Longitudinal Dynamics of Clinical and Neurophysiological Changes in Parkinson's Disease: A 4.5-Year Cohort Study

Longitudinal Dynamics of Clinical and Neurophysiological Changes in Parkinson's Disease: A 4.5-Year Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Longitudinal Dynamics of Clinical and Neurophysiological Changes in Parkinson's Disease: A 4.5-Year Cohort Study Estefanía Santana-Román, Emmanuel Ortega-Robles, Oscar Arias-Carrión This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6246415/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Parkinson's disease (PD) is characterized by progressive motor and non-motor symptoms, yet the longitudinal interplay between clinical progression and neurophysiological alterations remains underexplored. In this four-and-a-half-year longitudinal study, we assessed 22 individuals with PD using the Movement Disorder Society–Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and transcranial magnetic stimulation (TMS) to evaluate cortical excitability. We analyzed changes in resting motor threshold (rMT) and cortical silent period (CSP) across disease stages and examined the impact of the COVID-19 pandemic on disease progression. We observed significant motor function and cortical excitability deterioration over time, with CSP exhibiting potential as a biomarker of disease progression. These alterations were more pronounced in advanced PD and during the post-pandemic period, underscoring the susceptibility of PD patients to environmental stressors. No significant sex-related differences were found in clinical or neurophysiological measures. Our findings highlight the potential of TMS in monitoring PD progression and suggest that integrating neurophysiological assessments into routine clinical practice may enhance patient management. Longitudinal neurophysiological biomarkers could provide insights into disease trajectory and inform therapeutic interventions. Biological sciences/Neuroscience Biological sciences/Neuroscience/Motor control Biological sciences/Neuroscience/Motor control/Motor cortex Parkinson's disease Longitudinal study Motor asymmetry Transcranial magnetic stimulation Disease progression Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as bradykinesia, rigidity, resting tremor, and postural instability, alongside a broad spectrum of non-motor symptoms that significantly impair patients' quality of life 1 – 3 . Changes resulting from the disease's progression can be clinically correlated with the severity of motor and non-motor symptoms through instruments such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) 4 – 6 . Today it is well recognized that the pathophysiology of PD extends beyond the degeneration of dopaminergic neurons in the substantia nigra, involving widespread neurodegenerative processes across cortical and subcortical areas, as well as non-dopaminergic systems 7 – 12 . However, clinical scales cannot directly assess changes at these levels. Transcranial magnetic stimulation (TMS) is a non-invasive technique used to assess various neurophysiological parameters in the primary motor cortex, providing insights into cortical excitability (e.g., resting motor threshold [rMT], active motor threshold [aMT], motor-evoked potential [MEP] amplitude, input-output curves), inhibition (e.g., cortical silent period [CSP], short-interval intracortical inhibition [SICI], long-interval intracortical inhibition [LICI]), and plasticity (e.g., repetitive TMS [rTMS], theta burst stimulation [TBS]) 13 – 15 . In Parkinson's disease, TMS may contribute to the diagnosis, prognosis, and disease progression assessment, complementing clinical evaluations 16 , 17 . However, long-term studies integrating neurophysiological and clinical measures in PD remain scarce. Some studies have reported TMS alterations in patients with PD compared to a control group 18 – 21 . However, most of these reports assess patients with PD at a single time. Remembering that PD is a neurodegenerative process, the combined study of clinical features and neurophysiological measures over time in this type of patient becomes important. Furthermore, it could enhance our understanding of the neurophysiological alterations occurring during the disease course, facilitate comprehensive patient follow-up and predict disease evolution by establishing possible correlations between clinical scales and TMS-derived measurements. However, just a few studies have conducted clinical and TMS follow-ups in a cohort of PD subjects, with this follow-up not exceeding one year 22 – 24 . Therefore, in this longitudinal observational study, we followed a cohort of individuals with PD from 2018 to 2023, encompassing pre and post-pandemic periods. By integrating standardized clinical assessments, such as the MDS-UPDRS, with TMS-derived neurophysiological measures, including rMT, CSP, and MEP amplitudes, we aimed to investigate the evolution of motor and non-motor symptoms along with neurophysiological parameters and their potential associations. This is one of the few studies to perform a comprehensive follow-up in a group of subjects with PD and, to our knowledge, the most extended clinical-electrophysiological follow-up in PD reported. Results Recruitment and sample characteristics Twenty-two patients with PD met the inclusion criteria and consented to participate, comprising an equal distribution of 11 females and 11 males. The cohort was stratified into early-stage PD (15 participants with Hoehn & Yahr stage ≤ 2) and advanced-stage PD (7 participants with Hoehn & Yahr stage > 2). The demographic and clinical characteristics of the entire cohort and subgroups are detailed in Table 1 ​. Importantly, none of the participants tested positive for COVID-19 or reported known exposure to infected individuals throughout the study period. Table 1 Demographic and clinical characteristics of the sample. Total Early PD Advanced PD N (female/male) 22 (11 / 11) 15 (8 / 7) 7 (3 / 4) Age (years) 65.8 ± 9.1 [42–81] 65.2 ± 7.2 [52–77] 67.1 ± 12.9 [42–81] Age of onset (years) 59.5 ± 8.1 [47–75] 60.1 ± 6.6 [51–75] 55.1 ± 13.9 [36–75] Evolution (years) 8.9 ± 5.3 [2–19] 6.0 ± 3.6 [2–13] 13.0 ± 5.6 [7–19] UPDRS – Part I 1 10.9 ± 6.0 [2–26] 11.0 ± 6.9 [2–26] 10.7 ± 4.6 [3.5–15.8] UPDRS – Part II 1 14.7 ± 9.6 [2–43] 11.9 ± 7.7 [2–24.3] 19.3 ± 11.3 [9.7–43] UPDRS – Part III 1 39.9 ± 16.8 [10–76.9] 32.9 ± 12.3 [10–58] 51.9 ± 17.5 [27.8–76.9] Hoehn & Yahr 1 , 2 2 [1–4] 2 [1–2] 3 [2.5–4] More affected side (Left/Right) 15 / 7 12 / 3 3 / 4 Values are given as mean ± standard deviation and range. 1 Mean of evaluations during the first period in 2018. 2 Values are given as the median and range. Changes in clinical outcomes Longitudinal analyses revealed no significant sex-related differences in the progression of UPDRS scores across all subparts. However, the disease stage significantly influenced motor outcomes ( F ₁,₁₉.₃₈ = 5.59, p = 0.029, η²ₚ = 0.22). Patients with advanced PD exhibited higher UPDRS Part III scores compared to those with early-stage PD, with pronounced differences observed in lower limb function ( F ₁,₁₉.₂₉ = 8.63, p = 0.008, η²ₚ = 0.31) and on the less affected side ( F ₁,₁₉.₂₅ = 5.66, p = 0.028, η²ₚ = 0.23). These disparities were also reflected in the motor asymmetry index ( F ₁,₁₇ = 6.87, p = 0.018, η²ₚ = 0.29). The evaluation period significantly affected UPDRS scores in the overall cohort ( p < 0.003, η²ₚ = 0.24–0.47) and in the early PD subgroup ( p < 0.05, η²ₚ = 0.23–0.56) across all clinical domains. In contrast, for advanced PD, significant effects were restricted to Parts I, II, and III ( p < 0.03, η²ₚ = 0.45–0.60) ( Supplementary Table 1 ​). The most marked changes occurred between pre-pandemic (2018, 2019) and post-pandemic (2021, 2022) periods, as depicted in Fig. 1 and Supplementary Fig. 1 ​​. Changes in TMS parameters The S 50 intensity derived from recruitment curves remained stable across evaluation periods and hemispheres, with a mean of 133.40 ± 3.95% of the resting motor threshold (rMT), supporting the use of 130% rMT for motor-evoked potential (MEP) amplitude comparisons. Similar to clinical outcomes, sex did not significantly influence TMS parameters, nor were differences detected between early and advanced PD subgroups. However, time exerted a significant effect on several TMS measures. Across all PD patients, rMT ( p = 0.001, η²ₚ = 0.31–0.32), MEP duration ( p < 0.015, η²ₚ = 0.18–0.27), and cortical silent period (CSP) ( p < 0.001, η²ₚ = 0.34–0.51) exhibited significant longitudinal changes in both the more affected hemisphere (MAH) and less affected hemisphere (LAH) ( Supplementary Table 2 ​). In early PD, the rMT and CSP were significantly influenced bilaterally, alongside MEP latency in the MAH and the asymmetry index of MEP amplitudes (η²ₚ = 0.17–0.57). For advanced PD, significant time effects were restricted to the rMT and CSP in the MAH and MEP duration in the LAH (η²ₚ = 0.46–0.55). As observed with UPDRS scores, the most pronounced changes in TMS parameters occurred between pre- and post-pandemic periods, highlighted in Fig. 2 and Supplementary Figs. 2–4 ​​. Recruitment curves across the evaluation periods, stratified by hemisphere and disease stage, are presented in Fig. 3 ​. While the fitted sigmoid curves suggest potential differences over time, the variability in measurements and the absence of significant time effects in the area under the recruitment curve (AURC) preclude definitive conclusions regarding longitudinal changes. Correlation between clinical and electrophysiological measurements Exploratory correlation analyses between clinical and neurophysiological measures revealed some associations. The within-subject analysis identified a significant relationship between CSP in the LAH and UPDRS Part III scores (correlation coefficient r = 0.32–0.44, p < 0.05) in the overall cohort, with stronger correlations in early PD. However, these associations lost significance after correction for multiple correlations ( Supplementary Table 3 ​). Between-subject analyses revealed a robust correlation between AURC and clinical asymmetry indices ( r = 0.63–0.96, p < 0.05). Notably, after correcting for multiple correlations, significance persisted in the overall sample and advanced PD subgroup, with the strongest correlation observed in advanced PD ( Supplementary Table 4 ​). Discussion As Parkinson's disease is a neurodegenerative disorder, it is essential to have tools that measure changes over time to understand disease progression better and identify potential biomarkers 25,26 . This longitudinal study evaluated clinical and neurophysiological changes in 22 patients with early and advanced PD over four-and-a-half years, incorporating assessments before and after the COVID-19 pandemic. There is limited knowledge about the evolution of cortical excitability in PD and the potential role of TMS in complementing clinical scales for disease prognosis and biomarker identification. To our knowledge, this study is one of the few that have used TMS to monitor symptom progression in PD and represents one of the most extended clinical-electrophysiological follow-ups conducted in this population. Previous studies have explored longitudinal TMS-based assessments in PD. Strafella et al. 22 examined 10 newly diagnosed, untreated PD patients and compared them to 7 age-matched controls. Patients received levodopa/benserazide or pergolide therapy, and assessments were conducted at baseline and after 6 and 12 months. Similarly, Kojovic et al. 23 studied a cohort of 12 drug-naïve PD patients with no specific treatment, evaluating them at the same time points. Guerra et al. 24 followed 25 PD patients and 18 healthy controls to investigate the long-term effects of safinamide on motor cortex plasticity, with assessments at baseline, after 14 days, and after 12 months of treatment. All three studies measure resting and active motor thresholds, intracortical inhibition, and facilitation and motor function using the MDS-UPDRS. Additionally, Kojovic and Guerra assessed input-output curves, while Kojovic also measured the cortical silent period, and Guerra evaluated intermittent theta-burst stimulation (iTBS)-induced plasticity. Our findings reveal significant disease progression in motor function and neurophysiological measures. Similar to previous studies 4,5,27 , we observed a time-dependent worsening of motor symptoms, as reflected by increasing MDS-UPDRS Part III scores. Notably, advanced PD patients exhibited more significant motor impairments than those in early stages, particularly in lower limb function and on the less affected side, aligning with prior reports of more significant symptom deterioration in later disease stages due to increased neurodegenerative burden 6,25,28,29 . However, changes in the early PD subgroup may have been underestimated, as the MDS-UPDRS has limitations in detecting disease progression in early-stage PD 30 . Despite these limitations, no alternative scale has been developed explicitly for this population, and the MDS-UPDRS remains the most widely used tool for assessing PD symptoms and monitoring disease progression 31 . The asymmetry index also decreased over time, consistent with the findings of Kojovic et al. 23 and cross-sectional studies by Uitti et al. 32 and Marinus et al. 33 . However, this contrasts with other studies that reported preserved asymmetry over time 34,35 . Although the studies by Miller-Patterson and Fiorenzato also had long follow-up periods (5 and 4 years, respectively), these discrepancies may be attributed to patient demographics and treatment approaches. The most pronounced clinical changes occurred between the pre-pandemic (2018, 2019) and post-pandemic (2021, 2022) periods, with significantly higher UPDRS scores in the latter. This aligns with reports of accelerated disease progression in PD patients following prolonged periods of physical inactivity and reduced medical follow-up during the COVID-19 lockdown 36,37 . Ineichen et al. 38,39 followed a cohort of patients for 5 and 7 years, covering periods before, during, and after the COVID-19 pandemic-related isolation. They similarly found worsening motor symptoms after the isolation period, which persisted despite the resumption of everyday activities. While our study lacked a control group, these findings underscore the importance of regular rehabilitation and medical care in PD management. The non-motor symptoms (NMS) of daily living, as assessed by the UPDRS, also exhibited temporal changes. Although the progression of NMS in PD is well documented, its severity varies across studies depending on population characteristics and assessment methods. However, several studies agree that the progression of NMS is slow, heterogeneous, and domain-specific 40-43 . Although no significant changes in NMS were observed in our early PD group or overall during the first years of evaluation, some studies have reported only mild changes in early PD. For instance, Ou et al. 42 documented a subtle progression of NMS in an early PD cohort over a 3-year follow-up. In contrast, a 6-year study in a Taiwanese population found that symptom severity remained stable over the first two years but became evident only after six years, which aligns more closely with our findings 41 . Notably, in our study, the COVID-19 pandemic does not appear to have directly affected the severity of NMS; however, worsening was observed between the last two post-pandemic evaluation periods, particularly pronounced in the advanced PD group. This could be attributed to the pandemic's delayed or indirect effect or, alternatively, to a non-linear progression nature of symptoms. Unfortunately, our data cannot confirm these hypotheses, and further studies would be necessary to explore these possibilities. Given the limitations of MDS-UPDRS in detecting subtle functional changes, especially in early-stage PD 30,44 , we complemented our clinical assessments with transcranial magnetic stimulation to examine neurophysiological changes over time. Our findings are largely consistent with previous studies on cortical excitability in PD, though some discrepancies exist. Kojovic et al. 23 and Strafella et al. 22 reported no significant changes in resting motor threshold over one year. In contrast, we observed subtle but statistically significant changes over 4.5 years, suggesting increased corticospinal excitability and highlighting the importance of long-term follow-up. The cortical silent period exhibited significant longitudinal changes, particularly in early PD patients, with an overall duration increase, supporting its potential as a biomarker of disease progression. CSP is influenced by inhibitory cortical circuits mediated by GABAergic neurotransmission. In PD, it is typically shortened compared to healthy controls, while levodopa and dopamine agonists have been shown to prolong CSP 16 . Notably, Khedr et al. 45 found no differences in CSP between akinetic-rigid and tremor-dominant PD subtypes, suggesting that the heterogeneous nature of our sample may have had minimal impact on this measure. Similar to our findings, Kojovic et al. 23 observed an increase in CSP duration over one year, though only in the more affected hemisphere. This may indicate the restoration of intracortical inhibition, possibly due to the chronic effects of dopaminergic treatment. This interpretation is further supported by Strafella et al. 22 , who reported that levodopa improved cortical inhibitory circuits after 12 months. In contrast to Kojovic et al. 23 , who found differences in input-output curves between the more and less affected hemispheres, our analysis revealed no significant differences in MEP amplitude, as measured by recruitment curves. MEP amplitude and S 50 stimulation intensity remained stable over time, suggesting that corticospinal recruitment properties may be relatively preserved despite ongoing neurodegeneration or that recruitment curves may lack the sensitivity to detect slight changes in corticospinal excitability. Our findings are also consistent with Guerra et al. 24 , who reported no long-term changes in input-output curve steepness. The lack of significant differences in TMS measures between early- and advanced-stage PD groups contrasts with some prior studies reporting more significant alterations in later stages. Spagnolo et al. 18 reported lower rMT values, indicating higher corticospinal excitability in advanced compared to early PD, though their study lacked a longitudinal component. Given our longitudinal observation that rMT tends to decrease as the disease progresses, one would expect advanced PD patients to have lower rMT than those in the early stage. This discrepancy may be due to the small sample size of our advanced PD subgroup, individual variability in disease progression, or the possibility that the decline in rMT occurs gradually and reaches a plateau in the mid-stage of the disease. Regarding sex-related differences, while PD incidence is lower in women than men 46,47 , reports on sex-specific differences in symptom progression remain inconclusive. Some studies suggest distinct motor and non-motor symptom trajectories 48 , whereas others found no significant differences 49,50 . Our study observed no sex-related differences in UPDRS or TMS parameters. Kolmancic et al. 51 reported that women with early PD exhibit a more favourable TMS profile, suggesting sex-specific physiological processes. However, our analysis found no significant differences in cortical excitability between male and female patients. Although within-subject correlations did not remain significant after correcting for multiple comparisons, exploratory analyses suggested a moderate association between CSP in the less affected hemisphere and UPDRS Part III scores. Notably, this correlation was stronger in early PD and more pronounced in the upper limbs. Previous studies have also suggested a correlation between cortical inhibition and disease progression 23,52,53 . CSP in the less affected hemisphere may have the potential as a marker for tracking disease progression in personalized medicine or monitoring intervention outcomes in early PD. However, this hypothesis requires validation in a larger cohort, with adjustments for potential confounding factors. Additionally, we found a significant between-subject correlation linking motor symptom asymmetry to the asymmetry of the area under the recruitment curve, particularly strong in the advanced PD subgroup. This suggests that, at the population level, especially in advanced PD, patients with more significant motor asymmetry also tend to exhibit more pronounced asymmetry in input-output curves. While we did not observe longitudinal changes in these curves, this finding provides insights that may guide future research on the pathophysiology of the disease. Our findings offer valuable insights into the complex trajectory of Parkinson's disease and reinforce the clinical utility of transcranial magnetic stimulation as a potential tool for disease monitoring. The significant correlations between cortical silent period and UPDRS Part III scores highlight the importance of neurophysiological assessments in capturing motor deterioration, particularly in early-stage PD. Furthermore, our study highlights PD's susceptibility to environmental stressors, such as those experienced during the COVID-19 pandemic, which may have exposed latent vulnerabilities in motor circuits. The persistence of altered asymmetry indices and neurophysiological markers beyond the acute phase of the pandemic suggests long-term neuroadaptive processes rather than transient fluctuations. These changes, potentially linked to disruptions in medical care, reduced physical activity, social isolation, and psychological stress, were evident even in early-stage PD, suggesting that neurophysiological plasticity and motor asymmetry are highly dynamic and influenced by external factors. These findings raise important questions about the role of environmental stressors in triggering maladaptive plasticity and potentially accelerating disease progression in vulnerable individuals. They also reinforce the view of PD as a condition with a non-linear trajectory rather than a simple, linear decline 54-56 . Despite these compelling findings, our study is not without limitations. The small sample size, particularly in the advanced PD subgroup, limits our findings' statistical power and generalizability. Additionally, pandemic-related disruptions, including variations in the number and timing of clinical visits, introduced potential biases related to uneven follow-up intensity and a gap in data collection. While we verified the absence of COVID-19 infections in our cohort, we cannot entirely rule out the influence of undetected asymptomatic infections or indirect pandemic-related factors, such as changes in medication adherence, mental health, or daily physical activity, which were not systematically monitored. Furthermore, while our use of TMS provides valuable insights into cortical excitability, it offers a limited view of the complex basal ganglia-thalamo-cortical network dynamics central to PD pathophysiology. The absence of complementary neuroimaging data or molecular biomarkers restricts our ability to correlate functional changes with underlying structural or biochemical alterations. The heterogeneity of our sample, which included both akinetic-rigid and tremor-dominant PD subtypes, limits the strength of our conclusions, as these subtypes exhibit distinct neural activity patterns 57 . Additionally, we did not differentiate between early-onset and late-onset PD or account for patients' cognitive status, which could influence the observed outcomes. The lack of follow-up data on interventions such as pharmacological adjustments or rehabilitation further limits our ability to interpret the clinical and neurophysiological changes fully. Furthermore, although all measurements were conducted between 9:00 and 11:00 AM, we did not control for patients' clinical state at the time of assessment (ON or OFF dopaminergic treatment), which may have introduced variability in the results. Future research should aim for larger, more continuous datasets and adopt a multicenter, prospective cohort design with more diverse populations to address these limitations. Integrating TMS with other promising biomarkers, such as multimodal neuroimaging techniques (resting-state functional MRI and diffusion tensor imaging) and biochemical markers of neurodegeneration and neuroinflammation, could offer a more comprehensive understanding of functional and structural changes over time 26 . Additionally, systematically assessing psychosocial stressors, physical activity, cognitive status, and medication adherence would help disentangle the contributions of intrinsic disease progression from extrinsic environmental factors. Randomized controlled trials exploring interventions such as structured physical activity programs, stress reduction techniques, or telemedicine-based care models could further elucidate whether mitigating these external factors can alter PD's neurophysiological and clinical trajectory. In conclusion, our findings underscore the importance of longitudinal follow-up in Parkinson's disease to understand disease progression better. The significant influence of disease stage on motor function highlights the need for tailored therapeutic approaches, particularly for patients with advanced PD. Additionally, our results support the potential of TMS for tracking disease progression, with CSP emerging as a promising neurophysiological biomarker. Future research should investigate the integration of TMS parameters into routine clinical assessments to improve disease monitoring and optimize patient management. Methods Participants This study was conducted at the Movement and Sleep Disorders Unit of the General Hospital, Dr. Manuel Gea González, where 160 patients with PD receive regular care. All participants were diagnosed according to the clinical diagnostic criteria of the International Parkinson and Movement Disorder Society 58 , ensuring diagnostic consistency with internationally recognized standards. To maintain cohort homogeneity, individuals with atypical or secondary parkinsonism were excluded. Patients had to undergo at least one transcranial magnetic stimulation assessment within nine months before the COVID-19 lockdown (June 2019 to March 2020) to ensure reliable pre-pandemic baseline data for longitudinal comparison. The study followed the Declaration of Helsinki and received approval from the Ethics Committee of General Hospital, Dr. Manuel Gea González (protocol code 49-94-2021, approved on 13 October 2021). Written informed consent and privacy notices were obtained from all participants, safeguarding ethical compliance and participant confidentiality throughout the research process. TMS is a noninvasive and safe procedure that does not pose significant risks to patients with PD 59 . Moreover, we strictly adhered to established safety guidelines 60,61 . Study Design This longitudinal observational study retrospectively followed a cohort of PD patients from 2018 to 2021 and prospectively from 2021 to 2023 to evaluate clinical and neurophysiological parameters over time. Primary assessments included the MDS-UPDRS and TMS-derived neurophysiological measures. The study encompassed four distinct assessment periods: two nine-month intervals prior to the COVID-19 lockdown (September 2018 to June 2019 and June 2019 to March 2020) and two twelve-month intervals after the resumption of in-person visits (March 2021 to March 2022 and March 2022 to March 2023). In-person assessments were suspended between March 2020 and March 2021 due to pandemic-related safety concerns. For simplicity, these periods are referred to as 2018, 2019, 2021, and 2022 throughout the manuscript ( Figure 4 ). Participants attended multiple medical visits during each assessment period as part of routine clinical follow-up. While visits were intended to be regularly scheduled, individual patient factors and clinic scheduling constraints led to variations in the number and spacing of visits. A movement disorders specialist performed all clinical evaluations, which included MDS-UPDRS assessments and TMS procedures to monitor neurophysiological changes. This approach ensured clinical consistency and the accurate longitudinal tracking of disease progression and neurophysiological alterations. Clinical Outcomes Clinical outcomes were assessed using the MDS-UPDRS, which was divided into Part I (non-motor aspects of daily living), Part II (motor aspects of daily living), and Part III (motor examination). Part III was further subdivided into assessments of the upper limbs, lower limbs, the more affected side (MAS), and the less affected side (LAS) to enable a more detailed analysis of motor impairment. Given the multiple UPDRS evaluations per participant, mean scores were calculated for each part within each assessment period. The MAS was determined based on the side with the highest mean UPDRS Part III score across all evaluations throughout the study period, ensuring consistent classification. Motor symptom asymmetry was quantified using an asymmetry index, calculated as where MAS and LAS represent the lateralized UPDRS Part III scores 62 . This index provided a standardized measure of motor symptom distribution, facilitating the evaluation of asymmetry dynamics over time. Transcranial Magnetic Stimulation Following established protocols 63 , TMS was performed using a Magstim Rapid 2 stimulator (Magstim Co. Ltd., UK) equipped with a 70-mm figure-eight coil. Motor-evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle using an integrated two-channel electromyography (MEP Pod, Magstim Co. Ltd., UK). The stimulation coil was positioned over the primary motor cortex at a 45° angle relative to the sagittal plane and maintained consistently throughout the procedure using a neuronavigation system (NDI Polaris Vicra camera, Northern Digital Inc., Canada, and Visor 2 software, Eemagine Medical Imaging Solutions GmbH, Germany). TMS measurements were performed sequentially, starting with the left hemisphere, with a six-second interval between stimuli. The resting motor threshold (rMT) was determined using the adaptive parameter estimation by sequential testing (PEST) method, implemented through the ATH-tool application 64 . Recruitment curves were generated by acquiring 20 MEPs at 100% of rMT, followed by 10 MEPs at incremental intensities ranging from 110% to 180% of rMT in 10% steps. The cortical silent period (CSP) was assessed by recording 20 MEPs at 100% of rMT while participants maintained a tonic contraction of the index finger and thumb at approximately 30% of maximal voluntary contraction, measured using a digital pinch gauge. Neurophysiological parameters were analyzed separately for the more and less affected hemispheres, corresponding to the contralateral sides of the MAS and LAS. The measured outcomes included rMT, CSP, MEP amplitude at the S 50 stimulation intensity (the intensity required to elicit 50% of the maximum MEP response) 63,65 , the area under the recruitment curve (AURC), and MEP latency and duration. Asymmetry indices for rMT, CSP, MEP amplitude, and AURC were calculated similarly to the UPDRS asymmetry index using the formula: MEP latency and duration were measured automatically using the DELMEP algorithm 66 , which identified MEP onset. The endpoint was determined by reversing the MEP waveform in time and reapplying the algorithm. Amplitude was calculated as the difference between the maximum and minimum voltages within the identified MEP window. These analyses were performed on 10 MEPs elicited at S 50 intensity, with mean values for latency, duration, and amplitude calculated for each participant. Two independent experts conducted CSP measurements manually, and the average of their assessments was used for analysis. To characterize the corticospinal input-output properties and have an outcome that we could easily compare through time, we calculated the area under the recruitment curve using the trapezoidal rule 67 . Statistical Analysis Given the variability in the number of evaluations per participant across assessment periods—due to random factors such as missed appointments and personal obligations—as well as the relatively small sample size and presence of outliers, robust linear mixed-effects models were employed. The evaluation period (2018, 2019, 2021, and 2022) was treated as a fixed effect, with participants as a random effect to account for within-subject variability. Degrees of freedom estimation in robust models are complex, precluding direct computation of exact p -values. To address this, we approximated the F-statistic using coefficient estimates and the covariance matrix from the robust model, combined with Satterthwaite-estimated degrees of freedom derived from a corresponding non-robust model to calculate approximate significance levels 68,69 . Effect sizes were expressed as partial eta squared (η²ₚ), derived from the F-statistic and degrees of freedom. Pairwise comparisons across periods were performed using estimated marginal means, with p -values adjusted for multiple comparisons via Tukey's honest significant difference method. The homogeneity of variances was verified using Levene's test. To evaluate the influence of sex and disease stage (early vs. advanced PD), these variables were included as fixed effects in the models, with significance and effect sizes estimated accordingly. The S 50 parameter was calculated by fitting the recruitment curve data to a three-parameter Boltzmann sigmoid function 65 , with stimulation intensity as the independent variable and MEP amplitude as the dependent variable, performed separately for each hemisphere and assessment period. Within-subject correlation coefficients were computed using UPDRS scores as dependent variables and TMS measures as predictors to explore the relationship between clinical and neurophysiological changes 70 . Between-subject correlation coefficients were also calculated to assess overall, time-independent associations 71 . Multiple correlation p -values were corrected using the false discovery rate method to control for type I errors. Statistical significance was set at p < 0.05. All analyses were performed using R (version 4.4.1) 72 in RStudio (version 2023.06.1) 73 , with support from the following packages: ggpubr 74 , rstatix 75 , tidyr 76 , ggprism 77 , cowplot 78 , car 79 , lme4 80 , robustlmm 81 , emmeans 82 , lmerTest 83 , minpack.lm 84 , and rmcorr 85 . Declarations Data availability The clinical and transcranial magnetic stimulation measurements database is publicly available in our GitHub repository: https://github.com/UTMS-Gea/Parkinson-UPDRS-TMS-cohort. Code availability The R code used for the statistical analysis is available in our GitHub repository: https://github.com/UTMS-Gea/Parkinson-UPDRS-TMS-cohort. Author contributions E.O-R and O.A-C conceptualized and designed the study. E.S-R and E.O-R acquired the data. E.S-R and E.O-R completed data preprocessing. E.O-R and O.A-C developed statistical analysis. E.S-R, E.O-R and O.A-C performed analysis. E.S-R, E.O-R and O.A-C interpreted the data. E.S-R and E.O-R wrote the initial draft of the manuscript. E.S-R, E.O-R and O.A-C review, critique and final approval of the manuscript. O.A.-C.: conception and organization of the research project; review and critique of the statistical analysis; writing, editing, critique, and final approval of the manuscript. Competing Interests The authors declare no competing interests. Acknowledgements We thank Hospital General Dr. Manuel Gea González for their support. Estefanía Santana Román is a Ph.D. student in the Programa de Doctorado en Ciencias Biomédicas at the Universidad Nacional Autónoma de México (UNAM) and has received a SECIHTI fellowship (CVU 1225325). References Moustafa, A. A. et al. 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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-6246415","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":442832169,"identity":"9ce97548-f123-4f45-a664-8720e732b46f","order_by":0,"name":"Estefanía Santana-Román","email":"","orcid":"","institution":"Unidad de Trastornos del Movimiento y Sueño (TMS), Hospital General Dr. Manuel Gea González","correspondingAuthor":false,"prefix":"","firstName":"Estefanía","middleName":"","lastName":"Santana-Román","suffix":""},{"id":442832170,"identity":"0a057ec2-a99e-4006-bfc5-534362e32e46","order_by":1,"name":"Emmanuel Ortega-Robles","email":"","orcid":"","institution":"Unidad de Trastornos del Movimiento y Sueño (TMS), Hospital General Dr. Manuel Gea González","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Ortega-Robles","suffix":""},{"id":442832171,"identity":"cfe14971-b7b5-4af1-9abf-9aee6e7f8cd9","order_by":2,"name":"Oscar Arias-Carrión","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFACxgYwxc8Oog0sSNAi2XMApEWCBMsMbiSAKCK08M8+3PjxR8U2eYabz69u+FEgwcDf3p2AV4vEucRmaZ4ztw0bZ+eU3ewBOkzizNkN+K05w9ggzdh2O4FZOiftBg9Qi4FELn4t8mcYm3/+/Hc7gU3yTNrNP8RoMTjD2CbB23A7gUeC/dhtomwxBGqx5jl223AGTw7bbRkDCR6CfpE7w/745o+a2/L2x48/u/nmj40cf3svAe8jAI8BmCRWOQiwPyBF9SgYBaNgFIwgAAB/W0i3RkSVNgAAAABJRU5ErkJggg==","orcid":"","institution":"Unidad de Trastornos del Movimiento y Sueño (TMS), Hospital General Dr. Manuel Gea González","correspondingAuthor":true,"prefix":"","firstName":"Oscar","middleName":"","lastName":"Arias-Carrión","suffix":""}],"badges":[],"createdAt":"2025-03-17 16:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6246415/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6246415/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-13558-3","type":"published","date":"2025-07-26T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82132150,"identity":"7728f7ba-9d91-468c-bd2e-d4fd034809ba","added_by":"auto","created_at":"2025-05-07 05:41:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211302,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal changes in UPDRS Part I, II, and III scores, and the asymmetry index across evaluation periods. UPDRS scores significantly varied over time, with the most pronounced changes occurring between the pre-pandemic and post-pandemic periods. Error bars indicate 95% confidence intervals, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6246415/v1/9d9625a96689013520c6fa6e.png"},{"id":82131073,"identity":"38fadda4-e5d0-4a58-abd1-3386feb37bad","added_by":"auto","created_at":"2025-05-07 05:33:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198289,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal variations in resting motor threshold and cortical silent period in the more affected and less affected hemispheres. Both transcranial magnetic stimulation parameters significantly changed over time, with the most pronounced differences observed between the pre-pandemic and post-pandemic periods, mirroring the pattern in UPDRS scores. Error bars indicate 95% confidence intervals, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6246415/v1/392977ea181c9703b9b888cf.png"},{"id":82131070,"identity":"6c05343b-0161-4687-8a82-74a11094a0de","added_by":"auto","created_at":"2025-05-07 05:33:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":147921,"visible":true,"origin":"","legend":"\u003cp\u003eTranscranial magnetic stimulation recruitment curves across evaluation periods for the more affected and less affected hemispheres, stratified by all Parkinson's disease (PD) patients, early PD, and advanced PD subgroups. Solid lines represent fitted sigmoid curves; error bars indicate 95% confidence intervals; stimulus intensity is expressed as a percentage of the resting motor threshold (rMT).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6246415/v1/dfee44675e728e477ab80111.png"},{"id":82131066,"identity":"8b740b8d-b819-4832-a5f5-c3c38c8b7282","added_by":"auto","created_at":"2025-05-07 05:33:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110827,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the study design. This longitudinal study tracked a Parkinson's disease cohort from 2018 to 2022, assessing MDS-UPDRS scores and transcranial magnetic stimulation (TMS) parameters. Evaluations were conducted during two pre-pandemic periods (2018, 2019) and two post-pandemic periods (2021, 2022). In-person visits were suspended between March 2020 and March 2021 due to the COVID-19 pandemic.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6246415/v1/b0affac9b55d4a98dfd7fcee.png"},{"id":87756803,"identity":"6f8df384-3fc7-4f66-8048-107b6639b7dd","added_by":"auto","created_at":"2025-07-28 16:09:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1332903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6246415/v1/a826ad4d-7211-4296-b39c-adba18f74e6a.pdf"},{"id":82132157,"identity":"9ff22b3d-939c-4834-afab-0f9c20a390f8","added_by":"auto","created_at":"2025-05-07 05:41:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3950583,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6246415/v1/6e386e34b6f9a9ec8423c1b1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal Dynamics of Clinical and Neurophysiological Changes in Parkinson's Disease: A 4.5-Year Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as bradykinesia, rigidity, resting tremor, and postural instability, alongside a broad spectrum of non-motor symptoms that significantly impair patients' quality of life\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Changes resulting from the disease's progression can be clinically correlated with the severity of motor and non-motor symptoms through instruments such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS)\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Today it is well recognized that the pathophysiology of PD extends beyond the degeneration of dopaminergic neurons in the substantia nigra, involving widespread neurodegenerative processes across cortical and subcortical areas, as well as non-dopaminergic systems\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, clinical scales cannot directly assess changes at these levels.\u003c/p\u003e \u003cp\u003eTranscranial magnetic stimulation (TMS) is a non-invasive technique used to assess various neurophysiological parameters in the primary motor cortex, providing insights into cortical excitability (e.g., resting motor threshold [rMT], active motor threshold [aMT], motor-evoked potential [MEP] amplitude, input-output curves), inhibition (e.g., cortical silent period [CSP], short-interval intracortical inhibition [SICI], long-interval intracortical inhibition [LICI]), and plasticity (e.g., repetitive TMS [rTMS], theta burst stimulation [TBS])\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In Parkinson's disease, TMS may contribute to the diagnosis, prognosis, and disease progression assessment, complementing clinical evaluations\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, long-term studies integrating neurophysiological and clinical measures in PD remain scarce.\u003c/p\u003e \u003cp\u003eSome studies have reported TMS alterations in patients with PD compared to a control group\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, most of these reports assess patients with PD at a single time. Remembering that PD is a neurodegenerative process, the combined study of clinical features and neurophysiological measures over time in this type of patient becomes important. Furthermore, it could enhance our understanding of the neurophysiological alterations occurring during the disease course, facilitate comprehensive patient follow-up and predict disease evolution by establishing possible correlations between clinical scales and TMS-derived measurements. However, just a few studies have conducted clinical and TMS follow-ups in a cohort of PD subjects, with this follow-up not exceeding one year\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therefore, in this longitudinal observational study, we followed a cohort of individuals with PD from 2018 to 2023, encompassing pre and post-pandemic periods. By integrating standardized clinical assessments, such as the MDS-UPDRS, with TMS-derived neurophysiological measures, including rMT, CSP, and MEP amplitudes, we aimed to investigate the evolution of motor and non-motor symptoms along with neurophysiological parameters and their potential associations. This is one of the few studies to perform a comprehensive follow-up in a group of subjects with PD and, to our knowledge, the most extended clinical-electrophysiological follow-up in PD reported.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRecruitment and sample characteristics\u003c/h2\u003e \u003cp\u003eTwenty-two patients with PD met the inclusion criteria and consented to participate, comprising an equal distribution of 11 females and 11 males. The cohort was stratified into early-stage PD (15 participants with Hoehn \u0026amp; Yahr stage\u0026thinsp;\u0026le;\u0026thinsp;2) and advanced-stage PD (7 participants with Hoehn \u0026amp; Yahr stage\u0026thinsp;\u0026gt;\u0026thinsp;2). The demographic and clinical characteristics of the entire cohort and subgroups are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e​. Importantly, none of the participants tested positive for COVID-19 or reported known exposure to infected individuals throughout the study period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of the sample.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly PD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdvanced PD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (female/male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (11 / 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (8 / 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (3 / 4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1 [42\u0026ndash;81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 [52\u0026ndash;77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 [42\u0026ndash;81]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of onset (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 [47\u0026ndash;75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6 [51\u0026ndash;75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9 [36\u0026ndash;75]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvolution (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3 [2\u0026ndash;19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 [2\u0026ndash;13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 [7\u0026ndash;19]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPDRS \u0026ndash; Part I\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 [2\u0026ndash;26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9 [2\u0026ndash;26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 [3.5\u0026ndash;15.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPDRS \u0026ndash; Part II\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 [2\u0026ndash;43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7 [2\u0026ndash;24.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3 [9.7\u0026ndash;43]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPDRS \u0026ndash; Part III\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8 [10\u0026ndash;76.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3 [10\u0026ndash;58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5 [27.8\u0026ndash;76.9]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoehn \u0026amp; Yahr\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 [1\u0026ndash;4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 [1\u0026ndash;2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 [2.5\u0026ndash;4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore affected side (Left/Right)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 / 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 / 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eValues are given as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and range.\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eMean of evaluations during the first period in 2018.\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eValues are given as the median and range.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChanges in clinical outcomes\u003c/h3\u003e\n\u003cp\u003eLongitudinal analyses revealed no significant sex-related differences in the progression of UPDRS scores across all subparts. However, the disease stage significantly influenced motor outcomes (\u003cem\u003eF\u003c/em\u003e₁,₁₉.₃₈ = 5.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, η\u0026sup2;ₚ = 0.22). Patients with advanced PD exhibited higher UPDRS Part III scores compared to those with early-stage PD, with pronounced differences observed in lower limb function (\u003cem\u003eF\u003c/em\u003e₁,₁₉.₂₉ = 8.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, η\u0026sup2;ₚ = 0.31) and on the less affected side (\u003cem\u003eF\u003c/em\u003e₁,₁₉.₂₅ = 5.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028, η\u0026sup2;ₚ = 0.23). These disparities were also reflected in the motor asymmetry index (\u003cem\u003eF\u003c/em\u003e₁,₁₇ = 6.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, η\u0026sup2;ₚ = 0.29).\u003c/p\u003e \u003cp\u003eThe evaluation period significantly affected UPDRS scores in the overall cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.003, η\u0026sup2;ₚ = 0.24\u0026ndash;0.47) and in the early PD subgroup (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2;ₚ = 0.23\u0026ndash;0.56) across all clinical domains. In contrast, for advanced PD, significant effects were restricted to Parts I, II, and III (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.03, η\u0026sup2;ₚ = 0.45\u0026ndash;0.60) (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e​). The most marked changes occurred between pre-pandemic (2018, 2019) and post-pandemic (2021, 2022) periods, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e​​.\u003c/p\u003e\n\u003ch3\u003eChanges in TMS parameters\u003c/h3\u003e\n\u003cp\u003eThe S\u003csub\u003e50\u003c/sub\u003e intensity derived from recruitment curves remained stable across evaluation periods and hemispheres, with a mean of 133.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95% of the resting motor threshold (rMT), supporting the use of 130% rMT for motor-evoked potential (MEP) amplitude comparisons. Similar to clinical outcomes, sex did not significantly influence TMS parameters, nor were differences detected between early and advanced PD subgroups. However, time exerted a significant effect on several TMS measures.\u003c/p\u003e \u003cp\u003eAcross all PD patients, rMT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, η\u0026sup2;ₚ = 0.31\u0026ndash;0.32), MEP duration (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.015, η\u0026sup2;ₚ = 0.18\u0026ndash;0.27), and cortical silent period (CSP) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u0026sup2;ₚ = 0.34\u0026ndash;0.51) exhibited significant longitudinal changes in both the more affected hemisphere (MAH) and less affected hemisphere (LAH) (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e​). In early PD, the rMT and CSP were significantly influenced bilaterally, alongside MEP latency in the MAH and the asymmetry index of MEP amplitudes (η\u0026sup2;ₚ = 0.17\u0026ndash;0.57). For advanced PD, significant time effects were restricted to the rMT and CSP in the MAH and MEP duration in the LAH (η\u0026sup2;ₚ = 0.46\u0026ndash;0.55). As observed with UPDRS scores, the most pronounced changes in TMS parameters occurred between pre- and post-pandemic periods, highlighted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eSupplementary Figs.\u0026nbsp;2\u0026ndash;4\u003c/b\u003e​​.\u003c/p\u003e \u003cp\u003eRecruitment curves across the evaluation periods, stratified by hemisphere and disease stage, are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e​. While the fitted sigmoid curves suggest potential differences over time, the variability in measurements and the absence of significant time effects in the area under the recruitment curve (AURC) preclude definitive conclusions regarding longitudinal changes.\u003c/p\u003e\n\u003ch3\u003eCorrelation between clinical and electrophysiological measurements\u003c/h3\u003e\n\u003cp\u003eExploratory correlation analyses between clinical and neurophysiological measures revealed some associations. The within-subject analysis identified a significant relationship between CSP in the LAH and UPDRS Part III scores (correlation coefficient \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32\u0026ndash;0.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the overall cohort, with stronger correlations in early PD. However, these associations lost significance after correction for multiple correlations (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e​).\u003c/p\u003e \u003cp\u003eBetween-subject analyses revealed a robust correlation between AURC and clinical asymmetry indices (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.63\u0026ndash;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, after correcting for multiple correlations, significance persisted in the overall sample and advanced PD subgroup, with the strongest correlation observed in advanced PD (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e​).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs Parkinson\u0026apos;s disease is a neurodegenerative disorder, it is essential to have tools that measure changes over time to understand disease progression better and identify potential biomarkers\u003csup\u003e25,26\u003c/sup\u003e. This longitudinal study evaluated clinical and neurophysiological changes in 22 patients with early and advanced PD over four-and-a-half years, incorporating assessments before and after the COVID-19 pandemic. There is limited knowledge about the evolution of cortical excitability in PD and the potential role of TMS in complementing clinical scales for disease prognosis and biomarker identification. To our knowledge, this study is one of the few that have used TMS to monitor symptom progression in PD and represents one of the most extended clinical-electrophysiological follow-ups conducted in this population.\u003c/p\u003e\n\u003cp\u003ePrevious studies have explored longitudinal TMS-based assessments in PD. Strafella et al.\u003csup\u003e22\u003c/sup\u003e examined 10 newly diagnosed, untreated PD patients and compared them to 7 age-matched controls. Patients received levodopa/benserazide or pergolide therapy, and assessments were conducted at baseline and after 6 and 12 months. Similarly, Kojovic et al.\u003csup\u003e23\u003c/sup\u003e studied a cohort of 12 drug-na\u0026iuml;ve PD patients with no specific treatment, evaluating them at the same time points. Guerra et al.\u003csup\u003e24\u003c/sup\u003e followed 25 PD patients and 18 healthy controls to investigate the long-term effects of safinamide on motor cortex plasticity, with assessments at baseline, after 14 days, and after 12 months of treatment. All three studies measure resting and active motor thresholds, intracortical inhibition, and facilitation and motor function using the MDS-UPDRS. Additionally, Kojovic and Guerra assessed input-output curves, while Kojovic also measured the cortical silent period, and Guerra evaluated intermittent theta-burst stimulation (iTBS)-induced plasticity.\u003c/p\u003e\n\u003cp\u003eOur findings reveal significant disease progression in motor function and neurophysiological measures. Similar to previous studies\u003csup\u003e4,5,27\u003c/sup\u003e, we observed a time-dependent worsening of motor symptoms, as reflected by increasing MDS-UPDRS Part III scores. Notably, advanced PD patients exhibited more significant motor impairments than those in early stages, particularly in lower limb function and on the less affected side, aligning with prior reports of more significant symptom deterioration in later disease stages due to increased neurodegenerative burden\u003csup\u003e6,25,28,29\u003c/sup\u003e. However, changes in the early PD subgroup may have been underestimated, as the MDS-UPDRS has limitations in detecting disease progression in early-stage PD\u003csup\u003e30\u003c/sup\u003e. Despite these limitations, no alternative scale has been developed explicitly for this population, and the MDS-UPDRS remains the most widely used tool for assessing PD symptoms and monitoring disease progression\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe asymmetry index also decreased over time, consistent with the findings of Kojovic et al.\u003csup\u003e23\u003c/sup\u003e and cross-sectional studies by Uitti et al.\u003csup\u003e32\u003c/sup\u003e and Marinus et al.\u003csup\u003e33\u003c/sup\u003e. However, this contrasts with other studies that reported preserved asymmetry over time\u003csup\u003e34,35\u003c/sup\u003e. Although the studies by Miller-Patterson and Fiorenzato also had long follow-up periods (5 and 4 years, respectively), these discrepancies may be attributed to patient demographics and treatment approaches.\u003c/p\u003e\n\u003cp\u003eThe most pronounced clinical changes occurred between the pre-pandemic (2018, 2019) and post-pandemic (2021, 2022) periods, with significantly higher UPDRS scores in the latter. This aligns with reports of accelerated disease progression in PD patients following prolonged periods of physical inactivity and reduced medical follow-up during the COVID-19 lockdown\u003csup\u003e36,37\u003c/sup\u003e. Ineichen et al.\u003csup\u003e38,39\u003c/sup\u003e followed a cohort of patients for 5 and 7 years, covering periods before, during, and after the COVID-19 pandemic-related isolation. They similarly found worsening motor symptoms after the isolation period, which persisted despite the resumption of everyday activities. While our study lacked a control group, these findings underscore the importance of regular rehabilitation and medical care in PD management.\u003c/p\u003e\n\u003cp\u003eThe non-motor symptoms (NMS) of daily living, as assessed by the UPDRS, also exhibited temporal changes. Although the progression of NMS in PD is well documented, its severity varies across studies depending on population characteristics and assessment methods. However, several studies agree that the progression of NMS is slow, heterogeneous, and domain-specific\u003csup\u003e40-43\u003c/sup\u003e. Although no significant changes in NMS were observed in our early PD group or overall during the first years of evaluation, some studies have reported only mild changes in early PD. For instance, Ou et al.\u003csup\u003e42\u003c/sup\u003e documented a subtle progression of NMS in an early PD cohort over a 3-year follow-up.\u003c/p\u003e\n\u003cp\u003eIn contrast, a 6-year study in a Taiwanese population found that symptom severity remained stable over the first two years but became evident only after six years, which aligns more closely with our findings\u003csup\u003e41\u003c/sup\u003e. Notably, in our study, the COVID-19 pandemic does not appear to have directly affected the severity of NMS; however, worsening was observed between the last two post-pandemic evaluation periods, particularly pronounced in the advanced PD group. This could be attributed to the pandemic\u0026apos;s delayed or indirect effect or, alternatively, to a non-linear progression nature of symptoms. Unfortunately, our data cannot confirm these hypotheses, and further studies would be necessary to explore these possibilities.\u003c/p\u003e\n\u003cp\u003eGiven the limitations of MDS-UPDRS in detecting subtle functional changes, especially in early-stage PD\u003csup\u003e30,44\u003c/sup\u003e, we complemented our clinical assessments with transcranial magnetic stimulation to examine neurophysiological changes over time. Our findings are largely consistent with previous studies on cortical excitability in PD, though some discrepancies exist. Kojovic et al.\u003csup\u003e23\u003c/sup\u003e and Strafella et al.\u003csup\u003e22\u003c/sup\u003e reported no significant changes in resting motor threshold over one year. In contrast, we observed subtle but statistically significant changes over 4.5 years, suggesting increased corticospinal excitability and highlighting the importance of long-term follow-up.\u003c/p\u003e\n\u003cp\u003eThe cortical silent period exhibited significant longitudinal changes, particularly in early PD patients, with an overall duration increase, supporting its potential as a biomarker of disease progression. CSP is influenced by inhibitory cortical circuits mediated by GABAergic neurotransmission. In PD, it is typically shortened compared to healthy controls, while levodopa and dopamine agonists have been shown to prolong CSP\u003csup\u003e16\u003c/sup\u003e. Notably, Khedr et al.\u003csup\u003e45\u003c/sup\u003e found no differences in CSP between akinetic-rigid and tremor-dominant PD subtypes, suggesting that the heterogeneous nature of our sample may have had minimal impact on this measure. Similar to our findings, Kojovic et al.\u003csup\u003e23\u003c/sup\u003e observed an increase in CSP duration over one year, though only in the more affected hemisphere. This may indicate the restoration of intracortical inhibition, possibly due to the chronic effects of dopaminergic treatment. This interpretation is further supported by Strafella et al.\u003csup\u003e22\u003c/sup\u003e, who reported that levodopa improved cortical inhibitory circuits after 12 months.\u003c/p\u003e\n\u003cp\u003eIn contrast to Kojovic et al.\u003csup\u003e23\u003c/sup\u003e, who found differences in input-output curves between the more and less affected hemispheres, our analysis revealed no significant differences in MEP amplitude, as measured by recruitment curves. MEP amplitude and S\u003csub\u003e50\u003c/sub\u003e stimulation intensity remained stable over time, suggesting that corticospinal recruitment properties may be relatively preserved despite ongoing neurodegeneration or that recruitment curves may lack the sensitivity to detect slight changes in corticospinal excitability. Our findings are also consistent with Guerra et al.\u003csup\u003e24\u003c/sup\u003e, who reported no long-term changes in input-output curve steepness.\u003c/p\u003e\n\u003cp\u003eThe lack of significant differences in TMS measures between early- and advanced-stage PD groups contrasts with some prior studies reporting more significant alterations in later stages. Spagnolo et al.\u003csup\u003e18\u003c/sup\u003e reported lower rMT values, indicating higher corticospinal excitability in advanced compared to early PD, though their study lacked a longitudinal component. Given our longitudinal observation that rMT tends to decrease as the disease progresses, one would expect advanced PD patients to have lower rMT than those in the early stage. This discrepancy may be due to the small sample size of our advanced PD subgroup, individual variability in disease progression, or the possibility that the decline in rMT occurs gradually and reaches a plateau in the mid-stage of the disease.\u003c/p\u003e\n\u003cp\u003eRegarding sex-related differences, while PD incidence is lower in women than men\u003csup\u003e46,47\u003c/sup\u003e, reports on sex-specific differences in symptom progression remain inconclusive. Some studies suggest distinct motor and non-motor symptom trajectories\u003csup\u003e48\u003c/sup\u003e, whereas others found no significant differences\u003csup\u003e49,50\u003c/sup\u003e. Our study observed no sex-related differences in UPDRS or TMS parameters. Kolmancic et al.\u003csup\u003e51\u003c/sup\u003e reported that women with early PD exhibit a more favourable TMS profile, suggesting sex-specific physiological processes. However, our analysis found no significant differences in cortical excitability between male and female patients.\u003c/p\u003e\n\u003cp\u003eAlthough within-subject correlations did not remain significant after correcting for multiple comparisons, exploratory analyses suggested a moderate association between CSP in the less affected hemisphere and UPDRS Part III scores. Notably, this correlation was stronger in early PD and more pronounced in the upper limbs. Previous studies have also suggested a correlation between cortical inhibition and disease progression\u003csup\u003e23,52,53\u003c/sup\u003e. CSP in the less affected hemisphere may have the potential as a marker for tracking disease progression in personalized medicine or monitoring intervention outcomes in early PD. However, this hypothesis requires validation in a larger cohort, with adjustments for potential confounding factors.\u003c/p\u003e\n\u003cp\u003eAdditionally, we found a significant between-subject correlation linking motor symptom asymmetry to the asymmetry of the area under the recruitment curve, particularly strong in the advanced PD subgroup. This suggests that, at the population level, especially in advanced PD, patients with more significant motor asymmetry also tend to exhibit more pronounced asymmetry in input-output curves. While we did not observe longitudinal changes in these curves, this finding provides insights that may guide future research on the pathophysiology of the disease.\u003c/p\u003e\n\u003cp\u003eOur findings offer valuable insights into the complex trajectory of Parkinson\u0026apos;s disease and reinforce the clinical utility of transcranial magnetic stimulation as a potential tool for disease monitoring. The significant correlations between cortical silent period and UPDRS Part III scores highlight the importance of neurophysiological assessments in capturing motor deterioration, particularly in early-stage PD. Furthermore, our study highlights PD\u0026apos;s susceptibility to environmental stressors, such as those experienced during the COVID-19 pandemic, which may have exposed latent vulnerabilities in motor circuits. The persistence of altered asymmetry indices and neurophysiological markers beyond the acute phase of the pandemic suggests long-term neuroadaptive processes rather than transient fluctuations. These changes, potentially linked to disruptions in medical care, reduced physical activity, social isolation, and psychological stress, were evident even in early-stage PD, suggesting that neurophysiological plasticity and motor asymmetry are highly dynamic and influenced by external factors. These findings raise important questions about the role of environmental stressors in triggering maladaptive plasticity and potentially accelerating disease progression in vulnerable individuals. They also reinforce the view of PD as a condition with a non-linear trajectory rather than a simple, linear decline\u003csup\u003e54-56\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite these compelling findings, our study is not without limitations. The small sample size, particularly in the advanced PD subgroup, limits our findings\u0026apos; statistical power and generalizability. Additionally, pandemic-related disruptions, including variations in the number and timing of clinical visits, introduced potential biases related to uneven follow-up intensity and a gap in data collection. While we verified the absence of COVID-19 infections in our cohort, we cannot entirely rule out the influence of undetected asymptomatic infections or indirect pandemic-related factors, such as changes in medication adherence, mental health, or daily physical activity, which were not systematically monitored.\u003c/p\u003e\n\u003cp\u003eFurthermore, while our use of TMS provides valuable insights into cortical excitability, it offers a limited view of the complex basal ganglia-thalamo-cortical network dynamics central to PD pathophysiology. The absence of complementary neuroimaging data or molecular biomarkers restricts our ability to correlate functional changes with underlying structural or biochemical alterations.\u003c/p\u003e\n\u003cp\u003eThe heterogeneity of our sample, which included both akinetic-rigid and tremor-dominant PD subtypes, limits the strength of our conclusions, as these subtypes exhibit distinct neural activity patterns\u003csup\u003e57\u003c/sup\u003e. Additionally, we did not differentiate between early-onset and late-onset PD or account for patients\u0026apos; cognitive status, which could influence the observed outcomes. The lack of follow-up data on interventions such as pharmacological adjustments or rehabilitation further limits our ability to interpret the clinical and neurophysiological changes fully. Furthermore, although all measurements were conducted between 9:00 and 11:00 AM, we did not control for patients\u0026apos; clinical state at the time of assessment (ON or OFF dopaminergic treatment), which may have introduced variability in the results.\u003c/p\u003e\n\u003cp\u003eFuture research should aim for larger, more continuous datasets and adopt a multicenter, prospective cohort design with more diverse populations to address these limitations. Integrating TMS with other promising biomarkers, such as multimodal neuroimaging techniques (resting-state functional MRI and diffusion tensor imaging) and biochemical markers of neurodegeneration and neuroinflammation, could offer a more comprehensive understanding of functional and structural changes over time\u003csup\u003e26\u003c/sup\u003e. Additionally, systematically assessing psychosocial stressors, physical activity, cognitive status, and medication adherence would help disentangle the contributions of intrinsic disease progression from extrinsic environmental factors. Randomized controlled trials exploring interventions such as structured physical activity programs, stress reduction techniques, or telemedicine-based care models could further elucidate whether mitigating these external factors can alter PD\u0026apos;s neurophysiological and clinical trajectory.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our findings underscore the importance of longitudinal follow-up in Parkinson\u0026apos;s disease to understand disease progression better. The significant influence of disease stage on motor function highlights the need for tailored therapeutic approaches, particularly for patients with advanced PD. Additionally, our results support the potential of TMS for tracking disease progression, with CSP emerging as a promising neurophysiological biomarker. Future research should investigate the integration of TMS parameters into routine clinical assessments to improve disease monitoring and optimize patient management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted at the Movement and Sleep Disorders Unit of the General Hospital, \u0026nbsp;Dr. Manuel Gea Gonz\u0026aacute;lez, where 160 patients with PD receive regular care. All participants were diagnosed according to the clinical diagnostic criteria of the International Parkinson and Movement Disorder Society\u003csup\u003e58\u003c/sup\u003e, ensuring diagnostic consistency with internationally recognized standards. To maintain cohort homogeneity, individuals with atypical or secondary parkinsonism were excluded. Patients had to undergo at least one transcranial magnetic stimulation assessment within nine months before the COVID-19 lockdown (June 2019 to March 2020) to ensure reliable pre-pandemic baseline data for longitudinal comparison.\u003c/p\u003e\n\u003cp\u003eThe study followed the Declaration of Helsinki and received approval from the Ethics Committee of General Hospital, \u0026nbsp;Dr. Manuel Gea Gonz\u0026aacute;lez (protocol code 49-94-2021, approved on 13 October 2021). Written informed consent and privacy notices were obtained from all participants, safeguarding ethical compliance and participant confidentiality throughout the research process. TMS is a noninvasive and safe procedure that does not pose significant risks to patients with PD\u003csup\u003e59\u003c/sup\u003e. Moreover, we strictly adhered to established safety guidelines\u003csup\u003e60,61\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy Design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis longitudinal observational study retrospectively followed a cohort of PD patients from 2018 to 2021 and prospectively from 2021 to 2023 to evaluate clinical and neurophysiological parameters over time. Primary assessments included the MDS-UPDRS and TMS-derived neurophysiological measures. The study encompassed four distinct assessment periods: two nine-month intervals prior to the COVID-19 lockdown (September 2018 to June 2019 and June 2019 to March 2020) and two twelve-month intervals after the resumption of in-person visits (March 2021 to March 2022 and March 2022 to March 2023). In-person assessments were suspended between March 2020 and March 2021 due to pandemic-related safety concerns. For\u0026nbsp;simplicity, these periods are referred to as 2018, 2019, 2021, and 2022 throughout the manuscript (\u003cstrong\u003eFigure 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eParticipants attended multiple medical visits during each assessment period as part of routine clinical follow-up. While visits were intended to be regularly scheduled, individual patient factors and clinic scheduling constraints led to variations in the number and spacing of visits. A movement disorders specialist performed all clinical evaluations, which included MDS-UPDRS assessments and TMS procedures to monitor neurophysiological changes. This approach ensured clinical consistency and the accurate longitudinal tracking of disease progression and neurophysiological alterations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical Outcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClinical outcomes were assessed using the MDS-UPDRS, which was divided into Part I (non-motor aspects of daily living), Part II (motor aspects of daily living), and Part III (motor examination). Part III was further subdivided into assessments of the upper limbs, lower limbs, the more affected side (MAS), and the less affected side (LAS) to enable a more detailed analysis of motor impairment.\u003c/p\u003e\n\u003cp\u003eGiven the multiple UPDRS evaluations per participant, mean scores were calculated for each part within each assessment period. The MAS was determined based on the side with the highest mean UPDRS Part III score across all evaluations throughout the study period, ensuring consistent classification. Motor symptom asymmetry was quantified using an asymmetry index, calculated as \u003cimg src=\"data:image/png;base64,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\"\u003e where MAS and LAS represent the lateralized UPDRS Part III scores\u003csup\u003e62\u003c/sup\u003e. This index provided a standardized measure of motor symptom distribution, facilitating the evaluation of asymmetry dynamics over time.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTranscranial Magnetic Stimulation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFollowing established protocols\u003csup\u003e63\u003c/sup\u003e, TMS was performed using a Magstim Rapid\u003csup\u003e2\u003c/sup\u003e stimulator (Magstim Co. Ltd., UK) equipped with a 70-mm figure-eight coil. Motor-evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle using an integrated two-channel electromyography (MEP Pod, Magstim Co. Ltd., UK). The stimulation coil was positioned over the primary motor cortex at a 45\u0026deg; angle relative to the sagittal plane and maintained consistently throughout the procedure using a neuronavigation system (NDI Polaris Vicra camera, Northern Digital Inc., Canada, and Visor\u003csup\u003e2\u003c/sup\u003e software, Eemagine Medical Imaging Solutions GmbH, Germany).\u003c/p\u003e\n\u003cp\u003eTMS measurements were performed sequentially, starting with the left hemisphere, with a six-second interval between stimuli. The resting motor threshold (rMT) was determined using the adaptive parameter estimation by sequential testing (PEST) method, implemented through the ATH-tool application\u003csup\u003e64\u003c/sup\u003e. Recruitment curves were generated by acquiring 20 MEPs at 100% of rMT, followed by 10 MEPs at incremental intensities ranging from 110% to 180% of rMT in 10% steps.\u003c/p\u003e\n\u003cp\u003eThe cortical silent period (CSP) was assessed by recording 20 MEPs at 100% of rMT while participants maintained a tonic contraction of the index finger and thumb at approximately 30% of maximal voluntary contraction, measured using a digital pinch gauge. Neurophysiological parameters were analyzed separately for the more and less affected hemispheres, corresponding to the contralateral sides of the MAS and LAS.\u003c/p\u003e\n\u003cp\u003eThe measured outcomes included rMT, CSP, MEP amplitude at the S\u003csub\u003e50\u003c/sub\u003e stimulation intensity (the intensity required to elicit 50% of the maximum MEP response)\u003csup\u003e63,65\u003c/sup\u003e, the area under the recruitment curve (AURC), and MEP latency and duration. Asymmetry indices for rMT, CSP, MEP amplitude, and AURC were calculated similarly to the UPDRS asymmetry index using the formula: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eMEP latency and duration were measured automatically using the DELMEP algorithm\u003csup\u003e66\u003c/sup\u003e, which identified MEP onset. The endpoint was determined by reversing the MEP waveform in time and reapplying the algorithm. Amplitude was calculated as the difference between the maximum and minimum voltages within the identified MEP window. These analyses were performed on 10 MEPs elicited at S\u003csub\u003e50\u003c/sub\u003e intensity, with mean values for latency, duration, and amplitude calculated for each participant. Two independent experts conducted CSP measurements manually, and the average of their assessments was used for analysis. To characterize the corticospinal input-output properties and have an outcome that we could easily compare through time, we calculated the area under the recruitment curve using the trapezoidal rule\u003csup\u003e67\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGiven the variability in the number of evaluations per participant across assessment periods\u0026mdash;due to random factors such as missed appointments and personal obligations\u0026mdash;as well as the relatively small sample size and presence of outliers, robust linear mixed-effects models were employed. The evaluation period (2018, 2019, 2021, and 2022) was treated as a fixed effect, with participants as a random effect to account for within-subject variability.\u003c/p\u003e\n\u003cp\u003eDegrees of freedom estimation in robust models are complex, precluding direct computation of exact \u003cem\u003ep\u003c/em\u003e-values. To address this, we approximated the F-statistic using coefficient estimates and the covariance matrix from the robust model, combined with Satterthwaite-estimated degrees of freedom derived from a corresponding non-robust model to calculate approximate significance levels\u003csup\u003e68,69\u003c/sup\u003e. Effect sizes were expressed as partial eta squared (\u0026eta;\u0026sup2;ₚ), derived from the F-statistic and degrees of freedom.\u003c/p\u003e\n\u003cp\u003ePairwise comparisons across periods were performed using estimated marginal means, with \u003cem\u003ep\u003c/em\u003e-values adjusted for multiple comparisons via Tukey\u0026apos;s honest significant difference method. The homogeneity of variances was verified using Levene\u0026apos;s test. To evaluate the influence of sex and disease stage (early vs. advanced PD), these variables were included as fixed effects in the models, with significance and effect sizes estimated accordingly.\u003c/p\u003e\n\u003cp\u003eThe S\u003csub\u003e50\u003c/sub\u003e parameter was calculated by fitting the recruitment curve data to a three-parameter Boltzmann sigmoid function\u003csup\u003e65\u003c/sup\u003e, with stimulation intensity as the independent variable and MEP amplitude as the dependent variable, performed separately for each hemisphere and assessment period.\u003c/p\u003e\n\u003cp\u003eWithin-subject correlation coefficients were computed using UPDRS scores as dependent variables and TMS measures as predictors to explore the relationship between clinical and neurophysiological changes\u003csup\u003e70\u003c/sup\u003e. Between-subject correlation coefficients were also calculated to assess overall, time-independent associations\u003csup\u003e71\u003c/sup\u003e. Multiple correlation \u003cem\u003ep\u003c/em\u003e-values were corrected using the false discovery rate method to control for type I errors. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. All analyses were performed using R (version 4.4.1)\u003csup\u003e72\u003c/sup\u003e in RStudio (version 2023.06.1)\u003csup\u003e73\u003c/sup\u003e, with support from the following packages: \u003cem\u003eggpubr\u003c/em\u003e\u003cem\u003e\u003csup\u003e74\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003erstatix\u003c/em\u003e\u003cem\u003e\u003csup\u003e75\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003etidyr\u003c/em\u003e\u003cem\u003e\u003csup\u003e76\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003eggprism\u003c/em\u003e\u003cem\u003e\u003csup\u003e77\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003ecowplot\u003c/em\u003e\u003cem\u003e\u003csup\u003e78\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003ecar\u003c/em\u003e\u003cem\u003e\u003csup\u003e79\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003elme4\u003c/em\u003e\u003cem\u003e\u003csup\u003e80\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003erobustlmm\u003c/em\u003e\u003cem\u003e\u003csup\u003e81\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003eemmeans\u003c/em\u003e\u003cem\u003e\u003csup\u003e82\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003elmerTest\u003c/em\u003e\u003cem\u003e\u003csup\u003e83\u003c/sup\u003e\u003c/em\u003e, \u003cem\u003eminpack.lm\u003c/em\u003e\u003cem\u003e\u003csup\u003e84\u003c/sup\u003e\u003c/em\u003e, and \u003cem\u003ermcorr\u003c/em\u003e\u003cem\u003e\u003csup\u003e85\u003c/sup\u003e\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical and transcranial magnetic stimulation measurements database is publicly available in our GitHub repository: https://github.com/UTMS-Gea/Parkinson-UPDRS-TMS-cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R code used for the statistical analysis is available in our GitHub repository: https://github.com/UTMS-Gea/Parkinson-UPDRS-TMS-cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.O-R and O.A-C conceptualized and designed the study. E.S-R and E.O-R acquired the data. E.S-R and E.O-R completed data preprocessing. E.O-R and O.A-C developed statistical analysis. E.S-R, E.O-R and O.A-C performed analysis. E.S-R, E.O-R and O.A-C interpreted the data. E.S-R and E.O-R wrote the initial draft of the manuscript. E.S-R, E.O-R and O.A-C review, critique and final approval of the manuscript. O.A.-C.: conception and organization of the research project; review and critique of the statistical analysis; writing, editing, critique, and final approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Hospital General Dr. Manuel Gea González for their support. Estefanía Santana Román is a Ph.D. student in the Programa de Doctorado en Ciencias Biomédicas at the Universidad Nacional Autónoma de México (UNAM) and has received a SECIHTI fellowship (CVU 1225325).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMoustafa, A. A.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Motor symptoms in Parkinson\u0026apos;s disease: A unified framework. \u003cem\u003eNeurosci. Biobehav. Rev.\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 727-740, https://doi.org/10.1016/j.neubiorev.2016.07.010 (2016).\u003c/li\u003e\n \u003cli\u003eChaudhuri, K. R., Healy, D. G. \u0026amp; Schapira, A. H. Non-motor symptoms of Parkinson\u0026apos;s disease: diagnosis and management. \u003cem\u003eLancet Neurol\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 235-245, https://doi.org/10.1016/s1474-4422(06)70373-8 (2006).\u003c/li\u003e\n \u003cli\u003eLacy, B., Piotrowski, H. J., Dewey, R. B., Jr. \u0026amp; Husain, M. M. 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V., Mullen, K. M., Spiess, A.-N. \u0026amp; Bolker, B. minpack.lm: R Interface to the Levenberg-Marquardt Non-linear Least-Squares Algorithm Found in MINPACK, Plus Support for Bounds. (1.2.4). https://CRAN.R-project.org/package=minpack.lm (2023).\u003c/li\u003e\n \u003cli\u003eBakdash, J. Z. \u0026amp; Marusich, L. R. rmcorr: Repeated Measures Correlation. (0.7.0). https://CRAN.R-project.org/package=rmcorr (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson's disease, Longitudinal study, Motor asymmetry, Transcranial magnetic stimulation, Disease progression","lastPublishedDoi":"10.21203/rs.3.rs-6246415/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6246415/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson's disease (PD) is characterized by progressive motor and non-motor symptoms, yet the longitudinal interplay between clinical progression and neurophysiological alterations remains underexplored. In this four-and-a-half-year longitudinal study, we assessed 22 individuals with PD using the Movement Disorder Society\u0026ndash;Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and transcranial magnetic stimulation (TMS) to evaluate cortical excitability. We analyzed changes in resting motor threshold (rMT) and cortical silent period (CSP) across disease stages and examined the impact of the COVID-19 pandemic on disease progression. We observed significant motor function and cortical excitability deterioration over time, with CSP exhibiting potential as a biomarker of disease progression. These alterations were more pronounced in advanced PD and during the post-pandemic period, underscoring the susceptibility of PD patients to environmental stressors. No significant sex-related differences were found in clinical or neurophysiological measures. Our findings highlight the potential of TMS in monitoring PD progression and suggest that integrating neurophysiological assessments into routine clinical practice may enhance patient management. Longitudinal neurophysiological biomarkers could provide insights into disease trajectory and inform therapeutic interventions.\u003c/p\u003e","manuscriptTitle":"Longitudinal Dynamics of Clinical and Neurophysiological Changes in Parkinson's Disease: A 4.5-Year Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 05:33:44","doi":"10.21203/rs.3.rs-6246415/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-30T06:23:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T10:59:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-14T16:21:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129321218628988009036269472200829039386","date":"2025-04-09T09:31:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318672163061446183891627711122436456484","date":"2025-04-06T18:41:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T16:51:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-03T14:03:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-26T18:20:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T10:23:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-17T16:20:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0783d4eb-75c7-4d56-ad49-7168e4e764fe","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47144934,"name":"Biological sciences/Neuroscience"},{"id":47144935,"name":"Biological sciences/Neuroscience/Motor control"},{"id":47144936,"name":"Biological sciences/Neuroscience/Motor control/Motor cortex"}],"tags":[],"updatedAt":"2025-07-28T16:05:35+00:00","versionOfRecord":{"articleIdentity":"rs-6246415","link":"https://doi.org/10.1038/s41598-025-13558-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-26 15:57:36","publishedOnDateReadable":"July 26th, 2025"},"versionCreatedAt":"2025-05-07 05:33:44","video":"","vorDoi":"10.1038/s41598-025-13558-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-13558-3","workflowStages":[]},"version":"v1","identity":"rs-6246415","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6246415","identity":"rs-6246415","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}