Cerebellar functional reorganization across mandibular positions in painful temporomandibular disorder: an fMRI case report integrated with cerebellar voxel-based morphometry

Cerebellar functional reorganization across mandibular positions in painful temporomandibular disorder: an fMRI case report integrated with cerebellar voxel-based morphometry | 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 Case Report Cerebellar functional reorganization across mandibular positions in painful temporomandibular disorder: an fMRI case report integrated with cerebellar voxel-based morphometry Nataliia Savychuk, Vasil Pekhno, Ivan Riabko This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9177677/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background Painful temporomandibular disorder is common, but cerebellar responses to different mandibular states remain insufficiently described. This case is reported because it shows a novel within-subject pattern of state-dependent cerebellar blood oxygen level-dependent signal redistribution. Case presentation: A 27-year-old woman presented with persistent jaw tension, bruxism, and subjective chronic tinnitus of approximately 2 years duration. Brain magnetic resonance imaging with task-based functional imaging was performed using a block-design rest-task paradigm. During imaging, the patient performed jaw-closing tasks under three experimentally defined mandibular conditions: centric occlusion, a splint condition, and centric relation. Functional data were preprocessed in standard space and analyzed using voxelwise and region-of-interest-based cerebellar assessment with the SUIT Nettekoven Asym32 atlas. The splint condition showed the largest number of cerebellar clusters and the broadest topographic distribution, but these activations were mainly small and spatially fragmented. Centric relation showed the largest total activated volume and the highest peak signal, consistent with more consolidated activation fields. Centric occlusion showed fewer clusters overall but the highest mean signal intensity. Model-based analysis did not identify a robust global main effect of mandibular condition; however, region-specific effects and an interaction between cerebellar region and centric relation suggested condition-dependent redistribution of cerebellar activity. Conclusions This case demonstrates that cerebellar blood oxygen level-dependent responses in painful temporomandibular disorder may vary according to mandibular state in a region-specific manner. The findings provide pathophysiological insight into cerebellar involvement in temporomandibular disorder and suggest potential value for individualized functional assessment in complex cases. Temporomandibular disorders cerebellum functional magnetic resonance imaging voxel-based morphometry SUIT atlas Figures Figure 1 Figure 2 Background Temporomandibular disorders (TMD) are musculoskeletal pain conditions involving the temporomandibular joint and/or the masticatory muscles. They are among the most common chronic musculoskeletal disorders, affecting an estimated 6–12% of the general population. Despite their high prevalence, management remains largely multimodal rather than mechanism-specific. Pain is one of the main reasons patients seek care, and reported average pain intensity in TMD has been shown to be comparable to that of other clinically significant pain conditions, including chest and back pain [ 1 ]. The cerebellum plays an important role in motor coordination while integrating diverse sensory inputs [ 2 ]. Human neuroimaging studies have consistently demonstrated cerebellar involvement in both physiological and pathological pain states [ 3 – 7 ]. In TMD, a limited number of neuroimaging studies have reported cerebellar activation, including findings in the left cerebellar hemisphere [ 8 , 9 ]. In addition, several studies suggest that central nervous system activity may vary according to mandibular position or occlusal state, which is consistent with the cerebellum’s role in sensorimotor integration and adaptive motor control [ 10 – 13 ]. To our knowledge, no case-based functional magnetic resonance imaging (fMRI) report has described within-subject cerebellar BOLD signal redistribution across different mandibular positions in TMD. This case may therefore provide a novel pathophysiological perspective and potential diagnostic insight into state-dependent brain responses in TMD. Case presentation This case was evaluated within a study protocol approved by the Commission on Ethics and Academic Integrity of the Shupyk National Healthcare University of Ukraine (Protocol No. 13/10; Ukrainian Research Registration No. 0125U003930). Written informed consent for clinical and imaging assessment was obtained from the patient before participation. A 27-year old female presented to the Department of Therapeutic and Pediatric Dentistry and Department of Orthopedic Dentistry, Digital Technologies and Implantology, Faculty of Dentistry, Shupyk National Healthcare University of Ukraine, on October 22, 2024. The main complaints were persistent jaw tension, bruxism, and subjective chronic tinnitus that had been present for approximately 2 years. Clinical findings On clinical examination, the patient reported pain on palpation of the left temporalis muscle, left lateral pterygoid muscle, and left temporomandibular joint region. She also reported left-sided otalgia and pain radiating to the left periauricular region. Maximum mouth opening was 43 mm, with deviation to the left during opening. Joint clicking was present in the left temporomandibular joint. No neurological abnormalities were observed. These findings were considered consistent with a painful temporomandibular disorder phenotype. Diagnostic assessment Based on the clinical presentation, palpation findings, mandibular deviation during opening, joint clicking, and imaging work-up, the case was classified as painful temporomandibular disorder. Structural MRI was used to assess the anatomical context, whereas task-based fMRI was used to investigate condition-dependent cerebellar functional responses across different mandibular states. Outcome and follow-up No adverse events related to the imaging procedure were observed. The case was primarily investigated for diagnostic and pathophysiological characterization rather than for assessment of treatment efficacy. At the time of reporting, the patient remained under clinical observation, and the present report focuses on the imaging phenotype observed during the study protocol. Patient perspective: The patient reported that the most disturbing symptoms were persistent jaw tension and chronic tinnitus, both of which she considered clinically meaningful in everyday life. She perceived the imaging-based assessment as important because it could help clarify whether different mandibular positions were associated with different symptom-related brain responses. Timeline Symptoms of persistent jaw tension, bruxism, and subjective chronic tinnitus had been present for approximately 2 years before the patient sought specialist evaluation. She presented on October 22, 2024, to the Faculty of Dentistry of Shupyk National Healthcare University of Ukraine for clinical assessment. Following informed consent, structural MRI and task-based fMRI were performed at the National Children’s Specialized Hospital “Okhmatdyt.” Magnetic resonance imaging was performed at the Radiology Department of the National Children’s Specialized Hospital “Okhmatdyt,” Ministry of Health of Ukraine. Brain functional MRI was acquired using a block-design rest-task paradigm. During imaging, the patient performed jaw-closing tasks under three experimentally defined mandibular conditions: centric occlusion (CO), a splint condition, and centric relation (CR). These conditions were used as comparative functional states during imaging rather than as treatment outcomes. Functional activation maps were superimposed on anatomical T1-weighted brain MRI images. Functional data were preprocessed and co-registered to MNI152 space. Imaging parameters were as follows: repetition time 4000 ms, echo time 97 ms, slice thickness 5.0 mm, 25 slices, distance factor 30%, field of view 230 mm, phase field of view 100%, base resolution 128, phase resolution 100%, GRAPPA acceleration factor 2, reference lines 32, partial Fourier 6/8, distortion correction off, raw filter on, elliptical filter off, strong fat saturation, standard dynamic mode, magnitude reconstruction, and AutoAlign (Head > Brain). To characterize state-dependent cerebellar responses across mandibular conditions, both voxelwise and ROI-based analyses were performed. Voxelwise analysis was conducted in FEAT (FSL v6.0.7.18) using a general linear model to generate Z-statistic maps, with a threshold of Z > 3.1 and cluster-wise correction at p < 0.05 based on Gaussian random field theory. ROI-level cerebellar characterization was performed using the SUIT Nettekoven Asym32 atlas [ 14 ], selected because of its multimodal organization and sufficiently detailed cerebellar parcellation. For each mandibular condition, the activation index, voxel counts, peak uncorrected p-values, and FRD were calculated for activated cerebellar regions. To compare cerebellar response profiles across CO, splint condition, and CR, pairwise contrasts between conditions were also examined. Pattern similarity across conditions was further explored using cosine similarity, Euclidean distance, and mean absolute difference. Hierarchical clustering and consensus heatmaps were used to evaluate ROI-level organization across mandibular states. Structural cerebellar morphometric assessment was performed on anatomical MRI using FreeSurfer (v7.4.1). HOROS (v3.3.6) was used for MRI visualization, and MATLAB (R2024a) was used for statistical processing. Imaging findings As summarized in Table 1 , cerebellar activation varied across the three mandibular conditions, predominantly through ROI-specific redistribution rather than a uniform global shift. The splint condition showed the largest number of clusters (21) and the widest topographic distribution, involving 13 unique ROIs. However, this pattern was predominantly fragmented, with a median cluster size of 30 voxels, a mean cluster size of 578 voxels, a mean Z-statistic of 4.251, and a maximum Z-statistic of 6.023. Thus, under the splint condition, activation was distributed across a larger number of small and spatially fragmented cerebellar clusters. Table 1 General fMRI results across all conditions Mandibular position Number of clusters Number of voxels Cluster size Z-statistic Unique ROIs in rows median mean mean max CO 8 11 646 523 1455,75 5.065 6.898 8 Splint condition 21 12 157 30 578 4,251 6.023 13 CR 11 15 495 85 1408.63 4.809 7.398 8 The CR condition showed a distinct pattern. Although fewer clusters were identified (11), the total activated volume was the largest (15,495 voxels), with a mean cluster size of 1408.63 voxels, a median cluster size of 85 voxels, and the highest peak response in the dataset (Zmax = 7.398). This pattern was associated with more spatially consolidated activation fields and higher peak intensity in selected ROIs. CO showed an intermediate spatial pattern, with 8 clusters and a total activated volume of 11,646 voxels, but it had the highest mean Z-statistic (5.065; Zmax = 6.898). Compared with the splint condition, activation under CO was less spatially dispersed but relatively more intense at the voxel level, with larger clusters overall (median 523 voxels; mean 1455.75 voxels). Detailed ROI-level findings, including anatomical region, cluster overlap, activation index, voxel count, uncorrected peak p-values, FRD, and SUIT coordinates, are presented in Table 2 . Table 2 The results of preprocessing the data according to SUIT Nettekoven Asym32 atlas Anato-mical region Over-lap in cluster (%) Other regions (label: share%) Activa-tion index (peak Z/T) Number of voxels in cluster Uncorr. p (peak) FDR q (peaks) SUIT coords NMI152 (x y z) Centric occlusion 1. D2R 24.1 S4R:22.3%; S2R:18.2% 6,898 4379 5,27E-12 4,22E-11 35 -39 -41 2. S1L 24.8 D1L:12.8%; D3L:11.3% 6,403 4677 1,52E-10 6,1E-10 29 -56 34 3. S1L 42.2 S2L:33.9%; D1L:12.3% 6,048 495 1,47E-09 3,91E-09 40 -83 3 4. S2R 58.0 D2R:19.2%; S3R:7.1% 5,682 1484 1,33E-08 2,66E-08 24 -83 -28 5. M3R 36.9 S1R:32.5%; D4R:11.8% 4,577 550 4,71E-06 7,54E-06 35 -63 -16 6. A2R 60.0 M4R:16.4%; S5R:10.9% 4,396 55 1,1E-05 1,47E-05 40 -52 -20 7. D2R 60.0 D3R:40.0% 3,406 5 0,000659 0,000753 28 v -31 8. S2R 100.0 3,106 1 0,001896 0,001896 40 -54 -15 Splint condition 1. D3L 21.5 S2L:19.5%; S1L:17.8% 6,023 5416 1,71E-09 3,59E-08 29 -57 36 2. D2R 48.2 S4R:32.6%; S2R:12.6% 5,837 709 5,31E-09 5,57E-08 43 -44 -49 3. D1R 43.6 S1R:31.6%; M1R:12.6% 5,646 427 1,64E-08 1,15E-07 26 -69 -20 4. D3R 55.4 D2R:34.7%; D1R:6.4% 5,204 623 1,95E-07 1,02E-06 46 -69 -29 5. M2L 23.5 M3L:22.1%; D3L:21.9% 5,031 2204 4,87E-07 2,04E-06 9 -44 -3 6. D2R 52.2 S4R:34.1%; S2R:11.2% 4,821 1251 1,43E-06 4,77E-06 46 -59 -49 7. D2R 72.3 D4R:27.7% 4,799 47 1,59E-06 4,77E-06 31 -60 -37 8. M3L 60.4 M4L:36.7%; D2L:2.1% 4,742 283 2,12E-06 5,56E-06 18 -38 4 9. S2R 64.4 S1R:20.3%; S4R:12.3% 4,475 1061 7,64E-06 1,78E-05 26 -82 -27 10. S1L 60.0 S2L:40.0% 4,315 20 1,59E-05 3,35E-05 33 -67 19 11. M3R 100.0 4,051 42 5,1E-05 9,74E-05 41 -55 -29 12. D3R 100.0 3,969 8 7,23E-05 0,000127 29 -54 -33 13. M2R 76.7 D3R:16.7%; D2R:6.7% 3,755 30 0,000174 0,00028 24 -57 -26 14. M3L 87.5 M2L:12.5% 3,623 16 0,000291 0,000437 39 -58 10 15. M2R 100.0 3,547 4 0,000389 0,000545 22 -58 -10 16. D3R 100.0 3,44 6 0,000583 0,000737 23 -59 -52 17. A1R 100.0 3,433 3 0,000597 0,000737 42 -38 -40 18. M1R 100.0 3,169 2 0,001527 0,001722 11 -74 -16 19. D1R 100.0 3,164 3 0,001558 0,001722 15 -83 -20 20. S1L 100.0 3,117 1 0,001829 0,001864 31 -72 6 21. M2R 100.0 3,111 1 0,001864 0,001864 25 -61 -10 Centric Relation 1. S2R 47.1 D2R:21.4%; S4R:21.3% 7,398 4503 1,38E-13 1,52E-12 33 -52 -45 2. S1L 13.5 M2L:11.3%; D1L:10.0% 6,254 9086 4E-10 2,2E-09 1 -52 -5 3. D3L 45.7 D2L:36.9%; D4L:10.4% 5,755 833 8,64E-09 3,17E-08 29 -54 31 4. M4R 30.0 S5R:28.8%; S2R:11.6% 5,379 604 7,49E-08 2,06E-07 40 -49 -16 5. S1R 46.4 M3R:19.3 D3R:15.0% 5,032 274 4,84E-07 1,07E-06 36 -56 -16 6. D3R 31.8 A1R:31.8%; M2R:18.8% 4,801 85 1,58E-06 2,89E-06 51 -50 -42 7. M4L 89.7 D2L:5.1%; A2L:5.1% 4,049 39 5,15E-05 8,09E-05 15 -38 4 8. M4R 100.0 3,987 37 6,69E-05 9,2E-05 24 -36 -25 9. S1R 100.0 3,643 25 0,000269 0,000329 33 -70 -30 10. A3R 100.0 3,349 6 0,000811 0,000893 48 -44 -39 11. M3R 100.0 3,249 3 0,001158 0,001158 11 -52 -21 At the model level, generalized linear modeling did not identify a significant global main effect of mandibular condition on the activation index. In the condition-only model, neither CR nor the splint condition differed significantly from CO (all p > 0.5), and the overall F-test was not significant (p = 0.862). When ROI was added in the model, the ROI effect was significant (p = 0.019), whereas the condition effect remained non-significant. An ROI × condition interaction was observed for CR (p = 0.042), indicating that the relationship between activation index and ROI differed between CR and CO, although the overall model showed only borderline significance (F = 2.39, p = 0.068). For voxel count, the condition-only model also showed no global between-state differences (all p > 0.6). ROI remained significant in models including ROI (p = 0.011) and ROI × condition interaction terms (p = 0.028). A weak interaction signal was observed for the splint condition by ROI (p = 0.093), suggesting that the spatial distribution of cluster size may differ across ROIs under this condition, although no robust global state effect was detected. For false discovery rate-adjusted q-values (FRD), the splint condition showed only a trend toward higher values compared with CO (p = 0.107 in the condition-only model; p = 0.105 after inclusion of ROI; p = 0.059 in the interaction model), without reaching conventional statistical significance. Uncorrected p-values did not show systematic differences across states or ROIs in any of the tested models. Taken together, these analyses suggest that local ROI-specific changes were more prominent than global average shifts across mandibular conditions. Figure 1 illustrates the top 10 cerebellar ROIs across CO, the splint condition, and CR. Across all panels, the dominant pattern was not a uniform increase or decrease in signal, but rather a redistribution of activation among specific cerebellar regions. The highest activation index in S1L was observed under CR, with lower values under CO and the splint condition. In contrast, D2R and S2R were most prominent under CO, whereas D3L and M2L showed their highest activation under the splint condition. M3R, A2R, D3R, M4R, and S1R showed more moderate values without a single consistently dominant condition. The voxel-count profiles also varied by ROI. S1L showed its largest cluster under CR, with intermediate values under CO and minimal representation under the splint condition. D2R and S2R showed their largest clusters under CO, whereas D3L and M2L were most pronounced under the splint condition (Fig. 2 ). These findings support the interpretation that mandibular state was associated with ROI-specific redistribution of cerebellar activation rather than a consistent global increase or decrease. Pairwise comparisons between conditions further supported this regional patterning. CO was characterized by stronger right-sided involvement of D2R, S2R, and A2R. The splint condition shifted the dominant response toward left-sided D3L and M2L, whereas CR was associated with more consolidated activation in S1L and was additionally associated with right-sided S1R and M4R. Most of the strongest peaks remained significant after FDR correction. According to the ROI comparison analysis (Table 3 ), the main between-condition differences were therefore regional rather than global. Table 3 ROI comparison analysis ROI Metric CO-CR CO-Splint condition CR-Splint condition S1L AI 0,149 3,286 3,137 D2R AI 5,27 -0,567 -5,837 S2R AI -1,716 1,207 2,923 M3R AI 4,577 0,526 -4,051 A2R AI 4,396 4,396 0 D3L AI -5,755 -6,023 -0,268 M2L AI 0 -5,031 -5,031 D3R AI 0 -4,742 -4,742 M4R AI -5,379 0 5,379 S1R AI -5,032 0 5,032 S1L Vx -4409 4657 9066 D2R Vx 4379 3670 -709 S2R Vx -3019 423 3442 M3R Vx 550 508 -42 A2R Vx 55 55 0 D3L Vx -833 -5416 -4583 M2L Vx 0 -2204 -2204 D3R Vx 0 -637 -637 M4R Vx -604 0 604 S1R Vx -299 0 299 S1L p -2,48E-10 -1,58998E-05 -1,58996E-05 D2R p 5,27E-12 -5,30473E-09 -5,31E-09 S2R p 1,32999E-08 -7,6267E-06 -7,64E-06 M3R p 0,00000471 -0,00004629 -0,000051 A2R p 0,000011 0,000011 0 D3L p -8,64E-09 -1,71E-09 6,93E-09 M2L p 0 -0,000000487 -0,000000487 D3R p 0 -0,00000212 -0,00000212 M4R p -7,49E-08 0 7,49E-08 S1R p -0,000000484 0 0,000000484 S1L FDR -1,59E-09 -3,34994E-05 -3,34978E-05 D2R FDR 4,22E-11 -5,56578E-08 -5,57E-08 S2R FDR 2,65985E-08 -1,77734E-05 -1,78E-05 M3R FDR 0,00000754 -0,00008986 -0,0000974 A2R FDR 0,0000147 0,0000147 0 D3L FDR -3,17E-08 -3,59E-08 -4,2E-09 M2L FDR 0 -0,00000204 -0,00000204 D3R FDR 0 -0,00000556 -0,00000556 M4R FDR -0,000000206 0 0,000000206 S1R FDR -0,00000107 0 0,00000107 Additional analyses of ROI appearance and disappearance relative to CO are summarized in Table 4 , and hierarchical clustering with consensus heatmaps is presented in Table 5 . ROI labels were grouped into motor (M), action/attention (A), multi-demand/default-mode (D), and speech/semantic (S) territories. Under CO, clustering emphasized right-sided S2R and D2R together with stable A2R/M3R involvement. Under the splint condition, left-sided D3L and M2L became more distinctive, consistent with broader but less consolidated recruitment. Under CR, the clustering structure appeared more stable, with strong segregation of S1L, S2R, M4R, and S1R. Overall, these exploratory analyses reinforced the interpretation that mandibular state was associated with ROI-specific cerebellar redistribution rather than a uniform global shift. Table 4 ROI appearance and disappearance relative to CO Splint condition vs CO: Appeared in the Splint condition (absent in CO) Disappeared in the Splint condition (present in CO) D3L, M2L, D3R A2R Present in both S1L, D2R, S2R, M3R CR vs CO Appeared in CR (absent in CO) Disappeared in CR (present in CO) S1R, M4R, D3L S1R, M4R, D3L Present in both S1L, S2R Table 5 ROI clusters for activated ROI Ai, FDR, p-value, Voxels Voxels FDR q p-value Activation Index CO (mean s = 0.88): C1 : S2R; C2 : M3R, A2R, D3L, M2L, D3R, M4R, S1R; C3 : S1L, D2R CO (mean s = 1.00) C1 : M3R; C2 : A2R; C3 : S1L, D2R, S2R, D3L, M2L, D3R, M4R, S1R. CO (mean s = 1.00): The same as CO FDR q CO (mean s = 0.78): C1 : M3R, A2R; C2 : S1L, D2R, S2R; C3 : D3L, M2L, D3R, M4R, S1R. Splint condition (mean s = 0.77) : C1 : M2L; C2 : S1L, D2R, S2R, M3R, A2R, D3R, M4R, S1R; C3 : D3L. Splint condition (mean s = 0.80) C1 : D2R, A2R, D3L, M2L, D3R, M4R, S1R; C2 : S1L, S2R; C3 : M3R The same as Splint condition FDR q Splint condition (mean s = 0.68) : C1 : D2R, D3L; C2 : S1L, S2R, M3R, M2L, D3R; C3 : A2R, M4R, S1R. CR (mean s = 0.93) : C1 : S1L; C2 : S2R; C3 : D2R, M3R, A2R, D3L, M2L, D3R, M4R, S1R. CR (mean s = 0.96) C1 : M4R; C2 : S1L, D2R, S2R, M3R, A2R, D3L, M2L, D3R; C3 : S1R. CR (mean s = 0.97) C1 : M4R; C2 : S1L, D2R, S2R, M3R, A2R, D3L, M2L, D3R; C3 : S1R. CR (mean s = 0.83) : C1 : S2R; C2 : S1L, D3L, M4R, S1R; C3 : D2R, M3R, A2R, M2L, D3R. Discussion and conclusions Systematic reviews have shown that TMD is associated not only with abnormalities in the trigemino-thalamo-cortical system, but also with alterations in the lateral and medial pain systems, the default mode network, the descending antinociceptive periaqueductal gray-raphe magnus pathway, and motor-related circuits. At the same time, both reviews emphasized substantial methodological heterogeneity, which has limited cross-study comparability and made it difficult to define a single canonical neuroimaging phenotype for TMD [ 15 , 16 ]. Recent studies further support the view that TMD is characterized by distributed brain changes rather by than single uniforn biomarker. In a 2025 pilot whole-brain voxel-based analysis, patients with TMD showed increased gray matter volume in several frontal, sensorimotor, and temporal regions, while cerebral perfusion changes were more restricted, with significantly higher cerebral blood flow identified only in the medial segment right postcentral gyrus; importantly, pain intensity, anxiety, depression, and jaw functional limitation were differentially associated with these morphometric and perfusion findings. In parallel, a 2026 resting-state fMRI graph-theory study reported reduced clustering coefficient and local efficiency in TMD, with local efficiency correlating positively with depressive and anxious symptoms. Together, these findings suggest that TMD involves maladaptive plasticity at both structural and network levels, whereas the cerebellum has remained relatively underemphasized in most recent studies [ 17 , 18 ]. Against this background, the present case adds a distinct perspective by focusing specifically on the cerebellum and by examining cerebellar responses within the same patient across different mandibular states. This is relevant because contemporary pain literature increasingly recognizes the cerebellum as more than a motor structure. A 2024 review argued that lobules IV-VI and Crus I are particulary relevant to pain processing and that the cerebellum likely modulates pain through its communication with sensorimotor, executive, reward, and limbic systems. A 2025 narrative review further highlighted recurrent involvement of Crus I, lobules VI and VIII, and the vermis across chronic pain conditions, including migraine and chronic low back pain, with implications for both sensory-discriminative and affective-motivational dimensions of pain. Within such a framework, cerebellar BOLD changes in TMD can reasonably be interpreted as part of a multimodal pain-regulation network rather than as isolated motor co-activation [ 19 , 20 ]. The pattern observed in our results is consistent with this interpretation. The splint condition was associated with broader but fragmented cerebellar recruitment, CR by more spatially consolidated activation with the highest peak response, and CO by a more spatially restricted but statistically stronger average signal. At the same time, the generalized linear model did not demonstrate a robust global main effect of condition; instead, the dominant pattern was ROI-specific redistribution. This distinction is important. In light of recent cerebellar pain models, the present findings suggest that experimentally defined mandibular states may modulate the internal organization of cerebellar pain-related processing rather than shift cerebellar globally in a single direction. In other words, local cerebellar reweighting may be more informative than whole-structure averages in TMD [ 19 , 20 ]. Broader evidence from musculoskeletal pain disorders also supports this interpretation. A 2023 meta-analysis of voxel-based morphometric studies identified decreased gray matter in the left cerebellum, alongside abnormalities in affective and cognitive pain-related regions [ 21 ]. A 2022 diffusion tensor imaging study demonstrated widespread white-matter alterations, supporting the idea that chronic musculoskeletal pain is accompanied by central microstructural reorganization [ 22 ]. Similarly, newer work in chronic low back pain has described disrupted cerebellar connectivity with large-scale cortical systems, including salience, emotional, and locus coeruleus-related pathways. [ 23 ] Taken together, these findings indicate that chronic musculoskeletal pain is better understood as a heterogeneous network disorder in which cerebellar involvement may be recurrent but phenotype-dependent rather than disease-specific. This case therefore extends the current TMD neuroimaging literature in two ways. First, it brings the cerebellum to the foreground, whereas most recent TMD studies have focused on cortical, limbic, or graph-theoretical whole-brain findings. Second, it demonstrates within-subject state dependence across mandibular conditions, suggesting that cerebellar responses may vary dynamically with occlusal-functional context. These observations should not be interpreted as evidence of therapeutic efficacy of any mandibular intervention. Rather, they support the view that cerebellar reactivity in TMD may be condition-sensitive and region-specific, with potential relevance for future mechanism-oriented and diagnostic neuroimaging studies. The limitations of this report are inherent to its single-case design. The findings cannot establish causality, cannot be generalized to all patients with TMD, and should be regarded as hypothesis-generating. Nevertheless, the convergence between the present ROI-specific cerebellar results and the broader literature on pain neuroimaging suggests that the cerebellum deserves more explicit attention in future multimodal TMD studies, especially those combining TMJ MRI, task-based or resting-state fMRI, and cerebellar parcellation methods. Abbreviations AI Activation index BOLD Blood oxygen level-dependent CO Centric occlusion CR Centric relation FDR False discovery rate fMRI Functional magnetic resonance imaging GLM General linear model GRF Gaussian random field MRI Magnetic resonance imaging TMD Temporomandibular disorder ROI Region of interest SUIT Spatially unbiased infratentorial template VBM Voxel-based morphometry Declarations Funding statement: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of interests: The authors declare that they have no competing interests. Ethics approval: Written informed consent for publication and imaging analysis was obtained from the patient. This case report was prepared in accordance with institutional ethical requirements and the Declaration of Helsinki and was approved by the Commission on Ethics and Academic Integrity of the Shupyk National Healthcare University of Ukraine, Kyiv, Ukraine (Protocol No. 13/10, dated 17 December 2024). The data owner, the National Specialized Children’s Hospital “OHMATDYT,” Ministry of Health of Ukraine, Kyiv, Ukraine. Ukrainian Research Registration No. 0125U003930. Author Contributions : N.S. Validation ; Writing - review and editing ; Supervision , Investigation . V.P. Conceptualization ; Methodology ; Formal analysis ; Resources ; Visualization ; Writing - original draft , Validation . I.R. Data curation ; Software . Availability of data and materials: De-identified data supporting the findings of this study are not publicly available due to the risk of re-identification and institutional restrictions but are available from the corresponding author on reasonable request and with permission of the data owner (National Specialized Children’s Hospital “OHMATDYT”, Kyiv, Ukraine). Declaration of generative AI and AI-assisted technologies in the manuscript preparation process. During the preparation of this work the authors used the following AI-assisted tools: Cursor (Anysphere Inc.; version 1.0) and ChatGPT (OpenAI; version 5.2) for writing and refining MATLAB code used for statistical data processing and visualization (including correlation analyses, group comparisons, and figure generation). All AI-generated code and text were reviewed, modified where necessary, and validated by the authors. The authors take full responsibility for the content of the published article and for the correctness of the analyses performed. Corresponding author V asil Pekhno PhD., Doctoral student, Department of Therapeutic and Pediatric Dentistry, associate professor Department of Orthopedic Dentistry, Digital technologies and implantology, Shupyk National Healthcare University of Ukraine 9 Dorohozhytska Str., Kyiv, 04112 Ukraine e-mail: [email protected] References Jimsheleishvili S, Dididze M, Neuroanatomy. Cerebellum. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan– [updated 2023; cited 2026 Mar 20]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK538167/ Manto M, Bower JM, Conforto AB, Delgado-García JM, da Guarda SNF, Gerwig M, et al. Consensus paper: roles of the cerebellum in motor control-the diversity of ideas on cerebellar involvement in movement. Cerebellum. 2012;11(2):457–87. Borsook D, Moulton EA, Schmidt KF, Becerra LR. Human cerebellar responses to brush and heat stimuli in healthy and neuropathic pain subjects. Cerebellum. 2008;7(3):252–72. Moulton EA, Schmahmann JD, Becerra L, Borsook D. The cerebellum and pain: passive integrator or active participator? Brain Res Rev. 2010;65(1):14–27. Saab CY, Willis WD. The cerebellum: organization, functions and its role in nociception. Brain Res Brain Res Rev. 2003;42(1):85–95. Apkarian AV, Bushnell MC, Treede RD, Zubieta JK. Human brain mechanisms of pain perception and regulation in health and disease. Eur J Pain. 2005;9(4):463–84. Coombes SA, Misra G. Pain and motor processing in the human cerebellum. Pain. 2016;157(1):117–27. Lickteig R, Lotze M, Kordass B. Successful therapy for temporomandibular pain alters anterior insula and cerebellar representations of occlusion. Cephalalgia. 2013;33(15):1248–57. He SS, Li F, Gu T, Liu Y, Zou SJ, Huang XQ, et al. Altered neural activation pattern during teeth clenching in temporomandibular disorders. Oral Dis. 2016;22(5):406–14. 10.1111/odi.12465 . Tramonti Fantozzi MP, Diciotti S, Tessa C, Castagna B, Chiesa D, Barresi M, et al. Unbalanced occlusion modifies the pattern of brain activity during execution of a finger to thumb motor task. Front Neurosci. 2019;13:499. 10.3389/fnins.2019.00499 . Sato F, Tsutsumi Y, Oka A, Furuta T, Sohn J, Oi Y, et al. Projections from regions of the cerebellar nuclei receiving jaw muscle proprioceptive signals to trigeminal motoneurons and their premotoneurons in the rat pons and medulla. Cerebellum. 2025;24(4):113. 10.1007/s12311-025-01862-7 . Cho SY, Shin AS, Na BJ, Jahng GH, Park SU, Jung WS, et al. Brain activity associated with memory and cognitive function during jaw-tapping movement in healthy subjects using functional magnetic resonance imaging. Chin J Integr Med. 2013;19(6):409–17. 10.1007/s11655-012-1187-7 . Kobayashi T, Fukami H, Ishikawa E, Shibata K, Kubota M, Kondo H, et al. An fMRI study of the brain network involved in teeth tapping in elderly adults. Front Aging Neurosci. 2020;12:32. 10.3389/fnagi.2020.00032 . Nettekoven C, Diedrichsen J. Cerebellar asymmetries. Handb Clin Neurol. 2025;208:369–78. 10.1016/B978-0-443-15646-5.00005-1 . Yin Y, He S, Xu J, You W, Li Q, Long J, et al. The neuro-pathophysiology of temporomandibular disorders-related pain: a systematic review of structural and functional MRI studies. J Headache Pain. 2020;21(1):78. 10.1186/s10194-020-01131-4 . Shrivastava M, Ye L. Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders: a comprehensive review. Int J Oral Sci. 2023;15(1):58. 10.1038/s41368-023-00254-z . Li X, Jiang Y, Chen Z. Altered grey matter volume and cerebral perfusion over the whole brain in painful temporomandibular disorders: a pilot voxel-based analysis. Curr Med Imaging. 2025;21:e15734056373583. 10.2174/0115734056373583250531004637 . Jiang Y, Li X, Liu M, Chen Z. Investigating changes of functional brain networks in painful temporomandibular disorders: a resting-state fMRI study. J Oral Facial Pain Headache. 2026;40(1):61–70. 10.22514/jofph.2026.006 . Li CN, Keay KA, Henderson LA, Mychasiuk R. Re-examining the mysterious role of the cerebellum in pain. J Neurosci. 2024;44(17):e1538232024. 10.1523/JNEUROSCI.1538-23.2024 . Manda O, Hadjivassiliou M, Varrassi G, Zavridis P, Zis P. Exploring the role of the cerebellum in pain perception: a narrative review. Pain Ther. 2025;14(3):803–16. 10.1007/s40122-025-00724-8 . Xin M, Qu Y, Peng X, Zhu D, Cheng S. A systematic review and meta-analysis of voxel-based morphometric studies of fibromyalgia. Front Neurosci. 2023;17:1164145. 10.3389/fnins.2023.1164145 . Cheng S, Dong X, Zhou J, et al. Alterations of the white matter in patients with knee osteoarthritis: a diffusion tensor imaging study with tract-based spatial statistics. Front Neurol. 2022;13:835050. 10.3389/fneur.2022.835050 . Hall M, Dobson F, Klyne DM, Zheng CJ, Lima YL, Egorova-Brumley N. Neurobiology of osteoarthritis: a systematic review and activation likelihood estimation meta-analysis. Sci Rep. 2023;13(1):12442. 10.1038/s41598-023-39245-9 . Additional Declarations No competing interests reported. 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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-9177677","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":627270072,"identity":"172ce7db-00de-4aeb-b4e3-2d17c1e1ab02","order_by":0,"name":"Nataliia Savychuk","email":"","orcid":"","institution":"Shupyk National Healthcare University of Ukraine","correspondingAuthor":false,"prefix":"","firstName":"Nataliia","middleName":"","lastName":"Savychuk","suffix":""},{"id":627270079,"identity":"b67b32ab-cb31-44ea-9e6a-5bfc0f32aa6c","order_by":1,"name":"Vasil Pekhno","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBAC+wY2NhAtx8BDrBaDAxAtxlAtBsRrSWwgXsvxtrTHhW026RvOnH3AXFDxh7AW+55jx41ntqXlbjjbbsA84wwxtkikt0nzth3O3XCejYGZt40YLfLPwVrSDcBa/hFlC9sxkJYEg7NtQC0NxGjhSUuT5jmXZjjzzDGGwzzHjInQwn7MTJqnzEae70wa42OeGjnCWsCAkQ1CHyBSPQgQERujYBSMglEwggEAE1QzzjdsC9IAAAAASUVORK5CYII=","orcid":"","institution":"Shupyk National Healthcare University of Ukraine","correspondingAuthor":true,"prefix":"","firstName":"Vasil","middleName":"","lastName":"Pekhno","suffix":""},{"id":627270084,"identity":"e352c13b-f6f0-45b1-8753-be54cca5d634","order_by":2,"name":"Ivan Riabko","email":"","orcid":"","institution":"Taras Shevchenko National University of Kyiv","correspondingAuthor":false,"prefix":"","firstName":"Ivan","middleName":"","lastName":"Riabko","suffix":""}],"badges":[],"createdAt":"2026-03-20 10:08:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9177677/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9177677/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107616403,"identity":"1e8d78fe-47b5-49c0-bbe6-8da5de9c0832","added_by":"auto","created_at":"2026-04-23 09:13:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":558333,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9177677/v1/bcb743d008fe795031b59c2b.png"},{"id":107616405,"identity":"4481d6bd-f12b-4727-b935-c6dfc738b09e","added_by":"auto","created_at":"2026-04-23 09:13:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":442318,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9177677/v1/2f288da7921171812c5e2f1c.png"},{"id":107707215,"identity":"9ccec1d4-65fc-4e0f-924c-82e186f2382d","added_by":"auto","created_at":"2026-04-24 09:19:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1679168,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9177677/v1/b1b2ebe4-330d-4427-80ab-7721cf7ce039.pdf"},{"id":107616404,"identity":"252af03a-3d88-4617-ac17-c1b07ce085b3","added_by":"auto","created_at":"2026-04-23 09:13:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33156,"visible":true,"origin":"","legend":"","description":"","filename":"CAREChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-9177677/v1/5d47ca2e741b5d756a2dd770.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cerebellar functional reorganization across mandibular positions in painful temporomandibular disorder: an fMRI case report integrated with cerebellar voxel-based morphometry","fulltext":[{"header":"Background","content":"\u003cp\u003eTemporomandibular disorders (TMD) are musculoskeletal pain conditions involving the temporomandibular joint and/or the masticatory muscles. They are among the most common chronic musculoskeletal disorders, affecting an estimated 6\u0026ndash;12% of the general population. Despite their high prevalence, management remains largely multimodal rather than mechanism-specific. Pain is one of the main reasons patients seek care, and reported average pain intensity in TMD has been shown to be comparable to that of other clinically significant pain conditions, including chest and back pain [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe cerebellum plays an important role in motor coordination while integrating diverse sensory inputs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Human neuroimaging studies have consistently demonstrated cerebellar involvement in both physiological and pathological pain states [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In TMD, a limited number of neuroimaging studies have reported cerebellar activation, including findings in the left cerebellar hemisphere [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, several studies suggest that central nervous system activity may vary according to mandibular position or occlusal state, which is consistent with the cerebellum\u0026rsquo;s role in sensorimotor integration and adaptive motor control [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo our knowledge, no case-based functional magnetic resonance imaging (fMRI) report has described within-subject cerebellar BOLD signal redistribution across different mandibular positions in TMD. This case may therefore provide a novel pathophysiological perspective and potential diagnostic insight into state-dependent brain responses in TMD.\u003c/p\u003e"},{"header":"Case presentation","content":"\u003cp\u003eThis case was evaluated within a study protocol approved by the Commission on Ethics and Academic Integrity of the Shupyk National Healthcare University of Ukraine (Protocol No. 13/10; Ukrainian Research Registration No. 0125U003930). Written informed consent for clinical and imaging assessment was obtained from the patient before participation.\u003c/p\u003e \u003cp\u003eA 27-year old female presented to the Department of Therapeutic and Pediatric Dentistry and Department of Orthopedic Dentistry, Digital Technologies and Implantology, Faculty of Dentistry, Shupyk National Healthcare University of Ukraine, on October 22, 2024. The main complaints were persistent jaw tension, bruxism, and subjective chronic tinnitus that had been present for approximately 2 years.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical findings\u003c/h2\u003e \u003cp\u003eOn clinical examination, the patient reported pain on palpation of the left temporalis muscle, left lateral pterygoid muscle, and left temporomandibular joint region. She also reported left-sided otalgia and pain radiating to the left periauricular region. Maximum mouth opening was 43 mm, with deviation to the left during opening. Joint clicking was present in the left temporomandibular joint. No neurological abnormalities were observed. These findings were considered consistent with a painful temporomandibular disorder phenotype.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiagnostic assessment\u003c/h3\u003e\n\u003cp\u003eBased on the clinical presentation, palpation findings, mandibular deviation during opening, joint clicking, and imaging work-up, the case was classified as painful temporomandibular disorder. Structural MRI was used to assess the anatomical context, whereas task-based fMRI was used to investigate condition-dependent cerebellar functional responses across different mandibular states.\u003c/p\u003e\n\u003ch3\u003eOutcome and follow-up\u003c/h3\u003e\n\u003cp\u003eNo adverse events related to the imaging procedure were observed. The case was primarily investigated for diagnostic and pathophysiological characterization rather than for assessment of treatment efficacy. At the time of reporting, the patient remained under clinical observation, and the present report focuses on the imaging phenotype observed during the study protocol.\u003c/p\u003e\n\u003ch3\u003ePatient perspective:\u003c/h3\u003e\n\u003cp\u003eThe patient reported that the most disturbing symptoms were persistent jaw tension and chronic tinnitus, both of which she considered clinically meaningful in everyday life. She perceived the imaging-based assessment as important because it could help clarify whether different mandibular positions were associated with different symptom-related brain responses.\u003c/p\u003e\n\u003ch3\u003eTimeline\u003c/h3\u003e\n\u003cp\u003eSymptoms of persistent jaw tension, bruxism, and subjective chronic tinnitus had been present for approximately 2 years before the patient sought specialist evaluation. She presented on October 22, 2024, to the Faculty of Dentistry of Shupyk National Healthcare University of Ukraine for clinical assessment. Following informed consent, structural MRI and task-based fMRI were performed at the National Children\u0026rsquo;s Specialized Hospital \u0026ldquo;Okhmatdyt.\u0026rdquo;\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging was performed at the Radiology Department of the National Children\u0026rsquo;s Specialized Hospital \u0026ldquo;Okhmatdyt,\u0026rdquo; Ministry of Health of Ukraine. Brain functional MRI was acquired using a block-design rest-task paradigm. During imaging, the patient performed jaw-closing tasks under three experimentally defined mandibular conditions: centric occlusion (CO), a splint condition, and centric relation (CR). These conditions were used as comparative functional states during imaging rather than as treatment outcomes.\u003c/p\u003e \u003cp\u003eFunctional activation maps were superimposed on anatomical T1-weighted brain MRI images. Functional data were preprocessed and co-registered to MNI152 space. Imaging parameters were as follows: repetition time 4000 ms, echo time 97 ms, slice thickness 5.0 mm, 25 slices, distance factor 30%, field of view 230 mm, phase field of view 100%, base resolution 128, phase resolution 100%, GRAPPA acceleration factor 2, reference lines 32, partial Fourier 6/8, distortion correction off, raw filter on, elliptical filter off, strong fat saturation, standard dynamic mode, magnitude reconstruction, and AutoAlign (Head\u0026thinsp;\u0026gt;\u0026thinsp;Brain).\u003c/p\u003e \u003cp\u003eTo characterize state-dependent cerebellar responses across mandibular conditions, both voxelwise and ROI-based analyses were performed. Voxelwise analysis was conducted in FEAT (FSL v6.0.7.18) using a general linear model to generate Z-statistic maps, with a threshold of Z\u0026thinsp;\u0026gt;\u0026thinsp;3.1 and cluster-wise correction at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 based on Gaussian random field theory. ROI-level cerebellar characterization was performed using the SUIT Nettekoven Asym32 atlas [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], selected because of its multimodal organization and sufficiently detailed cerebellar parcellation. For each mandibular condition, the activation index, voxel counts, peak uncorrected p-values, and FRD were calculated for activated cerebellar regions.\u003c/p\u003e \u003cp\u003eTo compare cerebellar response profiles across CO, splint condition, and CR, pairwise contrasts between conditions were also examined. Pattern similarity across conditions was further explored using cosine similarity, Euclidean distance, and mean absolute difference. Hierarchical clustering and consensus heatmaps were used to evaluate ROI-level organization across mandibular states. Structural cerebellar morphometric assessment was performed on anatomical MRI using FreeSurfer (v7.4.1). HOROS (v3.3.6) was used for MRI visualization, and MATLAB (R2024a) was used for statistical processing.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImaging findings\u003c/h2\u003e \u003cp\u003eAs summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, cerebellar activation varied across the three mandibular conditions, predominantly through ROI-specific redistribution rather than a uniform global shift. The splint condition showed the largest number of clusters (21) and the widest topographic distribution, involving 13 unique ROIs. However, this pattern was predominantly fragmented, with a median cluster size of 30 voxels, a mean cluster size of 578 voxels, a mean Z-statistic of 4.251, and a maximum Z-statistic of 6.023. Thus, under the splint condition, activation was distributed across a larger number of small and spatially fragmented cerebellar clusters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral fMRI results across all conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMandibular position\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of clusters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of voxels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCluster size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eZ-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnique ROIs in rows\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1455,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSplint condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1408.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe CR condition showed a distinct pattern. Although fewer clusters were identified (11), the total activated volume was the largest (15,495 voxels), with a mean cluster size of 1408.63 voxels, a median cluster size of 85 voxels, and the highest peak response in the dataset (Zmax\u0026thinsp;=\u0026thinsp;7.398). This pattern was associated with more spatially consolidated activation fields and higher peak intensity in selected ROIs.\u003c/p\u003e \u003cp\u003eCO showed an intermediate spatial pattern, with 8 clusters and a total activated volume of 11,646 voxels, but it had the highest mean Z-statistic (5.065; Zmax\u0026thinsp;=\u0026thinsp;6.898). Compared with the splint condition, activation under CO was less spatially dispersed but relatively more intense at the voxel level, with larger clusters overall (median 523 voxels; mean 1455.75 voxels).\u003c/p\u003e \u003cp\u003eDetailed ROI-level findings, including anatomical region, cluster overlap, activation index, voxel count, uncorrected peak p-values, FRD, and SUIT coordinates, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of preprocessing the data according to SUIT Nettekoven Asym32 atlas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAnato-mical region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOver-lap in cluster (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther regions (label: share%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eActiva-tion index (peak Z/T)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of voxels in cluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUncorr. p (peak)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFDR q (peaks)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eSUIT coords NMI152\u003c/p\u003e \u003cp\u003e(x y z)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eCentric occlusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS4R:22.3%; S2R:18.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,27E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,22E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD1L:12.8%; D3L:11.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,52E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6,1E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS2L:33.9%; D1L:12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,47E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,91E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD2R:19.2%; S3R:7.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,33E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,66E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS1R:32.5%; D4R:11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,71E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,54E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM4R:16.4%; S5R:10.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,1E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,47E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD3R:40.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,000659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,000753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,001896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,001896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSplint condition\u003c/b\u003e\u003c/p\u003e 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align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS1R:31.6%; M1R:12.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,64E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD2R:34.7%; D1R:6.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,95E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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D2L:2.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,12E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5,56E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.4\u003c/p\u003e \u003c/td\u003e 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS2L:40.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,59E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,35E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,1E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9,74E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e 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colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD3R:16.7%; D2R:6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,000174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,00028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM2L:12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,000291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,000437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,000389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,000545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,000583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,000737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,000597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,000737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,001527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,001722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,001558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,001722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,001829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,001864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,001864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,001864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCentric Relation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD2R:21.4%; S4R:21.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,38E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,52E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM2L:11.3%; D1L:10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,2E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD2L:36.9%; D4L:10.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,64E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,17E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS5R:28.8%; S2R:11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,49E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,06E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM3R:19.3 D3R:15.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,84E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,07E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA1R:31.8%; M2R:18.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,58E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,89E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM4L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD2L:5.1%; A2L:5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,15E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,09E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,69E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9,2E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,000269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,000329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,000811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,000893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,001158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,001158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt the model level, generalized linear modeling did not identify a significant global main effect of mandibular condition on the activation index. In the condition-only model, neither CR nor the splint condition differed significantly from CO (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.5), and the overall F-test was not significant (p\u0026thinsp;=\u0026thinsp;0.862). When ROI was added in the model, the ROI effect was significant (p\u0026thinsp;=\u0026thinsp;0.019), whereas the condition effect remained non-significant. An ROI \u0026times; condition interaction was observed for CR (p\u0026thinsp;=\u0026thinsp;0.042), indicating that the relationship between activation index and ROI differed between CR and CO, although the overall model showed only borderline significance (F\u0026thinsp;=\u0026thinsp;2.39, p\u0026thinsp;=\u0026thinsp;0.068).\u003c/p\u003e \u003cp\u003eFor voxel count, the condition-only model also showed no global between-state differences (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.6). ROI remained significant in models including ROI (p\u0026thinsp;=\u0026thinsp;0.011) and ROI \u0026times; condition interaction terms (p\u0026thinsp;=\u0026thinsp;0.028). A weak interaction signal was observed for the splint condition by ROI (p\u0026thinsp;=\u0026thinsp;0.093), suggesting that the spatial distribution of cluster size may differ across ROIs under this condition, although no robust global state effect was detected.\u003c/p\u003e \u003cp\u003eFor false discovery rate-adjusted q-values (FRD), the splint condition showed only a trend toward higher values compared with CO (p\u0026thinsp;=\u0026thinsp;0.107 in the condition-only model; p\u0026thinsp;=\u0026thinsp;0.105 after inclusion of ROI; p\u0026thinsp;=\u0026thinsp;0.059 in the interaction model), without reaching conventional statistical significance. Uncorrected p-values did not show systematic differences across states or ROIs in any of the tested models. Taken together, these analyses suggest that local ROI-specific changes were more prominent than global average shifts across mandibular conditions.\u003c/p\u003e \u003cp\u003eFigure 1 illustrates the top 10 cerebellar ROIs across CO, the splint condition, and CR. Across all panels, the dominant pattern was not a uniform increase or decrease in signal, but rather a redistribution of activation among specific cerebellar regions. The highest activation index in S1L was observed under CR, with lower values under CO and the splint condition. In contrast, D2R and S2R were most prominent under CO, whereas D3L and M2L showed their highest activation under the splint condition. M3R, A2R, D3R, M4R, and S1R showed more moderate values without a single consistently dominant condition.\u003c/p\u003e \u003cp\u003eThe voxel-count profiles also varied by ROI. S1L showed its largest cluster under CR, with intermediate values under CO and minimal representation under the splint condition. D2R and S2R showed their largest clusters under CO, whereas D3L and M2L were most pronounced under the splint condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings support the interpretation that mandibular state was associated with ROI-specific redistribution of cerebellar activation rather than a consistent global increase or decrease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePairwise comparisons between conditions further supported this regional patterning. CO was characterized by stronger right-sided involvement of D2R, S2R, and A2R. The splint condition shifted the dominant response toward left-sided D3L and M2L, whereas CR was associated with more consolidated activation in S1L and was additionally associated with right-sided S1R and M4R. Most of the strongest peaks remained significant after FDR correction. According to the ROI comparison analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the main between-condition differences were therefore regional rather than global.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROI comparison analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCO-CR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO-Splint condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCR-Splint condition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5,837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1,716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7,6267E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7,64E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00000471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,00004629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,000051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,000011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,000011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8,64E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,71E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,93E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,000000487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,000000487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,00000212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,00000212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,49E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,49E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,000000484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,000000484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1,59E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3,34994E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3,34978E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,22E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5,56578E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5,57E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,65985E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,77734E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,78E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00000754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,00008986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,0000974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0000147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0000147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3,17E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3,59E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4,2E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,00000204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,00000204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,00000556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,00000556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,000000206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,000000206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,00000107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00000107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditional analyses of ROI appearance and disappearance relative to CO are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and hierarchical clustering with consensus heatmaps is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. ROI labels were grouped into motor (M), action/attention (A), multi-demand/default-mode (D), and speech/semantic (S) territories. Under CO, clustering emphasized right-sided S2R and D2R together with stable A2R/M3R involvement. Under the splint condition, left-sided D3L and M2L became more distinctive, consistent with broader but less consolidated recruitment. Under CR, the clustering structure appeared more stable, with strong segregation of S1L, S2R, M4R, and S1R. Overall, these exploratory analyses reinforced the interpretation that mandibular state was associated with ROI-specific cerebellar redistribution rather than a uniform global shift.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROI appearance and disappearance relative to CO\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSplint condition vs CO:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAppeared in the Splint condition (absent in CO)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisappeared in the Splint condition (present in CO)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD3L, M2L, D3R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA2R\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePresent in both\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eS1L, D2R, S2R, M3R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCR vs CO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAppeared in CR (absent in CO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisappeared in CR (present in CO)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1R, M4R, D3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS1R, M4R, D3L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePresent in both\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eS1L, S2R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROI clusters for activated ROI Ai, FDR, p-value, Voxels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoxels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDR q\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActivation Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003cp\u003e(mean s\u0026thinsp;=\u0026thinsp;0.88):\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: S2R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: M3R, A2R, D3L, M2L, D3R, M4R, S1R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: S1L, D2R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003cp\u003e(mean s\u0026thinsp;=\u0026thinsp;1.00)\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: M3R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: A2R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: S1L, D2R, S2R, D3L, M2L, D3R, M4R, S1R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003cp\u003e(mean s\u0026thinsp;=\u0026thinsp;1.00):\u003c/p\u003e \u003cp\u003eThe same as CO FDR q\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003cp\u003e(mean s\u0026thinsp;=\u0026thinsp;0.78):\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: M3R, A2R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: S1L, D2R, S2R; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: D3L, M2L, D3R, M4R, S1R.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSplint condition\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean s\u0026thinsp;=\u0026thinsp;0.77)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: M2L;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: S1L, D2R, S2R, M3R, A2R, D3R, M4R, S1R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: D3L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSplint condition\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean s\u0026thinsp;=\u0026thinsp;0.80)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: D2R, A2R, D3L, M2L, D3R, M4R, S1R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: S1L, S2R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: M3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe same as Splint condition FDR q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSplint condition\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean s\u0026thinsp;=\u0026thinsp;0.68)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: D2R, D3L;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: S1L, S2R, M3R, M2L, D3R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: A2R, M4R, S1R.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCR\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean s\u0026thinsp;=\u0026thinsp;0.93)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: S1L;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: S2R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: D2R, M3R, A2R, D3L, M2L, D3R, M4R, S1R.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCR\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean s\u0026thinsp;=\u0026thinsp;0.96)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: M4R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: S1L, D2R, S2R, M3R, A2R, D3L, M2L, D3R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: S1R.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCR\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean s\u0026thinsp;=\u0026thinsp;0.97)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: M4R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: S1L, D2R, S2R, M3R, A2R, D3L, M2L, D3R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: S1R.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCR\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean s\u0026thinsp;=\u0026thinsp;0.83)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC1\u003c/span\u003e: S2R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC2\u003c/span\u003e: S1L, D3L, M4R, S1R;\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC3\u003c/span\u003e: D2R, M3R, A2R, M2L, D3R.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion and conclusions","content":"\u003cp\u003eSystematic reviews have shown that TMD is associated not only with abnormalities in the trigemino-thalamo-cortical system, but also with alterations in the lateral and medial pain systems, the default mode network, the descending antinociceptive periaqueductal gray-raphe magnus pathway, and motor-related circuits. At the same time, both reviews emphasized substantial methodological heterogeneity, which has limited cross-study comparability and made it difficult to define a single canonical neuroimaging phenotype for TMD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies further support the view that TMD is characterized by distributed brain changes rather by than single uniforn biomarker. In a 2025 pilot whole-brain voxel-based analysis, patients with TMD showed increased gray matter volume in several frontal, sensorimotor, and temporal regions, while cerebral perfusion changes were more restricted, with significantly higher cerebral blood flow identified only in the medial segment right postcentral gyrus; importantly, pain intensity, anxiety, depression, and jaw functional limitation were differentially associated with these morphometric and perfusion findings. In parallel, a 2026 resting-state fMRI graph-theory study reported reduced clustering coefficient and local efficiency in TMD, with local efficiency correlating positively with depressive and anxious symptoms. Together, these findings suggest that TMD involves maladaptive plasticity at both structural and network levels, whereas the cerebellum has remained relatively underemphasized in most recent studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAgainst this background, the present case adds a distinct perspective by focusing specifically on the cerebellum and by examining cerebellar responses within the same patient across different mandibular states. This is relevant because contemporary pain literature increasingly recognizes the cerebellum as more than a motor structure. A 2024 review argued that lobules IV-VI and Crus I are particulary relevant to pain processing and that the cerebellum likely modulates pain through its communication with sensorimotor, executive, reward, and limbic systems. A 2025 narrative review further highlighted recurrent involvement of Crus I, lobules VI and VIII, and the vermis across chronic pain conditions, including migraine and chronic low back pain, with implications for both sensory-discriminative and affective-motivational dimensions of pain. Within such a framework, cerebellar BOLD changes in TMD can reasonably be interpreted as part of a multimodal pain-regulation network rather than as isolated motor co-activation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pattern observed in our results is consistent with this interpretation. The splint condition was associated with broader but fragmented cerebellar recruitment, CR by more spatially consolidated activation with the highest peak response, and CO by a more spatially restricted but statistically stronger average signal. At the same time, the generalized linear model did not demonstrate a robust global main effect of condition; instead, the dominant pattern was ROI-specific redistribution. This distinction is important. In light of recent cerebellar pain models, the present findings suggest that experimentally defined mandibular states may modulate the internal organization of cerebellar pain-related processing rather than shift cerebellar globally in a single direction. In other words, local cerebellar reweighting may be more informative than whole-structure averages in TMD [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBroader evidence from musculoskeletal pain disorders also supports this interpretation. A 2023 meta-analysis of voxel-based morphometric studies identified decreased gray matter in the left cerebellum, alongside abnormalities in affective and cognitive pain-related regions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A 2022 diffusion tensor imaging study demonstrated widespread white-matter alterations, supporting the idea that chronic musculoskeletal pain is accompanied by central microstructural reorganization [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, newer work in chronic low back pain has described disrupted cerebellar connectivity with large-scale cortical systems, including salience, emotional, and locus coeruleus-related pathways. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Taken together, these findings indicate that chronic musculoskeletal pain is better understood as a heterogeneous network disorder in which cerebellar involvement may be recurrent but phenotype-dependent rather than disease-specific.\u003c/p\u003e \u003cp\u003eThis case therefore extends the current TMD neuroimaging literature in two ways. First, it brings the cerebellum to the foreground, whereas most recent TMD studies have focused on cortical, limbic, or graph-theoretical whole-brain findings. Second, it demonstrates within-subject state dependence across mandibular conditions, suggesting that cerebellar responses may vary dynamically with occlusal-functional context. These observations should not be interpreted as evidence of therapeutic efficacy of any mandibular intervention. Rather, they support the view that cerebellar reactivity in TMD may be condition-sensitive and region-specific, with potential relevance for future mechanism-oriented and diagnostic neuroimaging studies.\u003c/p\u003e \u003cp\u003eThe limitations of this report are inherent to its single-case design. The findings cannot establish causality, cannot be generalized to all patients with TMD, and should be regarded as hypothesis-generating. Nevertheless, the convergence between the present ROI-specific cerebellar results and the broader literature on pain neuroimaging suggests that the cerebellum deserves more explicit attention in future multimodal TMD studies, especially those combining TMJ MRI, task-based or resting-state fMRI, and cerebellar parcellation methods.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eActivation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBOLD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood oxygen level-dependent\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentric occlusion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentric relation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003efMRI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFunctional magnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGLM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral linear model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGRF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGaussian random field\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMRI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTMD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTemporomandibular disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegion of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSUIT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpatially unbiased infratentorial template\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVBM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVoxel-based morphometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;statement:\u003c/strong\u003e This research did not receive\u0026nbsp;any specific grant from funding agencies in the public, commercial, or\u0026nbsp;not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of\u0026nbsp;interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eWritten informed consent for publication and imaging analysis was obtained from the patient. This case report was prepared in accordance with institutional ethical requirements and the Declaration of Helsinki and was approved by the Commission on Ethics and Academic Integrity of the Shupyk National Healthcare University of Ukraine, Kyiv, Ukraine (Protocol No. 13/10, dated 17 December 2024). The data owner, the National Specialized Children\u0026rsquo;s Hospital \u0026ldquo;OHMATDYT,\u0026rdquo; Ministry of Health of Ukraine, Kyiv, Ukraine.\u0026nbsp;Ukrainian Research Registration No. 0125U003930.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN.S. \u003cstrong\u003eValidation\u003c/strong\u003e\u003c/strong\u003e\u003cstrong\u003e; \u003cstrong\u003eWriting - review\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003e\u003cstrong\u003eand editing\u003c/strong\u003e\u003cstrong\u003e; \u003cstrong\u003eSupervision\u003c/strong\u003e\u003c/strong\u003e\u003cstrong\u003e, Investigation\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eV.P. \u003cstrong\u003eConceptualization\u003c/strong\u003e\u003c/strong\u003e\u003cstrong\u003e; \u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/strong\u003e\u003cstrong\u003e; \u003cstrong\u003eFormal analysis\u003c/strong\u003e\u003c/strong\u003e\u003cstrong\u003e; Resources\u003c/strong\u003e\u003cstrong\u003e; \u003cstrong\u003eVisualization\u003c/strong\u003e\u003c/strong\u003e\u003cstrong\u003e; Writing - original draft\u003c/strong\u003e\u003cstrong\u003e, Validation\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI.R. \u003cstrong\u003eData curation\u003c/strong\u003e\u003c/strong\u003e\u003cstrong\u003e; \u003cstrong\u003eSoftware\u003c/strong\u003e\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e De-identified data supporting the findings of this study are not publicly available due to the risk of re-identification and institutional restrictions but are available from the corresponding author on reasonable request and with permission of the data owner (National Specialized Children\u0026rsquo;s Hospital \u0026ldquo;OHMATDYT\u0026rdquo;, Kyiv, Ukraine).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used the following AI-assisted tools: Cursor (Anysphere Inc.; version 1.0) and ChatGPT (OpenAI; version 5.2) for writing and refining MATLAB code used for statistical data processing and visualization (including correlation analyses, group comparisons, and figure generation). All AI-generated code and text were reviewed, modified where necessary, and validated by the authors. The authors take full responsibility for the content of the published article and for the correctness of the analyses performed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003cstrong\u003easil Pekhno\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhD., Doctoral student,\u0026nbsp;Department of Therapeutic and Pediatric Dentistry,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eassociate professor Department\u0026nbsp;of Orthopedic Dentistry, Digital technologies and implantology, Shupyk National Healthcare University of Ukraine 9 Dorohozhytska Str.,\u0026nbsp;Kyiv, 04112 Ukraine\u003c/p\u003e\n\u003cp\u003ee-mail: \u003cem\[email protected]\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJimsheleishvili S, Dididze M, Neuroanatomy. 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Neurobiology of osteoarthritis: a systematic review and activation likelihood estimation meta-analysis. Sci Rep. 2023;13(1):12442. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-39245-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-39245-9\" targettype=\"DOI\" 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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Temporomandibular disorders, cerebellum, functional magnetic resonance imaging, voxel-based morphometry, SUIT atlas","lastPublishedDoi":"10.21203/rs.3.rs-9177677/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9177677/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePainful temporomandibular disorder is common, but cerebellar responses to different mandibular states remain insufficiently described. This case is reported because it shows a novel within-subject pattern of state-dependent cerebellar blood oxygen level-dependent signal redistribution.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCase presentation:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA 27-year-old woman presented with persistent jaw tension, bruxism, and subjective chronic tinnitus of approximately 2 years duration. Brain magnetic resonance imaging with task-based functional imaging was performed using a block-design rest-task paradigm. During imaging, the patient performed jaw-closing tasks under three experimentally defined mandibular conditions: centric occlusion, a splint condition, and centric relation. Functional data were preprocessed in standard space and analyzed using voxelwise and region-of-interest-based cerebellar assessment with the SUIT Nettekoven Asym32 atlas. The splint condition showed the largest number of cerebellar clusters and the broadest topographic distribution, but these activations were mainly small and spatially fragmented. Centric relation showed the largest total activated volume and the highest peak signal, consistent with more consolidated activation fields. Centric occlusion showed fewer clusters overall but the highest mean signal intensity. Model-based analysis did not identify a robust global main effect of mandibular condition; however, region-specific effects and an interaction between cerebellar region and centric relation suggested condition-dependent redistribution of cerebellar activity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis case demonstrates that cerebellar blood oxygen level-dependent responses in painful temporomandibular disorder may vary according to mandibular state in a region-specific manner. The findings provide pathophysiological insight into cerebellar involvement in temporomandibular disorder and suggest potential value for individualized functional assessment in complex cases.\u003c/p\u003e","manuscriptTitle":"Cerebellar functional reorganization across mandibular positions in painful temporomandibular disorder: an fMRI case report integrated with cerebellar voxel-based morphometry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:13:02","doi":"10.21203/rs.3.rs-9177677/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-30T06:28:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T11:41:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T02:48:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299201933013231202062089730027223083678","date":"2026-04-24T02:31:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T11:49:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239002820360654385037619854486912093451","date":"2026-04-22T16:41:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270624509755501276301636175066710572308","date":"2026-04-20T07:42:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T13:01:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293978623265518307774999965706284105684","date":"2026-04-19T12:29:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86553461686073473967023494538185390060","date":"2026-04-16T23:41:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336778046807507875191336687407045408383","date":"2026-04-15T05:34:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T17:18:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-25T13:54:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T03:50:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T03:50:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2026-03-20T09:58:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01390df6-4a7c-4d64-b5b5-495955fb864f","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-30T06:28:01+00:00","index":77,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:13:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:13:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9177677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9177677","identity":"rs-9177677","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}