Parkinson\u0026rsquo;s Disease Progression. \u003cem\u003eMovement Disorders\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 636\u0026ndash;645 (2023).\u003c/li\u003e\n\u003cli\u003eCombes, B. F. \u003cem\u003eet al.\u003c/em\u003e Spiral volumetric optoacoustic tomography of reduced oxygen saturation in the spinal cord of M83 mouse model of Parkinson\u0026rsquo;s disease. \u003cem\u003eEur J Nucl Med Mol Imaging\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 427\u0026ndash;443 (2025).\u003c/li\u003e\n\u003cli\u003eQuerin, G. \u003cem\u003eet al.\u003c/em\u003e Spinal cord multi-parametric magnetic resonance imaging for survival prediction in amyotrophic lateral sclerosis. \u003cem\u003eEur J Neurol\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1040\u0026ndash;1046 (2017).\u003c/li\u003e\n\u003cli\u003eQuerin, G. \u003cem\u003eet al.\u003c/em\u003e The spinal and cerebral profile of adult spinal-muscular atrophy: A multimodal imaging study. \u003cem\u003eNeuroimage Clin\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 101618 (2019).\u003c/li\u003e\n\u003cli\u003eB\u0026eacute;dard, S. \u0026amp; Cohen-Adad, J. Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. \u003cem\u003eFront Neuroimaging\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 1031253 (2022).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, Spinal cord, Quantitative MRI, Autonomic Dysfunction, Orthostatic Hypotension","lastPublishedDoi":"10.21203/rs.3.rs-7704958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7704958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Patients with Parkinson’s disease (PD) frequently present autonomic cardiovascular dysfunction. This study investigated the involvement of autonomic centers in the upper thoracic spinal cord in cardiovascular dysfunction in patients with PD using multimodal MRI and markers of orthostatic hypotension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We recruited 26 patients with PD, stratified based on the presence (PD\u003csub\u003eRBD(+)\u003c/sub\u003e, n=11) or absence (PD\u003csub\u003eRBD(-)\u003c/sub\u003e, n=15) of rapid-eye movement sleep behavior disorder (RBD), and 22 matched healthy controls (HC). Participants underwent multimodal MRI of the cervical and upper thoracic spinal cord. Quantitative metrics, including T1 relaxation times, diffusion metrics, and magnetization transfer ratio (MTR) values, were extracted from gray and white matter spinal cord regions. MRI metrics were compared across groups and examined for associations with blood pressure drops, both cross-sectionally and longitudinally, as indicators of orthostatic hypotension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: No significant differences in MRI metrics were found between patients with PD and HCs, nor between PD subgroups. A multivariate analysis pooling all MRI metrics together allowed for the separation of HCs and PD subgroups. In the PD\u003csub\u003eRBD(+)\u003c/sub\u003e subgroup, positive correlations were found between systolic blood pressure drop and T1 relaxation times as well as mean diffusivity values at the cervicothoracic junction. Longitudinal changes in blood pressure drops were associated with MRI measurements after adjusting for baseline blood pressure, age, and sex, suggesting that these metrics may serve as potential markers of future blood pressure changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Spinal cord quantitative MRI measurements at the cervicothoracic junction may be associated with orthostatic hypotension and its progression over time in PD\u003csub\u003eRBD(+)\u003c/sub\u003e patients.\u003c/p\u003e","manuscriptTitle":"Spinal cord involvement and cardiovascular autonomic dysfunction in Parkinson’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 06:51:11","doi":"10.21203/rs.3.rs-7704958/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-10T07:01:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T08:31:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116661805683376858466883057894729717044","date":"2025-11-26T16:50:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T17:21:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276058307499668838805670029394466580590","date":"2025-10-16T15:26:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-16T13:31:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-16T13:25:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T05:59:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-02T19:04:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-02T19:00:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20f0a7b1-3aef-4822-b2e5-b444ea0e3333","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56889971,"name":"Health sciences/Diseases"},{"id":56889972,"name":"Health sciences/Medical research"},{"id":56889973,"name":"Health sciences/Neurology"},{"id":56889974,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-03-23T16:08:36+00:00","versionOfRecord":{"articleIdentity":"rs-7704958","link":"https://doi.org/10.1038/s41598-026-38152-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-17 15:59:02","publishedOnDateReadable":"March 17th, 2026"},"versionCreatedAt":"2025-10-30 06:51:11","video":"","vorDoi":"10.1038/s41598-026-38152-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-38152-z","workflowStages":[]},"version":"v1","identity":"rs-7704958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7704958","identity":"rs-7704958","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}