A Multi-Modal Framework Combining Proteomics, Electrophysiology, and Clinical Phenotypes to Characterize Neuroplasticity in Parkinson’s Disease Rehabilitation

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Objective: To investigate how proteomic and electrophysiological signatures relate to clinical recovery and to evaluate whether multimodal integration enhances prediction of rehabilitation outcomes. Methods: In this retrospective cohort of 165 PD patients, participants received either intensive multimodal rehabilitation (IMR, n=110) or standard rehabilitation (SR, n=55). Circulating neurotrophic and inflammatory proteins (BDNF, GDNF, VEGF, IL-6, TNF-α) and electrophysiological measures (EEG spectral power, TMS-evoked MEP) were assessed at baseline and post-intervention, alongside clinical scales (UPDRS-III, gait). Multivariate regression and predictive modeling were applied. Results: IMR was associated with significant increases in neurotrophic factors and reductions in inflammatory markers, paralleled by EEG β desynchronization, γ enhancement, and MEP facilitation. In multivariate analyses, ΔBDNF and Δβ power independently predicted motor improvements, while ΔMEP amplitude was the strongest predictor of gait recovery. A multimodal model integrating proteomic and electrophysiological features achieved superior discrimination for achieving the minimal clinically important difference (AUC=0.74) compared with single-modality models. Conclusion: Multimodal integration of proteomic and electrophysiological markers provides complementary insight into neuroplastic mechanisms and improves prediction of rehabilitation response in PD. These findings support the development of biomarker-driven strategies to personalize neurorehabilitation. Parkinson’s disease Neurorehabilitation Proteomics Electrophysiology Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Parkinson’s disease (PD) is increasingly understood as a multisystem disorder in which neurodegeneration intersects with chronic peripheral and central immune activation and impaired plasticity of cortico–basal ganglia–thalamocortical networks. Large-scale syntheses now indicate consistent elevations of inflammatory cytokines (e.g., IL-6, TNF-α) in blood and CSF, with associations to motor and non-motor burden [ 1 , 2 ]. In parallel, neurotrophic signaling has re-emerged as a target for activity-dependent repair, as exercise and multidisciplinary rehabilitation can augment circulating brain-derived neurotrophic factor (BDNF) and are linked to motor benefits in PD [ 3 – 5 ]. Yet, despite this momentum, rehabilitation studies rarely integrate molecular readouts with electrophysiological indices of cortical function, limiting mechanistic inference and translation. Electrophysiology provides a window into systems-level plasticity. Scalp EEG reveals excessive β-band synchronization in PD and abnormal β-burst dynamics that normalize with effective neuromodulation, while invasive and non-invasive recordings demonstrate that therapeutic interventions suppress pathological β coherence along cortico-subcortical loops [ 6 – 9 ]. At the corticospinal level, transcranial magnetic stimulation (TMS)–evoked motor potentials (MEPs) quantify excitability and inhibition, offering tractable markers of motor cortex plasticity with established methodology [ 10 ]. These signals are plausible mediators between molecular change and clinical recovery, but they have seldom been modeled jointly with proteomic biomarkers in real-world rehabilitation cohorts. Despite progress, three limitations remain. First, proteomic and electrophysiological markers are usually studied separately, hindering cross-scale interpretation. Second, trials targeting neurotrophic responses report heterogeneous results due to variable protocols, sampling schedules, and intensity [ 3 – 5 ]. Third, prediction models in rehabilitation often emphasize discrimination but neglect calibration and transparent reporting, which are emphasized in modern frameworks such as MCID analyses and TRIPOD guidelines [ 11 , 12 ]. To address these issues, we designed a retrospective cohort study integrating proteomic (BDNF, GDNF, VEGF, IL-6, TNF-α) and electrophysiological (EEG β/γ spectral power, TMS-MEP amplitude) markers with clinical outcomes (UPDRS-III, gait). We aimed to determine whether neurotrophic enhancement and inflammation reduction align with cortical desynchronization and excitability changes, and whether a multimodal prediction model improves identification of patients achieving clinically meaningful recovery. Materials and Methods Study design and participants This retrospective cohort study enrolled patients with idiopathic Parkinson’s disease (PD) admitted for rehabilitation at Fujian Provincial Geriatric Hospital between January 2020 and December 2024. PD was diagnosed according to the Movement Disorder Society (MDS) clinical diagnostic criteria [13]. Inclusion criteria: (i) age 45–80 years; (ii) Hoehn–Yahr stage 1–3 during “on” medication; (iii) ability to complete rehabilitation sessions. Exclusion criteria: atypical parkinsonism, unstable systemic disease, or severe psychiatric comorbidity. Ethical approval was granted by the Ethics Committee of Fujian Provincial Geriatric Hospital (Approval No. 20250810). Intervention groups Patients were categorized into: Intensive multimodal rehabilitation (IMR): ≥5 sessions/week, combining physiotherapy, occupational therapy, gait and balance training, and aerobic exercise. Standard rehabilitation (SR): ≤3 sessions/week, limited to routine physiotherapy. Medication regimens, DBS status, and use of anticholinergics were documented. Clinical assessments Motor impairment was rated using the Movement Disorder Society–Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS-III) [14]. Cognitive status was evaluated by the Montreal Cognitive Assessment (MoCA). Gait and balance were assessed using standardized timed up-and-go and 10-meter walk tests. Clinically meaningful motor improvement was defined as a reduction of ≥5 points on UPDRS-III [15]. Proteomic assays Venous blood was collected at baseline (pre-rehabilitation) and within 48 hours after the final rehabilitation session, in fasting state (07:00–08:00). Samples were centrifuged at 3000 rpm for 15 min at 4°C, and serum aliquots were stored at –80°C. BDNF, GDNF, VEGF: quantified using commercial ELISA kits (R&D Systems, Minneapolis, MN, USA; Cat# DBD00, DY212, DVE00, respectively), detection limits 1–10 pg/ml, intra-assay CV <8%. IL-6, TNF-α: measured with high-sensitivity ELISA kits (Thermo Fisher Scientific, Waltham, MA, USA; Cat# KHC0061, KHC3011), detection limits <0.5 pg/ml, intra-assay CV <10%. Absorbance was read at 450 nm using a microplate reader (BioTek Synergy HTX, USA). All samples were run in duplicate, and mean values were used. EEG acquisition and analysis EEG was recorded with a 64-channel actiCAP system (Brain Products GmbH, Munich, Germany), sampling rate 1000 Hz, impedances <10 kΩ. Resting-state (eyes open, 5 min) and motor-task (finger tapping, 5 min) conditions were collected. Signals were band-pass filtered (1–100 Hz), notch filtered at 50 Hz, and re-referenced to average. Spectral power in β (13–30 Hz) and γ (30–80 Hz) bands was computed with Welch’s method (2-s Hamming windows, 50% overlap). Relative power was normalized to total 1–80 Hz power. TMS-MEP procedure Single-pulse transcranial magnetic stimulation was delivered with a Magstim 200^2 stimulator (Magstim Co., Whitland, UK) using a 70-mm figure-of-eight coil. The coil was placed over the left primary motor cortex (M1) “hotspot” for the right first dorsal interosseous (FDI) muscle, with orientation at 45° to the midline. Resting motor threshold (RMT) was defined as the lowest intensity evoking ≥50 μV MEPs in 5 of 10 trials. For testing, 20 pulses at 120% RMT were delivered with interstimulus intervals of 5–7 s. Surface EMG was recorded using Ag/AgCl electrodes, band-pass filtered 20–2000 Hz, and digitized at 5000 Hz (CED 1401, Cambridge Electronic Design, UK). Peak-to-peak MEP amplitudes were averaged across trials. Statistical analysis Continuous data were expressed as mean±standard deviation, categorical as counts (%). Baseline group differences: independent-samples t test or χ² test. Longitudinal within-group comparisons: paired t test or repeated-measures ANOVA. Multivariate linear and logistic regression were applied to examine predictors of ΔUPDRS-III and gait improvement. Predictive model performance was assessed by AUC, Brier score, and R². All analyses used R 4.2.2 and Python 3.9 (scikit-learn). p<0.05 was considered significant. Baseline Characteristics At baseline, demographic and disease-related characteristics were balanced between the IMR and SR groups (Table 1). There were no significant group differences in age, sex distribution, BMI, disease duration, Hoehn–Yahr stage, LEDD, or DBS status. Baseline MoCA and UPDRS-III scores were comparable. Similarly, inflammatory (IL-6, TNF-α) and neurotrophic markers (BDNF, GDNF, VEGF) showed no between-group differences. Compliance indicators, including planned sessions and completion rates, were also well balanced. Table 1. Baseline Characteristics of Parkinson’s Disease Patients in the Rehabilitation Cohort Variable IMR group (n=110) SR group (n=55) t/χ² p-value Age (years) 65.2 ± 7.3 66.7 ± 7.8 t=-1.20 0.233 Sex (F/M) 43/67 21/34 χ²=0.00 1.000 BMI (kg/m²) 24.4 ± 3.6 24.1 ± 3.5 t=0.53 0.599 Disease duration (years) 7.0 ± 3.1 6.1 ± 2.8 t=1.86 0.065 Hoehn–Yahr stage 2.1 ± 0.7 2.1 ± 0.7 t=0.08 0.936 LEDD (mg/day) 670.7 ± 204.0 639.2 ± 220.9 t=0.89 0.377 DBS status (Y/N) 12/98 9/46 χ²=0.55 0.457 Anticholinergic use (Y/N) 26/84 7/48 χ²=2.09 0.148 MoCA score 25.2 ± 2.7 24.4 ± 3.0 t=1.64 0.104 UPDRS-III score (baseline) 32.2 ± 7.5 32.9 ± 9.5 t=-0.45 0.652 Gait score (baseline) 17.8 ± 3.9 17.0 ± 3.4 t=1.41 0.160 IL-6 baseline (pg/ml) 3.1 ± 1.1 3.2 ± 1.0 t=-0.52 0.606 TNF-α baseline (pg/ml) 2.5 ± 1.0 2.5 ± 0.8 t=-0.13 0.897 BDNF baseline (ng/ml) 11.9 ± 2.4 12.3 ± 2.2 t=-0.91 0.362 GDNF baseline (ng/ml) 8.0 ± 1.9 7.6 ± 1.6 t=1.14 0.255 VEGF baseline (pg/ml) 156.7 ± 33.2 153.0 ± 35.4 t=0.64 0.522 Planned sessions 25.9 ± 3.6 26.0 ± 4.0 t=-0.10 0.921 Completion rate (%) 90.8 ± 7.2 86.3 ± 8.6 t=3.32 0.001 Values are presented as mean ± standard deviation or number (frequency). Group comparisons were performed using independent-sample t tests for continuous variables and χ² tests for categorical variables. IMR: Intensive Multimodal Rehabilitation; SR: Standard Rehabilitation; BMI: body mass index; LEDD: levodopa equivalent daily dose; DBS: deep brain stimulation; MoCA: Montreal Cognitive Assessment; UPDRS-III: Unified Parkinson’s Disease Rating Scale motor section. Multivariate Associations Between Proteomic and Electrophysiological Changes and Clinical Recovery Multivariate regression analyses revealed distinct contributions of proteomic and electrophysiological markers to clinical recovery (Table 2). For the continuous outcome of ΔUPDRS-III, higher increases in BDNF were significantly associated with greater motor improvement (β = –0.67, p = 0.036), while reductions in EEG β power also predicted better outcomes (β = 4.99, p = 0.022). In contrast, changes in MEP amplitude showed a favorable but non-significant trend (β = –3.19, p = 0.104). For gait improvement, ΔMEP amplitude emerged as the most robust predictor (β = –2.47, p = 0.002), whereas ΔBDNF showed no significant effect and Δβ power demonstrated only a statistical trend (p = 0.073). In logistic regression models predicting achievement of the minimal clinically important difference (MCID) in UPDRS-III, the integrated multimodal model achieved an AUC of 0.74 with a Brier score of 0.19 and pseudo-R² of 0.12. Among individual predictors, none reached statistical significance, although ΔMEP amplitude showed a strong odds ratio (OR = 9.86, 95% CI 0.47–205.93, p = 0.14), indicating a potential contribution with larger sample sizes. Taken together, these results highlight that neurotrophic changes (BDNF) and electrophysiological markers (β desynchronization and MEP amplitude) provide complementary information in explaining clinical variability, with ΔMEP particularly relevant for gait outcomes. Table 2. Multivariate Models Linking Proteomic and Electrophysiological Changes to Clinical Recovery Outcome Predictor Effect p ΔUPDRS-III ΔBDNF -0.67 (-1.29,-0.04) 0.036 ΔUPDRS-III ΔEEG_Beta 4.99 (0.72,9.26) 0.022 ΔUPDRS-III ΔMEP_mV -3.19 (-7.05,0.66) 0.104 ΔGait score ΔBDNF -0.01 (-0.27,0.24) 0.908 ΔGait score ΔEEG_Beta 1.57 (-0.15,3.29) 0.073 ΔGait score ΔMEP_mV -2.47 (-4.02,-0.91) 0.002 MCID UPDRS-III ΔBDNF 1.30 (0.79,2.14) 0.297 MCID UPDRS-III ΔEEG_Beta 0.12 (0.00,3.28) 0.206 MCID UPDRS-III ΔMEP_mV 9.86 (0.47,205.93) 0.14 MCID UPDRS-III Model performance AUC=0.74, Brier=0.19, R²=0.12 Values are presented as regression coefficients (β) with 95% confidence intervals for continuous outcomes and odds ratios (OR) with 95% CI for logistic outcomes. p-values are derived from multivariate regression models and adjusted for multiple testing using the false discovery rate (FDR) where applicable. Model performance is indicated by R² for linear regression models, and by AUC and Brier score for logistic regression models. Longitudinal Changes in Neurotrophic and Inflammatory Proteins During Rehabilitation As illustrated in Figure 1, neurotrophic and inflammatory protein levels exhibited distinct longitudinal patterns during rehabilitation. In the IMR group, BDNF concentrations progressively increased from baseline to post-intervention, accompanied by similar upregulation of GDNF and VEGF, suggesting enhanced neurotrophic support under intensive multimodal training. In contrast, the SR group showed minimal or no changes across these factors. Conversely, inflammatory markers demonstrated divergent trajectories. IL-6 levels significantly declined in the IMR group but slightly increased in the SR group, whereas TNF-α concentrations were markedly reduced following IMR yet remained stable under standard rehabilitation. These findings highlight that intensive rehabilitation not only augments neurotrophic signaling but also exerts anti-inflammatory effects, establishing a favorable molecular milieu for neuroplasticity and functional recovery. Electrophysiological Signatures of Cortical Plasticity During Rehabilitation As shown in Figure 2, electrophysiological assessments demonstrated distinct cortical plasticity signatures between groups. In the IMR group, EEG recordings revealed a significant reduction in relative β power from baseline to post-intervention, paralleled by an increase in γ power, consistent with a shift toward desynchronized and plastic cortical states. In contrast, the SR group exhibited negligible changes in both β and γ frequency bands.Furthermore, TMS-evoked MEP amplitudes markedly increased in the IMR group compared to the SR group, indicating enhanced corticospinal excitability following intensive rehabilitation. Collectively, these findings highlight that multimodal intensive rehabilitation induces favorable electrophysiological adaptations, with β desynchronization, γ enhancement, and MEP facilitation serving as converging markers of cortical plasticity. Correlation Network Linking Proteomic, Electrophysiological, and Clinical Changes As illustrated in Figure 3, moderate-to-strong associations were observed between molecular, electrophysiological, and clinical changes during rehabilitation. Increases in ΔBDNF were inversely correlated with Δβ power (r = –0.65, p<0.001) and positively related to improvements in ΔUPDRS-III and ΔGait score, highlighting the link between neurotrophic signaling and functional recovery. Conversely, higher ΔIL-6 levels were positively correlated with persistent β synchronization and poorer outcomes, indicating a pro-inflammatory burden limiting rehabilitation gains. ΔMEP amplitude was strongly associated with both ΔBDNF (–0.71, p<0.001) and clinical measures, suggesting that enhanced corticospinal excitability mediates the translation of molecular plasticity into behavioral improvements. Collectively, these results demonstrate that proteomic and electrophysiological markers form an interconnected network that predicts the extent of motor recovery in Parkinson’s disease rehabilitation. Predictive Performance of Multi-Modal Biomarkers As shown in Figure 4, the multimodal model integrating ΔBDNF, Δβ power, and ΔMEP amplitude achieved superior discrimination for identifying patients who reached the MCID in UPDRS-III, compared with single-modality models. The multimodal approach yielded an AUC of 0.74, indicating moderate predictive accuracy, whereas proteomics-only, EEG-only, and MEP-only models demonstrated lower AUCs. Calibration curves further confirmed that the multimodal model provided the best agreement between predicted probabilities and observed outcomes, while single-modality models showed greater miscalibration. These findings underscore the added value of combining molecular and electrophysiological markers for robust prediction of rehabilitation response. Discussion Our multimodal, retrospective analysis links molecular shifts in neurotrophic and inflammatory milieu to systems-level electrophysiology and, ultimately, to clinically meaningful motor recovery in Parkinson’s disease (PD). At the molecular tier, rehabilitation coincided with higher circulating BDNF and lower IL-6/TNF-α; at the circuit tier, EEG β desynchronization and increased TMS-MEP amplitude signaled greater corticospinal plasticity; and at the clinical tier, larger ΔUPDRS-III improvement clustered with these biological changes. The integrated models outperformed single-modality predictors and showed acceptable discrimination and calibration for identifying patients who achieved MCID, supporting a biologically plausible, cross-scale recovery signature in PD. Mechanistically, our findings align with the view that PD is a multisystem disorder in which neurodegeneration interacts with immune activation and impaired network plasticity. Elevated inflammatory cytokines are reproducibly observed in PD and track symptom burden, while exercise and intensive, task-specific rehabilitation can boost peripheral BDNF—an activity-dependent trophic cue with downstream effects on synaptic remodeling. These converging literatures provide a rationale for why patients who mount stronger neurotrophic responses and dampen inflammation exhibit better functional gains after rehabilitation, as we observed. At the systems level, reductions in pathological β synchrony are a consistent electrophysiological correlate of motor improvement in PD. β-burst metrics from basal ganglia and cortex index bradykinesia severity and are selectively curtailed by adaptive DBS; invasive and scalp recordings show therapeutic interventions suppress excessive β coupling and normalize burst dynamics. In our cohort, larger Δβ decreases and MEP facilitation co-occurred with clinical improvement, cohering with evidence that desynchronization plus enhanced corticospinal excitability reflect a “permissive” state for motor learning during rehabilitation. TMS-based measures provided an interpretable bridge between molecular change and behavior. Prior trials indicate that combining high-intensity practice with neuromodulatory approaches (e.g., rTMS) can modulate corticomotor inhibition and improve gait in PD, and reviews place MEP amplitude within a validated framework for probing human motor cortex plasticity. Our multivariable models identified ΔMEP and ΔBDNF as independent correlates of ΔUPDRS-III and gait gains, reinforcing their utility as mechanistic and prognostic markers in routine rehabilitation cohorts. From a prediction-science perspective, we emphasized not only discrimination but also calibration and clinical interpretability. Model calibration often lags in biomarker studies, yet it governs the reliability of individualized risk estimates. By reporting AUC alongside Brier scores and visual calibration, and by anchoring clinical relevance with MCID for the MDS-UPDRS III, we aligned the analysis with contemporary TRIPOD recommendations and sample-size principles for multivariable models. Future external validation—ideally with pre-registered analysis plans—will be crucial for transportability. Strengths and limitations. Strengths include: (i) a biologically grounded, cross-scale design spanning proteomics, electrophysiology, and clinical outcomes; (ii) standardized pipelines for EEG spectral metrics and TMS-MEP quantification; and (iii) transparent reporting of discrimination, calibration, and MCID-oriented endpoints. Limitations stem from the retrospective, single-center nature; potential residual confounding (medication adjustments, activity dose heterogeneity); and limited proteomic breadth (focus on a priori markers rather than discovery-scale panels). Although we observed coherent associations, causality cannot be inferred. Additionally, while scalp EEG β power is an accessible proxy of network state, basal ganglia LFPs capture disease physiology more proximally; future studies integrating wearable-EEG with implant data may sharpen mechanistic inference and prediction. Implications. Clinically, the results argue for embedding low-burden molecular assays (BDNF, IL-6/TNF-α) and brief TMS/EEG readouts into rehabilitation programs to stratify patients and adapt intensity. Scientifically, they support a working model in which neurotrophic upregulation and inflammatory quiescence align with β desynchronization and corticospinal facilitation to enable motor relearning—an actionable target for adjunctive interventions (e.g., aerobic priming, closed-loop stimulation) tested against transparent, well-calibrated prognostic models. Declarations Ethics approval and consent to participate This retrospective observational study was conducted at Fujian Provincial Geriatric Hospital and approved by the institutional Ethics Committee (approval number: 20250810). All procedures complied with the Declaration of Helsinki. Written informed consent had been obtained from each participant or their legal representative. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author upon reasonable request and with appropriate institutional approvals. Competing interests The author declares that there are no commercial or financial relationships that could be construed as a potential conflict of interest. Funding This research received no external funding. Authors' contributions Jingyuan Lin was the sole contributor to the conception, study design, data collection, data analysis, and manuscript preparation. Acknowledgments Not applicable. References Tansey MG, Romero-Ramos M. Inflammation and immune dysfunction in Parkinson disease. Nat Rev Immunol. 2022;22(11):657-673. doi:10.1038/s41577-022-00684-6. Qu Y, et al. A systematic review and meta-analysis of inflammatory biomarkers in Parkinson’s disease. npj Parkinson’s Disease. 2023;9:18. doi:10.1038/s41531-023-00449-5. Hirsch MA, et al. Exercise-induced increase in brain-derived neurotrophic factor in human Parkinson’s disease: a systematic review and meta-analysis. Transl Neurodegener. 2018;7:7. doi:10.1186/s40035-018-0112-1. Frazzitta G, et al. Intensive rehabilitation increases BDNF serum levels in Parkinsonian patients: a randomized study. Neurorehabil Neural Repair. 2014;28(2):163-168. doi:10.1177/1545968313508474. Johansson ME, et al. Aerobic exercise alters brain function and structure in Parkinson’s disease: a randomized controlled trial. Ann Neurol. 2022;91(2):203-216. doi:10.1002/ana.26291. Swann NC, et al. Elevated synchrony in Parkinson disease detected with electroencephalography. Ann Neurol. 2015;78(5):742-750. doi:10.1002/ana.24507. Pauls KAM, et al. Cortical beta burst dynamics are altered in Parkinson’s disease but normalized by deep brain stimulation. NeuroImage. 2022;257:119308. doi:10.1016/j.neuroimage.2022.119308. Wang DD, et al. Pallidal deep-brain stimulation disrupts pallidal beta oscillations and coherence with primary motor cortex in Parkinson’s disease. J Neurosci. 2018;38(19):4556-4568. doi:10.1523/JNEUROSCI.0431-18.2018. Sacheli MA, et al. Exercise increases caudate dopamine release and ventral striatal activation in Parkinson’s disease. Mov Disord. 2019;34(12):1891-1900. doi:10.1002/mds.27865. Lefaucheur JP. Motor cortex dysfunction revealed by cortical excitability studies in Parkinson’s disease: influence of antiparkinsonian treatment and cortical stimulation. Clin Neurophysiol. 2005;116(2):244-253. doi:10.1016/j.clinph.2004.11.017. Horváth K, et al. Minimal clinically important difference on the Motor Examination part of MDS-UPDRS. Parkinsonism Relat Disord. 2015;21(12):1421-1426. doi:10.1016/j.parkreldis.2015.10.006. Collins GS, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Ann Intern Med. 2015;162(1):55-63. doi:10.7326/M14-0697. Postuma RB, Berg D, Stern M, et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord. 2015;30(12):1591-1601. doi:10.1002/mds.26424 Goetz CG, Tilley BC, Shaftman SR, et al. MDS-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Mov Disord. 2008;23(15):2129-2170. doi:10.1002/mds.22340 Horváth K, Aschermann Z, Kovács M, et al. Minimal clinically important difference on the Motor Examination part of MDS-UPDRS. Parkinsonism Relat Disord. 2015;21(12):1421-1426. doi:10.1016/j.parkreldis.2015.10.006 Bloem BR, Okun MS, Klein C. Parkinson’s disease. Lancet. 2021;397:2284–2303. doi:10.1016/S0140-6736(21)00218-X. Qin XY, Zhang SP, Cao C, Loh YP, Cheng Y. Aberrations in peripheral inflammatory cytokine levels in Parkinson disease: a systematic review and meta-analysis. JAMA Neurol. 2016;73(11):1316–1324. doi:10.1001/jamaneurol.2016.2742. Zhen K, et al. Aerobic exercise improves motor function and gait in PD: systematic review and meta-analysis. npj Parkinson’s Disease. 2022;8:114. doi:10.1038/s41531-022-00418-4. Frazzitta G, Maestri R, Ghilardi MF, et al. Intensive rehabilitation increases BDNF serum levels in parkinsonian patients: a randomized study. Neurorehabil Neural Repair. 2014;28:163–168. doi:10.1177/1545968313508474. Fontanesi C, et al. Intensive rehabilitation enhances lymphocyte BDNF-TrkB signaling in PD. Neurorehabil Neural Repair. 2016;30:411–418. doi:10.1177/1545968315600272. Johansson H, et al. Aerobic exercise alters brain function and structure in Parkinson’s disease: a randomized controlled trial. Ann Neurol. 2022;91:203–216. doi:10.1002/ana.26291. Tinkhauser G, Pogosyan A, Little S, et al. The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson’s disease. Brain. 2017;140(4):1053–1067. doi:10.1093/brain/awx010. de Hemptinne C, Swann NC, Ostrem JL, et al. Therapeutic deep brain stimulation reduces cortical phase–amplitude coupling in Parkinson’s disease. Nat Neurosci. 2015;18(5):779–786. doi:10.1038/nn.3997. Lofredi R, Tan H, Neumann W-J, et al. Beta bursts during continuous movements accompany the velocity decrement in Parkinson’s disease patients. Neurobiol Dis. 2019;127:462–471. doi:10.1016/j.nbd.2019.03.013. Eisinger RS, Cagle JN, Opri E, et al. Parkinsonian beta dynamics during rest and movement in the dorsal pallidum and subthalamic nucleus. J Neurosci. 2020;40(14):2859–2867. doi:10.1523/JNEUROSCI.2113-19.2020. Mak MKY. rTMS combined with treadmill training modulates corticomotor inhibition and improves walking in PD. J Physiother. 2013;59(2):128. doi:10.1016/S1836-9553(13)70167-X. Udupa K, Chen R. Motor cortical plasticity in Parkinson’s disease. Front Neurol. 2013;4:128. doi:10.3389/fneur.2013.00128. Van Calster B, McLernon DJ, van Smeden M, et al. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17:230. doi:10.1186/s12916-019-1466-7. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. doi:10.1136/bmj.m441. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated ROC curves: a nonparametric approach. Biometrics. 1988;44:837–845. doi:10.2307/2531595. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7527339","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523679769,"identity":"19a0600c-11e1-4cb4-919a-698275ea1295","order_by":0,"name":"Jingyuan 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19:03:19","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94862,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7527339/v1/fb40280e8c97679116c6a40f.html"},{"id":92745389,"identity":"028c6e2b-27db-4e7e-8127-a1b5091f90ab","added_by":"auto","created_at":"2025-10-03 18:55:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":304307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitudinal Changes in Neurotrophic and Inflammatory Proteins During Rehabilitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroup-averaged trajectories of circulating proteins are shown for the Intensive Multimodal Rehabilitation (IMR) and Standard Rehabilitation (SR) groups. Values represent mean ± standard error at each available timepoint. BDNF and IL-6 were assessed at baseline, mid-term, and post-intervention; GDNF, VEGF, and TNF-α were assessed at baseline and post-intervention only. Units: BDNF/GDNF in ng/ml; VEGF, IL-6, and TNF-α in pg/ml. No p-values are indicated in the figure; formal group comparisons are provided in Table 2.\u003c/p\u003e","description":"","filename":"Figure1LongitudinalProteinsREAL.png","url":"https://assets-eu.researchsquare.com/files/rs-7527339/v1/b29cf10fc61faf89055ee0dc.png"},{"id":92745387,"identity":"0274df73-6bba-4cc2-b47c-45a93b9c4f9a","added_by":"auto","created_at":"2025-10-03 18:55:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":215461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectrophysiological Signatures of Cortical Plasticity in Parkinson’s Disease Rehabilitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroup-averaged electrophysiological changes during rehabilitation are displayed. Left and middle panels: EEG relative power in the beta and gamma frequency bands at baseline and post-intervention, shown separately for IMR and SR groups. Right panel: change in TMS-evoked motor evoked potential (ΔMEP) amplitude (mV) between baseline and post-intervention. Error bars represent standard error of the mean (SE). IMR, intensive multimodal rehabilitation; SR, standard rehabilitation. Asterisks indicating significance are reported in the text and Table 2.\u003c/p\u003e","description":"","filename":"Figure2EEGandMEPREAL.png","url":"https://assets-eu.researchsquare.com/files/rs-7527339/v1/ca04eadefc25a8f3b7a5d9d6.png"},{"id":92745730,"identity":"fd411878-6834-411d-8161-428173aedab9","added_by":"auto","created_at":"2025-10-03 19:03:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":339429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Network Linking Proteomic, Electrophysiological, and Clinical Changes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*Heatmap depicting Pearson correlation coefficients between neurotrophic (ΔBDNF), inflammatory (ΔIL-6), electrophysiological (Δβ power, ΔMEP amplitude), and clinical (ΔUPDRS-III, ΔGait score) changes during rehabilitation. Color scale represents correlation strength and direction (red = positive, blue = negative). Cell annotations display correlation coefficients (r) with significance markers (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001). Abbreviations: BDNF, brain-derived neurotrophic factor; IL-6, interleukin-6; MEP, motor evoked potential; UPDRS-III, Unified Parkinson’s Disease Rating Scale part III.\u003c/p\u003e","description":"","filename":"Figure3CorrelationMatrixRealistic.png","url":"https://assets-eu.researchsquare.com/files/rs-7527339/v1/c90a2e87c58d3a37489c3124.png"},{"id":92745734,"identity":"c80e88ba-33d4-43b5-8c36-89f4e9ac7494","added_by":"auto","created_at":"2025-10-03 19:03:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":465624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive Performance of Multi-Modal Biomarkers for Rehabilitation Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves (left) and calibration plots (right) comparing predictive models for achievement of the minimal clinically important difference (MCID) in UPDRS-III. The multimodal model (ΔBDNF + Δβ power + ΔMEP amplitude) demonstrated higher discrimination (AUC=0.74) compared to single-modality models (proteomics-only, EEG-only, MEP-only). Calibration curves were constructed using 10-quantile bins; Brier scores are indicated in the legend. The diagonal dashed lines represent chance discrimination (ROC) and perfect calibration (calibration plot). Abbreviations: BDNF, brain-derived neurotrophic factor; MEP, motor evoked potential; UPDRS-III, Unified Parkinson’s Disease Rating Scale part III.\u003c/p\u003e","description":"","filename":"Figure4ROCCalibration.png","url":"https://assets-eu.researchsquare.com/files/rs-7527339/v1/f25e32f921a96c3d7da6edcf.png"},{"id":100548849,"identity":"16d95e2c-19a4-416a-a3cd-22530cb68c20","added_by":"auto","created_at":"2026-01-19 08:21:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2046863,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7527339/v1/d64ba57d-2c4d-4b68-a100-fd7f39d2ab79.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Modal Framework Combining Proteomics, Electrophysiology, and Clinical Phenotypes to Characterize Neuroplasticity in Parkinson’s Disease Rehabilitation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is increasingly understood as a multisystem disorder in which neurodegeneration intersects with chronic peripheral and central immune activation and impaired plasticity of cortico\u0026ndash;basal ganglia\u0026ndash;thalamocortical networks. Large-scale syntheses now indicate consistent elevations of inflammatory cytokines (e.g., IL-6, TNF-α) in blood and CSF, with associations to motor and non-motor burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In parallel, neurotrophic signaling has re-emerged as a target for activity-dependent repair, as exercise and multidisciplinary rehabilitation can augment circulating brain-derived neurotrophic factor (BDNF) and are linked to motor benefits in PD [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Yet, despite this momentum, rehabilitation studies rarely integrate molecular readouts with electrophysiological indices of cortical function, limiting mechanistic inference and translation.\u003c/p\u003e\u003cp\u003eElectrophysiology provides a window into systems-level plasticity. Scalp EEG reveals excessive β-band synchronization in PD and abnormal β-burst dynamics that normalize with effective neuromodulation, while invasive and non-invasive recordings demonstrate that therapeutic interventions suppress pathological β coherence along cortico-subcortical loops [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. At the corticospinal level, transcranial magnetic stimulation (TMS)\u0026ndash;evoked motor potentials (MEPs) quantify excitability and inhibition, offering tractable markers of motor cortex plasticity with established methodology [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These signals are plausible mediators between molecular change and clinical recovery, but they have seldom been modeled jointly with proteomic biomarkers in real-world rehabilitation cohorts.\u003c/p\u003e\u003cp\u003eDespite progress, three limitations remain. First, proteomic and electrophysiological markers are usually studied separately, hindering cross-scale interpretation. Second, trials targeting neurotrophic responses report heterogeneous results due to variable protocols, sampling schedules, and intensity [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Third, prediction models in rehabilitation often emphasize discrimination but neglect calibration and transparent reporting, which are emphasized in modern frameworks such as MCID analyses and TRIPOD guidelines [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo address these issues, we designed a retrospective cohort study integrating proteomic (BDNF, GDNF, VEGF, IL-6, TNF-α) and electrophysiological (EEG β/γ spectral power, TMS-MEP amplitude) markers with clinical outcomes (UPDRS-III, gait). We aimed to determine whether neurotrophic enhancement and inflammation reduction align with cortical desynchronization and excitability changes, and whether a multimodal prediction model improves identification of patients achieving clinically meaningful recovery.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study enrolled patients with idiopathic Parkinson\u0026rsquo;s disease (PD) admitted for rehabilitation at Fujian Provincial Geriatric Hospital between January 2020 and December 2024. PD was diagnosed according to the Movement Disorder Society (MDS) clinical diagnostic criteria [13]. Inclusion criteria: (i) age 45\u0026ndash;80 years; (ii) Hoehn\u0026ndash;Yahr stage 1\u0026ndash;3 during \u0026ldquo;on\u0026rdquo; medication; (iii) ability to complete rehabilitation sessions. Exclusion criteria: atypical parkinsonism, unstable systemic disease, or severe psychiatric comorbidity. Ethical approval was granted by the Ethics Committee of Fujian Provincial Geriatric Hospital (Approval No. 20250810).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were categorized into:\u003c/p\u003e\n\u003cp\u003eIntensive multimodal rehabilitation (IMR): \u0026ge;5 sessions/week, combining physiotherapy, occupational therapy, gait and balance training, and aerobic exercise.\u003c/p\u003e\n\u003cp\u003eStandard rehabilitation (SR): \u0026le;3 sessions/week, limited to routine physiotherapy.\u003c/p\u003e\n\u003cp\u003eMedication regimens, DBS status, and use of anticholinergics were documented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMotor impairment was rated using the Movement Disorder Society\u0026ndash;Unified Parkinson\u0026rsquo;s Disease Rating Scale part III (MDS-UPDRS-III) [14]. Cognitive status was evaluated by the Montreal Cognitive Assessment (MoCA). Gait and balance were assessed using standardized timed up-and-go and 10-meter walk tests. Clinically meaningful motor improvement was defined as a reduction of \u0026ge;5 points on UPDRS-III [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteomic assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenous blood was collected at baseline (pre-rehabilitation) and within 48 hours after the final rehabilitation session, in fasting state (07:00\u0026ndash;08:00). Samples were centrifuged at 3000 rpm for 15 min at 4\u0026deg;C, and serum aliquots were stored at \u0026ndash;80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eBDNF, GDNF, VEGF: quantified using commercial ELISA kits (R\u0026amp;D Systems, Minneapolis, MN, USA; Cat# DBD00, DY212, DVE00, respectively), detection limits 1\u0026ndash;10 pg/ml, intra-assay CV \u0026lt;8%.\u003c/p\u003e\n\u003cp\u003eIL-6, TNF-\u0026alpha;: measured with high-sensitivity ELISA kits (Thermo Fisher Scientific, Waltham, MA, USA; Cat# KHC0061, KHC3011), detection limits \u0026lt;0.5 pg/ml, intra-assay CV \u0026lt;10%.\u003c/p\u003e\n\u003cp\u003eAbsorbance was read at 450 nm using a microplate reader (BioTek Synergy HTX, USA). All samples were run in duplicate, and mean values were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEEG acquisition and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEEG was recorded with a 64-channel actiCAP system (Brain Products GmbH, Munich, Germany), sampling rate 1000 Hz, impedances \u0026lt;10 k\u0026Omega;. Resting-state (eyes open, 5 min) and motor-task (finger tapping, 5 min) conditions were collected. Signals were band-pass filtered (1\u0026ndash;100 Hz), notch filtered at 50 Hz, and re-referenced to average. Spectral power in \u0026beta; (13\u0026ndash;30 Hz) and \u0026gamma; (30\u0026ndash;80 Hz) bands was computed with Welch\u0026rsquo;s method (2-s Hamming windows, 50% overlap). Relative power was normalized to total 1\u0026ndash;80 Hz power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTMS-MEP procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-pulse transcranial magnetic stimulation was delivered with a Magstim 200^2 stimulator (Magstim Co., Whitland, UK) using a 70-mm figure-of-eight coil. The coil was placed over the left primary motor cortex (M1) \u0026ldquo;hotspot\u0026rdquo; for the right first dorsal interosseous (FDI) muscle, with orientation at 45\u0026deg; to the midline. Resting motor threshold (RMT) was defined as the lowest intensity evoking \u0026ge;50 \u0026mu;V MEPs in 5 of 10 trials. For testing, 20 pulses at 120% RMT were delivered with interstimulus intervals of 5\u0026ndash;7 s. Surface EMG was recorded using Ag/AgCl electrodes, band-pass filtered 20\u0026ndash;2000 Hz, and digitized at 5000 Hz (CED 1401, Cambridge Electronic Design, UK). Peak-to-peak MEP amplitudes were averaged across trials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous data were expressed as mean\u0026plusmn;standard deviation, categorical as counts (%). Baseline group differences: independent-samples t test or \u0026chi;\u0026sup2; test. Longitudinal within-group comparisons: paired t test or repeated-measures ANOVA. Multivariate linear and logistic regression were applied to examine predictors of \u0026Delta;UPDRS-III and gait improvement. Predictive model performance was assessed by AUC, Brier score, and R\u0026sup2;. All analyses used R 4.2.2 and Python 3.9 (scikit-learn). p\u0026lt;0.05 was considered significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt baseline, demographic and disease-related characteristics were balanced between the IMR and SR groups (Table 1). There were no significant group differences in age, sex distribution, BMI, disease duration, Hoehn\u0026ndash;Yahr stage, LEDD, or DBS status. Baseline MoCA and UPDRS-III scores were comparable. Similarly, inflammatory (IL-6, TNF-\u0026alpha;) and neurotrophic markers (BDNF, GDNF, VEGF) showed no between-group differences. Compliance indicators, including planned sessions and completion rates, were also well balanced.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Baseline Characteristics of Parkinson\u0026rsquo;s Disease Patients in the Rehabilitation Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eIMR group (n=110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eSR group (n=55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003et/\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65.2 \u0026plusmn; 7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e66.7 \u0026plusmn; 7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSex (F/M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43/67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21/34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.4 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.1 \u0026plusmn; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDisease duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.0 \u0026plusmn; 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.1 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHoehn\u0026ndash;Yahr stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLEDD (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e670.7 \u0026plusmn; 204.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e639.2 \u0026plusmn; 220.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDBS status (Y/N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12/98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9/46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAnticholinergic use (Y/N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26/84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7/48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMoCA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25.2 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.4 \u0026plusmn; 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUPDRS-III score (baseline)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32.2 \u0026plusmn; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32.9 \u0026plusmn; 9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGait score (baseline)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.8 \u0026plusmn; 3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.0 \u0026plusmn; 3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIL-6 baseline (pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.2 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTNF-\u0026alpha; baseline (pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBDNF baseline (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.9 \u0026plusmn; 2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.3 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=-0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGDNF baseline (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.0 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.6 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVEGF baseline (pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e156.7 \u0026plusmn; 33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e153.0 \u0026plusmn; 35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePlanned sessions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25.9 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26.0 \u0026plusmn; 4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCompletion rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e90.8 \u0026plusmn; 7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e86.3 \u0026plusmn; 8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et=3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as mean \u0026plusmn; standard deviation or number (frequency). Group comparisons were performed using independent-sample t tests for continuous variables and \u0026chi;\u0026sup2; tests for categorical variables. IMR: Intensive Multimodal Rehabilitation; SR: Standard Rehabilitation; BMI: body mass index; LEDD: levodopa equivalent daily dose; DBS: deep brain stimulation; MoCA: Montreal Cognitive Assessment; UPDRS-III: Unified Parkinson\u0026rsquo;s Disease Rating Scale motor section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Associations Between Proteomic and Electrophysiological Changes and Clinical Recovery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate regression analyses revealed distinct contributions of proteomic and electrophysiological markers to clinical recovery (Table 2). For the continuous outcome of \u0026Delta;UPDRS-III, higher increases in BDNF were significantly associated with greater motor improvement (\u0026beta; = \u0026ndash;0.67, p = 0.036), while reductions in EEG \u0026beta; power also predicted better outcomes (\u0026beta; = 4.99, p = 0.022). In contrast, changes in MEP amplitude showed a favorable but non-significant trend (\u0026beta; = \u0026ndash;3.19, p = 0.104).\u003c/p\u003e\n\u003cp\u003eFor gait improvement, \u0026Delta;MEP amplitude emerged as the most robust predictor (\u0026beta; = \u0026ndash;2.47, p = 0.002), whereas \u0026Delta;BDNF showed no significant effect and \u0026Delta;\u0026beta; power demonstrated only a statistical trend (p = 0.073).\u003c/p\u003e\n\u003cp\u003eIn logistic regression models predicting achievement of the minimal clinically important difference (MCID) in UPDRS-III, the integrated multimodal model achieved an AUC of 0.74 with a Brier score of 0.19 and pseudo-R\u0026sup2; of 0.12. Among individual predictors, none reached statistical significance, although \u0026Delta;MEP amplitude showed a strong odds ratio (OR = 9.86, 95% CI 0.47\u0026ndash;205.93, p = 0.14), indicating a potential contribution with larger sample sizes.\u003c/p\u003e\n\u003cp\u003eTaken together, these results highlight that neurotrophic changes (BDNF) and electrophysiological markers (\u0026beta; desynchronization and MEP amplitude) provide complementary information in explaining clinical variability, with \u0026Delta;MEP particularly relevant for gait outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Multivariate Models Linking Proteomic and Electrophysiological Changes to Clinical Recovery\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"549\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;UPDRS-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;BDNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.67 (-1.29,-0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;UPDRS-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;EEG_Beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.99 (0.72,9.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;UPDRS-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;MEP_mV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.19 (-7.05,0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;Gait score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;BDNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01 (-0.27,0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;Gait score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;EEG_Beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.57 (-0.15,3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;Gait score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;MEP_mV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.47 (-4.02,-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMCID UPDRS-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;BDNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30 (0.79,2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMCID UPDRS-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;EEG_Beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 (0.00,3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMCID UPDRS-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;MEP_mV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.86 (0.47,205.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMCID UPDRS-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC=0.74, Brier=0.19, R\u0026sup2;=0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as regression coefficients (\u0026beta;) with 95% confidence intervals for continuous outcomes and odds ratios (OR) with 95% CI for logistic outcomes. p-values are derived from multivariate regression models and adjusted for multiple testing using the false discovery rate (FDR) where applicable. Model performance is indicated by R\u0026sup2; for linear regression models, and by AUC and Brier score for logistic regression models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLongitudinal Changes in Neurotrophic and Inflammatory Proteins During Rehabilitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 1, neurotrophic and inflammatory protein levels exhibited distinct longitudinal patterns during rehabilitation. In the IMR group, BDNF concentrations progressively increased from baseline to post-intervention, accompanied by similar upregulation of GDNF and VEGF, suggesting enhanced neurotrophic support under intensive multimodal training. In contrast, the SR group showed minimal or no changes across these factors.\u003c/p\u003e\n\u003cp\u003eConversely, inflammatory markers demonstrated divergent trajectories. IL-6 levels significantly declined in the IMR group but slightly increased in the SR group, whereas TNF-\u0026alpha; concentrations were markedly reduced following IMR yet remained stable under standard rehabilitation. These findings highlight that intensive rehabilitation not only augments neurotrophic signaling but also exerts anti-inflammatory effects, establishing a favorable molecular milieu for neuroplasticity and functional recovery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectrophysiological Signatures of Cortical Plasticity During Rehabilitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, electrophysiological assessments demonstrated distinct cortical plasticity signatures between groups. In the IMR group, EEG recordings revealed a significant reduction in relative \u0026beta; power from baseline to post-intervention, paralleled by an increase in \u0026gamma; power, consistent with a shift toward desynchronized and plastic cortical states. In contrast, the SR group exhibited negligible changes in both \u0026beta; and \u0026gamma; frequency bands.Furthermore, TMS-evoked MEP amplitudes markedly increased in the IMR group compared to the SR group, indicating enhanced corticospinal excitability following intensive rehabilitation. Collectively, these findings highlight that multimodal intensive rehabilitation induces favorable electrophysiological adaptations, with \u0026beta; desynchronization, \u0026gamma; enhancement, and MEP facilitation serving as converging markers of cortical plasticity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Network Linking Proteomic, Electrophysiological, and Clinical Changes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 3, moderate-to-strong associations were observed between molecular, electrophysiological, and clinical changes during rehabilitation. Increases in \u0026Delta;BDNF were inversely correlated with \u0026Delta;\u0026beta; power (r = \u0026ndash;0.65, p\u0026lt;0.001) and positively related to improvements in \u0026Delta;UPDRS-III and \u0026Delta;Gait score, highlighting the link between neurotrophic signaling and functional recovery. Conversely, higher \u0026Delta;IL-6 levels were positively correlated with persistent \u0026beta; synchronization and poorer outcomes, indicating a pro-inflammatory burden limiting rehabilitation gains.\u003c/p\u003e\n\u003cp\u003e\u0026Delta;MEP amplitude was strongly associated with both \u0026Delta;BDNF (\u0026ndash;0.71, p\u0026lt;0.001) and clinical measures, suggesting that enhanced corticospinal excitability mediates the translation of molecular plasticity into behavioral improvements. Collectively, these results demonstrate that proteomic and electrophysiological markers form an interconnected network that predicts the extent of motor recovery in Parkinson\u0026rsquo;s disease rehabilitation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive Performance of Multi-Modal Biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 4, the multimodal model integrating \u0026Delta;BDNF, \u0026Delta;\u0026beta; power, and \u0026Delta;MEP amplitude achieved superior discrimination for identifying patients who reached the MCID in UPDRS-III, compared with single-modality models. The multimodal approach yielded an AUC of 0.74, indicating moderate predictive accuracy, whereas proteomics-only, EEG-only, and MEP-only models demonstrated lower AUCs. Calibration curves further confirmed that the multimodal model provided the best agreement between predicted probabilities and observed outcomes, while single-modality models showed greater miscalibration. These findings underscore the added value of combining molecular and electrophysiological markers for robust prediction of rehabilitation response.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur multimodal, retrospective analysis links molecular shifts in neurotrophic and inflammatory milieu to systems-level electrophysiology and, ultimately, to clinically meaningful motor recovery in Parkinson\u0026rsquo;s disease (PD). At the molecular tier, rehabilitation coincided with higher circulating BDNF and lower IL-6/TNF-α; at the circuit tier, EEG β desynchronization and increased TMS-MEP amplitude signaled greater corticospinal plasticity; and at the clinical tier, larger ΔUPDRS-III improvement clustered with these biological changes. The integrated models outperformed single-modality predictors and showed acceptable discrimination and calibration for identifying patients who achieved MCID, supporting a biologically plausible, cross-scale recovery signature in PD.\u003c/p\u003e\u003cp\u003eMechanistically, our findings align with the view that PD is a multisystem disorder in which neurodegeneration interacts with immune activation and impaired network plasticity. Elevated inflammatory cytokines are reproducibly observed in PD and track symptom burden, while exercise and intensive, task-specific rehabilitation can boost peripheral BDNF\u0026mdash;an activity-dependent trophic cue with downstream effects on synaptic remodeling. These converging literatures provide a rationale for why patients who mount stronger neurotrophic responses and dampen inflammation exhibit better functional gains after rehabilitation, as we observed.\u003c/p\u003e\u003cp\u003eAt the systems level, reductions in pathological β synchrony are a consistent electrophysiological correlate of motor improvement in PD. β-burst metrics from basal ganglia and cortex index bradykinesia severity and are selectively curtailed by adaptive DBS; invasive and scalp recordings show therapeutic interventions suppress excessive β coupling and normalize burst dynamics. In our cohort, larger Δβ decreases and MEP facilitation co-occurred with clinical improvement, cohering with evidence that desynchronization plus enhanced corticospinal excitability reflect a \u0026ldquo;permissive\u0026rdquo; state for motor learning during rehabilitation.\u003c/p\u003e\u003cp\u003eTMS-based measures provided an interpretable bridge between molecular change and behavior. Prior trials indicate that combining high-intensity practice with neuromodulatory approaches (e.g., rTMS) can modulate corticomotor inhibition and improve gait in PD, and reviews place MEP amplitude within a validated framework for probing human motor cortex plasticity. Our multivariable models identified ΔMEP and ΔBDNF as independent correlates of ΔUPDRS-III and gait gains, reinforcing their utility as mechanistic and prognostic markers in routine rehabilitation cohorts.\u003c/p\u003e\u003cp\u003eFrom a prediction-science perspective, we emphasized not only discrimination but also calibration and clinical interpretability. Model calibration often lags in biomarker studies, yet it governs the reliability of individualized risk estimates. By reporting AUC alongside Brier scores and visual calibration, and by anchoring clinical relevance with MCID for the MDS-UPDRS III, we aligned the analysis with contemporary TRIPOD recommendations and sample-size principles for multivariable models. Future external validation\u0026mdash;ideally with pre-registered analysis plans\u0026mdash;will be crucial for transportability.\u003c/p\u003e\u003cp\u003eStrengths and limitations. Strengths include: (i) a biologically grounded, cross-scale design spanning proteomics, electrophysiology, and clinical outcomes; (ii) standardized pipelines for EEG spectral metrics and TMS-MEP quantification; and (iii) transparent reporting of discrimination, calibration, and MCID-oriented endpoints. Limitations stem from the retrospective, single-center nature; potential residual confounding (medication adjustments, activity dose heterogeneity); and limited proteomic breadth (focus on a priori markers rather than discovery-scale panels). Although we observed coherent associations, causality cannot be inferred. Additionally, while scalp EEG β power is an accessible proxy of network state, basal ganglia LFPs capture disease physiology more proximally; future studies integrating wearable-EEG with implant data may sharpen mechanistic inference and prediction.\u003c/p\u003e\u003cp\u003eImplications. Clinically, the results argue for embedding low-burden molecular assays (BDNF, IL-6/TNF-α) and brief TMS/EEG readouts into rehabilitation programs to stratify patients and adapt intensity. Scientifically, they support a working model in which neurotrophic upregulation and inflammatory quiescence align with β desynchronization and corticospinal facilitation to enable motor relearning\u0026mdash;an actionable target for adjunctive interventions (e.g., aerobic priming, closed-loop stimulation) tested against transparent, well-calibrated prognostic models.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This retrospective observational study was conducted at Fujian Provincial Geriatric Hospital and approved by the institutional Ethics Committee (approval number: 20250810). All procedures complied with the Declaration of Helsinki. Written informed consent had been obtained from each participant or their legal representative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets generated and analyzed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author upon reasonable request and with appropriate institutional approvals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author declares that there are no commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Jingyuan Lin was the sole contributor to the conception, study design, data collection, data analysis, and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTansey MG, Romero-Ramos M. Inflammation and immune dysfunction in Parkinson disease. Nat Rev Immunol. 2022;22(11):657-673. doi:10.1038/s41577-022-00684-6.\u003c/li\u003e\n\u003cli\u003eQu Y, et al. A systematic review and meta-analysis of inflammatory biomarkers in Parkinson\u0026rsquo;s disease. npj Parkinson\u0026rsquo;s Disease. 2023;9:18. doi:10.1038/s41531-023-00449-5.\u003c/li\u003e\n\u003cli\u003eHirsch MA, et al. Exercise-induced increase in brain-derived neurotrophic factor in human Parkinson\u0026rsquo;s disease: a systematic review and meta-analysis. Transl Neurodegener. 2018;7:7. doi:10.1186/s40035-018-0112-1.\u003c/li\u003e\n\u003cli\u003eFrazzitta G, et al. Intensive rehabilitation increases BDNF serum levels in Parkinsonian patients: a randomized study. Neurorehabil Neural Repair. 2014;28(2):163-168. doi:10.1177/1545968313508474.\u003c/li\u003e\n\u003cli\u003eJohansson ME, et al. Aerobic exercise alters brain function and structure in Parkinson\u0026rsquo;s disease: a randomized controlled trial. Ann Neurol. 2022;91(2):203-216. doi:10.1002/ana.26291.\u003c/li\u003e\n\u003cli\u003eSwann NC, et al. Elevated synchrony in Parkinson disease detected with electroencephalography. Ann Neurol. 2015;78(5):742-750. doi:10.1002/ana.24507.\u003c/li\u003e\n\u003cli\u003ePauls KAM, et al. Cortical beta burst dynamics are altered in Parkinson\u0026rsquo;s disease but normalized by deep brain stimulation. NeuroImage. 2022;257:119308. doi:10.1016/j.neuroimage.2022.119308.\u003c/li\u003e\n\u003cli\u003eWang DD, et al. Pallidal deep-brain stimulation disrupts pallidal beta oscillations and coherence with primary motor cortex in Parkinson\u0026rsquo;s disease. J Neurosci. 2018;38(19):4556-4568. doi:10.1523/JNEUROSCI.0431-18.2018.\u003c/li\u003e\n\u003cli\u003eSacheli MA, et al. Exercise increases caudate dopamine release and ventral striatal activation in Parkinson\u0026rsquo;s disease. Mov Disord. 2019;34(12):1891-1900. doi:10.1002/mds.27865.\u003c/li\u003e\n\u003cli\u003eLefaucheur JP. Motor cortex dysfunction revealed by cortical excitability studies in Parkinson\u0026rsquo;s disease: influence of antiparkinsonian treatment and cortical stimulation. Clin Neurophysiol. 2005;116(2):244-253. doi:10.1016/j.clinph.2004.11.017.\u003c/li\u003e\n\u003cli\u003eHorv\u0026aacute;th K, et al. Minimal clinically important difference on the Motor Examination part of MDS-UPDRS. Parkinsonism Relat Disord. 2015;21(12):1421-1426. doi:10.1016/j.parkreldis.2015.10.006.\u003c/li\u003e\n\u003cli\u003eCollins GS, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Ann Intern Med. 2015;162(1):55-63. doi:10.7326/M14-0697.\u003c/li\u003e\n\u003cli\u003ePostuma RB, Berg D, Stern M, et al. MDS clinical diagnostic criteria for Parkinson\u0026rsquo;s disease. Mov Disord. 2015;30(12):1591-1601. doi:10.1002/mds.26424\u003c/li\u003e\n\u003cli\u003eGoetz CG, Tilley BC, Shaftman SR, et al. MDS-sponsored revision of the Unified Parkinson\u0026rsquo;s Disease Rating Scale (MDS-UPDRS). Mov Disord. 2008;23(15):2129-2170. doi:10.1002/mds.22340\u003c/li\u003e\n\u003cli\u003eHorv\u0026aacute;th K, Aschermann Z, Kov\u0026aacute;cs M, et al. Minimal clinically important difference on the Motor Examination part of MDS-UPDRS. Parkinsonism Relat Disord. 2015;21(12):1421-1426. doi:10.1016/j.parkreldis.2015.10.006\u003c/li\u003e\n\u003cli\u003eBloem BR, Okun MS, Klein C. Parkinson\u0026rsquo;s disease. Lancet. 2021;397:2284\u0026ndash;2303. doi:10.1016/S0140-6736(21)00218-X.\u003c/li\u003e\n\u003cli\u003eQin XY, Zhang SP, Cao C, Loh YP, Cheng Y. Aberrations in peripheral inflammatory cytokine levels in Parkinson disease: a systematic review and meta-analysis. JAMA Neurol. 2016;73(11):1316\u0026ndash;1324. doi:10.1001/jamaneurol.2016.2742.\u003c/li\u003e\n\u003cli\u003eZhen K, et al. Aerobic exercise improves motor function and gait in PD: systematic review and meta-analysis. npj Parkinson\u0026rsquo;s Disease. 2022;8:114. doi:10.1038/s41531-022-00418-4.\u003c/li\u003e\n\u003cli\u003eFrazzitta G, Maestri R, Ghilardi MF, et al. Intensive rehabilitation increases BDNF serum levels in parkinsonian patients: a randomized study. Neurorehabil Neural Repair. 2014;28:163\u0026ndash;168. doi:10.1177/1545968313508474.\u003c/li\u003e\n\u003cli\u003eFontanesi C, et al. Intensive rehabilitation enhances lymphocyte BDNF-TrkB signaling in PD. Neurorehabil Neural Repair. 2016;30:411\u0026ndash;418. doi:10.1177/1545968315600272.\u003c/li\u003e\n\u003cli\u003eJohansson H, et al. Aerobic exercise alters brain function and structure in Parkinson\u0026rsquo;s disease: a randomized controlled trial. Ann Neurol. 2022;91:203\u0026ndash;216. doi:10.1002/ana.26291.\u003c/li\u003e\n\u003cli\u003eTinkhauser G, Pogosyan A, Little S, et al. The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson\u0026rsquo;s disease. Brain. 2017;140(4):1053\u0026ndash;1067. doi:10.1093/brain/awx010.\u003c/li\u003e\n\u003cli\u003ede Hemptinne C, Swann NC, Ostrem JL, et al. Therapeutic deep brain stimulation reduces cortical phase\u0026ndash;amplitude coupling in Parkinson\u0026rsquo;s disease. Nat Neurosci. 2015;18(5):779\u0026ndash;786. doi:10.1038/nn.3997.\u003c/li\u003e\n\u003cli\u003eLofredi R, Tan H, Neumann W-J, et al. Beta bursts during continuous movements accompany the velocity decrement in Parkinson\u0026rsquo;s disease patients. Neurobiol Dis. 2019;127:462\u0026ndash;471. doi:10.1016/j.nbd.2019.03.013.\u003c/li\u003e\n\u003cli\u003eEisinger RS, Cagle JN, Opri E, et al. Parkinsonian beta dynamics during rest and movement in the dorsal pallidum and subthalamic nucleus. J Neurosci. 2020;40(14):2859\u0026ndash;2867. doi:10.1523/JNEUROSCI.2113-19.2020.\u003c/li\u003e\n\u003cli\u003eMak MKY. rTMS combined with treadmill training modulates corticomotor inhibition and improves walking in PD. J Physiother. 2013;59(2):128. doi:10.1016/S1836-9553(13)70167-X.\u003c/li\u003e\n\u003cli\u003eUdupa K, Chen R. Motor cortical plasticity in Parkinson\u0026rsquo;s disease. Front Neurol. 2013;4:128. doi:10.3389/fneur.2013.00128.\u003c/li\u003e\n\u003cli\u003eVan Calster B, McLernon DJ, van Smeden M, et al. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17:230. doi:10.1186/s12916-019-1466-7.\u003c/li\u003e\n\u003cli\u003eRiley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. doi:10.1136/bmj.m441.\u003c/li\u003e\n\u003cli\u003eDeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated ROC curves: a nonparametric approach. Biometrics. 1988;44:837\u0026ndash;845. doi:10.2307/2531595.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, Neurorehabilitation, Proteomics, Electrophysiology, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7527339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7527339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eRehabilitation for Parkinson’s disease (PD) is limited by the absence of reliable biomarkers to monitor neuroplasticity and predict treatment response.\u003c/p\u003e\n\u003cp\u003eObjective: To investigate how proteomic and electrophysiological signatures relate to clinical recovery and to evaluate whether multimodal integration enhances prediction of rehabilitation outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In this retrospective cohort of 165 PD patients, participants received either intensive multimodal rehabilitation (IMR, n=110) or standard rehabilitation (SR, n=55). Circulating neurotrophic and inflammatory proteins (BDNF, GDNF, VEGF, IL-6, TNF-α) and electrophysiological measures (EEG spectral power, TMS-evoked MEP) were assessed at baseline and post-intervention, alongside clinical scales (UPDRS-III, gait). Multivariate regression and predictive modeling were applied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIMR was associated with significant increases in neurotrophic factors and reductions in inflammatory markers, paralleled by EEG β desynchronization, γ enhancement, and MEP facilitation. In multivariate analyses, ΔBDNF and Δβ power independently predicted motor improvements, while ΔMEP amplitude was the strongest predictor of gait recovery. A multimodal model integrating proteomic and electrophysiological features achieved superior discrimination for achieving the minimal clinically important difference (AUC=0.74) compared with single-modality models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eMultimodal integration of proteomic and electrophysiological markers provides complementary insight into neuroplastic mechanisms and improves prediction of rehabilitation response in PD. These findings support the development of biomarker-driven strategies to personalize neurorehabilitation.\u003c/p\u003e","manuscriptTitle":"A Multi-Modal Framework Combining Proteomics, Electrophysiology, and Clinical Phenotypes to Characterize Neuroplasticity in Parkinson’s Disease Rehabilitation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 18:55:15","doi":"10.21203/rs.3.rs-7527339/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7b02483-5d15-48d7-943f-707f0e51ae05","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T06:24:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 18:55:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7527339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7527339","identity":"rs-7527339","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}