Evaluation of cortical excitability patterns in patients undergoing glioblastoma resection in the motor area

Evaluation of cortical excitability patterns in patients undergoing glioblastoma resection in the motor area | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of cortical excitability patterns in patients undergoing glioblastoma resection in the motor area Lucas Schiavão, Gabriel Pokorny, Wellingson da Silva Paiva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7474809/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Feb, 2026 Read the published version in Neurosurgical Review → Version 1 posted 8 You are reading this latest preprint version Abstract Background: Brain tumors adjacent to the primary motor cortex may alter corticospinal physiology through mass effects, pathway disruption and maladaptive plasticity. Preoperative transcranial magnetic stimulation (TMS) maps cortical excitability more accurately than fMRI does and correlates with intraoperative stimulation, but it is unclear which TMS parameters predict postoperative motor outcomes. We investigated whether preoperative TMS cortical excitability measures differ from healthy norms and whether they identify signals potentially useful for predicting postoperative motor deficits. Methods: This was a retrospective, single-center observational study of consecutive adult patients (18–70 years) with glioblastoma adjacent to the precentral gyrus who underwent standardized preoperative TMS mapping (N = 60). The measured parameters included the resting motor threshold (RMT), MEP amplitudes at 120% and 140% RMT, the MEP140/120 ratio, paired-pulse relative MEPs at interstimulus intervals of 2, 4, 10 and 15 ms (Rel-02, Rel-04, Rel-10, Rel-15), short-interval intracortical inhibition (IICI) and intracortical facilitation (ICF). Patient values were compared to those of a healthy Brazilian reference cohort via robust one-sample median tests and age-stratified analyses; categorical comparisons were performed with normal ranges in the literature (Cueva et al.). Variables with correlation coefficients |r|>0.2 were considered for multivariable logistic modeling to explore associations with postoperative motor deficits. Results: Sixty patients were analyzed. Compared with the healthy cohort, the ill hemisphere showed significant alterations in paired-pulse and intracortical measures. In categorical analyses using established normal ranges, the ill hemisphere had significantly lower odds of being classified as “Low” (vs “High”) for Rel-04 (OR 0.21; 95% CI 0.07–0.57; p = 0.0067), Rel-15 (OR 0.25; 95% CI 0.10–0.63; p = 0.0117), IICI (OR 0.27; 95% CI 0.10–0.68; p = 0.0216) and ICF (OR 0.30; 95% CI 0.12–0.72; p = 0.0238). Age-stratified robust median tests versus healthy medians demonstrated multiple significant deviations—most prominently for MEP amplitudes and relative paired-pulse indices in patients <50 years. Several excitability measures met prespecified correlation thresholds and were entered into multivariable modeling (the results are reported in the main text). Conclusions: Preoperative TMS reveals consistent alterations in paired-pulse measures and intracortical inhibition/facilitation in glioblastomas adjacent to the motor cortex. Rel-04, Rel-15, IICI and ICF differ from healthy norms and merit prospective evaluation as predictors of postoperative motor outcomes. Future prospective studies should validate these parameters and determine their incremental predictive value for surgical planning and rehabilitation stratification. Glioblastomas cortical excitability motor evoked potential neurophysiology transcranial magnetic stimulation motor cortex Introduction Brain tumors affect the motor cortex through direct pressure, neural pathway obstruction, and neural plasticity. Neural plasticity occurs differently in slow- and fast-growing lesions 1 . Transcranial magnetic stimulation (TMS) is a noninvasive technique used to map brain function before tumor resection 2 , 3 . This group of techniques usually shows greater accuracy in motor area localization than does fMRI and correlates well with intraoperative direct cortical stimulation 4 – 6 . Moreover, TMS can influence surgical decisions and, when performed in the postoperative period, aid in the prediction of motor strength recovery after brain tumor surgery 7 . However, it is unclear whether specific parameters of TMS mapping, such as short-interval intracortical inhibition (IICI) or intracortical facilitation (ICF), in the preoperative period can aid in the prediction of postoperative motor deficits. Therefore, this study aimed to evaluate whether preoperative TMS parameters could predict postoperative motor deficits 8 – 10 . Therefore, the present study aimed to investigate the potential of TMS cortical excitability parameters to prevent or predict the occurrence of motor deficits after brain tumor surgery. Methods Retrospective, noncomparative, nonrandomized study This was an observational, exploratory, single-center, analytical study with nonadmitted patients who was carried out at a specialized cancer center in São Paulo. The present study was approved by an independent ethics committee and followed good clinical practices and the Helsinki guidelines. Patient Population and Inclusion Criteria The cohort of patients with glioblastoma was recruited from a specialized cancer center in São Paulo, Brazil. Patients aged between 18 and 70 years with glioblastoma located adjacent to the precentral gyrus were included, and only patients who signed a free consent form and agreed to participate in the study were included. Patients with tumors located closer than 5 mm to the precentral gyrus, patients with decompensated intracranial pressure or a Karnofsky scale score less than 50 points, those with decompensated epilepsy, and those with previous brain surgery were excluded. For comparative analysis, data were selected from healthy volunteer participants who made up the Brazilian standardization. The healthy participants had no history of neurological disease, traumatic brain injury, or psychiatric illness. Evaluation of cortical excitability EMT pulses were applied to the right primary motor and left primary motor cortices via a circular coil model C-100 MagVenture® positioned tangentially to the scalp in the anteroposterior direction and connected to the MagPro-X100® machine (MagVenture Tonika Elektronik, Farum, Denmark). Each individual being assessed sat in a comfortable reclining chair with armrests and wore a thin fabric cap that enabled anatomical reference points to be identified and marked, along with specific points on the scalp. On the basis of the international 10–20 reference system for electroencephalography, the following anatomical points were marked via a tape measure and an atomic brush pen: nacon-N, inion-I, central-Cz, left preauricular-pE, and right preauricular-pD. A line was drawn from point N to point I, following the median sagittal line, and another line from point pE to point pD, following a coronal orientation, both intersecting at point Cz. These orientation lines helped locate M1 in both hemispheres. The location of the stimulation target (hotspot) on the scalp of the individuals in this study was determined with the intensity of the machine set at 70% of its total power, and stimuli were applied every 1 to 2 s to find the stimulation point on M1 that caused the greatest possible contraction of the target muscle group. Once the hotspot was found, this point was marked to measure cortical excitability parameters. RMT was defined according to the recommendation of the IFCN Committee as the lowest stimulus intensity capable of causing an EMP amplitude of > 50 microvolts (µV) in at least five out of 10 trials, with the abductor pollicis brevis muscle at rest. The parameters selected for evaluation were the resting motor threshold (RMT), motor-evoked potential (PEM) recruitment curves, short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF). The amplitude of the MEPs was considered to be peak-to-peak (µV). To choose the target muscle group, surface electrodes were placed on the abductor pollicis brevis muscle on the insertion of the first dorsal interosseous muscle of the hand and forearm. The PEM responses were filtered and amplified via a surface electromyography device. The signals were transferred to a personal computer for offline analysis via a data collection software program (Nicolet Biomedical, Inc.) for each side of the motor cortex. Single pulses of EMT (sp-EMT) were used for RMT and EMP measurements (Fig. 10). EMP measurements were taken at 120% and 140% of the RMT to assess the relationship between stimulus intensity and the amplitude of the response curve. The average values of the four MEPs obtained at 120% RMT were used for the analysis. The same procedure was adopted for MEPs at 140% RMT. For ICF, paired pulses of EMT (pp-EMT) were used at intervals of 10 and 15 ms. The conditioning stimulus, the first pulse released, partially depolarizes the axons of the i-wave-generating neurons, making them hyperexcitable. The second pulse is released, and the test stimulus completes depolarization. The ICF has been reported to be dependent on NMDA receptors. The intensity of the conditioning stimulus was set at 80% of the RMT, and that of the test stimulus was set at 120% of the RMT. For IICIC, pp-EMT was performed in the same pattern as for ICF but at intervals of 2 and 4 ms. The overall IICIC and ICF values were obtained by averaging the overall indices at 02 and 04 ms (IICIC) and at 10 and 15 ms (ICF). The overall IICIC and ICF values are expressed as the average of the four measurements divided by the average of the MEPs at 120% of RMT42–56.108. The IICIC and ICF values were assessed for the fully relaxed target muscle, and information was recorded via a specific data collection instrument. Analyzed outcomes The following variables were analyzed in the present study. Demographics Age, sex Cortical excitability resting motor threshold (RMT), PEM 120%, PEM 140%, MEP140/120 ratio, ratio pp02/MEP120 (Rel-02), pp 04/MEP120 (Rel-04), pp 10/MEP120(Rel-10), pp 15/MEP120 (Rel-15), ICF, IICIC. Physical examination Upper limb motor strength, lower limb motor strength, and motor loss (yes if a patient presented a reduction in upper and/or lower limb strength between the preoperative period). Statistical analysis The patient data were inserted into an Excel spreadsheet and analyzed via R software (version 4.2). Continuous variables will be described as the mean, median, standard deviation, interquartile range, minimum, and maximum. Categorical variables are presented as numbers and percentages. Variable distribution was assessed via the Kolmogorov–Smirnov test, with p values above 0.05 indicating a normal distribution. To compare continuous variables, the Welch t test is used in the case of normally distributed variables, or the yuen–Welch t test is used in the case of variables that do not adhere to a distribution close enough to normal. To compare the values of cortical excitability between the ill hemisphere of the glioblastoma cohort and the healthy cohort, a one-sample median test using the “honest” estimator and the median value of the healthy cohort as a reference was utilized. To compare categorical variables between the two groups, the chi-square test was used in cases with more than two groups and in cases where more than 20% of the expected sum values were lower than 5. Furthermore, to investigate potential cortical excitability and demographic variables that have little potential for predicting postoperative motor deficits, variables that presented correlation coefficients greater than 0.2 or less than − 0.2 in the percentage bend correlation analysis were included in a multivariable logistic regression. The following tests were used to assess the correlations between the presence of deficits and other variables: robust correlation and the predictive power score. Finally, the marginal effects of the logistic regression model were evaluated via the average slope and average contrast functions of the WRS2 package, and uncertainty estimates were obtained via heteroskedasticity-consistent estimation of the covariance matrix HC3. The Benjamini‒Hochberg test was employed to adjust the p value of multiple tests. Statistical significance was set to 0.05. Results Sixty patients were included in the TMS cortex excitability protocol analysis, and their demographic data are detailed in Table 1. Table 1. Baseline demographic and clinical characteristics. Continuous variables are reported as medians [IQRs] and means (SDs); categorical variables are reported as n (%). Karnofsky = Karnofsky performance status; ECOG = Eastern Cooperative Oncology Group performance status. The motor strength was graded from 0–5. Note: there appears to be a discrepancy between the sex counts (Male 40 + Female 33 = 73) and the sample sizes reported for other variables (e.g., N = 59–60), suggesting a possible data entry or aggregation inconsistency that should be checked. Variable Statistic Value Age Min/Max 18.0/79.0 Median [IQR] 51.0 [41.5; 62.0] Mean (SD) 51.5 (15.6) N (missing) 59 (1) Biological sex Male 40 Female 33 Preop motor strength (Arms) Grade 0 2 (3.33%) Grade 2 2 (3.33%) Grade 3 3 (5.00%) Grade 4 23 (38.33%) Grade 5 30 (50.00%) Preop motor strength (Legs) Grade 0 1 (1.67%) Grade 2 2 (3.33%) Grade 3 4 (6.67%) Grade 4 23 (38.33%) Grade 5 30 (50.00%) Karnofsky Performance Status Min/Max 30/90 Median [IQR] 65.0 [50.0; 80.0] Mean (SD) 64.3 (16.1) N (missing) 60 (0) ECOG Performance Status 0 12 (20.00%) 1 21 (35.00%) 2 8 (13.33%) 3 7 (11.67%) 4 12 (20.00%) There were significant differences between the healthy and ill brain hemispheres of the patients in the following variables: Rel-02, Rel-04, Rel-10, Rel-15, and ICF. A full comparison of the variables between healthy and ill brain hemispheres is shown in Table 2. Table 2: Descriptive values and comparisons between healthy and ill hemispheres. Mean [95% CI]. std, standard deviation; NA: not applicable; IQR: interquartile range. Label Descriptive statistics Brain Hemisphere Difference of Trimmed Means Effect Size p Value Health Ill RMT Min/Max 36.0/85.0 27.0/86.0 -0,64 [-4,69; 3,41] 0,04 [0;0,31] p value: 0,804 (Yuen-Welch-T test) Med [IQR] 50.0 [46.0;58.5] 51.0 [45.0;60.0] Mean (std) 52.4 (10.4) 52.8 (12.5) N (NA) 67 (0) 61 (6) MEP120 Min/Max 94.0/4136.0 38.0/4434.8 50,07 [-114,34; 214,49] 0,07 [0;0,33] p value: 0,537 (Yuen-Welch-T test) Med [IQR] 525.0 [327.0;832.0] 465.6 [250.0;749.0] Mean (std) 715.7 (701.1) 654.4 (686.9) N (NA) 67 (0) 61 (6) MEP140 Min/Max 221.5/6140.0 66.0/7345.0 13,55 [-329,04; 356,14] 0,02 [0;0,29] p value: 0,931 (Yuen-Welch-T test) Med [IQR] 1075.5 [576.4;1619.5] 1020.8 [553.0;1736.8] Mean (std) 1353.8 (1126.4) 1303.6 (1187.6) N (NA) 66 (1) 58 (9) Ratio 140/120 Min/Max 0/7.5 1.0/5.9 -0,11 [-0,42; 0,21] 0,08 [0;0,3] p value: 0,482 (Yuen-Welch-T test) Med [IQR] 1.8 [1.4;2.5] 2.0 [1.6;2.6] Mean (std) 2.1 (1.1) 2.2 (0.9) N (NA) 67 (0) 58 (9) Med [IQR] 659.0 [504.5;1086.2] 1030.0 [353.0;1783.0] Mean (std) 900.5 (726.0) 1247.5 (1103.3) N (NA) 67 (0) 61 (6) Rel-02 Min/Max 0.05/7.4 0.1/2.9 -0,19 [-0,38; 0] 0,25 [0;0,49] p value: 0,058 (Yuen-Welch-T test) Med [IQR] 0.5 [0.3;0.8] 0.7 [0.4;1.2] Mean (std) 0.8 (1.2) 0.9 (0.6) N (NA) 67 (0) 61 (6) Rel-04 Min/Max 0.1/4.4 0.3/3.5 -0,27 [-0,53; -0,02] 0,27 [0,03;0,52] p value: 0,035 (Yuen-Welch-T test) Med [IQR] 0.8 [0.4;1.3] 1.0 [0.7;1.7] Mean (std) 1.0 (0.9) 1.3 (0.8) N (NA) 67 (0) 61 (6) Rel-10 Min/Max 0.3/6.4 0.3/6.4 -0,56 [-1,02; -0,1] 0,31 [0,1;0,53] p value: 0,022 (Yuen-Welch-T test) Med [IQR] 1.3 [0.8;2.1] 1.7 [1.3;2.8] Mean (std) 1.6 (1.2) 2.2 (1.4) N (NA) 67 (0) 61 (6) Rel-15 Min/Max 0.3/4.9 0.4/7.2 -0,48 [-0,87; -0,1] 0,32 [0,08;0,55] p value: 0,016 (Yuen-Welch-T test) Med [IQR] 1.4 [1.0;2.0] 1.7 [1.4;3.0] Mean (std) 1.6 (1.0) 2.3 (1.4) N (NA) 67 (0) 61 (6) IICI Min/Max 0.1/5.0 0.2/3.2 -0,22 [-0,45;0,01] 0,23 [0;0,46] p value: 0,069 (Yuen-Welch-T test) Med [IQR] 0.7 [0.4;1.0] 0.9 [0.6;1.3] Mean (std) 0.9 (0.9) 1.1 (0.7) N (NA) 67 (0) 61 (6) ICF Min/Max 0.3/1445.0 0.3/6.4 -0,48 [-0,91; -0,05] 0,3 [0,07;0,54] p value: 0,028 (Yuen-Welch-T test) Med [IQR] 1.4 [0.9;2.1] 1.8 [1.4;2.7] Mean (std) 23.2 (176.3) 2.2 (1.3) N (NA) 67 (0) 61 (6) When considering the established thresholds in the literature, a difference between the healthy and ill hemisphere of the patients was identified in the following variables: Rel-04, Rel-10, Rel-15, and IICI. In all the cases, there was a significantly lower odds ratio of Ill patients presenting a “low” classification than a “high” classification (Table 3). Table 3: Descriptive values and comparisons between healthy and ill hemispheres according to the normal ranges specified by Cueva et al. (2016). Mean [95% CI]. std, standard deviation; NA: not applicable; IQR: interquartile range. label variable Status Effect Size Odds ratio [95% Wald CI], ref='Ill vs Health Health Ill P Value RMT HIGH 34 (50.00%) 34 (50.00%) LOW vs HIGH: 1.25 [0.56 to 2.84] NORMAL vs HIGH: 0.41 [0.14 to 1.09] p value: 0.1142 (Pearson's Chi-squared test) LOW 16 (44.44%) 20 (55.56%) NORMAL 17 (70.83%) 7 (29.17%) NA 0 6 MEP 120 HIGH 27 (55.10%) 22 (44.90%) LOW vs HIGH: 1.55 [0.68 to 3.57] NORMAL vs HIGH: 0.88 [0.36 to 2.09] p value: 0.4037 (Pearson's Chi-squared test) LOW 19 (44.19%) 24 (55.81%) NORMAL 21 (58.33%) 15 (41.67%) NA 0 6 MEP 140 HIGH 22 (48.89%) 23 (51.11%) LOW vs HIGH: 0.89 [0.40 to 1.97] NORMAL vs HIGH: 0.56 [0.21 to 1.48] p value: 0.4957 (Pearson's Chi-squared test) LOW 27 (51.92%) 25 (48.08%) NORMAL 17 (62.96%) 10 (37.04%) NA 1 9 Ratio 140/120 HIGH 3 (60.00%) 2 (40.00%) LOW vs HIGH: 1.32 [0.21 to 10.35] NORMAL vs HIGH: 1.39 [0.20 to 11.89] p value: 1.0000 (Fisher's Exact Test for Count Data) LOW 49 (53.26%) 43 (46.74%) NORMAL 14 (51.85%) 13 (48.15%) NA 1 9 Rel-02 HIGH 27 (46.55%) 31 (53.45%) LOW vs HIGH: 0.49 [0.21 to 1.14] NORMAL vs HIGH: 0.87 [0.37 to 2.04] p value: 0.2493 (Pearson's Chi-squared test) LOW 23 (63.89%) 13 (36.11%) NORMAL 17 (50.00%) 17 (50.00%) NA 0 6 Rel-04 HIGH 23 (43.40%) 30 (56.60%) LOW vs HIGH: 0.21 [0.07 to 0.57] NORMAL vs HIGH: 0.87 [0.39 to 1.92] p value: 0.0067 (Pearson's Chi-squared test) LOW 22 (78.57%) 6 (21.43%) NORMAL 22 (46.81%) 25 (53.19%) NA 0 6 Rel-10 HIGH 10 (34.48%) 19 (65.52%) LOW vs HIGH: 0.34 [0.13 to 0.83] NORMAL vs HIGH: 0.53 [0.18 to 1.51] p value: 0.0580 (Pearson's Chi-squared test) LOW 43 (60.56%) 28 (39.44%) NORMAL 14 (50.00%) 14 (50.00%) NA 0 6 Rel-15 HIGH 10 (32.26%) 21 (67.74%) LOW vs HIGH: 0.25 [0.10 to 0.63] NORMAL vs HIGH: 0.48 [0.18 to 1.23] p value: 0.0117 (Pearson's Chi-squared test) LOW 36 (65.45%) 19 (34.55%) NORMAL 21 (50.00%) 21 (50.00%) NA 0 6 IICI HIGH 25 (42.37%) 34 (57.63%) LOW vs HIGH: 0.27 [0.10 to 0.68] NORMAL vs HIGH: 0.70 [0.31 to 1.57] p value: 0.0216 (Pearson's Chi-squared test) LOW 22 (73.33%) 8 (26.67%) NORMAL 20 (51.28%) 19 (48.72%) NA 0 6 ICF HIGH 11 (34.38%) 21 (65.62%) LOW vs HIGH: 0.30 [0.12 to 0.72] NORMAL vs HIGH: 0.56 [0.20 to 1.50] p value: 0.0238 (Pearson's Chi-squared test) LOW 40 (63.49%) 23 (36.51%) NORMAL 16 (48.48%) 17 (51.52%) NA 0 6 Furthermore, when the cortical excitability parameters of the ill hemisphere were evaluated and compared with the median values of the healthy Brazilian population, all the values were significantly different (p < 0.05) (Table 4). Table 4: Comparison of neurophysiological measures in the affected (ill) hemisphere versus reference healthy-population medians, stratified by age group (<50 years; ≥50 years). For each measure, the ill-hemisphere median and its 95% confidence interval (CI) are shown, together with the healthy population median, the median difference (calculated as the ill hemisphere median minus the healthy median via a robust one-sample test with the healthy median as the threshold), and the two-sided p value. Positive median differences indicate higher values in the ill hemisphere. Values are presented as medians (95% CI) or n.s./p where indicated; p<0.05 was considered statistically significant. Abbreviations: RMT = resting motor threshold; MEP120/MEP140 = peak-to-peak motor-evoked potential amplitude at 120%/140% of stimulator output; MEP140/120 = ratio of MEP140 to MEP120; Rel-02/Rel-04/Rel-10/Rel-15 = relative MEP (at interstimulus intervals of 2, 4, 10 and 15 ms, respectively); IICI = short-interval intracortical inhibition; ICF = intracortical facilitation. Variable Ill hemisphere median Confidence Interval 95- Confidence Interval 95-+ Health Population Median Median difference (Robust Test, Health as threshold) p Below 50 years RMT 49.00 46.00 54.00 47.0 2.00 0.111 MEP 120 737.00 448.00 1,226.00 315.0 422.00 0.000 MEP 140 1,622.00 1,196.00 2,145.00 773.0 849.00 0.021 MEP 140/120 1.86 1.65 2.60 2.0 -0.14 0.326 Rel-02 0.50 0.36 0.80 0.2 0.30 0.000 Rel-04 0.83 0.73 0.89 0.3 0.53 0.000 Rel-10 1.81 1.35 2.50 1.5 0.31 0.051 Rel-15 0.83 0.73 0.89 1.4 -0.57 0.014 IICI 0.69 0.58 1.02 0.3 0.39 0.000 ICF 1.94 1.65 2.49 1.4 0.54 0.000 Above 50 years RMT 58.00 48.0 51.00 61.00 10.00 0.0020 MEP 120 263.00 263.0 176.00 465.60 0.00 0.9975 MEP 140 611.10 799.0 352.00 1,018.00 -187.90 0.3610 MEP 140/120 2.12 2.8 1.64 2.57 -0.68 0.0010 Rel-02 0.96 0.5 0.71 1.37 0.46 0.0010 Rel-04 1.22 0.5 1.04 1.51 0.72 0.0000 Rel-10 1.47 1.5 1.30 2.03 -0.03 0.8520 Rel-15 1.22 1.4 1.04 1.51 -0.18 0.0650 IICI 1.20 0.5 0.96 1.29 0.70 0.0000 ICF 1.77 1.4 1.39 2.60 0.37 0.0660 Discussion Preoperative transcranial magnetic stimulation (TMS) has become an increasingly commonly used tool in neurosurgical planning, particularly for procedures involving eloquent brain regions that control motor, sensory, and language functions. The present study demonstrated that patients with brain tumors presented abnormal TMS parameters, both in the healthy hemisphere and in the diseased hemisphere of the brain, compared with healthy patients; however, only the Eastern Cooperative Oncology Group (ECOG) score was able to predict the occurrence of motor deficits during the postoperative period. Functional Mapping and Precision in Surgical Planning The integration of TMS into preoperative workflows enables detailed, noninvasive mapping of cortical regions responsible for critical functions. Studies have demonstrated that TMS is highly effective in identifying motor and language areas, which are often challenging to delineate with conventional imaging techniques such as MRI alone. The functional mapping provided by TMS is especially useful when lesions, such as tumors or vascular malformations, are located near or within eloquent cortices. By stimulating specific cortical areas and monitoring resultant motor or language responses, TMS allows neurosurgeons to plan resections that maximize pathological tissue removal while preserving essential functions 11 , 12 . Postoperative motor deficits One of the most significant impacts of using preoperative TMS is the reduction of postoperative deficits, such as motor weakness, aphasia, or sensory loss. This benefit is particularly pronounced in patients who undergo tumor resection in or near eloquent brain areas. TMS helps localize functional regions noninvasively, which reduces the likelihood of damaging critical tissue during surgery 13 . Multiple studies have reported that TMS-guided surgeries lead to better functional outcomes than surgeries that do not utilize functional mapping techniques 14 . For example, Picht et al. (2011) demonstrated that motor mapping with TMS significantly decreased the incidence of new motor deficits in patients with gliomas near motor areas. In their study, 94% of patients who underwent TMS-based mapping maintained their preoperative motor function postoperatively, whereas patients without TMS mapping had a greater risk of postoperative motor impairment 15 . Similarly, Narayana et al. (2021) reported that the use of preoperative TMS to map eloquent areas in children with epilepsy or brain tumors led to no language deficits (7 patients) and that 90% of the patients had no motor deficits (9 of 11 patients) in the postoperative period 16 . In a recent study, Moritz et al. (2024) reported that RMT values below 1.1 and pathological values of the cortical silent period (CSP) or recruitment curve (RC) were associated with worse motor outcomes after seven days or three months; however, when these values were combined in a multivariable regression, none of the parameters presented any significant impact on the occurrence of postoperative motor deficits 17 . Similar to the findings presented in this work, some TMS parameter variables demonstrated a small correlation with the occurrence of postoperative motor deficits; however, when analyzed together via multivariable regression, those values were no longer significant. Finally, in an extensive narrative review regarding the use of techniques for mapping and assessing neuroplasticity to improve surgical planning, Duffau (2020) reported that the TMS technique is capable of performing very specific and sensitive maps of the brain (both the ill and healthy hemispheres); however, a better understanding of how different neuroplasticity patterns impact the risk of postoperative deficits has yet to be described. Limitations The main limitation of this work lies in its small sample size, which makes it difficult to gain clear insights into the impact of several parameters of TMS on the occurrence of motor deficits, as different patients present distinct patterns of TMS parameters even when inside the “normal” ranges. Conclusion The present study demonstrated that the excitability parameters of patients with brain tumors are significantly altered compared with those of normal individuals and even compared with those of healthy individuals, revealing the changes and changes in neuroplasticity that occur in the eloquent areas of the ill hemisphere. However, the present study did not identify any preoperative TMS parameter capable of indicating or suggesting an increased risk of postoperative motor deficits. Further studies with more patients who are able to analyze the whole pattern of preoperative excitability as one large panel, not only as individual parameters, are needed to understand the overall changes in neuroplasticity that occur in these groups of patients and how it can lead to increased or decreased risks of postoperative deficits. Declarations Ethical Approval Ethical committee approval was obtained from an independent ethics committee for the present study. Funding No funding was received for this study. Availability of data and materials No data are available online; however, under reasonable request, the data can be shared by the author. Authors’ roles LS (study conception, data curation, investigation, writing-original draft, reviewing final draft) GP (formal analysis, reviewing the final draft, visualization) WSP (study conception, writing-original draft, reviewing final draft) Clinical Trial Number: Not applicable Consent to participate: Informed consent was obtained from all individual participants included in the study. References Kong NW, Gibb WR, Badhe S, Liu BP, Tate MC Plasticity of the Primary Motor Cortex in Patients with Primary Brain Tumors. Neural Plast [Internet]. 2020 Jan 1 [cited 2024 Sep 12];2020(1):3648517. Available from: https://onlinelibrary.wiley.com/doi/full/ 10.1155/2020/3648517 Frey D, Schilt S, Strack V, Zdunczyk A, Sler JR, Niraula B et al (2014) Navigated transcranial magnetic stimulation improves the treatment outcome in patients with brain tumors in motor eloquent locations. Neuro Oncol [Internet]. Oct 1 [cited 2024 Sep 12];16(10):1365–72. Available from: https://dx.doi.org/10.1093/neuonc/nou110 Raffa G, Scibilia A, Conti A, Ricciardo G, Rizzo V, Morelli A et al (2019) The role of navigated transcranial magnetic stimulation for surgery of motor-eloquent brain tumors: a systematic review and meta-analysis. Clin Neurol Neurosurg 180:7–17 Narayana S, Gibbs SK, Fulton SP, McGregor AL, Mudigoudar B, Weatherspoon SE et al (2021) Clinical Utility of Transcranial Magnetic Stimulation (TMS) in the Presurgical Evaluation of Motor, Speech, and Language Functions in Young Children With Refractory Epilepsy or Brain Tumor: Preliminary Evidence. Front Neurol [Internet]. May 19 [cited 2024 Sep 12];12:650830. Available from: www.frontiersin.org Umana GE, Scalia G, Graziano F, Maugeri R, Alberio N, Barone F et al Navigated transcranial magnetic stimulation motor mapping usefulness in the surgical management of patients affected by brain tumors in eloquent areas: A systematic review and meta-analysis. Front Neurol [Internet]. 2021 Mar 4 [cited 2024 Sep 12];12:644198. Available from: www.frontiersin.org Takahashi S, Vajkoczy P, focus TPN (2013) undefined Navigated transcranial magnetic stimulation for mapping the motor cortex in patients with rolandic brain tumors. thejns.org [Internet]. 2013 Apr [cited 2024 Sep 12];34(4). Available from: https://thejns.org/focus/view/journals/neurosurg-focus/34/4/article-pE3.xml Seidel K, Häni L, Lutz K, Zbinden C, Redmann A, Consuegra A et al (2019) Postoperative navigated transcranial magnetic stimulation to predict motor recovery after surgery of tumors in motor eloquent areas. Clin Neurophysiol 130(6):952–959 Neville IS, Gomes dos Santos A, Almeida CC, Hayashi CY, Solla DJF, Galhardoni R et al (2021) Evaluation of Changes in Preoperative Cortical Excitability by Navigated Transcranial Magnetic Stimulation in Patients With Brain Tumor. Front Neurol. ;11 Haddad AF, Young JS, Berger MS, Tarapore PE (2021) Preoperative Applications of Navigated Transcranial Magnetic Stimulation. Front Neurol [Internet]. Jan 22 [cited 2024 Sep 12];11:628903. Available from: www.frontiersin.org Schiavao LJV, Ribeiro IN, Hayashi CY, Figueiredo EG, Brunoni AR, Teixeira MJ et al (2022) Assessing the Capabilities of Transcranial Magnetic Stimulation (TMS) to Aid in the Removal of Brain Tumors Affecting the Motor Cortex: A Systematic Review. Neuropsychiatr Dis Treat [Internet]. [cited 2024 Sep 12];18:1219. Available from:/ pmc/articles/PMC9208734/ Lefaucheur JP, Picht T The value of preoperative functional cortical mapping using navigated TMS. Neurophysiol Clin [Internet]. 2016 Apr 1 [cited 2022 Apr 19];46(2):125–33. Available from: https://pubmed.ncbi.nlm.nih.gov/27229765/ Picht T, Strack V, Schulz J, Zdunczyk A, Frey D, Schmidt S et al Assessing the functional status of the motor system in brain tumor patients using transcranial magnetic stimulation. Acta Neurochirurgica 2012 154:11 [Internet]. 2012 Sep 5 [cited 2021 Oct 24];154(11):2075–81. Available from: https://link.springer.com/article/ 10.1007/s00701-012-1494-y Sollmann N, Krieg SM, Säisänen L, Julkunen P Mapping of Motor Function with Neuronavigated Transcranial Magnetic Stimulation: A Review on Clinical Application in Brain Tumors and Methods for Ensuring Feasible Accuracy. Brain Sci [Internet]. 2021 Jul 1 [cited 2022 Apr 19];11(7). Available from: https://pubmed.ncbi.nlm.nih.gov/34356131/ Picht T, Frey D, Thieme S, Kliesch S, Vajkoczy P (2016) Presurgical navigated TMS motor cortex mapping improves outcome in glioblastoma surgery: a controlled observational study. J Neurooncol [Internet]. Feb 1 [cited 2022 Apr 19];126(3):535–43. Available from: https://pubmed.ncbi.nlm.nih.gov/26566653/ Picht T, Schmidt S, Brandt S, Frey D, Hannula H, Neuvonen T et al (2011) Preoperative functional mapping for rolandic brain tumor surgery: comparison of navigated transcranial magnetic stimulation to direct cortical stimulation. Neurosurgery [Internet]. Sep [cited 2022 Apr 19];69(3):581–8. Available from: https://pubmed.ncbi.nlm.nih.gov/21430587/ Narayana S, Gibbs SK, Fulton SP, McGregor AL, Mudigoudar B, Weatherspoon SE et al Clinical Utility of Transcranial Magnetic Stimulation (TMS) in the Presurgical Evaluation of Motor, Speech, and Language Functions in Young Children With Refractory Epilepsy or Brain Tumor: Preliminary Evidence. Front Neurol [Internet]. 2021 May 19 [cited 2024 Oct 21];12. Available from: https://pubmed.ncbi.nlm.nih.gov/34093397/ Moritz I, Engelhardt M, Rosenstock T, Grittner U, Schweizerhof O, Khakhar R et al (2024) Preoperative nTMS analysis: a sensitive tool to detect imminent motor deficits in brain tumor patients. Acta Neurochir (Wien) [Internet]. Oct 21 [cited 2024 Oct 21];166(1):419. Available from: https://pubmed.ncbi.nlm.nih.gov/39432031/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Feb, 2026 Read the published version in Neurosurgical Review → Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 10 Nov, 2025 Editor assigned by journal 06 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 27 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-7474809","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544186146,"identity":"0bdc4195-d18c-464d-ac85-bcd0739c3c5d","order_by":0,"name":"Lucas Schiavão","email":"","orcid":"","institution":"Clinica Monitorizar","correspondingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Schiavão","suffix":""},{"id":544186147,"identity":"8271ac42-ad92-4e66-a408-033ff0cde5ec","order_by":1,"name":"Gabriel 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16:17:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1019795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7474809/v1/e4980c52-a295-4340-a87d-865031f7fbea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of cortical excitability patterns in patients undergoing glioblastoma resection in the motor area","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBrain tumors affect the motor cortex through direct pressure, neural pathway obstruction, and neural plasticity. Neural plasticity occurs differently in slow- and fast-growing lesions \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTranscranial magnetic stimulation (TMS) is a noninvasive technique used to map brain function before tumor resection\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This group of techniques usually shows greater accuracy in motor area localization than does fMRI and correlates well with intraoperative direct cortical stimulation \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.\u003c/p\u003e\u003cp\u003eMoreover, TMS can influence surgical decisions and, when performed in the postoperative period, aid in the prediction of motor strength recovery after brain tumor surgery\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, it is unclear whether specific parameters of TMS mapping, such as short-interval intracortical inhibition (IICI) or intracortical facilitation (ICF), in the preoperative period can aid in the prediction of postoperative motor deficits. Therefore, this study aimed to evaluate whether preoperative TMS parameters could predict postoperative motor deficits\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTherefore, the present study aimed to investigate the potential of TMS cortical excitability parameters to prevent or predict the occurrence of motor deficits after brain tumor surgery.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eRetrospective, noncomparative, nonrandomized study\u003c/p\u003e\u003cp\u003eThis was an observational, exploratory, single-center, analytical study with nonadmitted patients who was carried out at a specialized cancer center in S\u0026atilde;o Paulo.\u003c/p\u003e\u003cp\u003e The present study was approved by an independent ethics committee and followed good clinical practices and the Helsinki guidelines.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient Population and Inclusion Criteria\u003c/h2\u003e\u003cp\u003eThe cohort of patients with glioblastoma was recruited from a specialized cancer center in S\u0026atilde;o Paulo, Brazil. Patients aged between 18 and 70 years with glioblastoma located adjacent to the precentral gyrus were included, and only patients who signed a free consent form and agreed to participate in the study were included. Patients with tumors located closer than 5 mm to the precentral gyrus, patients with decompensated intracranial pressure or a Karnofsky scale score less than 50 points, those with decompensated epilepsy, and those with previous brain surgery were excluded.\u003c/p\u003e\u003cp\u003eFor comparative analysis, data were selected from healthy volunteer participants who made up the Brazilian standardization. The healthy participants had no history of neurological disease, traumatic brain injury, or psychiatric illness.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEvaluation of cortical excitability\u003c/h3\u003e\n\u003cp\u003eEMT pulses were applied to the right primary motor and left primary motor cortices via a circular coil model C-100 MagVenture\u0026reg; positioned tangentially to the scalp in the anteroposterior direction and connected to the MagPro-X100\u0026reg; machine (MagVenture Tonika Elektronik, Farum, Denmark). Each individual being assessed sat in a comfortable reclining chair with armrests and wore a thin fabric cap that enabled anatomical reference points to be identified and marked, along with specific points on the scalp.\u003c/p\u003e\u003cp\u003eOn the basis of the international 10\u0026ndash;20 reference system for electroencephalography, the following anatomical points were marked via a tape measure and an atomic brush pen: nacon-N, inion-I, central-Cz, left preauricular-pE, and right preauricular-pD.\u003c/p\u003e\u003cp\u003eA line was drawn from point N to point I, following the median sagittal line, and another line from point pE to point pD, following a coronal orientation, both intersecting at point Cz. These orientation lines helped locate M1 in both hemispheres. The location of the stimulation target (hotspot) on the scalp of the individuals in this study was determined with the intensity of the machine set at 70% of its total power, and stimuli were applied every 1 to 2 s to find the stimulation point on M1 that caused the greatest possible contraction of the target muscle group. Once the hotspot was found, this point was marked to measure cortical excitability parameters.\u003c/p\u003e\u003cp\u003eRMT was defined according to the recommendation of the IFCN Committee as the lowest stimulus intensity capable of causing an EMP amplitude of \u0026gt;\u0026thinsp;50 microvolts (\u0026micro;V) in at least five out of 10 trials, with the abductor pollicis brevis muscle at rest.\u003c/p\u003e\u003cp\u003eThe parameters selected for evaluation were the resting motor threshold (RMT), motor-evoked potential (PEM) recruitment curves, short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF). The amplitude of the MEPs was considered to be peak-to-peak (\u0026micro;V). To choose the target muscle group, surface electrodes were placed on the abductor pollicis brevis muscle on the insertion of the first dorsal interosseous muscle of the hand and forearm.\u003c/p\u003e\u003cp\u003eThe PEM responses were filtered and amplified via a surface electromyography device. The signals were transferred to a personal computer for offline analysis via a data collection software program (Nicolet Biomedical, Inc.) for each side of the motor cortex.\u003c/p\u003e\u003cp\u003eSingle pulses of EMT (sp-EMT) were used for RMT and EMP measurements (Fig.\u0026nbsp;10). EMP measurements were taken at 120% and 140% of the RMT to assess the relationship between stimulus intensity and the amplitude of the response curve. The average values of the four MEPs obtained at 120% RMT were used for the analysis. The same procedure was adopted for MEPs at 140% RMT.\u003c/p\u003e\u003cp\u003eFor ICF, paired pulses of EMT (pp-EMT) were used at intervals of 10 and 15 ms. The conditioning stimulus, the first pulse released, partially depolarizes the axons of the i-wave-generating neurons, making them hyperexcitable. The second pulse is released, and the test stimulus completes depolarization. The ICF has been reported to be dependent on NMDA receptors. The intensity of the conditioning stimulus was set at 80% of the RMT, and that of the test stimulus was set at 120% of the RMT. For IICIC, pp-EMT was performed in the same pattern as for ICF but at intervals of 2 and 4 ms.\u003c/p\u003e\u003cp\u003eThe overall IICIC and ICF values were obtained by averaging the overall indices at 02 and 04 ms (IICIC) and at 10 and 15 ms (ICF). The overall IICIC and ICF values are expressed as the average of the four measurements divided by the average of the MEPs at 120% of RMT42\u0026ndash;56.108. The IICIC and ICF values were assessed for the fully relaxed target muscle, and information was recorded via a specific data collection instrument.\u003c/p\u003e\n\u003ch3\u003eAnalyzed outcomes\u003c/h3\u003e\n\u003cp\u003eThe following variables were analyzed in the present study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003cp\u003eAge, sex\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCortical excitability\u003c/strong\u003e\u003cp\u003eresting motor threshold (RMT), PEM 120%, PEM 140%, MEP140/120 ratio, ratio pp02/MEP120 (Rel-02), pp 04/MEP120 (Rel-04), pp 10/MEP120(Rel-10), pp 15/MEP120 (Rel-15), ICF, IICIC.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePhysical examination\u003c/strong\u003e\u003cp\u003eUpper limb motor strength, lower limb motor strength, and motor loss (yes if a patient presented a reduction in upper and/or lower limb strength between the preoperative period).\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe patient data were inserted into an Excel spreadsheet and analyzed via R software (version 4.2). Continuous variables will be described as the mean, median, standard deviation, interquartile range, minimum, and maximum. Categorical variables are presented as numbers and percentages. Variable distribution was assessed via the Kolmogorov\u0026ndash;Smirnov test, with p values above 0.05 indicating a normal distribution. To compare continuous variables, the Welch t test is used in the case of normally distributed variables, or the yuen\u0026ndash;Welch t test is used in the case of variables that do not adhere to a distribution close enough to normal.\u003c/p\u003e\u003cp\u003eTo compare the values of cortical excitability between the ill hemisphere of the glioblastoma cohort and the healthy cohort, a one-sample median test using the \u0026ldquo;honest\u0026rdquo; estimator and the median value of the healthy cohort as a reference was utilized.\u003c/p\u003e\u003cp\u003eTo compare categorical variables between the two groups, the chi-square test was used in cases with more than two groups and in cases where more than 20% of the expected sum values were lower than 5.\u003c/p\u003e\u003cp\u003eFurthermore, to investigate potential cortical excitability and demographic variables that have little potential for predicting postoperative motor deficits, variables that presented correlation coefficients greater than 0.2 or less than \u0026minus;\u0026thinsp;0.2 in the percentage bend correlation analysis were included in a multivariable logistic regression. The following tests were used to assess the correlations between the presence of deficits and other variables: robust correlation and the predictive power score.\u003c/p\u003e\u003cp\u003eFinally, the marginal effects of the logistic regression model were evaluated via the average slope and average contrast functions of the WRS2 package, and uncertainty estimates were obtained via heteroskedasticity-consistent estimation of the covariance matrix HC3. The Benjamini‒Hochberg test was employed to adjust the p value of multiple tests.\u003c/p\u003e\u003cp\u003eStatistical significance was set to 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSixty patients were included in the TMS cortex excitability protocol analysis, and their demographic data are detailed in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline demographic and clinical characteristics. Continuous variables are reported as medians [IQRs] and means (SDs); categorical variables are reported as n (%). Karnofsky = Karnofsky performance status; ECOG = Eastern Cooperative Oncology Group performance status. The motor strength was graded from 0\u0026ndash;5.\u0026nbsp;\u003cem\u003eNote:\u003c/em\u003e there appears to be a discrepancy between the sex counts (Male 40 + Female 33 = 73) and the sample sizes reported for other variables (e.g., N = 59\u0026ndash;60), suggesting a possible data entry or aggregation inconsistency that should be checked.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.0/79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedian [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.0 [41.5; 62.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.5 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN (missing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eBiological sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003ePreop motor strength (Arms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (3.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (3.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (5.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (38.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003ePreop motor strength (Legs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (1.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (3.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (6.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (38.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrade 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eKarnofsky Performance Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30/90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedian [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.0 [50.0; 80.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.3 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN (missing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eECOG Performance Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (35.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (13.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (11.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThere were significant differences between the healthy and ill brain hemispheres of the patients in the following variables: Rel-02, Rel-04, Rel-10, Rel-15, and ICF. A full comparison of the variables between healthy and ill brain hemispheres is shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eDescriptive values and comparisons between healthy and ill hemispheres. Mean [95% CI]. std, standard deviation; NA: not applicable; IQR: interquartile range.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"737\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLabel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDescriptive statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrain Hemisphere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 141px;\"\u003e\n \u003cp\u003eDifference of Trimmed Means\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;RMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e36.0/85.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e27.0/86.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-0,64 [-4,69; 3,41]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,04 [0;0,31]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: \u0026nbsp;0,804\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e50.0 [46.0;58.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51.0 [45.0;60.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e52.4 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e52.8 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMEP120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e94.0/4136.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e38.0/4434.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 141px;\"\u003e\n \u003cp\u003e50,07 [-114,34; 214,49]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,07 [0;0,33]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: 0,537\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e525.0 [327.0;832.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e465.6 [250.0;749.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e715.7 (701.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e654.4 (686.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMEP140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e221.5/6140.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e66.0/7345.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 141px;\"\u003e\n \u003cp\u003e13,55 [-329,04; 356,14]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,02 [0;0,29]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: 0,931\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1075.5 [576.4;1619.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1020.8 [553.0;1736.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1353.8 (1126.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1303.6 (1187.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e66 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e58 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRatio 140/120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0/7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.0/5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-0,11 [-0,42; 0,21]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,08 [0;0,3]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: 0,482\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.8 [1.4;2.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.0 [1.6;2.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.2 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e58 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e659.0 [504.5;1086.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1030.0 [353.0;1783.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e900.5 (726.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1247.5 (1103.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRel-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.05/7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.1/2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-0,19 [-0,38; 0]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,25 [0;0,49]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: 0,058\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.5 [0.3;0.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.7 [0.4;1.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.8 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.9 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRel-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.1/4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.3/3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-0,27 [-0,53; -0,02]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,27 [0,03;0,52]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: 0,035\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.8 [0.4;1.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.0 [0.7;1.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.0 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRel-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.3/6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.3/6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-0,56 [-1,02; -0,1]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,31 [0,1;0,53]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: 0,022\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.3 [0.8;2.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.7 [1.3;2.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.6 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.2 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRel-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.3/4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.4/7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-0,48 [-0,87; -0,1]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,32 [0,08;0,55]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: 0,016\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.4 [1.0;2.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.7 [1.4;3.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.6 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.3 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003eIICI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.1/5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.2/3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-0,22 [-0,45;0,01]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,23 [0;0,46]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: 0,069\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.7 [0.4;1.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.9 [0.6;1.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.9 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.1 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003eICF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.3/1445.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.3/6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-0,48 [-0,91; -0,05]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0,3 [0,07;0,54]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp\u003ep value: 0,028\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Yuen-Welch-T test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMed [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.4 [0.9;2.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.8 [1.4;2.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMean (std)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e23.2 (176.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.2 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eN (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWhen considering the established thresholds in the literature, a difference between the\u0026nbsp;healthy\u0026nbsp;and\u0026nbsp;ill\u0026nbsp;hemisphere of the patients was identified in the following variables: Rel-04, Rel-10, Rel-15, and IICI. In all\u0026nbsp;the\u0026nbsp;cases, there was a significantly lower odds ratio of Ill patients presenting a\u0026nbsp;\u0026ldquo;low\u0026rdquo;\u0026nbsp;classification\u0026nbsp;than a \u0026ldquo;high\u0026rdquo;\u0026nbsp;classification (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003eDescriptive values and comparisons between healthy and ill hemispheres according to the normal ranges specified by Cueva et al. (2016). Mean [95% CI]. std, standard deviation; NA: not applicable; IQR: interquartile range.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"703\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elabel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003evariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect Size\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOdds ratio [95% Wald CI], ref=\u0026apos;Ill vs Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e34 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e34 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;LOW vs HIGH: 1.25 [0.56 to 2.84]\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 0.41 [0.14 to 1.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 0.1142\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Pearson\u0026apos;s Chi-squared test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e16 (44.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e20 (55.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e17 (70.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e7 (29.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eMEP 120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e27 (55.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e22 (44.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;LOW vs HIGH: 1.55 [0.68 to 3.57]\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 0.88 [0.36 to 2.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 0.4037\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Pearson\u0026apos;s Chi-squared test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e19 (44.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e24 (55.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e21 (58.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e15 (41.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eMEP 140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e22 (48.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e23 (51.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;LOW vs HIGH: 0.89 [0.40 to 1.97]\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 0.56 [0.21 to 1.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 0.4957\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Pearson\u0026apos;s Chi-squared test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e27 (51.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e25 (48.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e17 (62.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e10 (37.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRatio 140/120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e3 (60.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e2 (40.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;LOW vs HIGH: 1.32 [0.21 to 10.35]\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 1.39 [0.20 to 11.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 1.0000\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Fisher\u0026apos;s Exact Test for Count Data)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e49 (53.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e43 (46.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e14 (51.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e13 (48.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRel-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e27 (46.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e31 (53.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;LOW vs HIGH: 0.49 [0.21 to 1.14]\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 0.87 [0.37 to 2.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 0.2493\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Pearson\u0026apos;s Chi-squared test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e23 (63.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e13 (36.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e17 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e17 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRel-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e23 (43.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e30 (56.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003cstrong\u003eLOW vs HIGH: 0.21 [0.07 to 0.57]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 0.87 [0.39 to 1.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 0.0067\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Pearson\u0026apos;s Chi-squared test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e22 (78.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6 (21.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e22 (46.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e25 (53.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRel-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e10 (34.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e19 (65.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003cstrong\u003eLOW vs HIGH: 0.34 [0.13 to 0.83]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 0.53 [0.18 to 1.51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 0.0580\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Pearson\u0026apos;s Chi-squared test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e43 (60.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e28 (39.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e14 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e14 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRel-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e10 (32.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e21 (67.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003cstrong\u003eLOW vs HIGH: 0.25 [0.10 to 0.63]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 0.48 [0.18 to 1.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 0.0117\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Pearson\u0026apos;s Chi-squared test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e36 (65.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e19 (34.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e21 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e21 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eIICI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e25 (42.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e34 (57.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003cstrong\u003eLOW vs HIGH: 0.27 [0.10 to 0.68]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 0.70 [0.31 to 1.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 0.0216\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Pearson\u0026apos;s Chi-squared test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e22 (73.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e8 (26.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e20 (51.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e19 (48.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003eICF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eHIGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e11 (34.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e21 (65.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003cstrong\u003eLOW vs HIGH: 0.30 [0.12 to 0.72]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;NORMAL vs HIGH: 0.56 [0.20 to 1.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 117px;\"\u003e\n \u003cp\u003ep value: 0.0238\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Pearson\u0026apos;s Chi-squared test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e40 (63.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e23 (36.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNORMAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e16 (48.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e17 (51.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, when the cortical excitability parameters of the ill hemisphere were evaluated and compared with the median values of the healthy Brazilian population, all the values were significantly different (p \u0026lt; 0.05) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003eComparison of neurophysiological measures in the affected (ill) hemisphere versus reference healthy-population medians, stratified by age group (\u0026lt;50 years; \u0026ge;50 years). For each measure, the ill-hemisphere median and its 95% confidence interval (CI) are shown, together with the healthy population median, the median difference (calculated as the ill hemisphere median minus the healthy median via a robust one-sample test with the healthy median as the threshold), and the two-sided p value. Positive median differences indicate higher values in the ill hemisphere. Values are presented as medians (95% CI) or n.s./p where indicated; p\u0026lt;0.05 was considered statistically significant. Abbreviations: RMT = resting motor threshold; MEP120/MEP140 = peak-to-peak motor-evoked potential amplitude at 120%/140% of stimulator output; MEP140/120 = ratio of MEP140 to MEP120; Rel-02/Rel-04/Rel-10/Rel-15 = relative MEP (at interstimulus intervals of 2, 4, 10 and 15 ms, respectively); IICI = short-interval intracortical inhibition; ICF = intracortical facilitation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eIll hemisphere median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConfidence Interval 95-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConfidence Interval 95-+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Population Median\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian difference \u0026nbsp;(Robust Test, Health as threshold)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBelow 50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e49.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e46.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e54.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMEP 120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e737.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e448.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1,226.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e315.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e422.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.000\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMEP 140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1,622.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1,196.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2,145.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e773.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e849.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMEP 140/120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRel-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.000\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRel-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.000\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRel-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRel-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.014\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eIICI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.000\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eICF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.000\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cu\u003eAbove 50 years\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e58.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e48.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e51.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e61.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.0020\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMEP 120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e263.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e263.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e176.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e465.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.9975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMEP 140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e611.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e799.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e352.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1,018.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-187.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.3610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMEP 140/120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.0010\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRel-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.0010\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRel-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.0000\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRel-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.8520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRel-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.0650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eIICI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cu\u003e0.0000\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eICF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.0660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003ePreoperative transcranial magnetic stimulation (TMS) has become an increasingly commonly used tool in neurosurgical planning, particularly for procedures involving eloquent brain regions that control motor, sensory, and language functions. The present study demonstrated that patients with brain tumors presented abnormal TMS parameters, both in the healthy hemisphere and in the diseased hemisphere of the brain, compared with healthy patients; however, only the Eastern Cooperative Oncology Group (ECOG) score was able to predict the occurrence of motor deficits during the postoperative period.\u003c/p\u003e\n\u003ch3\u003eFunctional Mapping and Precision in Surgical Planning\u003c/h3\u003e\n\u003cp\u003eThe integration of TMS into preoperative workflows enables detailed, noninvasive mapping of cortical regions responsible for critical functions. Studies have demonstrated that TMS is highly effective in identifying motor and language areas, which are often challenging to delineate with conventional imaging techniques such as MRI alone. The functional mapping provided by TMS is especially useful when lesions, such as tumors or vascular malformations, are located near or within eloquent cortices. By stimulating specific cortical areas and monitoring resultant motor or language responses, TMS allows neurosurgeons to plan resections that maximize pathological tissue removal while preserving essential functions\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003ePostoperative motor deficits\u003c/h3\u003e\n\u003cp\u003eOne of the most significant impacts of using preoperative TMS is the reduction of postoperative deficits, such as motor weakness, aphasia, or sensory loss. This benefit is particularly pronounced in patients who undergo tumor resection in or near eloquent brain areas. TMS helps localize functional regions noninvasively, which reduces the likelihood of damaging critical tissue during surgery\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Multiple studies have reported that TMS-guided surgeries lead to better functional outcomes than surgeries that do not utilize functional mapping techniques\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor example, Picht et al. (2011) demonstrated that motor mapping with TMS significantly decreased the incidence of new motor deficits in patients with gliomas near motor areas. In their study, 94% of patients who underwent TMS-based mapping maintained their preoperative motor function postoperatively, whereas patients without TMS mapping had a greater risk of postoperative motor impairment \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Similarly, Narayana et al. (2021) reported that the use of preoperative TMS to map eloquent areas in children with epilepsy or brain tumors led to no language deficits (7 patients) and that 90% of the patients had no motor deficits (9 of 11 patients) in the postoperative period \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn a recent study, Moritz et al. (2024) reported that RMT values below 1.1 and pathological values of the cortical silent period (CSP) or recruitment curve (RC) were associated with worse motor outcomes after seven days or three months; however, when these values were combined in a multivariable regression, none of the parameters presented any significant impact on the occurrence of postoperative motor deficits \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Similar to the findings presented in this work, some TMS parameter variables demonstrated a small correlation with the occurrence of postoperative motor deficits; however, when analyzed together via multivariable regression, those values were no longer significant.\u003c/p\u003e\u003cp\u003eFinally, in an extensive narrative review regarding the use of techniques for mapping and assessing neuroplasticity to improve surgical planning, Duffau (2020) reported that the TMS technique is capable of performing very specific and sensitive maps of the brain (both the ill and healthy hemispheres); however, a better understanding of how different neuroplasticity patterns impact the risk of postoperative deficits has yet to be described.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThe main limitation of this work lies in its small sample size, which makes it difficult to gain clear insights into the impact of several parameters of TMS on the occurrence of motor deficits, as different patients present distinct patterns of TMS parameters even when inside the \u0026ldquo;normal\u0026rdquo; ranges.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study demonstrated that the excitability parameters of patients with brain tumors are significantly altered compared with those of normal individuals and even compared with those of healthy individuals, revealing the changes and changes in neuroplasticity that occur in the eloquent areas of the ill hemisphere.\u003c/p\u003e\u003cp\u003eHowever, the present study did not identify any preoperative TMS parameter capable of indicating or suggesting an increased risk of postoperative motor deficits.\u003c/p\u003e\u003cp\u003eFurther studies with more patients who are able to analyze the whole pattern of preoperative excitability as one large panel, not only as individual parameters, are needed to understand the overall changes in neuroplasticity that occur in these groups of patients and how it can lead to increased or decreased risks of postoperative deficits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical committee approval was obtained from an independent ethics committee for \u0026nbsp;the present study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo data are available online; however, under reasonable request, the data can be shared by the author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;\u0026nbsp;roles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLS (study conception, data curation, investigation,\u0026nbsp;writing-original draft, reviewing final draft)\u003c/p\u003e\n\u003cp\u003eGP (formal analysis, reviewing the final draft, visualization)\u003c/p\u003e\n\u003cp\u003eWSP (study conception,\u0026nbsp;writing-original draft, reviewing final draft)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eInformed consent was obtained from all individual participants included in the study.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKong NW, Gibb WR, Badhe S, Liu BP, Tate MC Plasticity of the Primary Motor Cortex in Patients with Primary Brain Tumors. Neural Plast [Internet]. 2020 Jan 1 [cited 2024 Sep 12];2020(1):3648517. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onlinelibrary.wiley.com/doi/full/\u003c/span\u003e\u003cspan address=\"https://onlinelibrary.wiley.com/doi/full/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2020/3648517\u003c/span\u003e\u003cspan address=\"10.1155/2020/3648517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrey D, Schilt S, Strack V, Zdunczyk A, Sler JR, Niraula B et al (2014) Navigated transcranial magnetic stimulation improves the treatment outcome in patients with brain tumors in motor eloquent locations. Neuro Oncol [Internet]. Oct 1 [cited 2024 Sep 12];16(10):1365\u0026ndash;72. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.doi.org/10.1093/neuonc/nou110\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/nou110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaffa G, Scibilia A, Conti A, Ricciardo G, Rizzo V, Morelli A et al (2019) The role of navigated transcranial magnetic stimulation for surgery of motor-eloquent brain tumors: a systematic review and meta-analysis. Clin Neurol Neurosurg 180:7\u0026ndash;17\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNarayana S, Gibbs SK, Fulton SP, McGregor AL, Mudigoudar B, Weatherspoon SE et al (2021) Clinical Utility of Transcranial Magnetic Stimulation (TMS) in the Presurgical Evaluation of Motor, Speech, and Language Functions in Young Children With Refractory Epilepsy or Brain Tumor: Preliminary Evidence. Front Neurol [Internet]. 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Front Neurol. ;11\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaddad AF, Young JS, Berger MS, Tarapore PE (2021) Preoperative Applications of Navigated Transcranial Magnetic Stimulation. Front Neurol [Internet]. Jan 22 [cited 2024 Sep 12];11:628903. Available from: www.frontiersin.org\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchiavao LJV, Ribeiro IN, Hayashi CY, Figueiredo EG, Brunoni AR, Teixeira MJ et al (2022) Assessing the Capabilities of Transcranial Magnetic Stimulation (TMS) to Aid in the Removal of Brain Tumors Affecting the Motor Cortex: A Systematic Review. Neuropsychiatr Dis Treat [Internet]. [cited 2024 Sep 12];18:1219. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/26566653/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/26566653/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePicht T, Schmidt S, Brandt S, Frey D, Hannula H, Neuvonen T et al (2011) Preoperative functional mapping for rolandic brain tumor surgery: comparison of navigated transcranial magnetic stimulation to direct cortical stimulation. Neurosurgery [Internet]. Sep [cited 2022 Apr 19];69(3):581\u0026ndash;8. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/21430587/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/21430587/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNarayana S, Gibbs SK, Fulton SP, McGregor AL, Mudigoudar B, Weatherspoon SE et al Clinical Utility of Transcranial Magnetic Stimulation (TMS) in the Presurgical Evaluation of Motor, Speech, and Language Functions in Young Children With Refractory Epilepsy or Brain Tumor: Preliminary Evidence. Front Neurol [Internet]. 2021 May 19 [cited 2024 Oct 21];12. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/34093397/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/34093397/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoritz I, Engelhardt M, Rosenstock T, Grittner U, Schweizerhof O, Khakhar R et al (2024) Preoperative nTMS analysis: a sensitive tool to detect imminent motor deficits in brain tumor patients. Acta Neurochir (Wien) [Internet]. Oct 21 [cited 2024 Oct 21];166(1):419. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/39432031/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/39432031/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"neurosurgical-review","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nrev","sideBox":"Learn more about [Neurosurgical Review](https://www.springer.com/journal/10143)","snPcode":"10143","submissionUrl":"https://submission.nature.com/new-submission/10143/3","title":"Neurosurgical Review","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Glioblastomas, cortical excitability, motor evoked potential, neurophysiology, transcranial magnetic stimulation, motor cortex","lastPublishedDoi":"10.21203/rs.3.rs-7474809/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7474809/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Brain tumors adjacent to the primary motor cortex may alter corticospinal physiology through mass effects, pathway disruption and maladaptive plasticity. Preoperative transcranial magnetic stimulation (TMS) maps cortical excitability more accurately than fMRI does and correlates with intraoperative stimulation, but it is unclear which TMS parameters predict postoperative motor outcomes. We investigated whether preoperative TMS cortical excitability measures differ from healthy norms and whether they identify signals potentially useful for predicting postoperative motor deficits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This was a retrospective, single-center observational study of consecutive adult patients (18–70 years) with glioblastoma adjacent to the precentral gyrus who underwent standardized preoperative TMS mapping (N = 60). The measured parameters included the resting motor threshold (RMT), MEP amplitudes at 120% and 140% RMT, the MEP140/120 ratio, paired-pulse relative MEPs at interstimulus intervals of 2, 4, 10 and 15 ms (Rel-02, Rel-04, Rel-10, Rel-15), short-interval intracortical inhibition (IICI) and intracortical facilitation (ICF). Patient values were compared to those of a healthy Brazilian reference cohort via robust one-sample median tests and age-stratified analyses; categorical comparisons were performed with normal ranges in the literature (Cueva et al.). Variables with correlation coefficients |r|\u0026gt;0.2 were considered for multivariable logistic modeling to explore associations with postoperative motor deficits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Sixty patients were analyzed. Compared with the healthy cohort, the ill hemisphere showed significant alterations in paired-pulse and intracortical measures. In categorical analyses using established normal ranges, the ill hemisphere had significantly lower odds of being classified as “Low” (vs “High”) for Rel-04 (OR 0.21; 95% CI 0.07–0.57; p = 0.0067), Rel-15 (OR 0.25; 95% CI 0.10–0.63; p = 0.0117), IICI (OR 0.27; 95% CI 0.10–0.68; p = 0.0216) and ICF (OR 0.30; 95% CI 0.12–0.72; p = 0.0238). Age-stratified robust median tests versus healthy medians demonstrated multiple significant deviations—most prominently for MEP amplitudes and relative paired-pulse indices in patients \u0026lt;50 years. Several excitability measures met prespecified correlation thresholds and were entered into multivariable modeling (the results are reported in the main text).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Preoperative TMS reveals consistent alterations in paired-pulse measures and intracortical inhibition/facilitation in glioblastomas adjacent to the motor cortex. Rel-04, Rel-15, IICI and ICF differ from healthy norms and merit prospective evaluation as predictors of postoperative motor outcomes. Future prospective studies should validate these parameters and determine their incremental predictive value for surgical planning and rehabilitation stratification.\u003c/p\u003e","manuscriptTitle":"Evaluation of cortical excitability patterns in patients undergoing glioblastoma resection in the motor area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 07:51:06","doi":"10.21203/rs.3.rs-7474809/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T11:40:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T19:19:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138488730243488352578159629497383889598","date":"2025-11-12T23:12:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39919071532485413294704317740190978471","date":"2025-11-12T19:39:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-10T21:20:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-06T18:11:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T08:15:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neurosurgical Review","date":"2025-08-27T21:55:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neurosurgical-review","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nrev","sideBox":"Learn more about [Neurosurgical Review](https://www.springer.com/journal/10143)","snPcode":"10143","submissionUrl":"https://submission.nature.com/new-submission/10143/3","title":"Neurosurgical Review","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4e022d5a-c234-49a4-b5cd-03fa25db661e","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:16:27+00:00","versionOfRecord":{"articleIdentity":"rs-7474809","link":"https://doi.org/10.1007/s10143-025-04094-9","journal":{"identity":"neurosurgical-review","isVorOnly":false,"title":"Neurosurgical Review"},"publishedOn":"2026-02-05 15:59:31","publishedOnDateReadable":"February 5th, 2026"},"versionCreatedAt":"2025-11-20 07:51:06","video":"","vorDoi":"10.1007/s10143-025-04094-9","vorDoiUrl":"https://doi.org/10.1007/s10143-025-04094-9","workflowStages":[]},"version":"v1","identity":"rs-7474809","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7474809","identity":"rs-7474809","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}