MRI-derived atrophy in multiple system atrophy aligns with mitochondrial and glial gene expression patterns

MRI-derived atrophy in multiple system atrophy aligns with mitochondrial and glial gene expression patterns | 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 MRI-derived atrophy in multiple system atrophy aligns with mitochondrial and glial gene expression patterns Lydia Chougar, Christina Tremblay, Aline Delva, Marie Filiatrault, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7095251/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in npj Parkinson's Disease → Version 1 posted 10 You are reading this latest preprint version Abstract Oligodendroglial pathology is a hallmark of multiple system atrophy (MSA), yet it remains unclear whether MRI-detected atrophy reflects underlying biological mechanisms. This study investigated whether regional atrophy aligns with gene expression and neurotransmitter systems. We recruited 65 MSA patients and derived brain atrophy measures from T1-weighted MRIs.Using postmortem data from the Allen Human Brain Atlas, partial least squares (PLS) regression identified gene expression components associated with atrophy. Gene enrichment analyses explored biological processes, and annotation mapping identified neurotransmitter systems matching atrophy patterns. Specificity was tested against 57 Parkinson’s disease patients. Atrophy primarily affected the cerebellar white matter, pons, putamen, olive, and substantia nigra. PLS revealed two latent variables explaining 27.5% of the covariance. Atrophic regions overexpressed genes linked to mitochondrial function and oligodendrocytes, showing patterns distinct from Parkinson’s disease. These regions also exhibited lower serotonin and GABA levels, and higher acetylcholine and noradrenaline receptor densities. MRI-derived atrophy in MSA is biologically grounded and may inform future therapeutic studies. Health sciences/Diseases Biological sciences/Genetics Health sciences/Neurology Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Multiple System Atrophy (MSA) is a rare, rapidly progressing synucleinopathy with a poor prognosis, characterized by autonomic failure and varying degrees of motor impairment, including poorly levodopa-responsive parkinsonism in the parkinsonian variant (MSAp) and predominant cerebellar symptoms in the cerebellar variant (MSAc). 1 α-synuclein aggregates mainly within the cytoplasm of oligodendrocytes, forming glial cytoplasmic inclusions. 2,3 Although mostly sporadic, rare familial cases linked MSA to SNCA , MAPT , and COQ2 variants. 4,5 However, the biological mechanisms underlying selective brain vulnerability in MSA remain poorly understood. Neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) provide sensitive, non-invasive biomarkers of disease-related brain changes. 6,7 Yet, these imaging-derived phenotypes remain largely descriptive and offer limited mechanistic insight into why certain brain regions are more susceptible to degeneration. The emerging field of imaging transcriptomics addresses this gap by linking spatial patterns of neuroimaging abnormalities to normative gene expression profiles from healthy postmortem human brains. 8–11 The core rationale of imaging transcriptomics is to move beyond purely descriptive neuroimaging findings and gain insight into the biological underpinnings of regional vulnerability in neurodegenerative diseases. In this context, MRI-derived atrophy patterns are not viewed as endpoints in themselves, but as in vivo phenotypic readouts of latent molecular predispositions. If regions showing pronounced atrophy overexpress genes involved in specific biological pathways, this suggests that neurodegeneration targets pre-existing transcriptomic landscape, thus positioning neuroimaging findings as biologically grounded and mechanistically interpretable markers of disease. This approach has already yielded disease-specific insights into synucleinopathies and other disorders. In Parkinson’s disease (PD), regional brain iron deposition has been associated with genes involved in metal detoxification and synaptic function, while cortical atrophy progression was associated with mitochondrial 12 and synaptic gene expression 13 and was reduced in regions enriched with oligodendrocyte and endothelial cell markers. 13 In isolated rapid eye movement sleep behaviour disorder (iRBD), a prodromal synucleinopathy, 14 regions showing cortical thinning overexpressed genes related to mitochondrial function and macroautophagy. 15 Imaging transcriptomics has also demonstrated specificity: in Alzheimer’s disease, atrophy correlates with regions overexpressing genes of the protein remodelling complex, with APOE (coding for apolipoprotein E) emerging as a key contributor. 15 Similarly, kidney-brain axis-related neurodegeneration was shown to follow spatial gene expression patterns including AGT (coding for angiotensinogen), 16 involved in vascular regulation. 17 Beyond transcriptomics, molecular annotation mapping enables the comparison of neuroimaging patterns with PET-based receptor density maps. 18 In iRBD, atrophy maps overlap with neurotransmitter systems such as dopamine, serotonin, and noradrenaline. 15 These complementary approaches offer a framework for uncovering the molecular and neurochemical architecture of regional vulnerability, yet they have not been applied to MSA, which remains poorly understood at the mechanistic level and urgently requires novel therapeutic targets. In this study, our objective was to quantify the spatial pattern of brain atrophy pattern in MSA and its subtypes and apply imaging transcriptomics and PET-based annotation mapping to uncover the molecular and neurochemical signatures of vulnerable brain regions. Using the Allen Human Brain Atlas, we identified gene expression components aligned with MSA-related atrophy and performed gene enrichment analyses to characterize the underlying biological processes. We further assessed which neurotransmitter systems best matched the spatial distribution of atrophy. To evaluate disease specificity, we repeated the same analyses in PD. We hypothesized that atrophy in MSA would primarily involve regions enriched in oligodendrocyte-related gene expression but also reveal additional disease-relevant molecular and neurochemical mechanisms contributing to neurodegeneration beyond oligodendroglial dysfunction. Methods Participants This is a retrospective single-center study of patients with clinically established MSA prospectively enrolled: 1) between 2007 and 2012 at the Paris Brain Institute-ICM as part of two research protocols (Genepark (LSHB-CT-2006-037544) and BBBIPPS (DGS 2006/0524)) and 2) between 2013 and 2020 in the movement disorders clinic at the Pitié-Salpêtrière Hospital. The diagnosis of MSA was confirmed by two movement disorders neurologists, based on international diagnostic criteria. 1 Participants were excluded if they had other neurological or psychiatric disorders, in case of evidence of vascular lesions on MRI (stroke, lacunar infarcts or Fazekas grade 3 vascular leukopathy) or if MRI findings contradicted the clinical diagnosis (e.g., midbrain atrophy suggestive of PSP in a clinically diagnosed MSA patient). Based on neurological examination, patients were clinically classified as the parkinsonian subtype (MSAp), cerebellar subtype (MSAc), or mixed subtype. The following clinical variables were collected: disease onset (defined as the first occurrence of either motor symptoms or autonomic dysfunction), levodopa equivalent daily dose (LEDD), Unified Parkinson's Disease Rating Scale (UPDRS) 19 part III scores, Hoehn and Yahr stage, 20 Mini Mental State Examination (MMSE), 21 and Parkinson Plus Scale (PPS), 22 the latter being available for a subset of participants (n = 20). We recruited age- and sex-matched healthy controls (HC) without a history of neurological or psychiatric disorders from the same sites and the Parkinson’s Progression Markers Initiative (PPMI) Database (RRID:SCR 006431, February 2024). 23 Additionally, patients with PD, age- and sex-matched to MSA patients, were recruited as a second control group through the Quebec Parkinson Network (QPN) at the Montreal Neurological Institute-MNI. 24 The local institutional review board approved the study (Genepark: CPP Paris II, 2007-A00208-45; BBBIPPS: CPP Paris VI, P040410–65 − 06; Parkatypique: CPP Ile-de-France VI08012015; C-BIG general protocol: 2017 − 330, 15-944-MUHC; C-BIG imaging protocol: 2019–4759; QPN protocol: 2015 − 143, MP-CUSM-NEU-14-053, MP-37-2015-143). Participants gave written informed consent. MRI acquisition Participants were scanned on three 3T MRI scanners (Siemens TRIO, Siemens SKYRA, General Electric SIGNA, 1.5T General Electric OPTIMA) using a three-dimensional gradient-recalled echo T1-weighted sequence (Tables S1-S2, PPMI imaging protocols). MRI processing Figure 1 outlines the pipeline. FreeSurfer (v7.1.1) was used for cortical parcellation and volumetric segmentation of T1-weighted images. 25 Maps passing quality control were segmented to derive 68 bilateral cortical thickness measurements from the Desikan-Killiany atlas and 14 volume measurements from the bilateral subcortical structures (putamen, caudate, pallidum, thalamus, nucleus accumbens, amygdala, hippocampus). The brainstem was segmented into subregions (midbrain, pons, medulla, superior cerebellar peduncles) using FreeSurfer’s brainstem toolbox. 25 The cerebellum was segmented into 28 volumes with CerebNet(v.2.1.2) 26 and the Schmahmann atlas. 27 All volumes were normalized by total intracranial volume. To extract morphological information from brainstem nuclei of interest, we performed deformation-based morphometry (DBM) on each subject’s T1-weighted image using the Computational Anatomy Toolbox (CAT12; r1742) in Statistical Parametric Mapping software (SPM12) 28 and MATLAB (vR2019b). The Brainstem Navigator toolkit (v0.9) 29,30 was applied to MNI-registered DBM maps to extract the extent of deformation in the substantia nigra (SN), inferior olive, and superior olive. Bilateral regions were analyzed separately. Processing of atrophy maps Morphological values were harmonized using ComBat to remove scanner-related variability preserving biological effects. 31 W-scoring was applied to ComBat-corrected values to remove the age and sex effects and derive deviations from what is expected for age and sex based on regressions generated within the HC group. 12,13,15 Negative W-scores indicate atrophy, whereas positive W-score indicate expansion. Regional gene expression extraction To characterize the gene expression patterns associated with atrophic regions, we performed an imaging transcriptomics approach. 11 Regional expression values of > 20,000 genes from the Allen Human Brain Atlas (AHBA) 32 were extracted in atrophic regions using abagen (v0.1.3). 33 Two cerebellar lobules which did not match with any AHBA region (vermis and vermis VII) were discarded, resulting in 26 cerebellar regions. The main analysis focused on deep brain regions, where atrophy predominates, using a region-by-gene expression matrix (50 regions, 15,611 genes). Since only two brains had available right hemisphere gene expression values, measurements from the left hemisphere were mirrored onto the right hemisphere to ensure whole-brain transcriptomic coverage. These gene expression values were used as predictors for the partial least squares regression. Partial least squares regression Partial least squares (PLS) regression was used to identify gene expression patterns associated with deep brain atrophy. PLS is a multivariate approach that identifies latent variables (LV) explaining maximal covariance between two matrices: atrophy (65 patients, 50 regions) and gene expression (15,611 genes, 50 regions). The matrices were multiplied, and the resulting correlation matrix was subjected to singular value decomposition. Significance of the LVs was assessed by comparing the empirical covariance explained by each LV to the covariance of 10,000 null models where atrophy was randomly permuted between regions (random null models). Given that the brain is characterized by a high degree of spatial autocorrelation between brain regions, the significance was also tested against 10,000 spatially-constrained null models generated with BrainSMASH. 34 A LV was considered significant if fewer than 5% of null models explained more covariance than the original LV (P < 0.05). To identify genes most robustly associated with each LV, we performed bootstrap resampling by randomly shuffling the matrix rows and repeating the PLS regression 5000 times to obtain bootstrap ratio weights. The ranked gene lists were used as inputs for gene set enrichment analysis. Gene set enrichment analysis To identify the biological processes, cellular components, and human diseases gene terms overexpressed in association with atrophy in MSA, we performed gene set enrichment analysis (GSEA) in WebGestalt 2024. 35 Gene Ontology terms 36 were used for biological processes and cellular components, and DisGeNET terms 37 for human diseases. GSEA assessed whether negatively-weighted genes (associated with atrophy) were found more frequently within certain gene terms. 35 Only gene terms with a minimum of 20 and maximum of 2000 genes were considered. Significance was determined using 1000 random permutations with false discovery rate (FDR) correction for multiple comparisons. To ensure that our results were independent of the enrichment platform used, we repeated the GSEA using PANTHER (version 19.0, 20240619). 38 To identify which terms were specifically overexpressed in each MSA subgroup, we ran additional sub-analyses in each subgroup. Cell type analysis Two transcriptomic-based approaches were performed to identify the cell types whose genes were expressed in relation to atrophy. First, we used a GSEA as described above using WebGestalt 2024 35 and the Human Cell Landscape, 39 which analyzes cells from > 50 human tissues. Second, using single-cell RNA sequencing performed on cortical samples, 40 we extracted the gene expression associated with oligodendrocytes, oligodendrocyte progenitor cells, microglia, astrocytes, excitatory neurons, inhibitory neurons, and endothelial cells. 18 For each cell type, we computed the regional average expression across all genes and calculated Pearson’s correlations between the average gene expression and atrophy maps. Correlations were corrected for 7 comparisons with Bonferroni (P < 0.007), tested against 10,000 spatial null models, and resulting P values were FDR-corrected. 34 Specificity analysis: comparison with PD To test whether the gene enrichment patterns in MSA were disease-specific, we replicated the gene set enrichment analysis on a sample of PD patients. Neurotransmitter mapping We investigated whether atrophy in MSA occurred in regions overexpressing certain neurochemical systems. We parcellated and z-scored the regional density maps of 19 receptors, transporters, and binding sites to quantify the density of dopamine (D1, D2, dopamine transporter [DAT]), serotonin (5-HT 1A , 5-HT 1B , 5-HT 2A , 5-HT 4 , 5-HT 6 , serotonin transporter [5-HTT]), noradrenaline (noradrenaline transporter [NET]), acetylcholine (α4β2, vesicular acetylcholine transporter [VAChT], M1), GABA (GABA A /BZ), glutamate (mGluR5, NMDA), histamine (H3), endocannabinoids (CB1), and opioids (µ) 18 in the same regions as atrophy using neuromaps (details and references in Appendix). 41 Cerebellar regions were excluded since PET images were normalized to the cerebellum for most tracers. Spearman’s correlation was calculated between each neurochemical regional density map and W-scores. Correlations were corrected for 19 comparisons with Bonferroni (P < 0.003), tested against 10,000 spatial null models, and resulting P values were further FDR-corrected. Specifics on the imaging and statistical methods are described in previous studies. 12,13,15,18 Statistical analysis Differences in sex proportion, age, and clinical scores were compared between groups using chi-squared test and Kruskal-Wallis test, respectively. Two-tailed one-sample t-tests with FDR correction were conducted on the W-scores to characterize the regions where MSA patients differed from HCs. Effects of laterality on the W-scores were investigated in MSA using two-tailed paired t-tests and FDR correction. Comparisons were also performed between the MSAp and MSAc subgroups. The associations between clinical variables and atrophy W-scores were investigated using partial Pearson’s correlation coefficients, with adjustment for age and sex and FDR correction. Data availability statement The data that support the findings of this study are not publicly available, but may be made available to qualified researchers on reasonable request from the corresponding author. Code availability statement The underlying code for this study is not publicly available, but may be made available to qualified researchers on reasonable request from the corresponding author. Results Participants The study included 69 MSA patients and 190 HCs. Of these, 13 (4 MSA, 9 HCs) failed image processing or quality control, resulting in 246 participants, namely 65 MSA patients and 181 HCs. The MSA group included 34 MSAp, 22 MSAc, and 9 mixed. Average disease duration was 4.0 ± 2.2 years. As expected, patients with MSAc (10.8 ± 6.2) and mixed MSA (16.0 ± 4.2) had higher cerebellar PPS subscores than those with MSAp (0.8 ± 1.2, p = 0.002). No other group difference was seen (Table 1 ). MSA patients show subcortical and cortical brain atrophy MSA patients had severe deep brain atrophy compared to HCs, particularly in the cerebellar white matter and cortex, pons, putamen, superior and inferior olive, and SN (all P FDR <0.0001, Table S3 ). Asymmetry was only observed in the inferior olive (P FDR <0.0001). Cortical atrophy was seen in 46 (68%) regions, particularly in the frontal and parietal cortices. The most atrophic regions were the bilateral precentral and caudal middle frontal cortices, the right pars opercularis and supramarginal cortices, the left precuneus, inferior parietal, paracentral, and rostral middle frontal cortices (P FDR <0.0001, Fig. 2 , Table S3 ). No asymmetry was found in cortical atrophy. When comparing MSAc and MSAp groups, as expected, MSAp patients exhibited more atrophy in the putamen (P FDR <0.0001) and caudate (left: P FDR =0.006, right: P FDR =0.04, Table S4). MSAc patients had greater atrophy in the cerebellar white matter (P FDR <0.0001) and cortex (P FDR <0.0001), followed by the pons (P FDR <0.0001), midbrain (P FDR <0.02), superior cerebellar peduncles (P FDR =0.02), and superior olive (P FDR ≤0.008). No differences were found in the SN, inferior olive, or cortical regions between MSAp and MSAc patients (Table S4). MRI subcortical atrophy correlates with disease severity We found significant negative correlation between UPDRS III scores and atrophy in the right (r=-0.46, P FDR =0.01) and left (r=-0.49, P FDR =0.01) putamen, left pallidum (r=-0.55, P FDR =0.01), left caudate (r=-0.37, P FDR =0.03), the right (r=-0.50, P FDR =0.01) and left (r=-0.46, P FDR =0.01) inferior olive, and medulla (r=-0.48, P FDR =0.01) (Fig. 3 a). The total PPS scores negatively correlated with atrophy in the left putamen (r=-0.51, P FDR =0.09), midbrain (r=-0.61, P FDR =0.04), medulla (r=-0.54, P FDR =0.07), bilateral SN (right: r=-0.59, P FDR =0.049, left: r=-0.66, P FDR =0.03), bilateral inferior olive (right: r=-0.63, P FDR =0.03, left: r=-0.53, P FDR =0.08), and superior olive (right: r=-0.61, P FDR =0.04, left: r=-0.56, P FDR =0.06). In addition, the cerebellar PPS subscore also strongly correlated with the pons (r=-0.77, P FDR =0.008) cerebellar white matter (right: r=-0.81, P FDR =0.004, left: r=-0.74, P FDR =0.007) (Fig. 3 b). No significant correlations were found between subcortical atrophy and either disease duration or LEDD values. Similarly, cortical atrophy did not correlate significantly with any clinical variables, including MMSE scores. The brain’s gene expression spatial distribution predicts atrophy in MSA We investigated the relationship between deep brain atrophy and the brain’s spatial gene expression distribution. Two LVs were significant, explaining 28% of the covariance in gene-atrophy compared to random (LV3: 19.8% of covariance explained vs. 11.3% in null models, P = 0.03; LV5: 7.6% vs. 3.7%, P = 0.02) and spatial null models (LV3: 12.1%, P = 0.04; LV5: 3.7%, P = 0.01; Fig. 4 a). For both variables, regions with more atrophy had more negative weights (LV3: r = 0.44, P < 0.001; LV5: r = 0.28, P = 0.05), meaning that negatively-weighted genes were more expressed in atrophic regions (Fig. 4 b, 4 d; Fig. S1 ). MRI atrophy in MSA is primarily associated with mitochondrial function Next, we performed GSEA to identify biological processes, cellular components, human disease terms enriched among genes most strongly associated with brain atrophy in MSA. For genes negatively weighted on LV3, 652 genes (4.2%) were significantly associated with atrophy after bootstrapping (Fig. 4 c, 4 e). Strikingly, the most significantly enriched biological processes in atrophic regions were related to mitochondrial function. These included proton transmembrane transport, complex I assembly, the electron transport chain, mitochondrial transport, and mitochondrion organization (all P FDR <0.0001). Other key processes included energy derivation by oxidation of organic compounds (P FDR <0.001) and nucleoside triphosphate metabolic process (P FDR <0.001) (Table S5, Fig. 5 ). Among biological processes, ensheathment of neurons (a function primarily mediated by oligodendrocytes) was also significantly enriched, but ranked twelfth among all significant terms, indicating that although oligodendrocyte-related functions are involved in regions showing neurodegeneration, mitochondrial processes are more prominently associated with regional vulnerability in MSA. Regarding cellular components, atrophic regions were enriched for genes associated with the mitochondrial protein-containing complex, respirasome, oxidoreductase complex, mitochondrial inner membrane, and myelin sheath (all P FDR <0.0001). Atrophic regions also overexpressed terms related to mitochondrial diseases (Fig. 5 , Table S5). Similar findings were obtained when performing GSEA using the PANTHER gene enrichment platform (Table S6). For genes negatively weighted on LV5, 219 (1.4%) were associated with greater atrophy (Fig. 4 e). GSEA revealed similar findings as in LV3, with processes related to energy production and metabolism. Additional sub-analyses on the MSA subgroups separately also identified terms related to mitochondrial functions in both subgroups (Table S7). MRI atrophy in MSA relates to cell-type vulnerability Using GSEA, cell type-related genes were overexpressed in relation to atrophy in MSA, including brain-related oligodendrocytes and endothelial cells (both P FDR <0.001) (Fig. 4 ). Similarly, using single-cell RNA sequencing specific to neural cells, we found a negative correlation between W-scores in deep brain regions and oligodendrocyte gene expression (r=-0.39, P = 0.0006; P FDR spatial = 0.002), meaning higher gene expression of these cell types in atrophic areas (Table S8). These results support that atrophic regions overexpress gene expression profiles related to oligodendrocytes. Specificity analysis: comparison with PD To assess the specificity of these findings to MSA, we repeated the PLS regression in 57 PD patients age- and sex-matched to the MSA patients. We found two significant LVs explaining in 20.0% of the gene expression-atrophy covariance in PD. Regions associated with negatively-weighted genes (atrophy) in PD were enriched for synaptic functions. In terms of cellular components, terms related to synapses were significant, including postsynaptic specialization, neuron spine, neuron-to-neuron synapse, GABA-ergic synapse (Fig. S2 ). There was no significant enrichment of cell types, including oligodendrocytes. These findings suggest that atrophy patterns in MSA and PD are supported by distinct gene expression profiles in MSA and PD, with mitochondrial functions and oligodendrocytes being specific to MSA. Brain atrophy maps onto specific neurotransmitter systems We further tested whether the pattern of cortical and deep brain regions atrophy in MSA mapped onto specific neurotransmitter systems. Atrophic regions had lower density of serotonin receptors, namely 5-HT 1A (r = 0.75, P < 0.0001; P FDR spatial < 0.0001) and 5-HT 2A (r = 0.36, P = 0.001; P FDR spatial = 0.046), and GABA A /BZ receptors (r = 0.42, P < 0.0001; P FDR spatial=0.046), as well as a higher density of α4β2 acetylcholine (r=-0.61, P < 0.0001; P FDR spatial < 0.0001) and NET noradrenaline transporters (r=-0.36, P = 0.001; P FDR spatial = 0.046) (Fig. S3 , Table S9). Discussion Our study provides novel evidence that brain atrophy patterns observed on MRI in MSA reflect specific underlying biological mechanisms. The pathological involvement of oligodendrocytes is well established given their role in glial cytoplasmic inclusions, but the molecular underpinnings of MRI-derived atrophy patterns have remained poorly characterized. Here, we showed that regions most affected by atrophy in MSA, including the putamen, cerebellar white matter, and pons, are not only enriched for oligodendrocyte-related gene expression but also display strong overexpression of genes involved in mitochondrial respiratory chain function. This finding suggests that MRI atrophy in MSA is not merely a structural readout but a phenotypic manifestation of region-specific molecular vulnerability. In addition, these atrophic regions exhibited a distinct neurochemical profile, with increased densities of acetylcholine and noradrenaline receptors/transporters and decreased densities of serotonin and GABA receptors. This extends current understanding of selective vulnerability in MSA by integrating transcriptomic and neurochemical context and support imaging transcriptomics as an approach to anchor neuroimaging observations in both established and novel disease-relevant biology. As expected, atrophy in MSA was more severe in deep brain regions, mainly involving the putamen in the parkinsonian variant, and the cerebellum, pons, and olive in the cerebellar subtype. 6,7 Subcortical atrophy was significantly associated with disease severity scores of motor and cerebellar dysfunction, supporting the functional relevance of these structural changes. In contrast, no significant correlation was found with autonomic dysfunction scores. This lack of association may be due to limited statistical power. Notably, a recent study linked gray matter loss in the medulla and cerebellum (cerebellar cortex and deep cerebellar nuclei) with cardiovascular autonomic failure, 42 suggesting that more targeted or higher-resolution analyses may be required to detect such associations. Atrophy was also present in specific cortical areas, notably in the bilateral precentral and caudal middle frontal cortices, the right pars opercularis region, and the left precuneus, in line with previous studies. 43 The involvement of the precentral cortices, more prominent in MSAp than MSAc, may reflect extrapyramidal and corticospinal involvement. Subcortical degeneration may alter cortico-subcortical loops, potentially leading to structural reorganization in connected cortical structures, and contributing to cortical atrophy. 43 Moreover, MSA patients often present with pyramidal signs, 1 reflecting corticospinal tract damage, as reported in neuropathological 44 and MRI studies. 45 Using imaging transcriptomics, we found that atrophic regions overexpressed genes involved in mitochondrial assembly and functioning, suggesting that mitochondrial dysfunction represents an important factor of selective vulnerability to neurodegeneration. Mitochondria play a crucial role in several neurodegenerative diseases. 46 As for PD, 47 there is evidence of mitochondrial dysfunction in the pathogenesis of MSA based on genome-wide association studies, 4 postmortem studies, 47,48 and cell 49,50 and animal models. 46 Mutations in the COQ2 gene, encoding an enzyme involved in Coenzyme Q10 (CoQ10) biosynthesis, have been linked to familial and sporadic cases of MSA. 4 CoQ10 is found in the inner mitochondrial membrane and is involved in the transfer of electrons from complexes I/II to complex III, thus playing an essential role in the functioning of the respiratory chain and the ATP production. Impaired CoQ10 activity increases vulnerability to oxidative stress. 46,47 Autopsy studies have shown reduced CoQ10 levels in the cerebrospinal fluid 51 and blood 52 of MSA patients as well as in cerebellar 47,48 and motor cortex postmortem samples. 48 A study investigating mitochondrial function within dopaminergic neurons derived from induced pluripotent stem cells (iPSCs) of MSA patients found evidence of impaired respiratory chain activity and up-regulation of CoQ10 biosynthesis, the latter indicating possible compensatory mechanism. 49 Another study found that oxidative stress exposure in neural progenitor cells derived from MSA patient iPSCs triggered excessive generation of reactive oxygen species and cell damage. 50 In line with these findings, a recent placebo-controlled phase 2 trial tested the efficacy of Ubiquinol, a coenzyme Q10 supplementation, in MSA patients and showed a significantly smaller decline of the Unified Multiple System Atrophy Rating Scale (UMSARS) in the treated group, 53 further supporting the evidence of mitochondrial dysfunction in MSA. Our results resemble those seen in other synucleinopathies, namely PD and iRBD, showing that cortical brain atrophy occurs in regions overexpressing genes involved in mitochondrial function or synaptic functioning. 12,15 However, when repeating the same analyses on deep brain regions in PD, we found an overexpression of synapse-related genes, as in previous studies. 13 Interestingly, no mitochondria-related terms were associated with deep brain regions in PD. These findings suggest distinct gene expression profiles in MSA and PD, with the relationship between deep brain atrophy, mitochondrial function and oligodendrocytes specific to MSA. An expected finding was the overexpression of oligodendrocyte-related genes in regions showing greater atrophy in MSA patients. Interestingly, this enrichment was not found in PD, aligning with the fact that oligodendrocytes are the primary target of α-synuclein aggregates in MSA. 2,3 This result mirrors the association with genes enriched for mitochondrial functions given the energetic need of oligodendrocytes, especially for the process of myelination. Whether mitochondrial dysfunction is a primary mechanism underlying oligodendropathy or is secondary to other pathological processes, especially α-synuclein accumulation, remains to be determined. Indeed, α-synuclein-mediated oligodendroglial pathology contributes to neuronal damage, as supported by the positive correlation between neuronal loss and glial cytoplasmic inclusion density. 3 Oligodendrocytes are crucial not only in the formation of the myelin sheath, but also for providing trophic support to neurons. Factors released by oligodendroglial precursors and mature oligodendrocytes are essential for neuronal survival. Consequently, the deficiency of oligodendroglia-derived neurotrophic factors resulting from oligodendroglial impairment may account for the neuronal loss. 46 In sum, our results support the theory that neurodegeneration in MSA may derive from neuronal energy failure and lack of trophic support from oligodendroglia. Regions most vulnerable to atrophy in MSA colocalized with areas with higher distribution of NET (noradrenaline) and α4β2 (acetylcholine), and lower distribution of serotonin (5-HT 1A /5-HT 2A ) and GABA receptors. These findings support the involvement of multi-neurochemical systems related to neuronal cell loss across several neurotransmitter projection systems, as demonstrated by postmortem and PET studies. 54 To date, only one study applied the spatial mapping approach in MSA to investigate the neurochemical underpinnings of iron deposition patterns, demonstrating that regions with higher cortical iron content overlapped with areas of higher density of noradrenaline and acetylcholine receptors. 55 Central autonomic pathways, crucial for autonomic cardiovascular and respiratory control, are impaired in MSA due to central noradrenergic deficiency. 56 This denervation is reflected by reduced cerebrospinal fluid levels of noradrenaline, coupled with decreased noradrenaline density in the frontal cortex and putamen in post-mortem tissue analyses. 57 The locus coeruleus (LC) is the main source of noradrenaline in the brain. Noradrenergic neurons project throughout the brain and spinal cord, regulating arousal, attention, and stress responses. 58 Central noradrenergic deficiency, possibly due to LC damage, may contribute to baroreflex dysfunction and orthostatic hypotension in MSA. The LC is also known to project to the nucleus of the solitary tract, where all baroreceptor afferents initially synapse in the brain, and to the rostral ventrolateral medulla, a major source of descending projections to sympathetic pre-ganglionic neurons, crucial for tonic maintenance of sympathetic vasomotor tone and blood pressure control. The association between atrophy and higher cholinergic receptor density shown here aligns with postmortem studies demonstrating severe cholinergic neurons depletion in the pedunculopontine and laterodorsal tegmental nuclei and LC. 58 Similarly, using 1-[ 11 C]Methylpiperidin-4-yl propionate, a PET radiotracer targeting acetylcholinesterase, a cholinergic activity reduction was shown in MSAp patients, similar to PD in the cortex, but more pronounced in subcortical regions, potentially accounting for the gait and cognitive disturbances observed in MSAp. 60 Furthermore, there is evidence of serotonergic neuronal loss in the caudal brainstem raphe nucleus in MSA, as shown by neuropathological 54 and PET imaging studies reporting decreased cortical serotonin transporter binding. 61 This may contribute to impaired autonomic and respiratory control. Our study has limitations. First, we lacked pathological confirmation of diagnoses. Instead, we only included patients meeting criteria for clinically established MSA, which is currently the highest level of diagnostic certainty. Second, while the sample size was relatively small given the rarity of the disease, it still provides valuable insights into brain abnormalities. Third, we performed a cross-sectional analysis on a cohort with variable disease duration and severity. Future longitudinal analyses will help investigate whether these molecular correlates are stage-dependent and how the spatial and biological landscape of atrophy evolves over time. Furthermore, gene expression data came from six healthy donors with varying ages and medical histories. Future studies should use transcriptomic atlases from a larger number of HCs closely matched to the MSA population. To conclude, we showed that brain atrophy in MSA, which is associated with clinical disease severity, affects deep regions enriched in genes related to mitochondrial function and oligodendrocytes and aligns with specific neurochemical systems. Using only MRI-derived morphological data, we identified biologically meaningful correlates of neurodegeneration in MSA. These results strengthen the link between imaging-derived phenotypes and underlying molecular mechanisms in MSA. Abbreviations DBM, deformation-based morphometry; FDR, false discovery rate; GO, gene ontology; HC, healthy control; iRBD, isolated rapid eye movement sleep behaviour disorder; LEDD, levodopa equivalent daily dose; LV, latent variable; MMSE, Mini Mental State Examination; MSA, multiple system atrophy; MSAp, parkinsonian variant of MSA; MSAc; cerebellar variant of MSA; PD, Parkinson’s disease; PLS, partial least squares; PPS, Parkinson Plus Scale; UPDRS, Unified Parkinson's Disease Rating Scale. Declarations Acknowledgments This work was supported by grants from Agence Nationale de la Recherche (grant numbers ANR-11-INBS-0006 [France Life Imaging], ANR-11-INBS-0011 [NeurATRIS, Investissements d’Avenir], ANR-19-P3IA-0001 [PRAIRIE 3IA Institute], and ANR-10-IAIHU-06 [IHU–Paris Institute of Neurosciences]), the European Union (EU) Framework Project 6–GENEPARK: Genomic Biomarkers for Parkinson’s Disease, Action Line: LIFESCIHEALTH Life sciences, genomics and biotechnology for health (LSH-2005-1.2.2.2)—Development of Innovative methods for diagnosis of nervous system disorders, the Programme Hospitalier de Recherche Clinique [grant Numbers: PHRC 2007-A00169-44 (LRRK) and PHRC 2004 (BBBIPPS), Association France Parkinson, Ecole Neuroscience de Paris, Electricité de France (Fondation d’Entreprise EDF), Institut National de la Santé et de la Recherche Médicale, Fondation Thérèse and René Planiol pour l’étude du Cerveau, Société Française de Radiologie (SFR) / Collège des Enseignants en Radiologie de France (CERF), and Société Française de Neuroradiologie (SFNR). The work was also supported by Fonds de recherche du Quebec – Santé (FRQS), the Canadian Institutes of Health Research (CIHR) and the Healthy Brains for Healthy Lives(HBHL) program of the Canada First Research Excellence Fund. Author information Lydia Chougar designed and conceptualized the study, collected and analyzed the data, drafted and revised the manuscript for intellectual content. Christina Tremblay, Aline Delva, Marie Filiatrault, Andrew Vo, Justine Y. Hansen, Asa Farahani, Parsa Khalafi, Charles-Etienne Castonguay, Guy Rouleau and Alain Dagher analyzed the data and revised the manuscript. Bratislav Misic assisted with the methodology and revised the manuscript. Jean-Christophe Corvol, Marie Vidailhet, Bertrand Degos, David Grabli, and Stéphane Lehéricy performed data collection and revised the manuscript. Shady Rahayel: designed and conceptualized the study, collected and analyzed the data, drafted and revised the manuscript for intellectual content Ethics declaration None of the authors report any competing interests related to the current work. J.C.C. has served in advisory boards for Alzprotect, Bayer, Ferrer, iRegene, Servier, UC; and received grants from the AXA and the ICM Foundations outside of this work. BD received honoraria for lectures from IPSEN, ORION, MERZ SR holds a research scholar award from the Fonds de recherche du Québec –Santé (FRQS) and receive grants from Parkinson Canada, Alzheimer Society of Canada, and The Michael J. Fox Foundation. References Wenning, G. K. et al. The Movement Disorder Society Criteria for the Diagnosis of Multiple System Atrophy. Movement Disorders mds.29005 (2022) doi:10.1002/mds.29005. Dickson, D. W. Parkinson’s Disease and Parkinsonism: Neuropathology. Cold Spring Harb Perspect Med 2 , (2012). Jellinger, K. A. 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Demographic and clinical characteristics of participants Controls MSA P value Post hoc tests Total MSAp MSAc MSA mixed n 181 65 34 22 9 Age (years) 63.2 ± 7.7 [46-83] 61.7 ± 8.0 [46-82] 63.3 ± 8.5 [46-82] 59.1 ± 6.2 [48-70] 62.4 ± 9.1 [50-75] 0.15 -- Females, n (%) 90 (49.7%) 27 (41.5%) 15 (44.1%) 8 (36.4%) 4 (44.4%) 0.66 -- Disease duration (years) -- 4.0 ± 2.2 [1-10] 3.7 ± 1.9 [1-8] 4.4 ± 2.4 [1-10] 3.9 ± 2.7 [2-10] 0.59 -- UPDRS III 1 -- 33.3 ± 17.8 [2-66] 35.9 ± 17.2 [11-60] 31.4 ± 17.4 [2-66] 30.4 ± 23.6 [4-64] 0.69 -- PPS 2 -- 99.7 ± 33.3 [38-168] 95.9 ± 30.3 [38-126] 95.2 ± 32.7 [62-168] 137.0 ± 43.8 [106-168] 0.34 -- Autonomic -- 20.1 ± 5.5 [11-33] 18.4 ± 7.2 [11-33] 20.2 ± 3.2 [15-25] 23.0 ± 18.4 [10-36] 0.10 -- Motor 58.4 ± 20.4 [19-97] 59.6 ± 20.9 [19-83] 54.2 ± 18.6 [26-87] 74.5 ± 31.8 [52-97] 0.55 -- Cerebellar -- 7.3 ± 7.2 [0-25] 0.8 ± 1.2 [0-3] 10.8 ± 6.2 [0-25] 16.0 ± 4.2 [13-19] 0.002 MSAc>MSAp** MSA mixed>MSAp* Oculomotor -- 2 ± 2.3 [0-6] 3.2 ± 2.2 [0-6] 1.3 ± 2.1 [0-6] ± 0.0 [0-0] 0.10 -- Mental -- 7.2 ± 3.1 [1–12] 6.8 ± 2.4 [3-11] 7.3 ± 3.8 [1-12] 9.0 ± 1.4 [8-10] 0.62 -- Hoehn and Yahr 3 -- 3.3 ± 1.1 [1-5] 3.0 ± 1.1 [1-5] 3.7 ± 0.8 [2.5-5] 3.3 ± 1.3 [1-5] 0.17 -- LEDD 4 (mg per day) -- 440.1 ± 355.5 [0-1395] 487.1 ± 327.6 [0-1169] 384.2 ± 398.6 [0-1395] 368.8 ± 409.2 [0-1044] 0.48 -- MMSE 5 -- 27.5 ± 2.2 [23-30] 27.6 ± 2.1 [23-30] 27.9 ± 2.3 [23-30] 26.3 ± 2.3 [23-30] 0.36 -- Quantitative variables are summarized as mean ± standard deviation [min-max], and qualitative variables as counts and percentages. Differences in sex proportion and age were compared between controls and MSA patients using fisher’s exact test and Kruskal-Wallis test, respectively. Differences in disease duration, UPDRS part III scores, PPS total score and subscores, Hoehn and Yahr scales, LEDD values and MMSE scores were compared between MSA subgroups using Kruskal-Wallis test, followed by post hoc Dunn tests with FDR correction in case of significant p value (<0.05). Asterisks indicate the significance level of the post hoc comparisons: adjusted p < 0.05 (*), adjusted p < 0.01 (**), adjusted p < 0.001 (***). Of note, PPS scores were only available for a subset of participants (n=20). 1 Higher UPDRS III values indicate worse motor symptoms, with a maximum score of 108. 2 Higher PPS values indicate higher disease severity, with a maximum score of 309. PPS encompasses different domains including motor, autonomic, cerebellar, oculomotor and mental functions. Refer to Payan et al. PLoS One 2011(doi: 10.1371/journal.pone.0022293). 3 Higher Hoehn and Yahr stage (ranging from 0 to 5) indicate more severe functional disability. 4 Higher LEDD values indicate a greater total dose of levodopa-equivalent medication. 5 Lower MMSE scores indicative worse cognitive function, with a maximum score of 10. Abbreviations: LEDD, levodopa equivalent daily dose; MMSE, Mini Mental State Examination; MSA, multiple system atrophy; MSAp, parkinsonian variant of MSA; MSAc; cerebellar variant of MSA; PPS, Parkinson Plus Scale; UPDRS, Unified Parkinson's Disease Rating Scale. Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.docx Supplementaryfigures.docx Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2025 Read the published version in npj Parkinson's Disease → Version 1 posted Editorial decision: Revision requested 28 Sep, 2025 Reviews received at journal 28 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 11 Jul, 2025 Editor assigned by journal 11 Jul, 2025 Submission checks completed at journal 11 Jul, 2025 First submitted to journal 10 Jul, 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. 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Scan harmonization corrected inter-scanner variability, and regional values were converted to W-scores.\u003c/p\u003e\n\u003cp\u003e(b)Regional gene expression values were extracted from postmortem brain data. Using PLS, the MSA brain atrophy vector was linked with gene expression, followed by gene set enrichment analysis to identify significant terms in MSA-relevant regions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: HC, healthy control; MSA, multiple system atrophy; PLS, Partial least squares regression; SD, standard deviation; T1-w, T1-weighted.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7095251/v1/1d62aeca7821653869d73efa.png"},{"id":87031092,"identity":"3b0f4d1a-9129-4bc6-a0b6-c8a0ad0cafdd","added_by":"auto","created_at":"2025-07-18 12:47:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3397764,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePattern of atrophy in MSA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegional W-scores for cortical (top row) and deep brain regions (bottom row) were projected onto a brain volume for all MSA patients (a), MSAp (b), and MSAc (c). Red represent lower W-scores relative to HCs (greater atrophy); white indicate no significant atrophy. Only regions with significant W-score differences are shown. The same color scale was used for cortical and deep brain regions to ensure comparability of the magnitude of atrophy.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: HC, healthy control; MSA, multiple system atrophy; MSAp, parkinsonian variant of MSA; MSAc; cerebellar variant of MSA.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7095251/v1/e42fade4ed258992a2f307cf.png"},{"id":87031085,"identity":"82d938e1-c07a-477c-8259-5cb78d5a7df1","added_by":"auto","created_at":"2025-07-18 12:47:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":624497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between subcortical atrophy and disease severity scores in MSA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Scatterplots with linear regression lines showing the associations between UPDRS III scores and regional W-scores in deep brain regions. Pearson’s partial correlation coefficients (r) after adjustment by age and sex and p values after FDR correction are reported. Only significant correlations are plotted.\u003c/p\u003e\n\u003cp\u003e(b) Heatmaps showing correlations between PPS total scores and autonomic, cerebellar, oculomotor and motor subscores, and regional W-scores in deep brain regions. Pearson’s partial correlation coefficients (r) after adjustment by age and sex and p values after FDR correction are reported. The color scale indicates the strength and direction of the correlation (blue for negative and red for positive correlations). Of note, PPS scores were only available for a subset of participants (n=20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e**0.001 \u0026lt; P\u003csub\u003eFDR\u003c/sub\u003e ≤ 0.01; *0.01 \u0026lt; P\u003csub\u003eFDR\u003c/sub\u003e ≤ 0.05; °P\u003csub\u003eFDR\u003c/sub\u003e ≤ 0.10.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: FDR, false discovery rate; ION, inferior olive nucleus; MSA, multiple system atrophy; PPS, Parkinson Plus Scale; SN, substantia nigra; SON, superior olive nucleus; UPDRS, Unified Parkinson's Disease Rating Scale.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7095251/v1/60e558ecf29d14c7ee7f7091.png"},{"id":87032315,"identity":"ebc73cfb-0abc-4f48-9340-3c9fffd32629","added_by":"auto","created_at":"2025-07-18 12:55:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":944990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatterns of gene expression in MSA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) PLS regression results on deep brain regions with violin plots representing the covariance percentage in W-scores explained by gene expression (y-axis). Black dots indicate empirical covariance, asterisks indicate significant latent variables (LV) against random (rd) and spatial (sp) null models (x-axis).\u003c/p\u003e\n\u003cp\u003e(b, d) Scatterplots of the correlation between W-scores associated with deep brain regions (x-axis) and regional weights of LV3 and LV5 (y axis).\u003c/p\u003e\n\u003cp\u003e(c, e) Histograms of bootstrapped gene weights on LV3 and LV5, with red weights indicating robust association with regions with greater atrophy (below -3.29).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: LV, latent variable; PLS, partial least square; rd, random null models; sp, spatial null models.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7095251/v1/53f5155910997604ca3bb604.png"},{"id":87031100,"identity":"6166c4f1-e325-4c3f-aee9-3e0a201a8bd3","added_by":"auto","created_at":"2025-07-18 12:47:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1655238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis in the whole MSA sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant biological processes (a), cellular components (b), and tissue cell types (c) enriched in the negatively-weighted genes associated with deep brain atrophy in MSA. Terms are ranked based on the normalized enrichment score; all enriched terms were significant after FDR correction. Only the top 15 terms are shown for visualization purposes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: FDR, false discovery rate; NES, normalized enrichment score.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7095251/v1/4fc152c16564d570022b7ae5.png"},{"id":99172248,"identity":"1e00b9c8-58c7-44ce-8af7-eacfbcebac5b","added_by":"auto","created_at":"2025-12-29 16:05:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9465569,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7095251/v1/d70cbb96-ed87-49e7-af76-aa1e717bf6db.pdf"},{"id":87031083,"identity":"4d7bd56f-6de7-455c-acb0-fd7823cdfb93","added_by":"auto","created_at":"2025-07-18 12:47:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":104919,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7095251/v1/6d0b22adf3d77a59ed754843.docx"},{"id":87032316,"identity":"250b31af-2912-43fa-a579-c7bed37401c8","added_by":"auto","created_at":"2025-07-18 12:55:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1561098,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7095251/v1/a072d7eb900afb6c80ea91c0.docx"},{"id":87031086,"identity":"91bf76ed-9cd6-4d4a-9199-4da3f9fe65d2","added_by":"auto","created_at":"2025-07-18 12:47:38","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":41114,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7095251/v1/26d0af83f19b8469fa14c4ce.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"MRI-derived atrophy in multiple system atrophy aligns with mitochondrial and glial gene expression patterns","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple System Atrophy (MSA) is a rare, rapidly progressing synucleinopathy with a poor prognosis, characterized by autonomic failure and varying degrees of motor impairment, including poorly levodopa-responsive parkinsonism in the parkinsonian variant (MSAp) and predominant cerebellar symptoms in the cerebellar variant (MSAc).\u003csup\u003e1\u003c/sup\u003e α-synuclein aggregates mainly within the cytoplasm of oligodendrocytes, forming glial cytoplasmic inclusions.\u003csup\u003e2,3\u003c/sup\u003e Although mostly sporadic, rare familial cases linked MSA to \u003cem\u003eSNCA\u003c/em\u003e, \u003cem\u003eMAPT\u003c/em\u003e, and \u003cem\u003eCOQ2\u003c/em\u003e variants.\u003csup\u003e4,5\u003c/sup\u003e However, the biological mechanisms underlying selective brain vulnerability in MSA remain poorly understood.\u003c/p\u003e\u003cp\u003eNeuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) provide sensitive, non-invasive biomarkers of disease-related brain changes.\u003csup\u003e6,7\u003c/sup\u003e Yet, these imaging-derived phenotypes remain largely descriptive and offer limited mechanistic insight into why certain brain regions are more susceptible to degeneration. The emerging field of imaging transcriptomics addresses this gap by linking spatial patterns of neuroimaging abnormalities to normative gene expression profiles from healthy postmortem human brains.\u003csup\u003e8–11\u003c/sup\u003e The core rationale of imaging transcriptomics is to move beyond purely descriptive neuroimaging findings and gain insight into the biological underpinnings of regional vulnerability in neurodegenerative diseases. In this context, MRI-derived atrophy patterns are not viewed as endpoints in themselves, but as \u003cem\u003ein vivo\u003c/em\u003e phenotypic readouts of latent molecular predispositions. If regions showing pronounced atrophy overexpress genes involved in specific biological pathways, this suggests that neurodegeneration targets pre-existing transcriptomic landscape, thus positioning neuroimaging findings as biologically grounded and mechanistically interpretable markers of disease.\u003c/p\u003e\u003cp\u003eThis approach has already yielded disease-specific insights into synucleinopathies and other disorders. In Parkinson’s disease (PD), regional brain iron deposition has been associated with genes involved in metal detoxification and synaptic function, while cortical atrophy progression was associated with mitochondrial\u003csup\u003e12\u003c/sup\u003e and synaptic gene expression\u003csup\u003e13\u003c/sup\u003e and was reduced in regions enriched with oligodendrocyte and endothelial cell markers.\u003csup\u003e13\u003c/sup\u003e In isolated rapid eye movement sleep behaviour disorder (iRBD), a prodromal synucleinopathy,\u003csup\u003e14\u003c/sup\u003e regions showing cortical thinning overexpressed genes related to mitochondrial function and macroautophagy.\u003csup\u003e15\u003c/sup\u003e Imaging transcriptomics has also demonstrated specificity: in Alzheimer’s disease, atrophy correlates with regions overexpressing genes of the protein remodelling complex, with \u003cem\u003eAPOE\u003c/em\u003e (coding for apolipoprotein E) emerging as a key contributor.\u003csup\u003e15\u003c/sup\u003e Similarly, kidney-brain axis-related neurodegeneration was shown to follow spatial gene expression patterns including \u003cem\u003eAGT\u003c/em\u003e (coding for angiotensinogen),\u003csup\u003e16\u003c/sup\u003e involved in vascular regulation.\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eBeyond transcriptomics, molecular annotation mapping enables the comparison of neuroimaging patterns with PET-based receptor density maps.\u003csup\u003e18\u003c/sup\u003e In iRBD, atrophy maps overlap with neurotransmitter systems such as dopamine, serotonin, and noradrenaline.\u003csup\u003e15\u003c/sup\u003e These complementary approaches offer a framework for uncovering the molecular and neurochemical architecture of regional vulnerability, yet they have not been applied to MSA, which remains poorly understood at the mechanistic level and urgently requires novel therapeutic targets.\u003c/p\u003e\u003cp\u003eIn this study, our objective was to quantify the spatial pattern of brain atrophy pattern in MSA and its subtypes and apply imaging transcriptomics and PET-based annotation mapping to uncover the molecular and neurochemical signatures of vulnerable brain regions. Using the Allen Human Brain Atlas, we identified gene expression components aligned with MSA-related atrophy and performed gene enrichment analyses to characterize the underlying biological processes. We further assessed which neurotransmitter systems best matched the spatial distribution of atrophy. To evaluate disease specificity, we repeated the same analyses in PD. We hypothesized that atrophy in MSA would primarily involve regions enriched in oligodendrocyte-related gene expression but also reveal additional disease-relevant molecular and neurochemical mechanisms contributing to neurodegeneration beyond oligodendroglial dysfunction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eThis is a retrospective single-center study of patients with clinically established MSA prospectively enrolled: 1) between 2007 and 2012 at the Paris Brain Institute-ICM as part of two research protocols (Genepark (LSHB-CT-2006-037544) and BBBIPPS (DGS 2006/0524)) and 2) between 2013 and 2020 in the movement disorders clinic at the Pitié-Salpêtrière Hospital. The diagnosis of MSA was confirmed by two movement disorders neurologists, based on international diagnostic criteria.\u003csup\u003e1\u003c/sup\u003e Participants were excluded if they had other neurological or psychiatric disorders, in case of evidence of vascular lesions on MRI (stroke, lacunar infarcts or Fazekas grade 3 vascular leukopathy) or if MRI findings contradicted the clinical diagnosis (e.g., midbrain atrophy suggestive of PSP in a clinically diagnosed MSA patient). Based on neurological examination, patients were clinically classified as the parkinsonian subtype (MSAp), cerebellar subtype (MSAc), or mixed subtype. The following clinical variables were collected: disease onset (defined as the first occurrence of either motor symptoms or autonomic dysfunction), levodopa equivalent daily dose (LEDD), Unified Parkinson's Disease Rating Scale (UPDRS) \u003csup\u003e19\u003c/sup\u003e part III scores, Hoehn and Yahr stage,\u003csup\u003e20\u003c/sup\u003e Mini Mental State Examination (MMSE),\u003csup\u003e21\u003c/sup\u003e and Parkinson Plus Scale (PPS),\u003csup\u003e22\u003c/sup\u003e the latter being available for a subset of participants (n = 20).\u003c/p\u003e\u003cp\u003eWe recruited age- and sex-matched healthy controls (HC) without a history of neurological or psychiatric disorders from the same sites and the Parkinson’s Progression Markers Initiative (PPMI) Database (RRID:SCR 006431, February 2024).\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAdditionally, patients with PD, age- and sex-matched to MSA patients, were recruited as a second control group through the Quebec Parkinson Network (QPN) at the Montreal Neurological Institute-MNI.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e The local institutional review board approved the study (Genepark: CPP Paris II, 2007-A00208-45; BBBIPPS: CPP Paris VI, P040410–65 − 06; Parkatypique: CPP Ile-de-France VI08012015; C-BIG general protocol: 2017 − 330, 15-944-MUHC; C-BIG imaging protocol: 2019–4759; QPN protocol: 2015 − 143, MP-CUSM-NEU-14-053, MP-37-2015-143). Participants gave written informed consent.\u003c/p\u003e\u003cp\u003eMRI acquisition\u003c/p\u003e\u003cp\u003eParticipants were scanned on three 3T MRI scanners (Siemens TRIO, Siemens SKYRA, General Electric SIGNA, 1.5T General Electric OPTIMA) using a three-dimensional gradient-recalled echo T1-weighted sequence (Tables S1-S2, PPMI imaging protocols).\u003c/p\u003e\u003cp\u003eMRI processing\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the pipeline. FreeSurfer (v7.1.1) was used for cortical parcellation and volumetric segmentation of T1-weighted images.\u003csup\u003e25\u003c/sup\u003e Maps passing quality control were segmented to derive 68 bilateral cortical thickness measurements from the Desikan-Killiany atlas and 14 volume measurements from the bilateral subcortical structures (putamen, caudate, pallidum, thalamus, nucleus accumbens, amygdala, hippocampus). The brainstem was segmented into subregions (midbrain, pons, medulla, superior cerebellar peduncles) using FreeSurfer’s brainstem toolbox.\u003csup\u003e25\u003c/sup\u003e The cerebellum was segmented into 28 volumes with CerebNet(v.2.1.2)\u003csup\u003e26\u003c/sup\u003e and the Schmahmann atlas.\u003csup\u003e27\u003c/sup\u003e All volumes were normalized by total intracranial volume.\u003c/p\u003e\u003cp\u003eTo extract morphological information from brainstem nuclei of interest, we performed deformation-based morphometry (DBM) on each subject’s T1-weighted image using the Computational Anatomy Toolbox (CAT12; r1742) in Statistical Parametric Mapping software (SPM12)\u003csup\u003e28\u003c/sup\u003e and MATLAB (vR2019b). The Brainstem Navigator toolkit (v0.9)\u003csup\u003e29,30\u003c/sup\u003e was applied to MNI-registered DBM maps to extract the extent of deformation in the substantia nigra (SN), inferior olive, and superior olive.\u003c/p\u003e\u003cp\u003eBilateral regions were analyzed separately.\u003c/p\u003e\u003cp\u003eProcessing of atrophy maps\u003c/p\u003e\u003cp\u003eMorphological values were harmonized using ComBat to remove scanner-related variability preserving biological effects.\u003csup\u003e31\u003c/sup\u003e W-scoring was applied to ComBat-corrected values to remove the age and sex effects and derive deviations from what is expected for age and sex based on regressions generated within the HC group.\u003csup\u003e12,13,15\u003c/sup\u003e Negative W-scores indicate atrophy, whereas positive W-score indicate expansion.\u003c/p\u003e\u003cp\u003eRegional gene expression extraction\u003c/p\u003e\u003cp\u003eTo characterize the gene expression patterns associated with atrophic regions, we performed an imaging transcriptomics approach.\u003csup\u003e11\u003c/sup\u003e Regional expression values of \u0026gt; 20,000 genes from the Allen Human Brain Atlas (AHBA)\u003csup\u003e32\u003c/sup\u003e were extracted in atrophic regions using abagen (v0.1.3).\u003csup\u003e33\u003c/sup\u003e Two cerebellar lobules which did not match with any AHBA region (vermis and vermis VII) were discarded, resulting in 26 cerebellar regions. The main analysis focused on deep brain regions, where atrophy predominates, using a region-by-gene expression matrix (50 regions, 15,611 genes). Since only two brains had available right hemisphere gene expression values, measurements from the left hemisphere were mirrored onto the right hemisphere to ensure whole-brain transcriptomic coverage. These gene expression values were used as predictors for the partial least squares regression.\u003c/p\u003e\u003cp\u003ePartial least squares regression\u003c/p\u003e\u003cp\u003ePartial least squares (PLS) regression was used to identify gene expression patterns associated with deep brain atrophy. PLS is a multivariate approach that identifies latent variables (LV) explaining maximal covariance between two matrices: atrophy (65 patients, 50 regions) and gene expression (15,611 genes, 50 regions). The matrices were multiplied, and the resulting correlation matrix was subjected to singular value decomposition. Significance of the LVs was assessed by comparing the empirical covariance explained by each LV to the covariance of 10,000 null models where atrophy was randomly permuted between regions (random null models). Given that the brain is characterized by a high degree of spatial autocorrelation between brain regions, the significance was also tested against 10,000 spatially-constrained null models generated with BrainSMASH.\u003csup\u003e34\u003c/sup\u003e A LV was considered significant if fewer than 5% of null models explained more covariance than the original LV (P \u0026lt; 0.05).\u003c/p\u003e\u003cp\u003eTo identify genes most robustly associated with each LV, we performed bootstrap resampling by randomly shuffling the matrix rows and repeating the PLS regression 5000 times to obtain bootstrap ratio weights. The ranked gene lists were used as inputs for gene set enrichment analysis.\u003c/p\u003e\u003cp\u003eGene set enrichment analysis\u003c/p\u003e\u003cp\u003eTo identify the biological processes, cellular components, and human diseases gene terms overexpressed in association with atrophy in MSA, we performed gene set enrichment analysis (GSEA) in WebGestalt 2024.\u003csup\u003e35\u003c/sup\u003e Gene Ontology terms\u003csup\u003e36\u003c/sup\u003e were used for biological processes and cellular components, and DisGeNET terms\u003csup\u003e37\u003c/sup\u003e for human diseases. GSEA assessed whether negatively-weighted genes (associated with atrophy) were found more frequently within certain gene terms.\u003csup\u003e35\u003c/sup\u003e Only gene terms with a minimum of 20 and maximum of 2000 genes were considered. Significance was determined using 1000 random permutations with false discovery rate (FDR) correction for multiple comparisons. To ensure that our results were independent of the enrichment platform used, we repeated the GSEA using PANTHER (version 19.0, 20240619).\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo identify which terms were specifically overexpressed in each MSA subgroup, we ran additional sub-analyses in each subgroup.\u003c/p\u003e\u003cp\u003eCell type analysis\u003c/p\u003e\u003cp\u003eTwo transcriptomic-based approaches were performed to identify the cell types whose genes were expressed in relation to atrophy. First, we used a GSEA as described above using WebGestalt 2024\u003csup\u003e35\u003c/sup\u003e and the Human Cell Landscape,\u003csup\u003e39\u003c/sup\u003e which analyzes cells from \u0026gt; 50 human tissues. Second, using single-cell RNA sequencing performed on cortical samples,\u003csup\u003e40\u003c/sup\u003e we extracted the gene expression associated with oligodendrocytes, oligodendrocyte progenitor cells, microglia, astrocytes, excitatory neurons, inhibitory neurons, and endothelial cells.\u003csup\u003e18\u003c/sup\u003e For each cell type, we computed the regional average expression across all genes and calculated Pearson’s correlations between the average gene expression and atrophy maps. Correlations were corrected for 7 comparisons with Bonferroni (P \u0026lt; 0.007), tested against 10,000 spatial null models, and resulting P values were FDR-corrected.\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eSpecificity analysis: comparison with PD\u003c/p\u003e\u003cp\u003eTo test whether the gene enrichment patterns in MSA were disease-specific, we replicated the gene set enrichment analysis on a sample of PD patients.\u003c/p\u003e\u003cp\u003eNeurotransmitter mapping\u003c/p\u003e\u003cp\u003eWe investigated whether atrophy in MSA occurred in regions overexpressing certain neurochemical systems. We parcellated and z-scored the regional density maps of 19 receptors, transporters, and binding sites to quantify the density of dopamine (D1, D2, dopamine transporter [DAT]), serotonin (5-HT\u003csub\u003e1A\u003c/sub\u003e, 5-HT\u003csub\u003e1B\u003c/sub\u003e, 5-HT\u003csub\u003e2A\u003c/sub\u003e, 5-HT\u003csub\u003e4\u003c/sub\u003e, 5-HT\u003csub\u003e6\u003c/sub\u003e, serotonin transporter [5-HTT]), noradrenaline (noradrenaline transporter [NET]), acetylcholine (α4β2, vesicular acetylcholine transporter [VAChT], M1), GABA (GABA\u003csub\u003eA\u003c/sub\u003e/BZ), glutamate (mGluR5, NMDA), histamine (H3), endocannabinoids (CB1), and opioids (µ)\u003csup\u003e18\u003c/sup\u003e in the same regions as atrophy using \u003cem\u003eneuromaps\u003c/em\u003e (details and references in Appendix).\u003csup\u003e41\u003c/sup\u003e Cerebellar regions were excluded since PET images were normalized to the cerebellum for most tracers. Spearman’s correlation was calculated between each neurochemical regional density map and W-scores. Correlations were corrected for 19 comparisons with Bonferroni (P \u0026lt; 0.003), tested against 10,000 spatial null models, and resulting P values were further FDR-corrected.\u003c/p\u003e\u003cp\u003eSpecifics on the imaging and statistical methods are described in previous studies.\u003csup\u003e12,13,15,18\u003c/sup\u003e\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDifferences in sex proportion, age, and clinical scores were compared between groups using chi-squared test and Kruskal-Wallis test, respectively. Two-tailed one-sample t-tests with FDR correction were conducted on the W-scores to characterize the regions where MSA patients differed from HCs. Effects of laterality on the W-scores were investigated in MSA using two-tailed paired t-tests and FDR correction. Comparisons were also performed between the MSAp and MSAc subgroups. The associations between clinical variables and atrophy W-scores were investigated using partial Pearson’s correlation coefficients, with adjustment for age and sex and FDR correction.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not publicly available, but may be made available to qualified researchers on reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe underlying code for this study is not publicly available, but may be made available to qualified researchers on reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipants\u003c/p\u003e\n\u003cp\u003eThe study included 69 MSA patients and 190 HCs. Of these, 13 (4 MSA, 9 HCs) failed image processing or quality control, resulting in 246 participants, namely 65 MSA patients and 181 HCs. The MSA group included 34 MSAp, 22 MSAc, and 9 mixed. Average disease duration was 4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 years. As expected, patients with MSAc (10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2) and mixed MSA (16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2) had higher cerebellar PPS subscores than those with MSAp (0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2, p\u0026thinsp;=\u0026thinsp;0.002). No other group difference was seen (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMSA patients show subcortical and cortical brain atrophy\u003c/p\u003e\n\u003cp\u003eMSA patients had severe deep brain atrophy compared to HCs, particularly in the cerebellar white matter and cortex, pons, putamen, superior and inferior olive, and SN (all P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.0001, Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). Asymmetry was only observed in the inferior olive (P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.0001). Cortical atrophy was seen in 46 (68%) regions, particularly in the frontal and parietal cortices. The most atrophic regions were the bilateral precentral and caudal middle frontal cortices, the right pars opercularis and supramarginal cortices, the left precuneus, inferior parietal, paracentral, and rostral middle frontal cortices (P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.0001, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). No asymmetry was found in cortical atrophy.\u003c/p\u003e\n\u003cp\u003eWhen comparing MSAc and MSAp groups, as expected, MSAp patients exhibited more atrophy in the putamen (P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.0001) and caudate (left: P\u003csub\u003eFDR\u003c/sub\u003e=0.006, right: P\u003csub\u003eFDR\u003c/sub\u003e=0.04, Table S4). MSAc patients had greater atrophy in the cerebellar white matter (P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.0001) and cortex (P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.0001), followed by the pons (P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.0001), midbrain (P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.02), superior cerebellar peduncles (P\u003csub\u003eFDR\u003c/sub\u003e=0.02), and superior olive (P\u003csub\u003eFDR\u003c/sub\u003e\u0026le;0.008). No differences were found in the SN, inferior olive, or cortical regions between MSAp and MSAc patients (Table S4).\u003c/p\u003e\n\u003cp\u003eMRI subcortical atrophy correlates with disease severity\u003c/p\u003e\n\u003cp\u003eWe found significant negative correlation between UPDRS III scores and atrophy in the right (r=-0.46, P\u003csub\u003eFDR\u003c/sub\u003e=0.01) and left (r=-0.49, P\u003csub\u003eFDR\u003c/sub\u003e=0.01) putamen, left pallidum (r=-0.55, P\u003csub\u003eFDR\u003c/sub\u003e=0.01), left caudate (r=-0.37, P\u003csub\u003eFDR\u003c/sub\u003e=0.03), the right (r=-0.50, P\u003csub\u003eFDR\u003c/sub\u003e=0.01) and left (r=-0.46, P\u003csub\u003eFDR\u003c/sub\u003e=0.01) inferior olive, and medulla (r=-0.48, P\u003csub\u003eFDR\u003c/sub\u003e=0.01) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e\n\u003cp\u003eThe total PPS scores negatively correlated with atrophy in the left putamen (r=-0.51, P\u003csub\u003eFDR\u003c/sub\u003e=0.09), midbrain (r=-0.61, P\u003csub\u003eFDR\u003c/sub\u003e=0.04), medulla (r=-0.54, P\u003csub\u003eFDR\u003c/sub\u003e=0.07), bilateral SN (right: r=-0.59, P\u003csub\u003eFDR\u003c/sub\u003e=0.049, left: r=-0.66, P\u003csub\u003eFDR\u003c/sub\u003e=0.03), bilateral inferior olive (right: r=-0.63, P\u003csub\u003eFDR\u003c/sub\u003e=0.03, left: r=-0.53, P\u003csub\u003eFDR\u003c/sub\u003e=0.08), and superior olive (right: r=-0.61, P\u003csub\u003eFDR\u003c/sub\u003e=0.04, left: r=-0.56, P\u003csub\u003eFDR\u003c/sub\u003e=0.06). In addition, the cerebellar PPS subscore also strongly correlated with the pons (r=-0.77, P\u003csub\u003eFDR\u003c/sub\u003e=0.008) cerebellar white matter (right: r=-0.81, P\u003csub\u003eFDR\u003c/sub\u003e=0.004, left: r=-0.74, P\u003csub\u003eFDR\u003c/sub\u003e=0.007) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eNo significant correlations were found between subcortical atrophy and either disease duration or LEDD values. Similarly, cortical atrophy did not correlate significantly with any clinical variables, including MMSE scores.\u003c/p\u003e\n\u003cp\u003eThe brain\u0026rsquo;s gene expression spatial distribution predicts atrophy in MSA\u003c/p\u003e\n\u003cp\u003eWe investigated the relationship between deep brain atrophy and the brain\u0026rsquo;s spatial gene expression distribution. Two LVs were significant, explaining 28% of the covariance in gene-atrophy compared to random (LV3: 19.8% of covariance explained vs. 11.3% in null models, P\u0026thinsp;=\u0026thinsp;0.03; LV5: 7.6% vs. 3.7%, P\u0026thinsp;=\u0026thinsp;0.02) and spatial null models (LV3: 12.1%, P\u0026thinsp;=\u0026thinsp;0.04; LV5: 3.7%, P\u0026thinsp;=\u0026thinsp;0.01; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). For both variables, regions with more atrophy had more negative weights (LV3: r\u0026thinsp;=\u0026thinsp;0.44, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; LV5: r\u0026thinsp;=\u0026thinsp;0.28, P\u0026thinsp;=\u0026thinsp;0.05), meaning that negatively-weighted genes were more expressed in atrophic regions (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed; Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMRI atrophy in MSA is primarily associated with mitochondrial function\u003c/p\u003e\n\u003cp\u003eNext, we performed GSEA to identify biological processes, cellular components, human disease terms enriched among genes most strongly associated with brain atrophy in MSA. For genes negatively weighted on LV3, 652 genes (4.2%) were significantly associated with atrophy after bootstrapping (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ee). Strikingly, the most significantly enriched biological processes in atrophic regions were related to mitochondrial function. These included proton transmembrane transport, complex I assembly, the electron transport chain, mitochondrial transport, and mitochondrion organization (all P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.0001). Other key processes included energy derivation by oxidation of organic compounds (P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001) and nucleoside triphosphate metabolic process (P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001) (Table S5, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Among biological processes, ensheathment of neurons (a function primarily mediated by oligodendrocytes) was also significantly enriched, but ranked twelfth among all significant terms, indicating that although oligodendrocyte-related functions are involved in regions showing neurodegeneration, mitochondrial processes are more prominently associated with regional vulnerability in MSA. Regarding cellular components, atrophic regions were enriched for genes associated with the mitochondrial protein-containing complex, respirasome, oxidoreductase complex, mitochondrial inner membrane, and myelin sheath (all P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.0001). Atrophic regions also overexpressed terms related to mitochondrial diseases (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, Table S5). Similar findings were obtained when performing GSEA using the PANTHER gene enrichment platform (Table S6).\u003c/p\u003e\n\u003cp\u003eFor genes negatively weighted on LV5, 219 (1.4%) were associated with greater atrophy (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ee). GSEA revealed similar findings as in LV3, with processes related to energy production and metabolism. Additional sub-analyses on the MSA subgroups separately also identified terms related to mitochondrial functions in both subgroups (Table S7).\u003c/p\u003e\n\u003cp\u003eMRI atrophy in MSA relates to cell-type vulnerability\u003c/p\u003e\n\u003cp\u003eUsing GSEA, cell type-related genes were overexpressed in relation to atrophy in MSA, including brain-related oligodendrocytes and endothelial cells (both P\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, using single-cell RNA sequencing specific to neural cells, we found a negative correlation between W-scores in deep brain regions and oligodendrocyte gene expression (r=-0.39, P\u0026thinsp;=\u0026thinsp;0.0006; P\u003csub\u003eFDR\u003c/sub\u003e spatial\u0026thinsp;=\u0026thinsp;0.002), meaning higher gene expression of these cell types in atrophic areas (Table S8). These results support that atrophic regions overexpress gene expression profiles related to oligodendrocytes.\u003c/p\u003e\n\u003cp\u003eSpecificity analysis: comparison with PD\u003c/p\u003e\n\u003cp\u003eTo assess the specificity of these findings to MSA, we repeated the PLS regression in 57 PD patients age- and sex-matched to the MSA patients. We found two significant LVs explaining in 20.0% of the gene expression-atrophy covariance in PD. Regions associated with negatively-weighted genes (atrophy) in PD were enriched for synaptic functions. In terms of cellular components, terms related to synapses were significant, including postsynaptic specialization, neuron spine, neuron-to-neuron synapse, GABA-ergic synapse (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). There was no significant enrichment of cell types, including oligodendrocytes. These findings suggest that atrophy patterns in MSA and PD are supported by distinct gene expression profiles in MSA and PD, with mitochondrial functions and oligodendrocytes being specific to MSA.\u003c/p\u003e\n\u003cp\u003eBrain atrophy maps onto specific neurotransmitter systems\u003c/p\u003e\n\u003cp\u003eWe further tested whether the pattern of cortical and deep brain regions atrophy in MSA mapped onto specific neurotransmitter systems. Atrophic regions had lower density of serotonin receptors, namely 5-HT\u003csub\u003e1A\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.75, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; P\u003csub\u003eFDR\u003c/sub\u003e spatial\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and 5-HT\u003csub\u003e2A\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.36, P\u0026thinsp;=\u0026thinsp;0.001; P\u003csub\u003eFDR\u003c/sub\u003espatial =\u0026thinsp;0.046), and GABA\u003csub\u003eA\u003c/sub\u003e/BZ receptors (r\u0026thinsp;=\u0026thinsp;0.42, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; P\u003csub\u003eFDR\u003c/sub\u003espatial=0.046), as well as a higher density of \u0026alpha;4\u0026beta;2 acetylcholine (r=-0.61, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; P\u003csub\u003eFDR\u003c/sub\u003e spatial\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and NET noradrenaline transporters (r=-0.36, P\u0026thinsp;=\u0026thinsp;0.001; P\u003csub\u003eFDR\u003c/sub\u003e spatial\u0026thinsp;=\u0026thinsp;0.046) (Fig. \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e, Table S9).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides novel evidence that brain atrophy patterns observed on MRI in MSA reflect specific underlying biological mechanisms. The pathological involvement of oligodendrocytes is well established given their role in glial cytoplasmic inclusions, but the molecular underpinnings of MRI-derived atrophy patterns have remained poorly characterized. Here, we showed that regions most affected by atrophy in MSA, including the putamen, cerebellar white matter, and pons, are not only enriched for oligodendrocyte-related gene expression but also display strong overexpression of genes involved in mitochondrial respiratory chain function. This finding suggests that MRI atrophy in MSA is not merely a structural readout but a phenotypic manifestation of region-specific molecular vulnerability. In addition, these atrophic regions exhibited a distinct neurochemical profile, with increased densities of acetylcholine and noradrenaline receptors/transporters and decreased densities of serotonin and GABA receptors. This extends current understanding of selective vulnerability in MSA by integrating transcriptomic and neurochemical context and support imaging transcriptomics as an approach to anchor neuroimaging observations in both established and novel disease-relevant biology.\u003c/p\u003e\u003cp\u003eAs expected, atrophy in MSA was more severe in deep brain regions, mainly involving the putamen in the parkinsonian variant, and the cerebellum, pons, and olive in the cerebellar subtype.\u003csup\u003e6,7\u003c/sup\u003e Subcortical atrophy was significantly associated with disease severity scores of motor and cerebellar dysfunction, supporting the functional relevance of these structural changes. In contrast, no significant correlation was found with autonomic dysfunction scores. This lack of association may be due to limited statistical power. Notably, a recent study linked gray matter loss in the medulla and cerebellum (cerebellar cortex and deep cerebellar nuclei) with cardiovascular autonomic failure,\u003csup\u003e42\u003c/sup\u003e suggesting that more targeted or higher-resolution analyses may be required to detect such associations. Atrophy was also present in specific cortical areas, notably in the bilateral precentral and caudal middle frontal cortices, the right pars opercularis region, and the left precuneus, in line with previous studies.\u003csup\u003e43\u003c/sup\u003e The involvement of the precentral cortices, more prominent in MSAp than MSAc, may reflect extrapyramidal and corticospinal involvement. Subcortical degeneration may alter cortico-subcortical loops, potentially leading to structural reorganization in connected cortical structures, and contributing to cortical atrophy.\u003csup\u003e43\u003c/sup\u003e Moreover, MSA patients often present with pyramidal signs,\u003csup\u003e1\u003c/sup\u003e reflecting corticospinal tract damage, as reported in neuropathological \u003csup\u003e44\u003c/sup\u003e and MRI studies.\u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eUsing imaging transcriptomics, we found that atrophic regions overexpressed genes involved in mitochondrial assembly and functioning, suggesting that mitochondrial dysfunction represents an important factor of selective vulnerability to neurodegeneration. Mitochondria play a crucial role in several neurodegenerative diseases.\u003csup\u003e46\u003c/sup\u003e As for PD,\u003csup\u003e47\u003c/sup\u003e there is evidence of mitochondrial dysfunction in the pathogenesis of MSA based on genome-wide association studies,\u003csup\u003e4\u003c/sup\u003e postmortem studies,\u003csup\u003e47,48\u003c/sup\u003e and cell\u003csup\u003e49,50\u003c/sup\u003e and animal models.\u003csup\u003e46\u003c/sup\u003e Mutations in the \u003cem\u003eCOQ2\u003c/em\u003e gene, encoding an enzyme involved in Coenzyme Q10 (CoQ10) biosynthesis, have been linked to familial and sporadic cases of MSA.\u003csup\u003e4\u003c/sup\u003e CoQ10 is found in the inner mitochondrial membrane and is involved in the transfer of electrons from complexes I/II to complex III, thus playing an essential role in the functioning of the respiratory chain and the ATP production. Impaired CoQ10 activity increases vulnerability to oxidative stress.\u003csup\u003e46,47\u003c/sup\u003e Autopsy studies have shown reduced CoQ10 levels in the cerebrospinal fluid \u003csup\u003e51\u003c/sup\u003e and blood \u003csup\u003e52\u003c/sup\u003e of MSA patients as well as in cerebellar\u003csup\u003e47,48\u003c/sup\u003e and motor cortex postmortem samples.\u003csup\u003e48\u003c/sup\u003e A study investigating mitochondrial function within dopaminergic neurons derived from induced pluripotent stem cells (iPSCs) of MSA patients found evidence of impaired respiratory chain activity and up-regulation of CoQ10 biosynthesis, the latter indicating possible compensatory mechanism.\u003csup\u003e49\u003c/sup\u003e Another study found that oxidative stress exposure in neural progenitor cells derived from MSA patient iPSCs triggered excessive generation of reactive oxygen species and cell damage.\u003csup\u003e50\u003c/sup\u003e In line with these findings, a recent placebo-controlled phase 2 trial tested the efficacy of Ubiquinol, a coenzyme Q10 supplementation, in MSA patients and showed a significantly smaller decline of the Unified Multiple System Atrophy Rating Scale (UMSARS) in the treated group,\u003csup\u003e53\u003c/sup\u003e further supporting the evidence of mitochondrial dysfunction in MSA. Our results resemble those seen in other synucleinopathies, namely PD and iRBD, showing that cortical brain atrophy occurs in regions overexpressing genes involved in mitochondrial function or synaptic functioning.\u003csup\u003e12,15\u003c/sup\u003e However, when repeating the same analyses on deep brain regions in PD, we found an overexpression of synapse-related genes, as in previous studies.\u003csup\u003e13\u003c/sup\u003e Interestingly, no mitochondria-related terms were associated with deep brain regions in PD. These findings suggest distinct gene expression profiles in MSA and PD, with the relationship between deep brain atrophy, mitochondrial function and oligodendrocytes specific to MSA.\u003c/p\u003e\u003cp\u003eAn expected finding was the overexpression of oligodendrocyte-related genes in regions showing greater atrophy in MSA patients. Interestingly, this enrichment was not found in PD, aligning with the fact that oligodendrocytes are the primary target of α-synuclein aggregates in MSA.\u003csup\u003e2,3\u003c/sup\u003e This result mirrors the association with genes enriched for mitochondrial functions given the energetic need of oligodendrocytes, especially for the process of myelination. Whether mitochondrial dysfunction is a primary mechanism underlying oligodendropathy or is secondary to other pathological processes, especially α-synuclein accumulation, remains to be determined. Indeed, α-synuclein-mediated oligodendroglial pathology contributes to neuronal damage, as supported by the positive correlation between neuronal loss and glial cytoplasmic inclusion density.\u003csup\u003e3\u003c/sup\u003e Oligodendrocytes are crucial not only in the formation of the myelin sheath, but also for providing trophic support to neurons. Factors released by oligodendroglial precursors and mature oligodendrocytes are essential for neuronal survival. Consequently, the deficiency of oligodendroglia-derived neurotrophic factors resulting from oligodendroglial impairment may account for the neuronal loss.\u003csup\u003e46\u003c/sup\u003e In sum, our results support the theory that neurodegeneration in MSA may derive from neuronal energy failure and lack of trophic support from oligodendroglia.\u003c/p\u003e\u003cp\u003eRegions most vulnerable to atrophy in MSA colocalized with areas with higher distribution of NET (noradrenaline) and α4β2 (acetylcholine), and lower distribution of serotonin (5-HT\u003csub\u003e1A\u003c/sub\u003e/5-HT\u003csub\u003e2A\u003c/sub\u003e) and GABA receptors. These findings support the involvement of multi-neurochemical systems related to neuronal cell loss across several neurotransmitter projection systems, as demonstrated by postmortem and PET studies.\u003csup\u003e54\u003c/sup\u003e To date, only one study applied the spatial mapping approach in MSA to investigate the neurochemical underpinnings of iron deposition patterns, demonstrating that regions with higher cortical iron content overlapped with areas of higher density of noradrenaline and acetylcholine receptors.\u003csup\u003e55\u003c/sup\u003e Central autonomic pathways, crucial for autonomic cardiovascular and respiratory control, are impaired in MSA due to central noradrenergic deficiency.\u003csup\u003e56\u003c/sup\u003e This denervation is reflected by reduced cerebrospinal fluid levels of noradrenaline, coupled with decreased noradrenaline density in the frontal cortex and putamen in post-mortem tissue analyses.\u003csup\u003e57\u003c/sup\u003e The locus coeruleus (LC) is the main source of noradrenaline in the brain. Noradrenergic neurons project throughout the brain and spinal cord, regulating arousal, attention, and stress responses.\u003csup\u003e58\u003c/sup\u003e Central noradrenergic deficiency, possibly due to LC damage, may contribute to baroreflex dysfunction and orthostatic hypotension in MSA. The LC is also known to project to the nucleus of the solitary tract, where all baroreceptor afferents initially synapse in the brain, and to the rostral ventrolateral medulla, a major source of descending projections to sympathetic pre-ganglionic neurons, crucial for tonic maintenance of sympathetic vasomotor tone and blood pressure control. The association between atrophy and higher cholinergic receptor density shown here aligns with postmortem studies demonstrating severe cholinergic neurons depletion in the pedunculopontine and laterodorsal tegmental nuclei and LC.\u003csup\u003e58\u003c/sup\u003e Similarly, using 1-[\u003csup\u003e11\u003c/sup\u003eC]Methylpiperidin-4-yl propionate, a PET radiotracer targeting acetylcholinesterase, a cholinergic activity reduction was shown in MSAp patients, similar to PD in the cortex, but more pronounced in subcortical regions, potentially accounting for the gait and cognitive disturbances observed in MSAp.\u003csup\u003e60\u003c/sup\u003e Furthermore, there is evidence of serotonergic neuronal loss in the caudal brainstem raphe nucleus in MSA, as shown by neuropathological\u003csup\u003e54\u003c/sup\u003e and PET imaging studies reporting decreased cortical serotonin transporter binding.\u003csup\u003e61\u003c/sup\u003e This may contribute to impaired autonomic and respiratory control.\u003c/p\u003e\u003cp\u003eOur study has limitations. First, we lacked pathological confirmation of diagnoses. Instead, we only included patients meeting criteria for clinically established MSA, which is currently the highest level of diagnostic certainty. Second, while the sample size was relatively small given the rarity of the disease, it still provides valuable insights into brain abnormalities. Third, we performed a cross-sectional analysis on a cohort with variable disease duration and severity. Future longitudinal analyses will help investigate whether these molecular correlates are stage-dependent and how the spatial and biological landscape of atrophy evolves over time. Furthermore, gene expression data came from six healthy donors with varying ages and medical histories. Future studies should use transcriptomic atlases from a larger number of HCs closely matched to the MSA population.\u003c/p\u003e\u003cp\u003eTo conclude, we showed that brain atrophy in MSA, which is associated with clinical disease severity, affects deep regions enriched in genes related to mitochondrial function and oligodendrocytes and aligns with specific neurochemical systems. Using only MRI-derived morphological data, we identified biologically meaningful correlates of neurodegeneration in MSA. These results strengthen the link between imaging-derived phenotypes and underlying molecular mechanisms in MSA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDBM, deformation-based morphometry; FDR, false discovery rate; GO, gene ontology; HC, healthy control; iRBD, isolated rapid eye movement sleep behaviour disorder;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eLEDD, levodopa equivalent daily dose; LV, latent variable; MMSE, Mini Mental State Examination; MSA, multiple system atrophy; MSAp, parkinsonian variant of MSA; MSAc; cerebellar variant of MSA; PD, Parkinson\u0026rsquo;s disease; PLS, partial least squares; PPS, Parkinson Plus Scale; UPDRS, Unified Parkinson\u0026apos;s Disease Rating Scale.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from Agence Nationale de la Recherche (grant numbers ANR-11-INBS-0006 [France Life Imaging], ANR-11-INBS-0011 [NeurATRIS, Investissements d\u0026rsquo;Avenir], ANR-19-P3IA-0001 [PRAIRIE 3IA Institute], and ANR-10-IAIHU-06 [IHU\u0026ndash;Paris Institute of Neurosciences]), the European Union (EU) Framework Project 6\u0026ndash;GENEPARK: Genomic Biomarkers for Parkinson\u0026rsquo;s Disease, Action Line: LIFESCIHEALTH Life sciences, genomics and biotechnology for health (LSH-2005-1.2.2.2)\u0026mdash;Development of Innovative methods for diagnosis of nervous system disorders, the Programme Hospitalier de Recherche Clinique [grant Numbers: PHRC 2007-A00169-44 (LRRK) and PHRC 2004 (BBBIPPS), Association France Parkinson, Ecole Neuroscience de Paris, Electricit\u0026eacute; de France (Fondation d\u0026rsquo;Entreprise EDF), Institut National de la Sant\u0026eacute; et de la Recherche M\u0026eacute;dicale, Fondation Th\u0026eacute;r\u0026egrave;se and Ren\u0026eacute; Planiol pour l\u0026rsquo;\u0026eacute;tude du Cerveau, Soci\u0026eacute;t\u0026eacute; Fran\u0026ccedil;aise de Radiologie (SFR) / Coll\u0026egrave;ge des Enseignants en Radiologie de France (CERF), and Soci\u0026eacute;t\u0026eacute; Fran\u0026ccedil;aise de Neuroradiologie (SFNR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe work was also supported by Fonds de recherche du Quebec \u0026ndash; Sant\u0026eacute; (FRQS), the Canadian Institutes of Health Research (CIHR) and the Healthy Brains for Healthy Lives(HBHL) program of the Canada First Research Excellence Fund.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLydia Chougar designed and conceptualized the study, collected and analyzed the data, drafted and\u003c/p\u003e\n\u003cp\u003erevised the manuscript for intellectual content. Christina Tremblay, Aline Delva, Marie Filiatrault, Andrew Vo, Justine Y. Hansen, Asa Farahani, Parsa Khalafi, Charles-Etienne Castonguay, Guy Rouleau and Alain Dagher analyzed the data and revised the manuscript. Bratislav Misic assisted with the methodology and revised the manuscript. Jean-Christophe Corvol, Marie Vidailhet, Bertrand Degos, David Grabli, and St\u0026eacute;phane Leh\u0026eacute;ricy performed data collection and revised the manuscript. Shady Rahayel: designed and conceptualized the study, collected and analyzed the data, drafted and revised the manuscript for intellectual content\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors report any competing interests related to the current work.\u003c/p\u003e\n\u003cp\u003eJ.C.C. has served in advisory boards for Alzprotect, Bayer, Ferrer, iRegene, Servier, UC; and received grants from the AXA and the ICM Foundations outside of this work.\u003c/p\u003e\n\u003cp\u003eBD received honoraria for lectures from IPSEN, ORION, MERZ\u003c/p\u003e\n\u003cp\u003eSR holds a research scholar award from the Fonds de recherche du Qu\u0026eacute;bec \u0026ndash;Sant\u0026eacute; (FRQS) and receive grants from Parkinson Canada, Alzheimer Society of Canada, and The Michael J. Fox Foundation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWenning, G. 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C. \u003cem\u003eet al.\u003c/em\u003e Cerebral mitochondrial electron transport chain dysfunction in multiple system atrophy and Parkinson\u0026rsquo;s disease. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 6559 (2019).\u003c/li\u003e\n\u003cli\u003eHsiao, J.-H. T., Purushothuman, S., Jensen, P. H., Halliday, G. M. \u0026amp; Kim, W. S. Reductions in COQ2 Expression Relate to Reduced ATP Levels in Multiple System Atrophy Brain. \u003cem\u003eFront Neurosci\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1187 (2019).\u003c/li\u003e\n\u003cli\u003eMonzio Compagnoni, G. \u003cem\u003eet al.\u003c/em\u003e Mitochondrial Dysregulation and Impaired Autophagy in iPSC-Derived Dopaminergic Neurons of Multiple System Atrophy. \u003cem\u003eStem Cell Reports\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1185\u0026ndash;1198 (2018).\u003c/li\u003e\n\u003cli\u003eHerrera-Vaquero, M. \u003cem\u003eet al.\u003c/em\u003e Signs of early cellular dysfunction in multiple system atrophy. \u003cem\u003eNeuropathology and Applied Neurobiology\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 268\u0026ndash;282 (2021).\u003c/li\u003e\n\u003cli\u003eCompta, Y. \u003cem\u003eet al.\u003c/em\u003e Cerebrospinal fluid levels of coenzyme Q10 are reduced in multiple system atrophy. \u003cem\u003eParkinsonism Relat Disord\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 16\u0026ndash;23 (2018).\u003c/li\u003e\n\u003cli\u003eKasai, T. \u003cem\u003eet al.\u003c/em\u003e Serum Levels of Coenzyme Q10 in Patients with Multiple System Atrophy. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, e0147574 (2016).\u003c/li\u003e\n\u003cli\u003eMitsui, J. \u003cem\u003eet al.\u003c/em\u003e High-dose ubiquinol supplementation in multiple-system atrophy: a multicentre, randomised, double-blinded, placebo-controlled phase 2 trial. \u003cem\u003eeClinicalMedicine\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 101920 (2023).\u003c/li\u003e\n\u003cli\u003eBenarroch, E. E., Schmeichel, A. M., Low, P. A. \u0026amp; Parisi, J. E. Involvement of medullary serotonergic groups in multiple system atrophy. \u003cem\u003eAnn Neurol\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 418\u0026ndash;422 (2004).\u003c/li\u003e\n\u003cli\u003eYan, S. \u003cem\u003eet al.\u003c/em\u003e Quantitative susceptibility mapping of multiple system atrophy and Parkinson\u0026rsquo;s disease correlates with neurotransmitter reference maps. \u003cem\u003eNeurobiol. Dis.\u003c/em\u003e \u003cstrong\u003e198\u003c/strong\u003e, 106549 (2024).\u003c/li\u003e\n\u003cli\u003eChelban, V. \u003cem\u003eet al.\u003c/em\u003e An update on MSA: premotor and non-motor features open a window of opportunities for early diagnosis and intervention. \u003cem\u003eJ Neurol\u003c/em\u003e \u003cstrong\u003e267\u003c/strong\u003e, 2754\u0026ndash;2770 (2020).\u003c/li\u003e\n\u003cli\u003eGoldstein, D. S. \u003cem\u003eet al.\u003c/em\u003e Differential abnormalities of cerebrospinal fluid dopaminergic versus noradrenergic indices in synucleinopathies. \u003cem\u003eJ Neurochem\u003c/em\u003e \u003cstrong\u003e158\u003c/strong\u003e, 554\u0026ndash;568 (2021).\u003c/li\u003e\n\u003cli\u003eBenarroch, E. E., Schmeichel, A. M. \u0026amp; Parisi, J. E. Depletion of mesopontine cholinergic and sparing of raphe neurons in multiple system atrophy. \u003cem\u003eNeurology\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 944\u0026ndash;946 (2002).\u003c/li\u003e\n\u003cli\u003eCoon, E. A., Cutsforth-Gregory, J. K. \u0026amp; Benarroch, E. E. Neuropathology of autonomic dysfunction in synucleinopathies. \u003cem\u003eMovement Disorders\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 349\u0026ndash;358 (2018).\u003c/li\u003e\n\u003cli\u003eGilman, S. \u003cem\u003eet al.\u003c/em\u003e Cerebral cortical and subcortical cholinergic deficits in parkinsonian syndromes. \u003cem\u003eNeurology\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 1416\u0026ndash;1423 (2010).\u003c/li\u003e\n\u003cli\u003eChou, K. L. \u003cem\u003eet al.\u003c/em\u003e Serotonin Transporter Imaging in Multiple System Atrophy and Parkinson\u0026rsquo;s Disease. \u003cem\u003eMov Disord\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 2301\u0026ndash;2307 (2022).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1. Demographic and clinical characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"775\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 387px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePost hoc tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSAp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSAc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSA mixed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e63.2 \u0026plusmn; 7.7\u003c/p\u003e\n \u003cp\u003e[46-83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e61.7 \u0026plusmn; 8.0\u003c/p\u003e\n \u003cp\u003e[46-82]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e63.3 \u0026plusmn; 8.5\u003c/p\u003e\n \u003cp\u003e[46-82]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e59.1 \u0026plusmn; 6.2\u003c/p\u003e\n \u003cp\u003e[48-70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e62.4 \u0026plusmn; 9.1\u003c/p\u003e\n \u003cp\u003e[50-75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemales, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e90 (49.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e27 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e15 (44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease duration (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 2.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3.7 \u0026plusmn; 1.9\u003c/p\u003e\n \u003cp\u003e[1-8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.4 \u0026plusmn; 2.4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.9 \u0026plusmn; 2.7\u003c/p\u003e\n \u003cp\u003e[2-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPDRS III \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e33.3 \u0026plusmn; 17.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[2-66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e35.9 \u0026plusmn; 17.2\u003c/p\u003e\n \u003cp\u003e[11-60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e31.4 \u0026plusmn; 17.4\u003c/p\u003e\n \u003cp\u003e[2-66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e30.4 \u0026plusmn; 23.6\u003c/p\u003e\n \u003cp\u003e[4-64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPS \u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e99.7 \u0026plusmn; 33.3\u003c/p\u003e\n \u003cp\u003e[38-168]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e95.9 \u0026plusmn; 30.3\u003c/p\u003e\n \u003cp\u003e[38-126]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e95.2 \u0026plusmn; 32.7\u003c/p\u003e\n \u003cp\u003e[62-168]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e137.0 \u0026plusmn; 43.8\u003c/p\u003e\n \u003cp\u003e[106-168]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAutonomic\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e20.1 \u0026plusmn; 5.5\u003c/p\u003e\n \u003cp\u003e[11-33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e18.4 \u0026plusmn; 7.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[11-33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e20.2 \u0026plusmn; 3.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[15-25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e23.0 \u0026plusmn; 18.4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[10-36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eMotor\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e58.4 \u0026plusmn; 20.4\u003c/p\u003e\n \u003cp\u003e[19-97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e59.6 \u0026plusmn; 20.9 [19-83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e54.2 \u0026plusmn; 18.6 [26-87]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e74.5 \u0026plusmn; 31.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[52-97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eCerebellar\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.3 \u0026plusmn; 7.2\u003c/p\u003e\n \u003cp\u003e[0-25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.8 \u0026plusmn; 1.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0-3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.8 \u0026plusmn; 6.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0-25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e16.0 \u0026plusmn; 4.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[13-19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMSAc\u0026gt;MSAp**\u003c/p\u003e\n \u003cp\u003eMSA mixed\u0026gt;MSAp*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eOculomotor\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2 \u0026plusmn; 2.3\u003c/p\u003e\n \u003cp\u003e[0-6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3.2 \u0026plusmn; 2.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0-6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.3 \u0026plusmn; 2.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0-6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003col start=\"0\"\u003e\n \u003cli\u003e\u0026plusmn; 0.0\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;[0-0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eMental\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.2 \u0026plusmn; 3.1\u003c/p\u003e\n \u003cp\u003e[1\u0026ndash;12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.8 \u0026plusmn; 2.4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[3-11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.3 \u0026plusmn; 3.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1-12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9.0 \u0026plusmn; 1.4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[8-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHoehn and Yahr \u003csup\u003e3\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 1.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1-5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 1.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1-5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.7 \u0026plusmn; 0.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[2.5-5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 1.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1-5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLEDD \u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mg per day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e440.1 \u0026plusmn; 355.5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0-1395]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;487.1 \u0026plusmn; 327.6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0-1169]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;384.2 \u0026plusmn; 398.6 [0-1395]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e368.8 \u0026nbsp;\u0026plusmn; 409.2 [0-1044]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMSE \u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e27.5 \u0026plusmn; 2.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[23-30]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e27.6 \u0026plusmn; 2.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[23-30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e27.9 \u0026plusmn; 2.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[23-30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e26.3 \u0026plusmn; 2.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[23-30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cs\u003e--\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Quantitative variables are summarized as mean \u0026plusmn; standard deviation [min-max], and qualitative variables as counts and percentages. Differences in sex proportion and age were compared between controls and MSA patients using fisher\u0026rsquo;s exact test and Kruskal-Wallis test, respectively. Differences in disease duration, UPDRS part III scores, PPS total score and subscores, Hoehn and Yahr scales, LEDD values and MMSE scores were compared between MSA subgroups using Kruskal-Wallis test, followed by post hoc Dunn tests with FDR correction in case of significant p value (\u0026lt;0.05). Asterisks indicate the significance level of the post hoc comparisons: adjusted p \u0026lt; 0.05 (*), adjusted p \u0026lt; 0.01 (**), adjusted p \u0026lt; 0.001 (***). Of note, PPS scores were only available for a subset of participants (n=20).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eHigher UPDRS III values indicate worse motor symptoms, with a maximum score of 108.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eHigher PPS values indicate higher disease severity, with a maximum score of 309. PPS encompasses different domains including motor, autonomic, cerebellar, oculomotor and mental functions. Refer to Payan et al. PLoS One 2011(doi: 10.1371/journal.pone.0022293).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eHigher Hoehn and Yahr stage (ranging from 0 to 5) indicate more severe functional disability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e4\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eHigher LEDD values indicate a greater total dose of levodopa-equivalent medication.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u0026nbsp;\u003c/sup\u003eLower MMSE scores indicative worse cognitive function, with a maximum score of 10.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: LEDD, levodopa equivalent daily dose; MMSE, Mini Mental State Examination; MSA, multiple system atrophy; MSAp, parkinsonian variant of MSA; MSAc; cerebellar variant of MSA; PPS, Parkinson Plus Scale; UPDRS, Unified Parkinson\u0026apos;s Disease Rating Scale.\u003c/em\u003e\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":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7095251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7095251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOligodendroglial pathology is a hallmark of multiple system atrophy (MSA), yet it remains unclear whether MRI-detected atrophy reflects underlying biological mechanisms. This study investigated whether regional atrophy aligns with gene expression and neurotransmitter systems. We recruited 65 MSA patients and derived brain atrophy measures from T1-weighted MRIs.Using postmortem data from the Allen Human Brain Atlas, partial least squares (PLS) regression identified gene expression components associated with atrophy. Gene enrichment analyses explored biological processes, and annotation mapping identified neurotransmitter systems matching atrophy patterns. Specificity was tested against 57 Parkinson\u0026rsquo;s disease patients. Atrophy primarily affected the cerebellar white matter, pons, putamen, olive, and substantia nigra. PLS revealed two latent variables explaining 27.5% of the covariance. Atrophic regions overexpressed genes linked to mitochondrial function and oligodendrocytes, showing patterns distinct from Parkinson\u0026rsquo;s disease. These regions also exhibited lower serotonin and GABA levels, and higher acetylcholine and noradrenaline receptor densities. MRI-derived atrophy in MSA is biologically grounded and may inform future therapeutic studies.\u003c/p\u003e","manuscriptTitle":"MRI-derived atrophy in multiple system atrophy aligns with mitochondrial and glial gene expression patterns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 12:47:33","doi":"10.21203/rs.3.rs-7095251/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-28T19:21:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T10:04:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271418983745908816317761745507763779119","date":"2025-09-18T09:16:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38515154164797632036991177184042883492","date":"2025-08-26T09:21:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T08:47:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321057441759762444732898731573830310720","date":"2025-07-14T15:15:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-11T18:41:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-11T12:06:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-11T09:56:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Parkinson's Disease","date":"2025-07-10T17:17:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2475ec12-001c-4235-a491-150942fc4d56","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51514620,"name":"Health sciences/Diseases"},{"id":51514621,"name":"Biological sciences/Genetics"},{"id":51514622,"name":"Health sciences/Neurology"},{"id":51514623,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-12-29T15:59:36+00:00","versionOfRecord":{"articleIdentity":"rs-7095251","link":"https://doi.org/10.1038/s41531-025-01227-1","journal":{"identity":"npj-parkinsons-disease","isVorOnly":false,"title":"npj Parkinson's Disease"},"publishedOn":"2025-12-26 15:56:59","publishedOnDateReadable":"December 26th, 2025"},"versionCreatedAt":"2025-07-18 12:47:33","video":"","vorDoi":"10.1038/s41531-025-01227-1","vorDoiUrl":"https://doi.org/10.1038/s41531-025-01227-1","workflowStages":[]},"version":"v1","identity":"rs-7095251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7095251","identity":"rs-7095251","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}