Brain Structure Mediates Impact of Pulmonary Dysfunction on Cognition in Spinocerebellar Ataxia Type 3

Brain Structure Mediates Impact of Pulmonary Dysfunction on Cognition in Spinocerebellar Ataxia Type 3 | 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 Research Article Brain Structure Mediates Impact of Pulmonary Dysfunction on Cognition in Spinocerebellar Ataxia Type 3 Maolin Cui, xiaoting lv, wei Lin, mengcheng li, Zhuo-Ying Huang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7952676/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Spinocerebellar ataxia type 3 (SCA3) is a common autosomal dominant disorder marked by both cognitive and pulmonary dysfunction. Although a connection between these impairments exists, the mechanisms underlying this relationship are not well understood. In this study, we aim to explore the neural and physiological pathways linking pulmonary and cognitive deficits in SCA3 through multimodal integration. Methods Seventy-six SCA3 patients from the OSCCAR cohort underwent assessments including pulmonary function testing (classified as normal [NPF] or impaired [IPF]), cognitive evaluations (MoCA, MMSE, CVLT-II), and multimodal MRI (3T Siemens). Structural brain volumes were analyzed using CAT12/SPM12, and resting-state functional connectivity was assessed with the CONN toolbox. Mediation analysis was employed to determine whether gray matter volume mediated the relationship between pulmonary and cognitive impairments. Results SCA3 patients with IPF exhibited global cognition and verbal memory that is significantly worse compared to those with NPF. For example, recall-discrimination decreases, intrusions increase, and forget quickly ( p < 0.05). IPF patients also had significant gray matter atrophy, predominantly in temporal regions (β = −0.16 to − 0.91, p < 0.05), extending to the frontal, parietal, insular, and cerebellar areas (β = −0.03 to − 0.34, p < 0.05). Additionally, the connectivity between rITG-medial temporal and intra-cerebellar was impaired (β ≤ −0.21, p < 0.05). Conclusion Pulmonary dysfunction in SCA3 is associated with greater cognitive impairment and cortical gray matter atrophy. Our findings suggest that gray matter volume may serve as a mediator in the pathway linking pulmonary and cognitive dysfunction. SCA3 Pulmonary Dysfunction Cognitive Impairments Brain structure Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Spinocerebellar Ataxia Type 3 (SCA3), also known as Machado-Joseph Disease (MJD), is an autosomal dominant neurodegenerative disorder caused by an expanded CAG repeat in the ATXN3 gene 1 , 2 . SCA3 is the most prevalent form of spinocerebellar ataxia 3 , representing 72.5% of all SCA cases in Southeastern China 4 . The hallmark of the disease is the accumulation of toxic polyglutamine-expanded ataxin-3 aggregates, which promote neurodegeneration in the cerebellum, brainstem, basal ganglia, and cerebral cortex 5 – 7 . This progressive neuronal loss results in a diverse clinical phenotype, encompassing both motor symptoms (such as ataxia and gait instability) and non-motor manifestations (including dysphagia, dysarthria, and neuropsychiatric symptoms like depression and anxiety) 8 . Cognitive impairment is among the most common non-motor symptoms of SCA3 and tends to worsen as the disease progresses 9 – 11 . This impairment is multifactorial, with neuroimaging studies consistently linking it to widespread gray matter (GM) atrophy in the cerebral cortex 12 . Our previous research has further shown that atrophy of cerebellar GM also plays a significant role in cognitive dysfunction in SCA3 13 . In addition to these central neuroanatomical changes, clinical comorbidities significantly influence cognitive outcomes. For example, depression and sleep disturbances have been identified as key factors that accelerate cognitive decline in SCA3 9 . Accumulating evidence demonstrates a strong link between pulmonary dysfunction and cognitive impairment 14 – 16 , as well as an increased risk of dementia in middle-aged and older adults with impaired lung function 15 . Longitudinal studies show that reduced pulmonary function accelerates progression from mild cognitive impairment (MCI) to dementia 17 and correlates with poorer cognitive performance 14 , 18 . Several mechanisms may explain this relationship. Pulmonary dysfunction can cause chronic cerebral hypoxia, which promotes gray matter atrophy and drives cognitive decline 19 . Hypoxia may also disrupt the glymphatic system, impairing waste clearance and further worsening cognitive function 20 . Pulmonary dysfunction is a frequent and early manifestation of SCAs 21 , 22 , yet its role in cognitive impairment remains unclear. Because pulmonary dysfunction is associated with cortical gray matter atrophy - a central feature of cognitive deficits in SCA3 - we hypothesize that structural and functional cortical changes mediate the link between pulmonary and cognitive function. To test this, we combined neuropsychological evaluations with structural and functional neuroimaging to examine relationships among pulmonary function, cognition, gray matter volume, and functional connectivity. Our findings delineate potential pathways connecting pulmonary and cognitive dysfunction in SCA3. These insights establish a mechanistic framework applicable to other disorders with overlapping pulmonary and cognitive features and highlight pulmonary health as a potential therapeutic target to slow cognitive decline. Methods Standard Protocol Approvals and Patient Consent The study protocol for the participants received approval from the Ethics Committee for Medical Research at the First Affiliated Hospital of Fujian Medical University ([2019]195). Study Participants We prospectively recruited 76 genetically confirmed SCA3 patients from the Organization in Southeast China for Cerebellar Ataxia Research (OSCCAR) cohort, housed within the Department of Neurology, First Affiliated Hospital of Fujian Medical University, between January 2021 and March 2024. Patients were eligible if they met the following inclusion criteria: (1) confirmed genetic diagnosis of SCA3; (2) absence of secondary central nervous system disorders; and (3) willingness to participate with signed informed consent. Exclusion criteria included: (1) comorbidities or injuries interfering with pulmonary function testing or cognitive assessment; (2) concurrent participation in other interventional clinical trials; and (3) age younger than 14 years. Genotype and Phenotype Analyses Genomic DNA was extracted from peripheral blood with a QIAamp DNA Blood Mini Kit (Qiagen, Germany). CAG repeat expansions in the ATXN3 gene were characterized through PCR and Sanger sequencing, following previous methods 4 , 23 . Demographic and clinical variables, including sex, age, CAG repeat length, smoking history, educational attainment, and body mass index (BMI), were systematically collected by Dr. Gan, an experienced neurologist. Assessment of pulmonary function We evaluated pulmonary function using the Vyaire Medical Pulmonary Function System (Vyaire Medical GmbH, Bavaria, Germany). All assessments were performed with participants in a seated position. Each subject completed three maneuvers, separated by a two-minute rest, and the best effort was used for analysis. The following spirometric parameters were obtained: forced vital capacity (FVC), forced expiratory volume in one second (FEV1), the FEV1/FVC ratio, peak expiratory flow (PEF), maximal expiratory flows at 75%, 50%, and 25% of vital capacity (MEF75, MEF50, MEF25), and mean mid-expiratory flow (MMEF75/25). In addition, lung volumes were measured, including total lung capacity (TLC) and the residual volume to total lung capacity ratio (RV/TLC) 24 , 25 . Pulmonary function was categorized as either normal (NPF) or impaired (IPF). NPF was defined as all measured values within the predicted reference ranges. IPF was diagnosed when any of the following conditions were met: (1) obstructive ventilatory dysfunction, defined by a reduced FEV1/FVC ratio (< 92% of predicted, calculated from equations adjusted for height, age, sex, and ethnicity) with accompanying elevations in RV and TLC; (2) restrictive ventilatory dysfunction, identified when both FVC and TLC were < 80% of predicted; (3) mixed ventilatory dysfunction, indicated by concurrent reductions in FVC, FEV1, and FEV1/FVC; or (4) small airway dysfunction, diagnosed when at least two of the three indices - MEF50, MEF25, or MMEF75/25 - were < 65% of predicted. All classifications were based on established diagnostic criteria 26 , 27 . Assessment of cognitive function We evaluated both global and domain-specific cognition. Global cognitive status was measured using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) 28 – 30 . Verbal learning and memory, a central domain-specific function, was assessed with the California Verbal Learning Test-II (CVLT-II) 31 . In this standardized task, participants were presented with a 16-word target list (List A) over five consecutive free recall trials, followed by a single recall of an interference list (List B). After a 20-minute delay filled with nonverbal tasks, memory was reassessed through short-delay free recall (SDFR) and cued recall, long-delay free recall (LDFR) and cued recall, and finally a yes/no recognition test. From these procedures, six key outcome measures were derived. Total learning: cumulative number of target items correctly recalled across the five immediate recall trials of List A. Intrusions: total number of extra-list errors recorded during the five learning trials. Learning efficiency: number of target words recalled on the fifth learning trial (Trial 5), reflecting maximal encoding performance. Recall discrimination: calculated as Z(SDFR correct /16) − Z(SDFR intrusion /16). Delayed memory retention: calculated as (LDFR correct / Trial 5 correct ) × 100%. Forgetting rate: calculated as 1 − (SDFR correct / Trial 5 correct ) 31 – 34 . To ensure consistency and reliability, all neuropsychological assessments were administered by the same trained team (MLC, WL, and BNY). Participants were evaluated in a standardized manner, during daytime sessions, in a quiet environment, and following a fixed test order. MRI Data Processing All participants underwent MRI on a Siemens 3.0T scanner. Following acquisition, an experienced radiologist performed quality control to ensure data integrity, including motion correction and exclusion based on excessive head movement. Structural MRI Data Acquisition and Analysis High-resolution T1-weighted images were collected using a magnetization-prepared rapid acquisition gradient echo (MP-RAGE) sequence in the sagittal plane with the following parameters: repetition time (TR) = 2300 ms, echo time (TE) = 2.3 ms, inversion time (TI) = 900 ms, flip angle = 8°, field of view (FOV) = 240 × 256 mm, matrix size = 240 × 256, bandwidth = 200 Hz/pixel, 192 slices, voxel size = 1 × 1 × 1 mm³, and total scan duration = 5 min 12 s. Preprocessing of structural images was performed using the Computational Anatomy Toolbox (CAT12) within SPM12. The pipeline included tissue segmentation into GM, white matter, and cerebrospinal fluid; spatial normalization to Montreal Neurological Institute (MNI) space using the DARTEL algorithm; and smoothing with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel 13 , 35 . Regions of interest (ROIs) were defined based on the Automated Anatomical Labeling (AAL) atlas (version 3). For each ROI, modulated GM volume values were extracted and used for subsequent statistical analyses. Functional MRI Data Acquisition and Analysis Resting-state functional MRI (rs-fMRI) data were acquired using a T2*-weighted gradient-echo echo-planar imaging (EPI) sequence. Participants were instructed to rest quietly with their eyes closed while remaining awake. Acquisition parameters were: number of volumes = 205, slice thickness = 3 mm, interslice gap = 3 mm, TR = 3000 ms, TE = 30 ms, FOV = 256 × 256 mm, matrix size = 64 × 64, and total scan duration = 10 min 26 s. Post-scan quality control was performed by an experienced radiologist. The resulting voxel dimensions were 4 × 4 × 6 mm³. Data preprocessing was carried out using the CONN functional connectivity toolbox (v22.a; RRID: SCR_009550) in MATLAB R2024a (MathWorks, Natick, MA) with SPM12. Of the 237 acquired volumes, the first five were discarded to allow for signal equilibration. Preprocessing included realignment and motion correction, slice-timing correction, identification of outlier volumes (≥ 0.5 mm framewise displacement or ≥ 3 standard deviations in global signal intensity), segmentation, normalization to the MNI template, and spatial smoothing with an 8-mm FWHM Gaussian kernel. To reduce noise, we applied temporal bandpass filtering (0.008–0.09 Hz) and ordinary least squares (OLS) regression to remove nuisance covariates from the BOLD time series. Additional artifact correction was performed using the anatomical component-based noise correction method (aCompCor) 36 , which identifies and removes physiological noise components. Resting-state functional connectivity (rsFC) was analyzed using a ROI-to-ROI framework. ROIs were defined from the Brainnetome Atlas 37 and the Atlas of the Basal Ganglia 38 . For each participant, pairwise temporal correlations of BOLD signals were computed across 378 ROI pairs (28 ROIs). At the group level, functional connectivity strength was quantified by calculating bivariate correlations between ROIs, followed by Fisher’s z-transformation. Statistical Analysis We first assessed the normality of continuous variables using the Shapiro–Wilk test. Parametric data were analyzed with independent-samples t-tests and expressed as mean ± standard deviation (SD), whereas nonparametric data were evaluated with Mann–Whitney U tests and reported as median with interquartile range (Q1–Q3). To control for potential confounders, we compared demographic variables, including sex, educational attainment, smoking history, CAG repeat length in the expanded allele (CAGexp), BMI, and age, between patients with impaired pulmonary function (IPF) and those with normal pulmonary function (NPF). Categorical variables (sex, educational attainment, smoking history) were tested with chi-square analyses, while continuous variables (CAGexp, BMI, age) were examined with independent-samples t-tests. To evaluate differences in cognitive performance between IPF and NPF groups, we used independent-samples t -tests and Mann–Whitney U tests to analyze MoCA, MMSE, total learning, intrusions, learning efficiency, recall discrimination, delayed memory, and forgetting rate. Associations between pulmonary function and brain structure were examined using multiple regression analyses. We compared ROI volumes between IPF and NPF groups while adjusting for age, sex, and total intracranial volume (TIV) as covariates. Group differences in resting-state functional connectivity were analyzed with independent-samples t -tests. Functional connectivity was observed thorough MATLAB’s BrainNet Viewer toolbox. We tested whether GM volume mediated the association between pulmonary dysfunction and cognitive performance using the PROCESS macro (v4.1, Model 4) for SPSS. All models included TIV as a covariate. We evaluated the significance of the Average Causal Mediation Effect (ACME) with a bias-corrected bootstrapping procedure based on 5000 resamples, generating 95% confidence intervals (CIs). Mediation was considered significant when the CI excluded zero. All statistical analyses were performed in SPSS version 25.0 (SPSS Inc., Chicago, IL, USA), with the exception of functional connectivity analyses, which we conducted using the CONN toolbox in MATLAB. We created statistical graphs in GraphPad Prism (v10.1.2). Statistical significance was defined as p < 0.05. Result Baseline Data and Cognitive Profiles in SCA3 Patients We analyzed baseline demographic and clinical data from 35 patients with normal pulmonary function (NPF; 24 men, 68.6%) and 41 patients with impaired pulmonary function (IPF; 27 men, 65.9%). The two groups did not differ significantly in age ( p = 0.086), sex ( p = 0.802), years of education ( p = 0.584), smoking history ( p = 0.684), CAGexp ( p = 0.743), or BMI ( p = 0.836) (Table 1 ). In terms of cognitive performance, the IPF group scored significantly lower on global assessments, including MoCA ( p = 0.019) and MMSE ( p = 0.015), compared with the NPF group. Domain-specific tests revealed that IPF patients showed poorer recall discrimination ( p = 0.049) and delayed memory ( p = 0.023), as well as higher intrusion ( p = 0.006) and forgetting rates ( p = 0.035). By contrast, no group differences emerged for total learning ( p = 0.466) or learning efficiency ( p = 0.538) (Fig. 1 ). Mediation analysis of pulmonary and cognitive functions in SCA3 Mediation analysis revealed distinct lung–brain–cognition pathways, showing that impaired pulmonary function contributed to cognitive decline through reduced gray matter volume in specific brain regions. Gray matter loss in the rIFGorb mediated the association between pulmonary dysfunction and poorer outcomes on CVLT-Total Learning (indirect effect: β = −2.09, p < 0.001; 56.38% of the total effect) and CVLT-Learning Efficiency (indirect effect: β = −0.62, p < 0.001; 20.84%). Similarly, reduced gray matter volume in the rCER6 mediated the effects of pulmonary impairment on CVLT-Recall Discrimination (indirect effect: β = −0.04, p = 0.04; 22.67%), CVLT-Delayed Memory (indirect effect: β = −0.04, p = 0.04; 26.10%), and MoCA (indirect effect: β = −0.64, p = 0.04; 38.3%). In addition, gray matter reduction in the left lPHG significantly mediated poorer MMSE performance (indirect effect: β = −0.69, p = 0.032; 40.54%). These findings are illustrated in Fig. 4 . Discussion Our study demonstrates that pulmonary impairment is closely linked to greater cognitive decline and widespread cortical structural and functional abnormalities in patients with SCA3. Stratification by pulmonary function revealed that patients with impaired respiration showed marked reductions in both global and domain-specific cognitive performance, along with decreased cortical gray matter volume and weakened functional connectivity. Importantly, mediation analysis indicated that gray matter atrophy partially explained the association between pulmonary dysfunction and cognitive decline, suggesting that respiratory compromise contributes to cognitive deterioration in SCA3 through neural structural changes. These emphasize the need to account for systemic factors, like pulmonary health, when defining SCA3’s cognitive phenotype. Although prior studies have explored how pulmonary function affects cognition, significant questions remain regarding its effects on distinct cognitive domains and the specific neural circuits that mediate these relationships 14 , 17 , 19 , 39 . In our cohort, mediation analyses showed that gray matter loss in the lPHG and rCER6 primarily mediated the effect of pulmonary dysfunction on global cognitive performance. The PHG, a central hub of the medial temporal lobe (MTL), plays a critical role in integrating spatial, contextual, and scene-based information 40 – 42 . By contrast, the ITG, located at the endpoint of the ventral visual stream, transforms perceptual signals into semantic and mnemonic representations, supporting recognition and processing of complex object features 43 , 44 . Patients with impaired pulmonary function exhibited reduced gray matter volume in both the lPHG and rITG, alongside decreased functional connectivity between these regions. Together, these neuroimaging results corroborate the mediation analyses, pointing to structural and functional disruption of the MTL–temporal network as a key pathway through which pulmonary impairment drives cognitive deficits in SCA3. The rCER6 plays a central role in non-motor cognitive functions, including working memory and language processing. It is a key component of the "triple representation" model of cerebellar organization 45 , 46 , which conceptualizes the cerebellum’s involvement in non-motor processes. In addition to its critical role in global cognition, our mediation analysis identified rCER6, along with rIFGorb, as a key mediator linking pulmonary dysfunction to deficits in domain-specific cognition. The rIFGorb is essential for integrating feedback during decision-making and post-choice value evaluation, both of which are vital to higher-order cognitive processes 47 – 49 . Consistent with these functional roles, our study observed significant gray matter loss in both rIFGorb and rCER6 in patients with idiopathic pulmonary fibrosis, providing a structural basis for their involvement in cognitive dysfunction. Furthermore, our previous findings demonstrated a robust association between rCER6 levels and cognitive impairment 13 . Interestingly, we found that cortical gray matter atrophy was more widespread than cerebellar atrophy in IPF patients, whereas alterations in cerebellar functional connectivity were more extensive. Specifically, reduced connectivity was observed between lCER1 and several other cerebellar regions, including lCER4_5, rCER8, rCER9, and Vermis10. In contrast, only the rITG showed reduced connectivity with the lPHG and lTFG within the cerebral cortex. Notably, the co-occurrence of structural atrophy and functional disconnection in lCER4_5 suggests that this region serves as a critical hub for disease pathology, potentially functioning as a node from which dysfunction spreads throughout the cerebellar motor network. This finding indicates a possible pathway through which pulmonary impairment affects motor networks via cerebellar involvement. Specifically, gray matter atrophy in lCER4_5, a region crucial for motor coordination 46 , 50 , may disrupt its intrinsic connectivity with lCER1, subsequently leading to altered functional connections between lCER1 and other cerebellar regions, such as rCER8, rCER9, and Vermis10, which are densely interconnected. Our study has several limitations that offer directions for future research. First, the cross-sectional design prevents an assessment of how pulmonary decline evolves over time in SCA3 and limits the ability to draw causal conclusions about its relationship with cognitive trajectories. Second, while our mediation analyses suggest a potential pathway linking pulmonary function, brain structure, and cognition, these findings are inferential and require direct anatomical or histopathological confirmation. Finally, we did not explore other potential mediating mechanisms, such as glymphatic dysfunction, which has been identified as a key factor in the lung-brain axis. Future longitudinal and multimodal studies that include biomarkers of glymphatic function are needed to further validate and extend our results. In conclusion, we show that impaired pulmonary function is associated with worse cognitive performance and significant reductions in cortical gray matter volume, along with decreased functional connectivity in SCA3. By integrating multimodal data, our study moves beyond simple correlation to identify specific neural pathways within the lung-brain axis, positioning pulmonary function as a potential modulator of central nervous system integrity and a target for future therapeutic approaches. Declarations Declaration of competing interest All authors report no conflict of interest. Acknowledgments The authors would like to thank the kind patients, families, caregivers, and members who participated in this research. Financial Disclosures of all authors None of the authors have any financial disclosures to report for the preceding 12 months . Funding This work was supported by the National Natural Science Foundation of China (8237062035, Beijing; S-R-G), the National Natural Science Foundation of China (82230039, Beijing; N-W), the Local Science and Technology Development Project guided by the central government grants (2022L3011, Fujian; N-W). Author acknowledgments: S.-R.G., N.W., J.-P.H and X.-Y.C. formulated and designed the study concept; M.-L.C., X.-T.L., W.L., M.-C.L. and S.-R.G. drafted and revised the manuscript; M.-L.C., X.-T.L., W.L., Z.-Y.H., B.-N.Y., C.-Y.L., M.-T.L., H.L., and S.-R.G. performed data analysis and prepared figures and tables; , M.-L.C., X.-T.L. and S.-R.G. enrolled the patients and conducted clinical assessments; all authors approved the final version of the manuscript; all authors agree that any questions related to the work are appropriately resolved. S.-R.G. finalized, certified, and submitted the manuscript. Consent Statement Written informed consent was obtained from all participants before enrolment. For individuals younger than 18 years, informed consent was obtained from their legal guardians. References Stevanin G, Le Guern E, Ravisé N, et al. A third locus for autosomal dominant cerebellar ataxia type I maps to chromosome 14q24.3-qter: evidence for the existence of a fourth locus. Am J Hum Genet. 1994;54(1):11–20. Matos CA, de Almeida LP, Nóbrega C. Machado-Joseph disease/spinocerebellar ataxia type 3: lessons from disease pathogenesis and clues into therapy. J Neurochem. 2019;148(1):8–28. Buijsen RAM, Toonen LJA, Gardiner SL, van Roon-Mom WMC. Genetics, Mechanisms, and Therapeutic Progress in Polyglutamine Spinocerebellar Ataxias. Neurotherapeutics. 2019;16(2):263–86. Gan SR, Shi SS, Wu JJ, et al. High frequency of Machado-Joseph disease identified in southeastern Chinese kindreds with spinocerebellar ataxia. BMC Med Genet. 2010;11:47. Wang Y, Zhai Y, Wang J. Insight into the early pathogenesis and therapeutic strategies of spinocerebellar ataxia type 3/machado-joseph disease from mouse models. Parkinsonism Relat Disord. 2024;126:106991. Klockgether T, Mariotti C, Paulson HL. Spinocerebellar ataxia. Nat Rev Dis Primers. 2019;5(1):24. Gitai LLG, Sobreira-Neto MA, Diniz PRB et al. Voxel-Based Morphometry and Relaxometry Demonstrate Macro- and Microstructural Damages in Spinocerebellar Ataxia Type 3. Cerebellum. 2023;22(5):818–824. Tezenas du Montcel S, Petit E, Olubajo T, et al. Baseline Clinical and Blood Biomarkers in Patients With Preataxic and Early-Stage Disease Spinocerebellar Ataxia 1 and 3. Neurology. 2023;100(17):e1836–48. Hengel H, Martus P, Faber J, et al. The frequency of non-motor symptoms in SCA3 and their association with disease severity and lifestyle factors. J Neurol. 2023;270(2):944–52. Garrard P, Martin NH, Giunti P, Cipolotti L. Cognitive and social cognitive functioning in spinocerebellar ataxia: a preliminary characterization. J Neurol. 2008;255(3):398–405. Braga-Neto P, Pedroso JL, Alessi H, et al. Cerebellar cognitive affective syndrome in Machado Joseph disease: core clinical features. Cerebellum. 2012;11(2):549–56. Lopes TM, D'Abreu A, Franca MC Jr., et al. Widespread neuronal damage and cognitive dysfunction in spinocerebellar ataxia type 3. J Neurol. 2013;260(9):2370–9. Ye ZX, Bi J, Qiu LL, et al. Cognitive impairment associated with cerebellar volume loss in spinocerebellar ataxia type 3. J Neurol. 2024;271(2):918–28. Hu P, Song D, Heng T, et al. Interactions of physical activity and lung function on cognitive health in older adults: Joint association and mediation analysis. J Prev Alzheimers Dis. 2025;12(5):100090. Russ TC, Kivimaki M, Batty GD. Respiratory Disease and Lower Pulmonary Function as Risk Factors for Dementia: A Systematic Review With Meta-analysis. Chest. 2020;157(6):1538–58. Aretz B, Janssen F, Vonk JM, Heneka MT, Boezen HM, Doblhammer G. Long-term exposure to fine particulate matter, lung function and cognitive performance: A prospective Dutch cohort study on the underlying routes. Environ Res. 2021;201:111533. Wang J, Dove A, Song R, et al. Poor pulmonary function is associated with mild cognitive impairment, its progression to dementia, and brain pathologies: A community-based cohort study. Alzheimers Dement. 2022;18(12):2551–9. Duong M, Usman A, Ma J, et al. Associations between lung function and physical and cognitive health in the Canadian Longitudinal Study on Aging (CLSA): A cross-sectional study from a multicenter national cohort. PLoS Med. 2022;19(2):e1003909. Wang J, Song R, Dove A, et al. Pulmonary function is associated with cognitive decline and structural brain differences. Alzheimers Dement. 2022;18(7):1335–44. Gallina P, Boschi A, Angelini L, Lastrucci G. The Possible Link Between Poor Pulmonary Function and Dementia. Chest. 2020;158(2):833–4. Biswas DD, El Haddad L, Sethi R, et al. Neuro-respiratory pathology in spinocerebellar ataxia. J Neurol Sci. 2022;443:120493. Sriranjini SJ, Pal PK, Krishna N, Sathyaprabha TN. Subclinical pulmonary dysfunction in spinocerebellar ataxias 1, 2 and 3. Acta Neurol Scand. 2010;122(5):323–8. Gan SR, Ni W, Dong Y, Wang N, Wu ZY. Population genetics and new insight into range of CAG repeats of spinocerebellar ataxia type 3 in the Han Chinese population. PLoS ONE. 2015;10(8):e0134405. Pal PK, Sathyaprabha TN, Tuhina P, Thennarasu K. Pattern of subclinical pulmonary dysfunctions in Parkinson's disease and the effect of levodopa. Mov disorders: official J Mov Disorder Soc. 2007;22(3):420–4. Bhagavatula ID, Bhat DI, Sasidharan GM, et al. Subclinical respiratory dysfunction in chronic cervical cord compression: a pulmonary function test correlation. NeuroSurg Focus. 2016;40(6):E3. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J. 2005;26(2):319–38. Graham BL, Steenbruggen I, Miller MR, et al. Standardization of Spirometry 2019 Update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am J Respir Crit Care Med. 2019;200(8):e70–88. Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9. Lo AX, Heinemann AW, Gray E, et al. Inter-rater Reliability of Clinical Frailty Scores for Older Patients in the Emergency Department. Acad Emerg Med. 2021;28(1):110–3. Jia X, Wang Z, Huang F, et al. A comparison of the Mini-Mental State Examination (MMSE) with the Montreal Cognitive Assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study. BMC Psychiatry. 2021;21(1):485. Delis DC, Wetter SR, Jacobson MW, et al. Recall discriminability: utility of a new CVLT-II measure in the differential diagnosis of dementia. J Int Neuropsychological Society: JINS. 2005;11(6):708–15. Thiruselvam I, Hoelzle JB. Refined Measurement of Verbal Learning and Memory: Application of Item Response Theory to California Verbal Learning Test - Second Edition (CVLT-II) Learning Trials. Arch Clin Neuropsychol. 2020;35(1):90–104. Alioto AG, Kramer JH, Borish S, et al. Long-term test-retest reliability of the California Verbal Learning Test - second edition. Clin Neuropsychol. 2017;31(8):1449–58. Donders J. Subtypes of learning and memory on the California Verbal Learning Test-Second Edition (CVLT-II) in the standardization sample. J Clin Exp Neuropsychol. 2008;30(7):741–8. Li M, Chen X, Xu HL, et al. Brain structural abnormalities in the preclinical stage of Machado-Joseph disease/spinocerebellar ataxia type 3 (MJD/SCA3): evaluation by MRI morphometry, diffusion tensor imaging and neurite orientation dispersion and density imaging. J Neurol. 2022;269(6):2989–98. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage. 2007;37(1):90–101. Fan L, Li H, Zhuo J, et al. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cereb Cortex. 2016;26(8):3508–26. Keuken MC, Bazin PL, Backhouse K, et al. Effects of aging on T₁, T₂*, and QSM MRI values in the subcortex. Brain Struct function. 2017;222(6):2487–505. Patrizz A, El Hamamy A, Maniskas M, et al. Stroke-Induced Respiratory Dysfunction Is Associated With Cognitive Decline. Stroke. 2023;54(7):1863–74. Rocchi F, Oya H, Balezeau F, et al. Common fronto-temporal effective connectivity in humans and monkeys. Neuron. 2021;109(5):852–68. e858. Martin-Elkins CL, Horel JA. Cortical afferents to behaviorally defined regions of the inferior temporal and parahippocampal gyri as demonstrated by WGA-HRP. J Comp Neurol. 1992;321(2):177–92. McDonald B, Highley JR, Walker MA, et al. Anomalous asymmetry of fusiform and parahippocampal gyrus gray matter in schizophrenia: A postmortem study. Am J Psychiatry. 2000;157(1):40–7. Mayes A, Montaldi D, Migo E. Associative memory and the medial temporal lobes. Trends Cogn Sci. 2007;11(3):126–35. Squire LR, Stark CE, Clark RE. The medial temporal lobe. Annu Rev Neurosci. 2004;27:279–306. Krienen FM, Buckner RL. Segregated fronto-cerebellar circuits revealed by intrinsic functional connectivity. Cereb Cortex. 2009;19(10):2485–97. Guell X, Gabrieli JDE, Schmahmann JD. Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort. NeuroImage. 2018;172:437–49. Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex. Trends Cogn Sci. 2004;8(4):170–7. Tomov MS, Truong VQ, Hundia RA, Gershman SJ. Dissociable neural correlates of uncertainty underlie different exploration strategies. Nat Commun. 2020;11(1):2371. Morris RW, Dezfouli A, Griffiths KR, Balleine BW. Action-value comparisons in the dorsolateral prefrontal cortex control choice between goal-directed actions. Nat Commun. 2014;5:4390. Mottolese C, Richard N, Harquel S, Szathmari A, Sirigu A, Desmurget M. Mapping motor representations in the human cerebellum. Brain. 2013;136(Pt 1):330–42. Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7952676","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545802035,"identity":"cdb6f171-ede3-4b0a-bed6-1fee7f1bf518","order_by":0,"name":"Maolin Cui","email":"","orcid":"","institution":"The First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Maolin","middleName":"","lastName":"Cui","suffix":""},{"id":545802037,"identity":"141f0423-b96a-4726-8b4b-9f675216bdf2","order_by":1,"name":"xiaoting lv","email":"","orcid":"","institution":"the First Affiliated Hospital of Fujian 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07:40:40","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134296,"visible":true,"origin":"","legend":"","description":"","filename":"39031c7fe8e149bbb890d75e04910e6f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7952676/v1/8fc1f445e3043088b1f1a954.xml"},{"id":96252158,"identity":"91693763-7312-4718-8d0f-0cc96d40c096","added_by":"auto","created_at":"2025-11-19 07:40:32","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147540,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7952676/v1/921d4be4eee543833d108d5e.html"},{"id":96250892,"identity":"10d8d3f4-b97d-405c-8365-8803fb8ee455","added_by":"auto","created_at":"2025-11-19 07:39:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":522706,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in cognitive performance between SCA3 patients with normal \u003cem\u003evs\u003c/em\u003e. impaired pulmonary function. (A) Domain-specific cognitive measures, including Total Learning, Intrusions, Learning Efficiency, Recall Discrimination, Delayed Memory, and Forgetting Rate. (B) Global cognition assessed using Mini-Mental State Examination (MMSE). (C) Global cognition assessed using Montreal Cognitive Assessment (MoCA). ****\u003cem\u003ep\u003c/em\u003e \u0026lt;0.001, ***\u003cem\u003ep\u003c/em\u003e \u0026lt;0.005, **\u003cem\u003ep\u003c/em\u003e \u0026lt;0.01, *\u003cem\u003ep\u003c/em\u003e \u0026lt;0.05.\u003c/p\u003e","description":"","filename":"FIG16.png","url":"https://assets-eu.researchsquare.com/files/rs-7952676/v1/0ce96c8fa5bc282fdef979e4.png"},{"id":96159983,"identity":"84ce227f-ea52-4bf0-97b7-63051d9d1ef1","added_by":"auto","created_at":"2025-11-18 08:46:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1540088,"visible":true,"origin":"","legend":"\u003cp\u003ePulmonary dysfunction impact on gray matter volume across different brain regions. Spheres represent various lobes, with colors indicating specific regions and sizes proportional to the effect magnitude. (A) Sagittal view. (B) Coronal view. (C) Axial view.\u003c/p\u003e","description":"","filename":"FIG24.png","url":"https://assets-eu.researchsquare.com/files/rs-7952676/v1/bba7efefe30ce4a31c779fcc.png"},{"id":96159980,"identity":"f6f77970-4966-467a-ae5e-86fb325bd17a","added_by":"auto","created_at":"2025-11-18 08:46:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1467377,"visible":true,"origin":"","legend":"\u003cp\u003eWidespread functional connectivity disruptions in SCA3 patients with impaired pulmonary function (IPF) vs. those with normal pulmonary function (NPF).\u003c/p\u003e\n\u003cp\u003e(A-C) Brain networks showing significantly reduced connectivity in the IPF group (FDR-corrected, *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Nodes represent brain regions and edges represent significant functional connections. 2D and 3D schematic representations are shown for each seed region. (A) Right inferior temporal gyrus (rITG) showing hypoconnectivity with left medial temporal lobe structures (parahippocampal gyrus, lPHG; temporal fusiform gyrus, lTFG). (B) Left cerebellar lobule 4_5 (lCER4_5) showing hypoconnectivity with the right lobule VIII (rCER8) and left lobule I (lCER1). (C) Left cerebellar lobule I (lCER1) showing hypoconnectivity with multiple regions, including right lobules VIII-IX (rCER8, rCER9), left lobule 4–5 (lCER4_5), and vermis X. (D) Comprehensive summary of all significant hypoconnected pathways between the IPF and NPF groups.\u003c/p\u003e","description":"","filename":"FIG33.png","url":"https://assets-eu.researchsquare.com/files/rs-7952676/v1/1d4ded094b10e69670d37e3a.png"},{"id":96159988,"identity":"80cd5857-ab59-4904-8b3d-af61e2c7a6b5","added_by":"auto","created_at":"2025-11-18 08:46:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1463387,"visible":true,"origin":"","legend":"\u003cp\u003eMediation effects of gray matter volume in the relationship between pulmonary function and cognitive performance for SCA3. Results of mediation analyses show the indirect (mediated) and direct effects of pulmonary function on cognitive outcomes. β-values represent standardized path coefficients, and p-values indicate statistical significance. Percentages above each indirect effect path indicate proportion of the total effect mediated by gray matter volume.\u003c/p\u003e","description":"","filename":"FIG42.png","url":"https://assets-eu.researchsquare.com/files/rs-7952676/v1/e00809a4562e94f4e4b90e7f.png"},{"id":97670757,"identity":"8f6ca559-3dc9-466d-b737-e831a5e26c3b","added_by":"auto","created_at":"2025-12-08 09:31:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5347945,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7952676/v1/61176b97-a5c7-446f-8d0a-b3f495af94fd.pdf"},{"id":96250176,"identity":"e2115479-9231-43f4-89be-a7de277c9dcf","added_by":"auto","created_at":"2025-11-19 07:37:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21475,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7952676/v1/f8cb482c0db1a5381572c9ff.docx"},{"id":96159984,"identity":"5f772d38-87f6-4602-9e6f-da9482a7719c","added_by":"auto","created_at":"2025-11-18 08:46:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21717,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7952676/v1/802f424196fcd0f542f579ae.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain Structure Mediates Impact of Pulmonary Dysfunction on Cognition in Spinocerebellar Ataxia Type 3","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpinocerebellar Ataxia Type 3 (SCA3), also known as Machado-Joseph Disease (MJD), is an autosomal dominant neurodegenerative disorder caused by an expanded CAG repeat in the ATXN3 gene \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. SCA3 is the most prevalent form of spinocerebellar ataxia \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, representing 72.5% of all SCA cases in Southeastern China \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The hallmark of the disease is the accumulation of toxic polyglutamine-expanded ataxin-3 aggregates, which promote neurodegeneration in the cerebellum, brainstem, basal ganglia, and cerebral cortex \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This progressive neuronal loss results in a diverse clinical phenotype, encompassing both motor symptoms (such as ataxia and gait instability) and non-motor manifestations (including dysphagia, dysarthria, and neuropsychiatric symptoms like depression and anxiety) \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCognitive impairment is among the most common non-motor symptoms of SCA3 and tends to worsen as the disease progresses \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This impairment is multifactorial, with neuroimaging studies consistently linking it to widespread gray matter (GM) atrophy in the cerebral cortex \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Our previous research has further shown that atrophy of cerebellar GM also plays a significant role in cognitive dysfunction in SCA3 \u003csup\u003e13\u003c/sup\u003e. In addition to these central neuroanatomical changes, clinical comorbidities significantly influence cognitive outcomes. For example, depression and sleep disturbances have been identified as key factors that accelerate cognitive decline in SCA3 \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccumulating evidence demonstrates a strong link between pulmonary dysfunction and cognitive impairment \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, as well as an increased risk of dementia in middle-aged and older adults with impaired lung function \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Longitudinal studies show that reduced pulmonary function accelerates progression from mild cognitive impairment (MCI) to dementia \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and correlates with poorer cognitive performance \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Several mechanisms may explain this relationship. Pulmonary dysfunction can cause chronic cerebral hypoxia, which promotes gray matter atrophy and drives cognitive decline \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Hypoxia may also disrupt the glymphatic system, impairing waste clearance and further worsening cognitive function \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePulmonary dysfunction is a frequent and early manifestation of SCAs \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, yet its role in cognitive impairment remains unclear. Because pulmonary dysfunction is associated with cortical gray matter atrophy - a central feature of cognitive deficits in SCA3 - we hypothesize that structural and functional cortical changes mediate the link between pulmonary and cognitive function. To test this, we combined neuropsychological evaluations with structural and functional neuroimaging to examine relationships among pulmonary function, cognition, gray matter volume, and functional connectivity.\u003c/p\u003e\u003cp\u003eOur findings delineate potential pathways connecting pulmonary and cognitive dysfunction in SCA3. These insights establish a mechanistic framework applicable to other disorders with overlapping pulmonary and cognitive features and highlight pulmonary health as a potential therapeutic target to slow cognitive decline.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStandard Protocol Approvals and Patient Consent\u003c/p\u003e\u003cp\u003e The study protocol for the participants received approval from the Ethics Committee for Medical Research at the First Affiliated Hospital of Fujian Medical University ([2019]195).\u003c/p\u003e\u003cp\u003eStudy Participants\u003c/p\u003e\u003cp\u003eWe prospectively recruited 76 genetically confirmed SCA3 patients from the Organization in Southeast China for Cerebellar Ataxia Research (OSCCAR) cohort, housed within the Department of Neurology, First Affiliated Hospital of Fujian Medical University, between January 2021 and March 2024. Patients were eligible if they met the following inclusion criteria: (1) confirmed genetic diagnosis of SCA3; (2) absence of secondary central nervous system disorders; and (3) willingness to participate with signed informed consent. Exclusion criteria included: (1) comorbidities or injuries interfering with pulmonary function testing or cognitive assessment; (2) concurrent participation in other interventional clinical trials; and (3) age younger than 14 years.\u003c/p\u003e\u003cp\u003eGenotype and Phenotype Analyses\u003c/p\u003e\u003cp\u003eGenomic DNA was extracted from peripheral blood with a QIAamp DNA Blood Mini Kit (Qiagen, Germany). CAG repeat expansions in the ATXN3 gene were characterized through PCR and Sanger sequencing, following previous methods\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDemographic and clinical variables, including sex, age, CAG repeat length, smoking history, educational attainment, and body mass index (BMI), were systematically collected by Dr. Gan, an experienced neurologist.\u003c/p\u003e\u003cp\u003eAssessment of pulmonary function\u003c/p\u003e\u003cp\u003eWe evaluated pulmonary function using the Vyaire Medical Pulmonary Function System (Vyaire Medical GmbH, Bavaria, Germany). All assessments were performed with participants in a seated position. Each subject completed three maneuvers, separated by a two-minute rest, and the best effort was used for analysis. The following spirometric parameters were obtained: forced vital capacity (FVC), forced expiratory volume in one second (FEV1), the FEV1/FVC ratio, peak expiratory flow (PEF), maximal expiratory flows at 75%, 50%, and 25% of vital capacity (MEF75, MEF50, MEF25), and mean mid-expiratory flow (MMEF75/25). In addition, lung volumes were measured, including total lung capacity (TLC) and the residual volume to total lung capacity ratio (RV/TLC) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePulmonary function was categorized as either normal (NPF) or impaired (IPF). NPF was defined as all measured values within the predicted reference ranges. IPF was diagnosed when any of the following conditions were met: (1) obstructive ventilatory dysfunction, defined by a reduced FEV1/FVC ratio (\u0026lt;\u0026thinsp;92% of predicted, calculated from equations adjusted for height, age, sex, and ethnicity) with accompanying elevations in RV and TLC; (2) restrictive ventilatory dysfunction, identified when both FVC and TLC were \u0026lt;\u0026thinsp;80% of predicted; (3) mixed ventilatory dysfunction, indicated by concurrent reductions in FVC, FEV1, and FEV1/FVC; or (4) small airway dysfunction, diagnosed when at least two of the three indices - MEF50, MEF25, or MMEF75/25 - were \u0026lt;\u0026thinsp;65% of predicted. All classifications were based on established diagnostic criteria \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAssessment of cognitive function\u003c/p\u003e\u003cp\u003eWe evaluated both global and domain-specific cognition. Global cognitive status was measured using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) \u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Verbal learning and memory, a central domain-specific function, was assessed with the California Verbal Learning Test-II (CVLT-II) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In this standardized task, participants were presented with a 16-word target list (List A) over five consecutive free recall trials, followed by a single recall of an interference list (List B). After a 20-minute delay filled with nonverbal tasks, memory was reassessed through short-delay free recall (SDFR) and cued recall, long-delay free recall (LDFR) and cued recall, and finally a yes/no recognition test. From these procedures, six key outcome measures were derived.\u003c/p\u003e\u003cp\u003eTotal learning: cumulative number of target items correctly recalled across the five immediate recall trials of List A.\u003c/p\u003e\u003cp\u003eIntrusions: total number of extra-list errors recorded during the five learning trials.\u003c/p\u003e\u003cp\u003eLearning efficiency: number of target words recalled on the fifth learning trial (Trial 5), reflecting maximal encoding performance.\u003c/p\u003e\u003cp\u003eRecall discrimination: calculated as Z(SDFR \u003csub\u003ecorrect\u003c/sub\u003e/16)\u0026thinsp;\u0026minus;\u0026thinsp;Z(SDFR \u003csub\u003eintrusion\u003c/sub\u003e/16).\u003c/p\u003e\u003cp\u003eDelayed memory retention: calculated as (LDFR \u003csub\u003ecorrect\u003c/sub\u003e / Trial 5 \u003csub\u003ecorrect\u003c/sub\u003e) \u0026times; 100%.\u003c/p\u003e\u003cp\u003eForgetting rate: calculated as 1 \u0026minus; (SDFR \u003csub\u003ecorrect\u003c/sub\u003e / Trial 5 \u003csub\u003ecorrect\u003c/sub\u003e) \u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo ensure consistency and reliability, all neuropsychological assessments were administered by the same trained team (MLC, WL, and BNY). Participants were evaluated in a standardized manner, during daytime sessions, in a quiet environment, and following a fixed test order.\u003c/p\u003e\u003cp\u003eMRI Data Processing\u003c/p\u003e\u003cp\u003eAll participants underwent MRI on a Siemens 3.0T scanner. Following acquisition, an experienced radiologist performed quality control to ensure data integrity, including motion correction and exclusion based on excessive head movement.\u003c/p\u003e\u003cp\u003eStructural MRI Data Acquisition and Analysis\u003c/p\u003e\u003cp\u003eHigh-resolution T1-weighted images were collected using a magnetization-prepared rapid acquisition gradient echo (MP-RAGE) sequence in the sagittal plane with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;2300 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;2.3 ms, inversion time (TI)\u0026thinsp;=\u0026thinsp;900 ms, flip angle\u0026thinsp;=\u0026thinsp;8\u0026deg;, field of view (FOV)\u0026thinsp;=\u0026thinsp;240 \u0026times; 256 mm, matrix size\u0026thinsp;=\u0026thinsp;240 \u0026times; 256, bandwidth\u0026thinsp;=\u0026thinsp;200 Hz/pixel, 192 slices, voxel size\u0026thinsp;=\u0026thinsp;1 \u0026times; 1 \u0026times; 1 mm\u0026sup3;, and total scan duration\u0026thinsp;=\u0026thinsp;5 min 12 s.\u003c/p\u003e\u003cp\u003ePreprocessing of structural images was performed using the Computational Anatomy Toolbox (CAT12) within SPM12. The pipeline included tissue segmentation into GM, white matter, and cerebrospinal fluid; spatial normalization to Montreal Neurological Institute (MNI) space using the DARTEL algorithm; and smoothing with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Regions of interest (ROIs) were defined based on the Automated Anatomical Labeling (AAL) atlas (version 3). For each ROI, modulated GM volume values were extracted and used for subsequent statistical analyses.\u003c/p\u003e\u003cp\u003eFunctional MRI Data Acquisition and Analysis\u003c/p\u003e\u003cp\u003eResting-state functional MRI (rs-fMRI) data were acquired using a T2*-weighted gradient-echo echo-planar imaging (EPI) sequence. Participants were instructed to rest quietly with their eyes closed while remaining awake. Acquisition parameters were: number of volumes\u0026thinsp;=\u0026thinsp;205, slice thickness\u0026thinsp;=\u0026thinsp;3 mm, interslice gap\u0026thinsp;=\u0026thinsp;3 mm, TR\u0026thinsp;=\u0026thinsp;3000 ms, TE\u0026thinsp;=\u0026thinsp;30 ms, FOV\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm, matrix size\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, and total scan duration\u0026thinsp;=\u0026thinsp;10 min 26 s. Post-scan quality control was performed by an experienced radiologist. The resulting voxel dimensions were 4 \u0026times; 4 \u0026times; 6 mm\u0026sup3;.\u003c/p\u003e\u003cp\u003eData preprocessing was carried out using the CONN functional connectivity toolbox (v22.a; RRID: SCR_009550) in MATLAB R2024a (MathWorks, Natick, MA) with SPM12. Of the 237 acquired volumes, the first five were discarded to allow for signal equilibration. Preprocessing included realignment and motion correction, slice-timing correction, identification of outlier volumes (\u0026ge;\u0026thinsp;0.5 mm framewise displacement or \u0026ge;\u0026thinsp;3 standard deviations in global signal intensity), segmentation, normalization to the MNI template, and spatial smoothing with an 8-mm FWHM Gaussian kernel.\u003c/p\u003e\u003cp\u003eTo reduce noise, we applied temporal bandpass filtering (0.008\u0026ndash;0.09 Hz) and ordinary least squares (OLS) regression to remove nuisance covariates from the BOLD time series. Additional artifact correction was performed using the anatomical component-based noise correction method (aCompCor) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, which identifies and removes physiological noise components.\u003c/p\u003e\u003cp\u003eResting-state functional connectivity (rsFC) was analyzed using a ROI-to-ROI framework. ROIs were defined from the Brainnetome Atlas \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and the Atlas of the Basal Ganglia \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. For each participant, pairwise temporal correlations of BOLD signals were computed across 378 ROI pairs (28 ROIs). At the group level, functional connectivity strength was quantified by calculating bivariate correlations between ROIs, followed by Fisher\u0026rsquo;s z-transformation.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe first assessed the normality of continuous variables using the Shapiro\u0026ndash;Wilk test. Parametric data were analyzed with independent-samples t-tests and expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), whereas nonparametric data were evaluated with Mann\u0026ndash;Whitney U tests and reported as median with interquartile range (Q1\u0026ndash;Q3). To control for potential confounders, we compared demographic variables, including sex, educational attainment, smoking history, CAG repeat length in the expanded allele (CAGexp), BMI, and age, between patients with impaired pulmonary function (IPF) and those with normal pulmonary function (NPF). Categorical variables (sex, educational attainment, smoking history) were tested with chi-square analyses, while continuous variables (CAGexp, BMI, age) were examined with independent-samples t-tests.\u003c/p\u003e\u003cp\u003eTo evaluate differences in cognitive performance between IPF and NPF groups, we used independent-samples \u003cem\u003et\u003c/em\u003e-tests and Mann\u0026ndash;Whitney U tests to analyze MoCA, MMSE, total learning, intrusions, learning efficiency, recall discrimination, delayed memory, and forgetting rate.\u003c/p\u003e\u003cp\u003eAssociations between pulmonary function and brain structure were examined using multiple regression analyses. We compared ROI volumes between IPF and NPF groups while adjusting for age, sex, and total intracranial volume (TIV) as covariates. Group differences in resting-state functional connectivity were analyzed with independent-samples \u003cem\u003et\u003c/em\u003e-tests. Functional connectivity was observed thorough MATLAB\u0026rsquo;s BrainNet Viewer toolbox.\u003c/p\u003e\u003cp\u003eWe tested whether GM volume mediated the association between pulmonary dysfunction and cognitive performance using the PROCESS macro (v4.1, Model 4) for SPSS. All models included TIV as a covariate. We evaluated the significance of the Average Causal Mediation Effect (ACME) with a bias-corrected bootstrapping procedure based on 5000 resamples, generating 95% confidence intervals (CIs). Mediation was considered significant when the CI excluded zero.\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed in SPSS version 25.0 (SPSS Inc., Chicago, IL, USA), with the exception of functional connectivity analyses, which we conducted using the CONN toolbox in MATLAB. We created statistical graphs in GraphPad Prism (v10.1.2). Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Data and Cognitive Profiles in SCA3 Patients\u003c/h2\u003e\u003cp\u003eWe analyzed baseline demographic and clinical data from 35 patients with normal pulmonary function (NPF; 24 men, 68.6%) and 41 patients with impaired pulmonary function (IPF; 27 men, 65.9%). The two groups did not differ significantly in age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.086), sex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.802), years of education (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.584), smoking history (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.684), CAGexp (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.743), or BMI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.836) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn terms of cognitive performance, the IPF group scored significantly lower on global assessments, including MoCA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) and MMSE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), compared with the NPF group. Domain-specific tests revealed that IPF patients showed poorer recall discrimination (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) and delayed memory (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), as well as higher intrusion (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and forgetting rates (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035). By contrast, no group differences emerged for total learning (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.466) or learning efficiency (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.538) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMediation analysis of pulmonary and cognitive functions in SCA3\u003c/h3\u003e\n\u003cp\u003eMediation analysis revealed distinct lung\u0026ndash;brain\u0026ndash;cognition pathways, showing that impaired pulmonary function contributed to cognitive decline through reduced gray matter volume in specific brain regions. Gray matter loss in the rIFGorb mediated the association between pulmonary dysfunction and poorer outcomes on CVLT-Total Learning (indirect effect: β = \u0026minus;2.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 56.38% of the total effect) and CVLT-Learning Efficiency (indirect effect: β = \u0026minus;0.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 20.84%). Similarly, reduced gray matter volume in the rCER6 mediated the effects of pulmonary impairment on CVLT-Recall Discrimination (indirect effect: β = \u0026minus;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; 22.67%), CVLT-Delayed Memory (indirect effect: β = \u0026minus;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; 26.10%), and MoCA (indirect effect: β = \u0026minus;0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; 38.3%). In addition, gray matter reduction in the left lPHG significantly mediated poorer MMSE performance (indirect effect: β = \u0026minus;0.69, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032; 40.54%). These findings are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study demonstrates that pulmonary impairment is closely linked to greater cognitive decline and widespread cortical structural and functional abnormalities in patients with SCA3. Stratification by pulmonary function revealed that patients with impaired respiration showed marked reductions in both global and domain-specific cognitive performance, along with decreased cortical gray matter volume and weakened functional connectivity. Importantly, mediation analysis indicated that gray matter atrophy partially explained the association between pulmonary dysfunction and cognitive decline, suggesting that respiratory compromise contributes to cognitive deterioration in SCA3 through neural structural changes. These emphasize the need to account for systemic factors, like pulmonary health, when defining SCA3\u0026rsquo;s cognitive phenotype.\u003c/p\u003e\u003cp\u003eAlthough prior studies have explored how pulmonary function affects cognition, significant questions remain regarding its effects on distinct cognitive domains and the specific neural circuits that mediate these relationships \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In our cohort, mediation analyses showed that gray matter loss in the lPHG and rCER6 primarily mediated the effect of pulmonary dysfunction on global cognitive performance. The PHG, a central hub of the medial temporal lobe (MTL), plays a critical role in integrating spatial, contextual, and scene-based information \u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. By contrast, the ITG, located at the endpoint of the ventral visual stream, transforms perceptual signals into semantic and mnemonic representations, supporting recognition and processing of complex object features \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Patients with impaired pulmonary function exhibited reduced gray matter volume in both the lPHG and rITG, alongside decreased functional connectivity between these regions. Together, these neuroimaging results corroborate the mediation analyses, pointing to structural and functional disruption of the MTL\u0026ndash;temporal network as a key pathway through which pulmonary impairment drives cognitive deficits in SCA3.\u003c/p\u003e\u003cp\u003eThe rCER6 plays a central role in non-motor cognitive functions, including working memory and language processing. It is a key component of the \"triple representation\" model of cerebellar organization \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, which conceptualizes the cerebellum\u0026rsquo;s involvement in non-motor processes. In addition to its critical role in global cognition, our mediation analysis identified rCER6, along with rIFGorb, as a key mediator linking pulmonary dysfunction to deficits in domain-specific cognition. The rIFGorb is essential for integrating feedback during decision-making and post-choice value evaluation, both of which are vital to higher-order cognitive processes \u003csup\u003e\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Consistent with these functional roles, our study observed significant gray matter loss in both rIFGorb and rCER6 in patients with idiopathic pulmonary fibrosis, providing a structural basis for their involvement in cognitive dysfunction. Furthermore, our previous findings demonstrated a robust association between rCER6 levels and cognitive impairment \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInterestingly, we found that cortical gray matter atrophy was more widespread than cerebellar atrophy in IPF patients, whereas alterations in cerebellar functional connectivity were more extensive. Specifically, reduced connectivity was observed between lCER1 and several other cerebellar regions, including lCER4_5, rCER8, rCER9, and Vermis10. In contrast, only the rITG showed reduced connectivity with the lPHG and lTFG within the cerebral cortex. Notably, the co-occurrence of structural atrophy and functional disconnection in lCER4_5 suggests that this region serves as a critical hub for disease pathology, potentially functioning as a node from which dysfunction spreads throughout the cerebellar motor network. This finding indicates a possible pathway through which pulmonary impairment affects motor networks via cerebellar involvement. Specifically, gray matter atrophy in lCER4_5, a region crucial for motor coordination \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, may disrupt its intrinsic connectivity with lCER1, subsequently leading to altered functional connections between lCER1 and other cerebellar regions, such as rCER8, rCER9, and Vermis10, which are densely interconnected.\u003c/p\u003e\u003cp\u003eOur study has several limitations that offer directions for future research. First, the cross-sectional design prevents an assessment of how pulmonary decline evolves over time in SCA3 and limits the ability to draw causal conclusions about its relationship with cognitive trajectories. Second, while our mediation analyses suggest a potential pathway linking pulmonary function, brain structure, and cognition, these findings are inferential and require direct anatomical or histopathological confirmation. Finally, we did not explore other potential mediating mechanisms, such as glymphatic dysfunction, which has been identified as a key factor in the lung-brain axis. Future longitudinal and multimodal studies that include biomarkers of glymphatic function are needed to further validate and extend our results.\u003c/p\u003e\u003cp\u003eIn conclusion, we show that impaired pulmonary function is associated with worse cognitive performance and significant reductions in cortical gray matter volume, along with decreased functional connectivity in SCA3. By integrating multimodal data, our study moves beyond simple correlation to identify specific neural pathways within the lung-brain axis, positioning pulmonary function as a potential modulator of central nervous system integrity and a target for future therapeutic approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors report no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the kind patients, families, caregivers, and members who participated in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Disclosures of all authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNone of the authors have any financial disclosures to report for the preceding 12 months\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (8237062035, Beijing; S-R-G), the National Natural Science Foundation of China (82230039, Beijing; N-W), the Local Science and Technology Development Project guided by the central government grants (2022L3011, Fujian; N-W).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor acknowledgments:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.-R.G., N.W., J.-P.H and X.-Y.C. formulated and designed the study concept; M.-L.C., X.-T.L., W.L., M.-C.L. and S.-R.G. drafted and revised the manuscript; M.-L.C., X.-T.L., W.L., Z.-Y.H., B.-N.Y., C.-Y.L., M.-T.L., H.L., and S.-R.G. performed data analysis and prepared figures and tables; , M.-L.C., X.-T.L. and S.-R.G. enrolled the patients and conducted clinical assessments; all authors approved the final version of the manuscript; all authors agree that any questions related to the work are appropriately resolved. S.-R.G. finalized, certified, and submitted the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants before enrolment. For individuals younger than 18 years, informed consent was obtained from their legal guardians.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStevanin G, Le Guern E, Ravis\u0026eacute; N, et al. A third locus for autosomal dominant cerebellar ataxia type I maps to chromosome 14q24.3-qter: evidence for the existence of a fourth locus. Am J Hum Genet. 1994;54(1):11\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatos CA, de Almeida LP, N\u0026oacute;brega C. Machado-Joseph disease/spinocerebellar ataxia type 3: lessons from disease pathogenesis and clues into therapy. J Neurochem. 2019;148(1):8\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuijsen RAM, Toonen LJA, Gardiner SL, van Roon-Mom WMC. Genetics, Mechanisms, and Therapeutic Progress in Polyglutamine Spinocerebellar Ataxias. Neurotherapeutics. 2019;16(2):263\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGan SR, Shi SS, Wu JJ, et al. High frequency of Machado-Joseph disease identified in southeastern Chinese kindreds with spinocerebellar ataxia. BMC Med Genet. 2010;11:47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Zhai Y, Wang J. Insight into the early pathogenesis and therapeutic strategies of spinocerebellar ataxia type 3/machado-joseph disease from mouse models. Parkinsonism Relat Disord. 2024;126:106991.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlockgether T, Mariotti C, Paulson HL. Spinocerebellar ataxia. Nat Rev Dis Primers. 2019;5(1):24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGitai LLG, Sobreira-Neto MA, Diniz PRB et al. Voxel-Based Morphometry and Relaxometry Demonstrate Macro- and Microstructural Damages in Spinocerebellar Ataxia Type 3. Cerebellum. 2023;22(5):818\u0026ndash;824.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTezenas du Montcel S, Petit E, Olubajo T, et al. Baseline Clinical and Blood Biomarkers in Patients With Preataxic and Early-Stage Disease Spinocerebellar Ataxia 1 and 3. Neurology. 2023;100(17):e1836\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHengel H, Martus P, Faber J, et al. The frequency of non-motor symptoms in SCA3 and their association with disease severity and lifestyle factors. J Neurol. 2023;270(2):944\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarrard P, Martin NH, Giunti P, Cipolotti L. Cognitive and social cognitive functioning in spinocerebellar ataxia: a preliminary characterization. J Neurol. 2008;255(3):398\u0026ndash;405.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraga-Neto P, Pedroso JL, Alessi H, et al. Cerebellar cognitive affective syndrome in Machado Joseph disease: core clinical features. Cerebellum. 2012;11(2):549\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLopes TM, D'Abreu A, Franca MC Jr., et al. Widespread neuronal damage and cognitive dysfunction in spinocerebellar ataxia type 3. J Neurol. 2013;260(9):2370\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe ZX, Bi J, Qiu LL, et al. Cognitive impairment associated with cerebellar volume loss in spinocerebellar ataxia type 3. J Neurol. 2024;271(2):918\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu P, Song D, Heng T, et al. Interactions of physical activity and lung function on cognitive health in older adults: Joint association and mediation analysis. J Prev Alzheimers Dis. 2025;12(5):100090.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuss TC, Kivimaki M, Batty GD. Respiratory Disease and Lower Pulmonary Function as Risk Factors for Dementia: A Systematic Review With Meta-analysis. Chest. 2020;157(6):1538\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAretz B, Janssen F, Vonk JM, Heneka MT, Boezen HM, Doblhammer G. Long-term exposure to fine particulate matter, lung function and cognitive performance: A prospective Dutch cohort study on the underlying routes. Environ Res. 2021;201:111533.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Dove A, Song R, et al. Poor pulmonary function is associated with mild cognitive impairment, its progression to dementia, and brain pathologies: A community-based cohort study. Alzheimers Dement. 2022;18(12):2551\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuong M, Usman A, Ma J, et al. Associations between lung function and physical and cognitive health in the Canadian Longitudinal Study on Aging (CLSA): A cross-sectional study from a multicenter national cohort. PLoS Med. 2022;19(2):e1003909.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Song R, Dove A, et al. Pulmonary function is associated with cognitive decline and structural brain differences. Alzheimers Dement. 2022;18(7):1335\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGallina P, Boschi A, Angelini L, Lastrucci G. The Possible Link Between Poor Pulmonary Function and Dementia. Chest. 2020;158(2):833\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBiswas DD, El Haddad L, Sethi R, et al. Neuro-respiratory pathology in spinocerebellar ataxia. J Neurol Sci. 2022;443:120493.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSriranjini SJ, Pal PK, Krishna N, Sathyaprabha TN. Subclinical pulmonary dysfunction in spinocerebellar ataxias 1, 2 and 3. Acta Neurol Scand. 2010;122(5):323\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGan SR, Ni W, Dong Y, Wang N, Wu ZY. Population genetics and new insight into range of CAG repeats of spinocerebellar ataxia type 3 in the Han Chinese population. PLoS ONE. 2015;10(8):e0134405.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePal PK, Sathyaprabha TN, Tuhina P, Thennarasu K. Pattern of subclinical pulmonary dysfunctions in Parkinson's disease and the effect of levodopa. Mov disorders: official J Mov Disorder Soc. 2007;22(3):420\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhagavatula ID, Bhat DI, Sasidharan GM, et al. Subclinical respiratory dysfunction in chronic cervical cord compression: a pulmonary function test correlation. NeuroSurg Focus. 2016;40(6):E3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J. 2005;26(2):319\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGraham BL, Steenbruggen I, Miller MR, et al. Standardization of Spirometry 2019 Update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am J Respir Crit Care Med. 2019;200(8):e70\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNasreddine ZS, Phillips NA, B\u0026eacute;dirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLo AX, Heinemann AW, Gray E, et al. Inter-rater Reliability of Clinical Frailty Scores for Older Patients in the Emergency Department. Acad Emerg Med. 2021;28(1):110\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia X, Wang Z, Huang F, et al. A comparison of the Mini-Mental State Examination (MMSE) with the Montreal Cognitive Assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study. BMC Psychiatry. 2021;21(1):485.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelis DC, Wetter SR, Jacobson MW, et al. Recall discriminability: utility of a new CVLT-II measure in the differential diagnosis of dementia. J Int Neuropsychological Society: JINS. 2005;11(6):708\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThiruselvam I, Hoelzle JB. Refined Measurement of Verbal Learning and Memory: Application of Item Response Theory to California Verbal Learning Test - Second Edition (CVLT-II) Learning Trials. Arch Clin Neuropsychol. 2020;35(1):90\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlioto AG, Kramer JH, Borish S, et al. Long-term test-retest reliability of the California Verbal Learning Test - second edition. Clin Neuropsychol. 2017;31(8):1449\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDonders J. Subtypes of learning and memory on the California Verbal Learning Test-Second Edition (CVLT-II) in the standardization sample. J Clin Exp Neuropsychol. 2008;30(7):741\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi M, Chen X, Xu HL, et al. Brain structural abnormalities in the preclinical stage of Machado-Joseph disease/spinocerebellar ataxia type 3 (MJD/SCA3): evaluation by MRI morphometry, diffusion tensor imaging and neurite orientation dispersion and density imaging. J Neurol. 2022;269(6):2989\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBehzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage. 2007;37(1):90\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan L, Li H, Zhuo J, et al. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cereb Cortex. 2016;26(8):3508\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeuken MC, Bazin PL, Backhouse K, et al. Effects of aging on T₁, T₂*, and QSM MRI values in the subcortex. Brain Struct function. 2017;222(6):2487\u0026ndash;505.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatrizz A, El Hamamy A, Maniskas M, et al. Stroke-Induced Respiratory Dysfunction Is Associated With Cognitive Decline. Stroke. 2023;54(7):1863\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRocchi F, Oya H, Balezeau F, et al. Common fronto-temporal effective connectivity in humans and monkeys. Neuron. 2021;109(5):852\u0026ndash;68. e858.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartin-Elkins CL, Horel JA. Cortical afferents to behaviorally defined regions of the inferior temporal and parahippocampal gyri as demonstrated by WGA-HRP. J Comp Neurol. 1992;321(2):177\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcDonald B, Highley JR, Walker MA, et al. Anomalous asymmetry of fusiform and parahippocampal gyrus gray matter in schizophrenia: A postmortem study. Am J Psychiatry. 2000;157(1):40\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayes A, Montaldi D, Migo E. Associative memory and the medial temporal lobes. Trends Cogn Sci. 2007;11(3):126\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSquire LR, Stark CE, Clark RE. The medial temporal lobe. Annu Rev Neurosci. 2004;27:279\u0026ndash;306.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrienen FM, Buckner RL. Segregated fronto-cerebellar circuits revealed by intrinsic functional connectivity. Cereb Cortex. 2009;19(10):2485\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuell X, Gabrieli JDE, Schmahmann JD. Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort. NeuroImage. 2018;172:437\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex. Trends Cogn Sci. 2004;8(4):170\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTomov MS, Truong VQ, Hundia RA, Gershman SJ. Dissociable neural correlates of uncertainty underlie different exploration strategies. Nat Commun. 2020;11(1):2371.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorris RW, Dezfouli A, Griffiths KR, Balleine BW. Action-value comparisons in the dorsolateral prefrontal cortex control choice between goal-directed actions. Nat Commun. 2014;5:4390.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMottolese C, Richard N, Harquel S, Szathmari A, Sirigu A, Desmurget M. Mapping motor representations in the human cerebellum. Brain. 2013;136(Pt 1):330\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SCA3, Pulmonary Dysfunction, Cognitive Impairments, Brain structure","lastPublishedDoi":"10.21203/rs.3.rs-7952676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7952676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSpinocerebellar ataxia type 3 (SCA3) is a common autosomal dominant disorder marked by both cognitive and pulmonary dysfunction. Although a connection between these impairments exists, the mechanisms underlying this relationship are not well understood. In this study, we aim to explore the neural and physiological pathways linking pulmonary and cognitive deficits in SCA3 through multimodal integration.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eSeventy-six SCA3 patients from the OSCCAR cohort underwent assessments including pulmonary function testing (classified as normal [NPF] or impaired [IPF]), cognitive evaluations (MoCA, MMSE, CVLT-II), and multimodal MRI (3T Siemens). Structural brain volumes were analyzed using CAT12/SPM12, and resting-state functional connectivity was assessed with the CONN toolbox. Mediation analysis was employed to determine whether gray matter volume mediated the relationship between pulmonary and cognitive impairments.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSCA3 patients with IPF exhibited global cognition and verbal memory that is significantly worse compared to those with NPF. For example, recall-discrimination decreases, intrusions increase, and forget quickly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). IPF patients also had significant gray matter atrophy, predominantly in temporal regions (β = \u0026minus;0.16 to \u0026minus;\u0026thinsp;0.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), extending to the frontal, parietal, insular, and cerebellar areas (β = \u0026minus;0.03 to \u0026minus;\u0026thinsp;0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the connectivity between rITG-medial temporal and intra-cerebellar was impaired (β \u0026le; \u0026minus;0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePulmonary dysfunction in SCA3 is associated with greater cognitive impairment and cortical gray matter atrophy. Our findings suggest that gray matter volume may serve as a mediator in the pathway linking pulmonary and cognitive dysfunction.\u003c/p\u003e","manuscriptTitle":"Brain Structure Mediates Impact of Pulmonary Dysfunction on Cognition in Spinocerebellar Ataxia Type 3","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 08:46:16","doi":"10.21203/rs.3.rs-7952676/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20b2117b-7e6b-4b16-9941-84b17290f5be","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-05T08:40:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 08:46:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7952676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7952676","identity":"rs-7952676","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}