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Investigating the genetic association of mitochondrial DNA copy number with neurodegenerative diseases

BMC Neurology volume 25, Article number: 160 (2025) Cite this article

Abstract

Objective

This study aims to investigate the causal relationship between Mitochondrial DNA (mtDNA) copy number and several common neurodegenerative diseases (NDs).

Methods

We conducted a bidirectional two-sample Mendelian randomization (MR) analysis using data from genome-wide association studies (GWAS) as instrumental variables (IVs). After screening for relevance and potential confounders, we estimated the association between mtDNA copy number and NDs, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS), and Multiple sclerosis (MS). Additionally, we validated our findings using GWAS data on mtDNA copy number from Longchamps et al., sourced from the Genetics Epidemiology Consortium and the UK Biobank (UKB) aging study cohort.

Results

A GWAS analysis of 395,718 UKB participants found no significant association between mtDNA copy number and the risk of NDs, including AD (OR = 0.956, P = 0.708), PD (OR = 1.223, P = 0.179), ALS (OR = 0.972, P = 0.374), and MS (OR = 0.932, P = 0.789). Similarly, reverse MR analysis revealed no significant relationship between genetic predictions of NDs and mtDNA copy number: AD (OR = 0.987, P = 0.062), PD (OR = 0.997, P = 0.514), ALS (OR = 0.974, P = 0.706), and MS (OR = 1.003, P = 0.181).

Conclusion

Although mitochondrial dysfunction is implicated in the pathogenesis of NDs, no clear evidence supports a causal role for mtDNA copy number. The relationship between mtDNA copy number and NDs is likely mediated by more complex molecular regulatory mechanisms. Further research is required to elucidate these intricate interactions.

Peer Review reports

Introduction

Neurodegenerative diseases (NDs) impact the central nervous system, characterized by progressive degradation of neuronal function and structure, typically resulting in cognitive and motor impairment [1]. Neuroinflammation, mediated by microglia and astrocytes, is a prominent feature common to NDs, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS), and Multiple sclerosis (MS) [2,3,4]. Although the primary therapeutic strategy for NDs targets harmful protein aggregates, the causal link between these aggregates and neuronal loss or synaptic damage remains under debate. With the increase in human life expectancy, the prevalence of NDs is on the rise [5, 6].

Mitochondria, essential subcellular organelles, produce cellular energy via oxidative phosphorylation in the respiratory chain [7]. This process occurs through the electron transport chain and oxidative phosphorylation [8]. Additionally, mitochondria produce molecules that protect against oxidative stress, aid in programmed cell death, and support cellular respiration [9]. Mitochondrial enzymes, rich in various oxidoreductases, and mitochondrial dysfunction are thought to be contributors to reactive oxygen species (ROS) production within the cellular environment [10]. Human mitochondrial DNA (mtDNA) is made up of 16,569 base pairs that encode 37 genes; this includes 22 transfer RNAs (tRNAs), 2 ribosomal RNAs (rRNAs), and 13 peptides involved in the electron transport chain [11]. The 13 peptides are essential for assembling respiratory chain complexes I to V. Markesbery’s hypothesis centers on the 13 proteins needed for the electron transport chain, encoded by mtDNA [12]. The mtDNA is especially vulnerable to oxidative stress due to its close proximity to oxidative metabolism sites and lack of protective histones and repair mechanisms [13]. Damaged neurons induce mitochondrial dysfunction, leading to mtDNA damage and potentially triggering NDs onset and progression [14]. Recent studies in human fibroblasts indicate that point mutations in mtDNA’s non-coding regulatory regions may significantly influence human aging [15].

Mounting evidence suggests an association between NDs and mitochondrial dysfunction caused by mtDNA mutations or proteomic abnormalities [16]. The mitochondrial cascade hypothesis proposes that, with age, modifications and mutations in mtDNA accumulate in somatic brain cells, influencing disease phenotypes. Certain mutations, such as those associated with Leber’s hereditary optic neuropathy, can induce neuronal damage by promoting ROS production [17]. Many studies report lower mtDNA levels in the blood and CSF of PD patients, though results vary [18]. Studies have reported reduced CSF mtDNA concentrations in PD patients compared to healthy controls, whereas others have found no notable differences. For specific groups, such as LRRK2 mutation carriers, mtDNA levels in the CSF are higher [19]. A prospective cohort study conducted using the UK Biobank (UKB) that involved whole-exome sequencing (WES) of approximately 50,000 individuals and genotyping of around 500,000 individuals found that mtDNA copy number is significantly associated with the prevalence and incidence of dementia and PD, and a higher mtDNA copy number is associated with a lower risk of developing NDs [20]. In AD research, a cohort study involving 1,201 individuals observed a reduction in mtDNA copy number in AD patients [21]. For PD, a two-year longitudinal study tracking 27 newly diagnosed, untreated PD patients and 22 healthy controls noted an increase in mtDNA copy number in PD patients [22]. Conversely, a South African case-control study found no significant link between mtDNA copy number and PD [23]. Additionally, an analysis of 363 peripheral blood samples, 151 substantia nigra pars compacta (SNpc) tissue samples, and 120 frontal cortex tissue samples from community-based PD cases (diagnosed according to UK PD Society Brain Bank criteria) and 262 peripheral blood samples, 33 SNpc tissue samples, and 37 frontal cortex tissue samples from controls without clinical evidence of PD found a reduction in mtDNA copy number in PD patients [24]. Research on MS also revealed a similar trend. A case-control study showed a decrease in mtDNA copy number in MS patients [25]. Furthermore, familial studies suggested that mtDNA copy numbers are elevated in patients with ALS [26]. Currently, the causal relationship between mtDNA copy number and NDs remains unconfirmed. Additionally, the question of whether NDs influence mtDNA copy number in reverse causality remains unresolved.

Mendelian randomization (MR) is a novel method in genetic epidemiology research design. It utilizes genetic variants closely linked to specific exposure factors, known as single nucleotide polymorphisms (SNPs), as instrumental variables (IVs). This approach minimizes confounding variables and bypasses reverse causation, enhancing the precision of causal inference between exposures and outcomes in the study [27]. Previous studies have examined the causal relationships between mtDNA copy number and diseases such as diabetes [28], stroke [29], and cardiac metabolic disorders [30] through MR designs. However, the causal links between mtDNA copy number and NDs (such as AD, PD, ALS, and MS) have not yet been investigated using MR. NDs may not only be influenced by mtDNA copy number, but may also affect it. To examine this bidirectional causal relationship, we used bidirectional MR analysis. This approach enables the simultaneous evaluation of the causal effects between mtDNA copy number and NDs.

Materials and methods

Study design

This study utilized the latest genome-wide association studies (GWAS) summary statistics for a bidirectional two-sample MR analysis to investigate the association between mtDNA copy number and NDs. Furthermore, the potential for NDs to induce changes in mtDNA copy number was examined. MR analysis employs SNPs as IVs, allowing us to test the causal relationship between risk factors and outcomes, overcoming reverse causation bias and confounding. To ensure the validity of the MR analysis, we followed three key assumptions: (1) the chosen SNPs have an association with mtDNA copy number; (2) the association between SNPs and NDs is not influenced by confounding factors; (3) SNPs only indirectly influence NDs outcomes by affecting mtDNA copy number.

The analysis encompassed four neurodegenerative disorders, specifically AD, PD, ALS, and MS. Figure 1 depicts the bidirectional MR workflow. All the projects in the GWAS and related consortia upon which this study is based have been approved by the respective ethical committees and participants involved have provided signed informed consent.

Fig. 1
figure 1

This study depicts the bidirectional MR framework utilized to examine the causal link between mtDNA copy number and NDs. Eight MR analyses were conducted to explore the reciprocal relationships between mtDNA copy number and several NDs. All employed genetic instruments were SNPs. AD, Alzheimer’s disease; PD, Parkinson’s disease; ALS, Amyotrophic lateral sclerosis; and MS, Multiple sclerosis; GWAS, genome-wide association studies; SNPs, single nucleotide polymorphisms

Full size image

Data source: MtDNA copy number

The GWAS summary statistics for mtDNA copy number included data from 395,718 UKB participants with varied ancestries [31]. Table 1 presents the relevant information from the GWAS summary statistics. This study employed “AutoMitoC,” a novel method by Chong et al. for automated mitochondrial replication. This method provides an effective means for estimating mtDNA copy number through SNP array intensity. AutoMitoC encompasses preprocessing, background correction, probe hybridization detection, and mtDNA copy number estimation, facilitating swift and precise measurement of mtDNA levels in blood samples. The GWAS data were adjusted for age, sex, chip type, and blood cell counts.

Additionally, the study incorporated validation research by Longchamps et al. on data from 465,809 Caucasian subjects in a genomic epidemiology consortium and the UKB’s aging cohort [32]. The UKB is a large longitudinal research project designed to explore the effects of genetics and environmental factors on human health. The project has recruited approximately 500,000 participants across the UK, collecting detailed information on their health, lifestyles, and genetics, providing researchers worldwide with a data resource for health studies.

Table 1 Information on the GWAS study data utilized in this study
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Data source: NDs

We analyzed four large-scale GWAS datasets recently published to assess NDs. Genetic data for AD originated from the study conducted by Brian et al., providing a GWAS dataset of 63,926 Europeans, which includes 21,982 AD patients and 41,944 controls [33]. Data for PD were derived from a recently completed GWAS conducted by the International Parkinson Disease Genomics Consortium (IPDGC) [34]. This study integrated data from three earlier GWASs and 13 new datasets, including proxy cases from the UKB, totaling 33,674 cases and 449,056 controls. The analysis of ALS was based on GWAS data from Project MinE, covering 36,052 Europeans, with 12,577 patients and 23,475 controls [35]. The genetic data for MS were sourced from the International MS Genetics Consortium, featuring a dataset of 115,803 Europeans, including 47,429 patients and 68,374 controls [36]. Refer to Table 1 for detailed information.

Screening IVs

This study used the TwoSampleMR and MR-PRESSO packages to identify suitable IVs. We implemented rigorous criteria for screening. Specifically, we focused on genetic loci with P-values less than 5.0 × 10− 8 to pinpoint SNPs with strong associations to disease risk. Furthermore, we required a linkage disequilibrium coefficient greater than 0.001 and a genetic distance below 10,000 kb for SNPs, to maintain independence among selected IVs and reduce biases from weak instruments. An F-statistic above 10 signifies strong instruments, whereas one below 10 suggests weak SNP-exposure associations. All analyses automatically excluded palindromic SNPs. The formula for the F-statistic is F = R2 / (1 – R2) × (N - K − 1) / K, where N is the sample size of the exposure GWAS, K is the number of SNPs, and R2 is the proportion of variance explained by SNPs in the exposure database [37].

Statistical analysis

This study primarily utilizes the inverse variance weighted (IVW) method, supplemented by the weighted median, simple mode, MR-Egger, and weighted mode approaches to bolster result credibility. The IVW method assesses the influence on outcomes by weighting SNPs according to their variance magnitudes. Sensitivity analyses included Cochran’s Q test, leave-one-out analysis, MR-PRESSO, and the MR-Egger intercept to ensure robustness of the findings. Specifically, Cochran’s Q evaluates IV heterogeneity; MR-PRESSO identifies and rectifies outlier-induced biases in IVs; the MR-Egger intercept examines pleiotropy, and the leave-one-out method concentrates on the stability and impact of each IV on the cumulative effect. We conducted a MR study to ascertain the mtDNA copy number’s potential causal impact on NDs such as AD, PD, ALS, and MS. We employed genetic variants linked to mtDNA expression as IVs. GWASs of four NDs provided outcome data for MR analyses, with validation using mtDNA copy number GWAS data from 465,809 Caucasian UKB participants. Considering multiple association analyses (n = 8 tests) of mtDNA copy number and NDs, we applied Bonferroni correction to adjust P-value thresholds, establishing a significance cutoff at 0.00625 (0.05/8). A P-value ≤ 0.05, falling short of the Bonferroni-adjusted significance level, indicated a potential causal link.

Results

The relationship between genetically predicted MtDNA copy number and NDs

The GWAS meta-analysis of mtDNA copy number identified 237,961 SNPs associated with mtDNA copy number, with 6,702 of them reaching genome-wide significance (P < 5 × 10− 8). The dataset was refined by excluding 6,634 SNPs because of linkage disequilibrium or considerable imbalance (r2 < 0.001), leaving 68 SNPs for in-depth analysis (Supplementary Table 1). The F-statistic for the genetic instrument associated with mtDNA copy number was 94.29 (ranging from 30.45 to 473.58). The IVW method showed no significant association between genetically predicted mtDNA copy number and AD (OR = 0.956; 95% CI 0.756–1.209; P = 0.708), PD (OR = 1.223; 95% CI 0.912–1.642; P = 0.179), ALS (OR = 0.972; 95% CI 0.912–1.035; P = 0.374), or MS (OR = 0.932; 95% CI 0.557–1.035; P = 0.789), as depicted in Fig. 2. The analysis suggested that mtDNA copy number does not statistically affect the risk of developing NDs. Supplementary Table 2 encompasses findings from four distinct analytical methods: weighted median, mode-based estimation, MR-Egger, and weighted mode. Aside from the causal relationship between mtDNA copy number and MS approaching statistical significance in heterogeneity analyses (P < 0.001) and MR-PRESSO tests (P < 0.001), there was no evidence of heterogeneity or horizontal pleiotropy in other outcomes (Supplementary Table 3). Leave-one-out analyses for each SNP-NDs association are presented in Supplementary Fig. 1.

Fig. 2
figure 2

MR analysis of the relationship between mtDNA copy number and the risk of AD, PD, ALS, and MS. CI, confidence interval; OR, odds ratio; SNP, single nucleotide polymorphism

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The relationship between gene-predicted NDs and MtDNA copy number

Our bidirectional MR analysis investigated the link between neurodegenerative disorders and mtDNA copy number, incorporating 17 SNPs associated with AD, 19 with PD, 5 with ALS, and 67 with MS. Genetic variants served as IVs in this analysis (Supplementary Table 4). The IVW method results showed genetic predictions for AD (OR = 0.987; 95% CI 0.974–1.001; P = 0.062), PD (OR = 0.997; 95% CI 0.989–1.006; P = 0.514), ALS (OR = 0.974; 95% CI 0.849–1.117; P = 0.706), and MS (OR = 1.003; 95% CI 0.999–1.008; P = 0.181) relative to mtDNA copy number, as seen in Fig. 3. The results of the four methods, including the weighted median, simple mode, MR-Egger, and weighted mode (Supplementary Table 5). We found no strong evidence of a significant correlation between mtDNA copy number and AD, PD, ALS, or MS. Statistical analyses identified significant pleiotropy and heterogeneity in the associations between mtDNA copy number and AD and MS (P < 0.001), but no such effects were observed for PD or ALS (Supplementary Table 6). Supplementary Fig. 2 illustrates the leave-one-out sensitivity analysis for the association between each SNP and mtDNA copy number.

Fig. 3
figure 3

Reverse MR analysis of the relationship between mtDNA copy number and the risks of AD, PD, ALS, and MS

Full size image

Bidirectional MR of validation data

We incorporated 70 SNPs related to mtDNA copy number in our validation dataset (Supplementary Table 7). The forward MR IVW method indicated that genetically predicted mtDNA copy number is not associated with AD (OR = 0.964; P = 0.757), PD (OR = 1.220; P = 0.187), ALS (OR = 0.957; P = 0.187), MS (OR = 0.837; P = 0.488) (Supplementary Table 8). The reverse MR IVW method results showed that the genetic prediction of AD (OR = 0.988; P = 0.079), PD (OR = 0.998; P = 0.579), ALS (OR = 0.973; P = 0.701), MS (OR = 1.003; P = 0.182) is not related to mtDNA copy number (Supplementary Table 9). IVW analysis of the validation dataset failed to provide robust evidence for a correlation between mtDNA copy number and NDs including AD, PD, ALS, and MS. The MR-Egger intercept analysis revealed no indication of horizontal pleiotropy influencing the outcomes. The relationship between MS and mtDNA copy number exhibits significant heterogeneity (Cochran’s Q = 70.84, P < 0.001), as well as pleiotropy according to MR-PRESSO analysis (P < 0.001). There is heterogeneity (Cochran’s Q = 42.14, P = 0.07) and pleiotropy (MR-PRESSO, P = 0.12) in the association between ALS and mtDNA copy number. The mtDNA copy number exhibits significant statistical heterogeneity (Cochran’s Q, P < 0.001) and pleiotropy (MR-PRESSO, P < 0.001) when evaluated as a potential exposure factor linking AD and MS (Supplementary Table 10). The results of the leave-one-out analysis are presented in Supplementary Figs. 3 and 4.

Discussion

Mitochondrial diseases arise from mutations in nuclear genes, which reduce mtDNA expression, or from primary mtDNA mutations that impair the function or abundance of mtDNA copy number [38]. To accommodate metabolic demands, the regulation of mtDNA content within cells and tissues is necessary. Regulation of mtDNA levels is a complex process that involves a balance between replication and degradation [39]. Observational studies have investigated the relationship between mtDNA and diseases such as AD [40], PD [23], ALS [26], and MS [25]. Among these studies, few have focused on the relationship between mtDNA copy number and AD, PD, ALS, and MS, and there have been contentious findings regarding diseases [20]. Different observational studies have presented conflicting results regarding the association between mtDNA copy number and PD. Some studies have revealed a decreased correlation between mtDNA copy number and PD [24], yet others have identified an increased correlation [22]. In addition, some studies have not observed a significant correlation between PD and mtDNA copy number [23]. An analysis of the blood DNA from 114 individuals—including ALS patients, asymptomatic genetic carriers, and family members without the gene—revealed a significant increase in mtDNA copy number in the ALS cohort [26]. Another neuropathological autopsy excluded the possibility of reduced mitochondrial copy numbers in ALS, determining that the observed decrease in mitochondrial gene expression and related metabolic deficiencies were not caused by diminished mtDNA [41], aligning with the outcomes of this study. Although mitochondrial DNA copy number may be associated with the risk of NDs, insufficient direct evidence exists to support it as a mechanistic factor. The regulation of mtDNA copy number is likely to involve a range of complex molecular mechanisms, such as mitochondrial dysfunction, alterations in energy metabolism, modulation of oxidative stress responses, and activation of apoptotic pathways. These mechanisms, including mitochondrial dysfunction and oxidative stress modulation, may serve as targets for future therapeutic interventions.

This study is the first to use bidirectional two-sample MR to comprehensively explore the link between mtDNA copy number and NDs. Despite the large sample size, we found no causal link between genetically predicted mtDNA copy number and NDs. Furthermore, existing research indicates that oxidative stress and immune dysregulation associated with NDs could intensify fluctuations in mtDNA copy number [42,43,44]. Our study did not reveal a significant correlation between genetically predicted NDs and mtDNA copy number levels.We believe the underlying reasons for these findings are likely complex. To explain these findings, we propose the following hypotheses: The progression of NDs may be indirectly associated with mtDNA copy number, influenced by various biological processes and molecular regulatory mechanisms. This includes mitochondrial dysfunction, alterations in energy metabolism, regulation of oxidative stress responses, and activation of apoptotic pathways [45,46,47]. Additionally, the interplay between genetic predispositions [48] and environmental factors [49] may significantly influence mtDNA replication, repair, and maintenance, subsequently altering the risk associated with NDs.

Our study has several strengths. Initially, we utilized a two-sample MR analysis, leveraging SNPs as IVs to investigate the putative causal relationship between mtDNA copy number and NDs. Subsequently, we performed MR analysis with extensive GWAS datasets, further validated by independent datasets, thereby reinforcing the assessment validity and the causality’s convincingness between mtDNA copy number and NDs. Moreover, to thoroughly explore the causal nexus between mtDNA copy number and various NDs, we conducted eight MR analyses to uncover bidirectional correlations. In addition to verifying the association between mtDNA copy number and NDs risk, this study represents the first systematic application of MR analysis to evaluate NDs’ conceivable impact on mtDNA copy number.

However, this study is subject to certain limitations. Firstly, given the maternal inheritance of the mitochondrial genome, as well as studies suggesting that sex, hormone levels, and other biological factors may contribute to differences in mitochondrial DNA copy number between females and males [50], sex differences are possible. Although the MR approach reduces confounding bias, the lack of individual-level data limits our ability to perform direct age- or sex-stratified analyses. Secondly, this study found that directional pleiotropy had a minor impact on the research outcomes through MR-Egger regression analysis. Nevertheless, we cannot overlook the potential bias introduced by pleiotropy or IVs events. Thirdly, the abundance of mtDNA copy number in this study was determined by the intensity of mtDNA genotyping probes, whereas assessments based on WES are generally considered more reliable estimates of mtDNA copy number. Since mtDNA copy number is measured in blood samples instead of specific neural tissues, direct inferences regarding mitochondrial changes in affected neurons across various NDs are not feasible. Currently, the relationship between blood mtDNA copy number and tissue-specific mitochondrial changes in these affected neurons remains unclear. Future research should adopt cell-type-specific strategies, such as single-cell sequencing and tissue-specific profiling, to further investigate the role of mitochondrial dysfunction in different neuronal populations within various NDs. Furthermore, large-scale analyses of diverse population samples, focusing on GWAS and comprehensive omics data, will be crucial for investigating the impact of mitochondrial genome variations on NDs.

Conclusion

Although mitochondrial dysfunction is implicated in the pathogenesis of NDs, no clear evidence supports a causal role for mtDNA copy number. The relationship between mtDNA copy number and NDs is likely mediated by more complex molecular regulatory mechanisms. Further research is required to elucidate these intricate interactions.

Data availability

All data are available from public databases (https://gwas.mrcieu.ac.uk, https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics, https://www.ukbiobank.ac.uk).

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Acknowledgements

The authors thank all individuals and researchers who contributed to and shared data with the GWAS study, IEU OpenGWAS project, NHGRI-EBI Catalog, and additional GWAS datasets.

Funding

This research was supported by the Autonomous Region Key Research and Development Project (2023B03003), the Tian-Shan Talent Program (2022TSYCLJ0066), the National Natural Science Foundation of China (82371258), the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2023D01C131), and the Central Guiding Local Science and Technology Development Special Fund Project (ZYYD2022C17).

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Author notes

  1. Huan Xia, Zihao Wang, and Xiaobei Wang have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Neurology, The Second Affiliated Hospital of Xinjiang Medical University, Xinjiang, China

    Huan Xia, Zi-Hao Wang & Xiao-Bei Wang

  2. Department of Dermatology, The Second Hospital of Jilin University, Changchun, China

    Mei-Rong Gao

  3. Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Sen Jiang

  4. Department of Neurology, Jiangsu Provincial People’s Hospital, Jiangsu, China

    Xin-Yu Du

  5. Xinjiang Medical University, Xinjiang, China

    Huan Xia & Xin-Ling Yang

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  1. Huan Xia
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Contributions

HX: Writing - review & editing, Data curation, Formal analysis. ZW: Writing - original draft, Formal analysis, Writing - review & editing. XW: Funding acquisition, Formal analysis. MG: Data curation, Resources. SJ: Supervision. XD: Investigation, Writing - review & editing. XY: Resources, Software. LY: Conceptualization, Funding acquisition.

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Correspondence to Xin-Ling Yang.

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Xia, H., Wang, ZH., Wang, XB. et al. Investigating the genetic association of mitochondrial DNA copy number with neurodegenerative diseases. BMC Neurol 25, 160 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12883-025-04176-7

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BMC Neurology

ISSN: 1471-2377

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