The Reorganization of Cerebellar Functional Network Topology in Post-Stroke Aphasia: A Resting-State fMRI Study

The Reorganization of Cerebellar Functional Network Topology in Post-Stroke Aphasia: A Resting-State fMRI Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Reorganization of Cerebellar Functional Network Topology in Post-Stroke Aphasia: A Resting-State fMRI Study Liting Chen, Zhenye Luo, Wenfeng Mai, Xiaole Fan, Lifeng Li, Yongqiang Shu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6379167/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Mar, 2026 Read the published version in Molecular Neurobiology → Version 1 posted 10 You are reading this latest preprint version Abstract Objective: This study investigated cerebellar functional network topology and connectivity in post-stroke aphasia (PSA) using resting-state fMRI and graph theory. We further explored associations between these alterations and language/cognitive functions to clarify the cerebellum’s role in PSA. Materials and Methods: Seventy-three right-handed PSA patients and 75 matched healthy controls underwent 3T rs-fMRI. A cerebellar functional network was constructed using the Seitzman-27 atlas. Graph theory was applied to assess global/local topological properties and functional connectivity (FC). Correlations with language and cognitive performance were analyzed. Results: Although the cerebellar network in PSA preserved a small-world organization (σ > 1), key metrics (σ, γ, λ, clustering coefficient [Cp], local efficiency [Eloc]) were significantly reduced (p < 0.05), indicating impaired network integration and local processing. Cp and Eloc correlated with multiple cognitive and language domains. Nodal centrality was diminished in the default mode network (DMN), frontoparietal network (FPN), cingulo-opercular network (CON), and dorsal sensorimotor network (SMN [Dor]). Specific nodal metrics correlated with fluency, repetition, and disease duration. FC analysis revealed widespread reductions in intra- and inter-network connectivity, primarily involving FPN and DMN. Conclusion: PSA is characterized by cerebellar network disorganization and extensive FC alterations that are closely linked to language and cognitive impairments. Topological metrics such as Cp and Eloc may serve as biomarkers for assessing functional deficits. These findings highlight the cerebellum’s integrative role in higher-order functions beyond motor control and provide a neurobiological basis for targeted neuromodulation or rehabilitation strategies aimed at restoring cerebellar-cortical connectivity in PSA. Aphasia Stroke Cerebellum Graph Theory Functional Magnetic Resonance Imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Post-stroke aphasia (PSA) is an acquired language disorder generally caused by ischemic stroke involving occlusion of the left middle cerebral artery, which primarily impairs various language functions such as auditory comprehension, speech production, naming, reading, and writing. PSA is one of the most common and severe complications of stroke [ 1 ] , as it not only exacerbates motor, cognitive, and social impairments but also increases mortality risk and reduces quality of life. In addition, PSA substantially influences multiple cognitive networks in the brain, particularly those regions engaged in language processing. Recent findings have emphasized the critical role of the cerebellum in language function, indicating that it may play a significant role in the reorganization of neural networks in PSA. Graph-theoretical analyses provide effective tools for the investigation of complex brain networks. By employing such methods, researchers gain profound insights into the topological principles of these networks, thereby offering valuable information on brain function as well as its alterations under pathological conditions. Previous work constructed whole-brain functional connectivity networks in PSA based on fMRI and employed graph theory to evaluate the topological properties of the PSA brain, revealing that the global functional network in PSA exhibits a “small-world” organization. Furthermore, both global and local topological attributes were disrupted, while certain core nodes of the dual-stream model showed abnormal centrality, suggesting that network topological impairment may be a potential marker of speech dysfunction in PSA [ 2 ] . In a related study, Cao et al. [ 3 ] reported that individuals with PSA demonstrated gut microbiota imbalance, increased inflammation, and impaired language function; graph-theoretical analyses showed compensatory activation in right-hemisphere language areas, whereas functional decline appeared near the left-hemisphere lesion. Tao and Rapp et al. [ 4 ] explored functional network characteristics in PSA and noted that networks with higher local integration (i.e., stronger modularity) were associated with superior therapeutic responses and milder deficits. Notably, both global modularity and local integration improved following therapy in intact ventral occipitotemporal regions related to spelling. Johnson et al. [ 5 ] also adopted a graph-theoretical approach to analyze semantic-processing networks in aphasia, finding that individuals with higher pre-treatment network metrics achieved better naming outcomes. These results suggest that greater global efficiency and network strength in semantic regions may serve as favorable prognostic factors for naming recovery. Baliki et al. [ 6 ] using rs-fMRI combined with graph theory, discovered that resting-state functional connectivity (rsFC) in PSA was closely related to treatment outcomes; for instance, global efficiency and interhemispheric connectivity were positively associated with improvements in language and visual attention. Moreover, connectivity between the default mode network (DMN) and auditory areas was strongly linked to language recovery, whereas connectivity between the salience network and visual regions correlated with enhanced visual attention. Gleichgerrcht et al. [ 7 ] investigated white matter structural networks via DTI, focusing on “rich club” regions, and showed that a greater number of intact left-hemisphere rich-club hubs was negatively correlated with aphasia severity. Taken together, these studies suggest that network topological metrics are reliable predictors of therapeutic efficacy in comprehensive language rehabilitation for aphasia. Although many studies have addressed the relationship between cortical network topology and aphasia, research on the role of the cerebellum—particularly its topological organization—remains limited. This gap constrains a more thorough understanding of cerebellar function, especially regarding its involvement in advanced cognitive processes [ 8 , 9 ] . Resting-state functional connectivity studies indicate that, aside from its participation in motor control networks, the cerebellum also takes part in “cognitive” networks, forming functional interactions with prefrontal and parietal association cortices. Task-dependent activation patterns further support the idea that the cerebellum can be functionally divided in accordance with its anatomical connectivity with sensorimotor and association areas of the cerebral cortex [ 10 ] . According to the internal model theory of cerebellar function, the cerebellum plays a pivotal role in motor and speech planning by predicting forthcoming linguistic information and adjusting speech production as necessary [ 11 ] . Due to the close structural connections between the cerebellum and the cerebrum, cerebellar regions associated with language receive inputs from frontal-temporal areas [ 12 ] and relay predictive information to the prefrontal cortex to ensure accurate language output [ 13 ] . One diffusion tensor imaging (DTI) study showed that the cerebellum and the cerebral cortex are interconnected via the thalamus in a contralateral manner [ 14 ] ; similarly, the dentate nucleus is structurally linked to multiple regions of the contralateral frontal lobe [ 15 ] . Functioning as part of a subcortical circuit, the cerebellum receives input from Broca’s area through the left anterior insula, then transmits this information to the left ventral premotor cortex, which eventually projects to the left primary motor cortex. This model positions the cerebellum below left frontal motor planning centers in the speech-processing hierarchy [ 16 ] , which reinforces evidence of cerebellar-cortical interactions during language processing. Keser et al. [ 17 ] investigated cerebellar white matter integrity and its connections with the cortex in PSA using DTI. The findings indicated that lower fractional anisotropy (FA) and higher mean diffusivity (MD) in the right cerebellum and its left-hemisphere pathways were associated with poorer performance in picture naming, underlining the importance of cerebellar-cortical circuits and white matter integrity in post-stroke language recovery. Satoer et al. [ 18 ] reported that individuals with cerebellar stroke demonstrated severe language deficits, including challenges in lexical retrieval, phonology, semantics, and syntax, and neither lesion side nor lesion volume significantly influenced outcomes. These results imply that “cerebellar aphasia” is not strictly lateralized and entails extensive interactions with cortical and subcortical language areas, highlighting the cerebellum’s role in both motor regulation and cognitive-linguistic functions. A graph-theoretical investigation by Chen et al. [ 19 ] in combination with hidden Markov model analyses showed that Crus I and Crus II exhibit high degree centrality in the cerebellar functional connectome. The cerebellar connectome displays small-world, modular, and hierarchical organization, comprising three main modules (attention/executive, default mode, and task-positive networks), reflecting pronounced functional interactions between the cerebrum and cerebellum. Seitzman et al. [ 20 ] designed a refined cerebellar ROI atlas, which enhanced the precision of network analyses and demonstrated that these cerebellar ROIs have a highly specific functional organization. Building on these developments, the present study employed the Seitzman-27 cerebellar atlas to construct cerebellar functional networks in PSA, with the goal of investigating topological changes in the network and their relationship with language and cognitive abilities. Considering the importance of the cerebellum in language function and its extensive connections with the cerebral cortex, analyzing the topological characteristics of the cerebellar functional network in PSA through graph-theoretical approaches is of high significance. Based on the above research, this study collected resting-state fMRI data from individuals with PSA. According to the Seitzman atlas, 27 cerebellar cortical and subcortical regions were extracted as nodes to form a 27×27 cerebellar functional connectivity matrix. Subsequently, functional connectivity methods and graph theory were used to examine the small-world properties, global and nodal metrics, and intrinsic connectivity patterns of the cerebellum. In addition, the study assessed how these aberrant FC values and network topological indices correlate with language and cognitive functions in PSA, in order to enhance our understanding of cerebellar involvement in post-stroke aphasia. Materials And Methods Participants From October 2019 to June 2024, we recruited 73 PSA at the Department of Rehabilitation Medicine (outpatient and inpatient) of the First Affiliated Hospital of Jinan University. Additionally, 75 age-, sex-, and education-matched healthy controls (HC) were recruited, all of whom met the head motion criteria. All participants were right-handed native Chinese speakers. The inclusion criteria for PSA were: 1. First-ever stroke, confirmed by MRI or CT, primarily involving the left middle cerebral artery territory. 2. Aphasia diagnosed using the Chinese version of the Western Aphasia Battery (WAB), with no prior language intervention. 3. Clear consciousness, able to cooperate with interviews and examinations. 4. Age 25–80 years, at least elementary education, native Chinese speaker, and right-handed (based on the Edinburgh Handedness Inventory). 5. Stroke onset 2–12 months before enrollment. The exclusion criteria for both PSA patients and HC were: Intracranial lesions on routine MRI, history of psychiatric or organic brain disease, head trauma, or unconsciousness lasting over 5 minutes. Language, reading, or writing difficulties due to severe sensory or motor impairments. Congenital or childhood learning disabilities affecting language development. Severe physical illness, drug side effects, or substance/alcohol abuse. Severe depression, anxiety, suicidal behavior, or agitation. Contraindications to MRI or inability to cooperate during scanning. Pregnancy or breastfeeding. This study was approved by the Ethics Committee of the First Affiliated Hospital of Jinan University (Approval No. KY-2024-096). Clinical trial number: not applicable. All procedures were conducted in accordance with the principles of the Declaration of Helsinki. All participants received a full explanation of the study and signed written informed consent forms prior to enrollment. fMRI Data Acquisition Resting-state fMRI data were collected from all PSA patients before any language therapy or rTMS treatment. Data for all participants were acquired using a GE Discovery MR 750 3.0T superconducting MRI scanner equipped with an 8-channel phased-array surface head coil. During scanning, participants lay supine with eyes closed, remained awake and relaxed, and were instructed to minimize active thinking. Foam padding and earplugs were used to reduce head motion and scanner noise, respectively. A high-resolution T1-weighted structural MRI was obtained via a 3D-BRAVO sequence with the following parameters: repetition time (TR) = 4,500 ms, echo time (TE) = 3.22 ms, slice thickness = 1.0 mm, interslice gap = 0.5 mm, field of view (FOV) = 240 mm × 240 mm, flip angle = 15°, voxel size = 0.47 × 0.47 × 1.0 mm³, and 164 slices. Resting-state functional images were acquired using a gradient-recalled echo-planar imaging (GRE-EPI) sequence: TR = 2,100 ms, TE = 30 ms, slice thickness = 3.0 mm, interslice gap = 0.6 mm, voxel size = 3.125 × 3.125 × 3.6 mm³, flip angle = 90°, matrix size = 64 × 64, number of volumes = 160, and 42 slices. Conventional T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) were also performed to exclude any additional lesions or acute infarctions. Data Preprocessing Routine MRI images were first examined to exclude individuals with other intracranial lesions. Subsequently, both resting-state fMRI (rs-fMRI) and high-resolution T1-weighted images were checked using MRIcro ( www.MRIcro.com ) for incomplete coverage or significant head motion/artifacts. Preprocessing of rs-fMRI data was conducted using DPARSF 6.0 (Data Processing Assistant for Resting-State fMRI; http://www.restfmri.net ) in MATLAB 2016b (MathWorks, Natick, MA, USA) and SPM8 ( http://www.fil.ion.ucl.ac.uk/spm ). The first 10 volumes of each rs-fMRI dataset were discarded to allow for signal stabilization. Slice timing correction was then performed, followed by 3D head motion correction using a 24-parameter rigid-body transformation. Participants with head translation > 2.5 mm or rotation > 2.5° were excluded. Lesion masks for PSA patients were generated from their high-resolution T1-weighted images using ITK-SNAP, employing semi-automatic segmentation and manual correction by two experienced imaging physicians. The masks were later used for normalization. T1 image segmentation was performed to separate gray matter, white matter, and cerebrospinal fluid. For patients, segmentation was carried out with the Clinical Toolbox ( https://www.nitrc.org/projects/clinicaltbx/ ) in SPM12 using enantiomorphic normalization with a six-tissue parameter model, shown to be more effective than traditional lesion-masked normalization[14,15]. For healthy controls, the standard SPM New Segment method was used. All resting-state functional images were co-registered to the corresponding T1-weighted images and normalized to the Montreal Neurological Institute (MNI) space with a voxel size of 3 × 3 × 3 mm³. To further reduce confounding influences on the BOLD signal, the global signal, white matter signal, cerebrospinal fluid signal, and Friston-24 head motion parameters were regressed out. Finally, a temporal band-pass filter (0.01–0.08 Hz) was applied to mitigate high-frequency physiological noise and low-frequency drift. Clinical Scale Assessments (1) Chinese Version of the Western Aphasia Battery (WAB) The WAB [ 21 ] is an assessment tool adapted from the original WAB through translation and cultural modification. It is specifically designed to evaluate language impairments and classify aphasia types in Chinese-speaking patients. The original WAB was developed by Kertesz in 1982 and provides a systematic evaluation of core language abilities, including spontaneous speech, auditory comprehension, repetition, and naming. The primary outcome measure, the Aphasia Quotient (AQ), ranges from 0 to 100; higher scores indicate better language function. AQ scores are categorized as follows: 93.8–100 (normal), 75–93.7 (mild aphasia), 50–74.9 (moderate aphasia), and 0–49.9 (severe aphasia). The Chinese version underwent rigorous localization and validation to ensure its suitability for clinical and research applications in Mandarin-speaking populations. It is widely used in aphasia diagnosis, rehabilitation assessment, and related research in China. (2) Non-Language Cognitive Assessment (NLCA) The NLCA [ 22 ] is a specialized instrument for evaluating nonverbal cognitive functions, particularly useful for individuals with speech impairments or language disorders. It provides an objective measure of cognition independent of language skills, covering multiple domains such as memory, attention, reasoning, executive function, and visuospatial ability. By incorporating five non-language tasks, the NLCA avoids potential bias introduced by speech or language deficits, making it especially suitable for patients with post-stroke aphasia (PSA) and other populations with language impairment. This tool offers reliable support for clinical diagnosis and cognitive rehabilitation, allowing more accurate insights into patients’ cognitive status and guiding personalized therapeutic strategies [ 22 ] . Construction of the Cerebellar Functional Connectivity Network Based on ROIs In this study, we employed the Seitzman-27 cerebellar atlas [ 20 ] (Table 1 ) to construct a cerebellar functional network. This atlas comprises 27 cerebellar cortical and subcortical regions, defined as network nodes. Using the GRETNA toolbox ( http://www.nitrc.org/projects/gretna/ ) [ 23 ] , we performed network analysis by first averaging the time series of all voxels within each node and extracting the mean time series for each region of interest (ROI). Pearson’s correlation coefficients were then computed between every pair of ROIs for each participant to generate a 27 × 27 functional connectivity matrix. To improve the normality of these correlations, we applied Fisher’s r-to-z transformation [ 24 ] . Table 1 Seitzman-27 cerebellar atlas s [ 20 ] Brain Region, Abbreviation ROI Number MNI Coordinates X Y Z Dorsal Attention Network, DAN 2 -13 -52 -50 3 14 -48 -52 Default Mode Network, DMN 4 -32 -78 -38 5 32 -81 -38 6 -24 -76 -28 7 24 -76 -28.01 8 -5.72 -50.8 -40.84 9 8 -50 -40 Visual Network, VN 10 0 -74 -25 Fronto Parietal Network, FPN 11 -10 -78 -28 12 10 -78 -28 13 -34 -72.01 -48 14 34 -72 -48 15 -30.5 -66 -30 16 31.68 -62.83 -30.4 17 40 -44 -38 Cingulo Opercular Network, CON 1 32 -49 -51 20 -34 -42 -44 21 -33 -51 -50 Salience Network, SN 18 43.5 -60 -30 19 -43.5 -60 -30 Somatomotor Network-Dorsal, SMN (Dor) 22 -6 -74 -42 23 7.5 -72 -39 26 -12 -44 -18 27 12 -44 -18 Somatomotor Network-Lateral, SMN (Lat) 24 -10 -62 -18 25 10 -62 -18 The mean time series of each ROI was extracted using a 4-mm radius spherical mask. Abbreviations, MNI: Montreal Neurological Institute; +: right hemisphere; −: left hemisphere. Functional Network Analysis Because no consensus exists on the optimal functional network threshold, we applied a commonly used range of sparsity thresholds (0.05 to 0.40, in increments of 0.01) to the correlation matrices. A lower sparsity limit of 0.05 ensured that the average node degree was greater than the natural logarithm of the node count, while the upper limit of 0.40 did not exceed 50% network density. Within this 0.05–0.40 range, we calculated the small-world index, global network metrics, and nodal metrics of the cerebellar functional network for each participant in both the PSA and HC groups [ 25 , 26 ] . Statistical Analysis All demographic and clinical data from the PSA and HC groups were analyzed using IBM SPSS 27.0 (IBM SPSS Inc., Chicago, IL, USA; http://www.spss.com ). First, skewness and kurtosis values were checked and fell within an acceptable range (− 2 to + 2). An independent-samples t-test was used to compare age, WAB, and NLCA scores between the two groups, whereas sex distribution was assessed by the chi-square test. A p-value < 0.05 was considered statistically significant. To explore group differences in global and local network measures, we conducted an independent-samples t-test on the area under the curve (AUC) for each measure, applying 5,000 permutations to ensure robust results. For local network metrics (such as nodal betweenness, nodal degree, and nodal clustering coefficient), Bonferroni correction was used (p < 0.00185); we also reported trends of potential biological significance at a more lenient threshold (p < 0.037). Group comparisons of ROI-ROI FC within the cerebellar network were performed using network-based statistics (NBS), with a significance threshold of p < 0.005. A permutation test with 5,000 iterations further confirmed the robustness of the findings. Age, head motion, and sex were included as covariates. To investigate the relationships among cerebellar functional network topological measures, cerebellar average FC, and language/cognitive function in PSA patients, we employed Pearson’s correlation analysis. Specifically, we examined whether global measures, local measures, and mean FC values correlated with language and cognitive test scores. A p-value < 0.05 was considered statistically significant. Results Demographic and Clinical Data Demographic and clinical characteristics of the PSA group (n = 73) and the HC group (n = 75) are summarized in Table 2 . No significant difference in age was observed between the two groups. The lesion types included cerebral infarction (n = 65), infarction with hemorrhagic transformation (n = 7), and intracerebral hemorrhage (n = 1), all located in the left cerebral hemisphere. Table 2 Demographic and Clinical Data of PSA and HC Characteristic PSA Group (n = 73) HC Group (n = 75) χ 2 / t value p value Age 48.59 ± 10.56 46.16 ± 11.50 1.340 0.184 1 Gender (Male: Female) 59:14 55:20 1.172 0.279 2 Duration of Illness (months) 4.47 ± 2.81 — — — Western Aphasia Battery (WAB) Fluency 2.91 ± 2.028 — — — Spontaneous Speech 8.22 ± 3.64 — — — Auditory Comprehension 91.55 ± 48.10 — — — Repetition 48.82 ± 23.59 — — — Naming 19.78 ± 14.12 — — — Aphasia Quotient (AQ) 37.76 ± 17.18 — — — Non-Linguistic Cognitive Assessment Scale Memory 7.03 ± 4.80 — — — Logical Reasoning Ability 3.11 ± 1.71 — — — Visuospatial Ability 5.08 ± 3.11 — — — Attention 6.41 ± 4.54 — — — Executive Function 3.64 ± 2.13 — — — Total Cognitive Score 25.14 ± 15.05 — — — Note : 1. Comparison using independent sample t-test; 2. Gender comparison using chi-square test. Abbreviations: Western Aphasia Battery (WAB), Aphasia Quotient (AQ), Aphasia after Stroke (PSA), Healthy Controls (HC). Global Topological Measures of the Cerebellar Network in PSA Within the predefined sparsity range (0.05 to 0.40, step = 0.01), both the PSA and HC groups showed γ > 1 and λ ≈ 1, resulting in a small-world index σ (σ = γ/λ) > 1. These findings indicate that both groups retained the characteristic high-efficiency, low-cost “small-world” organization in the cerebellar network. However, the PSA group displayed lower σ, γ, and λ than the HC group, suggesting impairments in small-world topological organization of the cerebellar functional network (Fig. 1 ). Additionally, we compared the AUC values of these global indices between groups. Compared with the HC group, the PSA group exhibited significant decreases in sigma (t = − 2.596, p = 0.010), gamma (t = − 3.299, p = 0.001), lambda (t = − 3.061, p = 0.003), Cp (t = − 2.132, p = 0.035), and Eloc (t = − 2.760, p = 0.007). No significant differences were observed in Lp (t = 0.110, p = 0.913) or Eglob (t = − 0.374, p = 0.709) (Fig. 2 ). Local Topological Measures of the Cerebellar Network Compared with HCs, the PSA group showed significantly reduced nodal betweenness in the DMN, reduced nodal degree in the FPN, CON, and SMN (dorsal) networks, reduced nodal clustering coefficient in the CON, and reduced nodal efficiency in the FPN and CON, as well as lower local efficiency in the FPN, CON, and SMN (dorsal) (Table 3 and Fig. 3 ). Table 3 Group Differences in Nodal Properties of the Cerebellar Network Between PSA and HC Group ROI Number Region MNI Nodal Betweenness Nodal Degree Nodal Clustering Coefficient Nodal Efficiency Nodal Local Efficiency X Y Z t -value p-value t -value p-value t -value p-value t -value p -value t -value p-value 4 DMN -32 -78 -38 2.52 0.01 1.56 0.12 -0.29 0.77 1.30 0.96 0.05 0.96 7 DMN 24 -76 -28.01 -2.02 0.05 -1.60 0.11 0.08 0.93 -1.48 0.56 -0.59 0.56 12 FPN 10 -78 -28 -1.27 0.21 -1.21 0.23 -1.60 0.11 -1.84 0.04 # -2.03 0.04 # 14 FPN 34 -72 -48 -0.92 0.36 -1.54 0.13 -1.73 0.09 -1.75 0.04 # -2.09 0.04 # 16 FPN 31.68 -62.83 -30.4 -1.72 0.09 -2.88 < 0.001 ** -0.78 0.44 -2.93 0.08 -1.77 0.08 17 FPN 40 -44 -38 -2.53 0.01 -2.82 0.01 * -0.95 0.34 -2.62 0.12 -1.55 0.12 20 CON -34 -42 -44 1.57 0.12 2.43 0.02 * 2.22 0.03 * 2.95 0.02 * 2.45 0.02 * 21 CON -33 -51 -50 1.77 0.08 2.16 0.03 * 0.41 0.68 1.99 0.40 0.84 0.40 23 SMN (Dor) 7.5 -72 -39 1.94 0.05 2.83 0.01 * 0.22 0.83 2.76 0.32 1.01 0.32 Note: p < 0.05 was considered statistically significant (uncorrected). # indicates uncorrected results. Bonferroni correction was applied, setting the significance level for each node at **p < 0.00185 (i.e., 0.05/27). Additionally, node-level results at a more lenient threshold of *p < 0.037 (i.e., 1/27) are also reported for reference. Abbreviations: MNI, Montreal Neurological Institute; DMN, default mode network; FPN, frontoparietal network; CON, cingulo-opercular network; SMN (Dorsal), dorsal somatomotor network. Alterations in Intranetwork FC Within the Cerebellar Functional Network in PSA Compared with HCs, the PSA group showed significantly decreased FC primarily in the FPN, DMN, and SMN (dorsal). In particular, FC within the FPN-FPN was markedly reduced (t = − 3.47, p = 0.001; t = − 4.648, p < 0.001; t = − 3.465, p = 0.001), as well as FPN-CON (t = − 4.388, p < 0.001), FPN-SN (t = − 3.819, p < 0.001), FPN-SMN (dorsal) (t = 3.733, p < 0.001), FPN-DMN (t = − 3.93, p < 0.001), DMN-DMN (t = − 3.976, p = 0.002), and DMN-SMN (dorsal) (t = − 3.527, p = 0.001) (Table 4 , Fig. 4 ). Table 4 Alterations in Intra-Cerebellar FC Between the PSA and HC Groups. ROI Number ROI Number t-value p-value FPN 12 FPN 11 -3.47 0.001 FPN 14 FPN 12 -4.648 < 0.001 FPN 14 FPN 13 -3.465 0.001 FPN 16 FPN 15 -3.611 < 0.001 FPN 17 CON 1 -4.388 < 0.001 FPN 16 SN 18 -3.819 < 0.001 FPN 17 CON 20 -3.641 < 0.001 FPN 14 SMN (Dor)23 3.733 < 0.001 FPN 16 DMN 7 -3.93 < 0.001 DMN 7 DMN 5 -3.976 0.002 DMN 8 SMN (Dor) 27 -3.527 0.001 Note: Differences were considered statistically significant at p < 0.005, corrected using network-based statistics (NBS) with 5,000 non-parametric permutations. Abbreviations: FPN, frontoparietal network; DMN, default mode network; SN, salience network; CON, cingulo-opercular network; SMN (Dorsal), dorsal somatomotor network. Correlations Between Cerebellar Network Metrics and Clinical Scales in PSA Pearson’s correlation analysis showed that the mean FC in PSA patients was significantly related to sigma, Cp, Lp, Eg, and Eloc, as well as nodal betweenness in the DMN, nodal degree in the SMN (dorsal), nodal efficiency in the FPN/CON, and local efficiency in the FPN. Cp was significantly associated with auditory comprehension, memory, reasoning, visuospatial ability, attention, executive function, and total cognitive scores. Eloc was similarly correlated with auditory comprehension, memory, reasoning, visuospatial ability, attention, executive function, and total cognitive scores. Locally, DMN nodal betweenness correlated with speech fluency and repetition, while FPN nodal efficiency correlated with speech fluency and course of disease. Local efficiency in the FPN was also significantly correlated with disease duration (Table 5 , Fig. 5 ). Table 5 Correlations Between Cerebellar Network Metrics and Language-Cognitive Functions in PSA Patients Clinical Measure Global Metrics Bc DC aNe aNe aNLe sigma Cp Lp Eg Eloc DMN ROI4 SMN (Dor) ROI23 FPN ROI12 CON ROI20 FPN ROI12 Fluency r 0.047 0.062 0.095 -0.010 0.089 -0.262 * -0.101 0.234 * -0.079 0.143 p 0.693 0.604 0.424 0.936 0.452 0.025 0.395 0.046 0.506 0.228 Auditory Comprehension r -0.160 -0.320 ** -0.151 0.141 -0.339 ** -0.077 0.026 0.056 0.072 -0.145 p 0.176 0.006 0.203 0.235 0.003 0.518 0.828 0.637 0.546 0.222 Repetition r -0.092 -0.174 0.015 0.062 -0.204 -0.262 * 0.030 0.077 0.039 -0.047 p 0.438 0.142 0.900 0.603 0.083 0.025 0.800 0.519 0.746 0.693 Memory r -0.154 -0.332 ** -0.076 0.101 -0.376 ** -0.096 0.034 0.097 0.006 -0.138 p 0.200 0.005 0.528 0.401 0.001 0.423 0.779 0.421 0.960 0.253 Logical Reasoning Ability r -0.178 -0.305 ** 0.012 0.033 -0.336 ** -0.096 0.042 0.052 0.020 -0.113 p 0.138 0.010 0.923 0.783 0.004 0.427 0.726 0.668 0.868 0.349 Visuospatial Ability r -0.118 -0.254 * 0.004 0.052 -0.276 * -0.086 0.102 -0.003 0.005 -0.167 p 0.325 0.032 0.972 0.666 0.020 0.477 0.399 0.977 0.964 0.165 Attention r 0.013 -0.328 ** -0.105 0.158 -0.343 ** -0.009 0.088 0.062 0.061 -0.179 p 0.916 0.005 0.385 0.188 0.003 0.942 0.467 0.607 0.616 0.136 Executive Function r -0.116 -0.361 ** -0.171 0.174 -0.382 ** -0.019 0.093 0.007 0.083 -0.192 p 0.337 0.002 0.153 0.147 0.001 0.873 0.438 0.957 0.489 0.109 Total Cognitive Score r -0.103 -0.335 ** -0.077 0.117 -0.364 ** -0.063 0.075 0.054 0.035 -0.168 p 0.392 0.004 0.524 0.331 0.002 0.603 0.536 0.652 0.772 0.160 Disease Duration (months) r 0.012 -0.098 -0.065 0.119 -0.081 -0.061 -0.122 -0.366 ** 0.043 -0.277 * p 0.921 0.408 0.587 0.317 0.497 0.608 0.304 0.001 0.721 0.018 PSA Average FC r -0.241 * 0.617 ** 0.554 ** -0.742 ** 0.473 ** -0.386 ** 0.246 * 0.136 -0.386 ** 0.409 ** p 0.040 < 0.001 < 0.001 < 0.001 < 0.001 0.001 0.036 0.250 0.001 < 0.001 Notes: p < 0.05 was considered statistically significant (*), and p < 0.01 was highly significant (**). PSA, Post-stroke Aphasia; σ (sigma), Small-world index; Cp, Average clustering coefficient; Lp, Average characteristic path length; Eg, Global efficiency; Eloc, Local efficiency; DMN, Default Mode Network; FPN, Fronto-Parietal Network; SN, Salience Network; CON, Cingulo-Opercular Network; SMN (Dor), Somatomotor Network-Dorsal; Bc, Betweenness centrality; DC, Degree centrality; aNe, Nodal efficiency; aNLe, Nodal local efficiency. Discussion In this study, based on resting-state fMRI and the Seitzman-27 cerebellar atlas, we constructed a cerebellar functional connectivity network and employed graph-theoretical methods to systematically analyze the global and local topological properties of the cerebellar functional network in PSA. We also examined the relationships between these network metrics and clinical variables. The results demonstrated that the cerebellar functional network in PSA retained “small-world” characteristics; however, σ, γ, λ, Cp, and Eloc were significantly decreased. Moreover, both Cp and Eloc were closely associated with language and cognitive functions. At the nodal level, the node centrality of the DMN, FPN, CON, and SMN (dorsal) networks was significantly reduced. Specifically, DMN nodal betweenness showed significant correlations with fluency and repetition, while FPN nodal efficiency and local efficiency correlated with fluency and disease duration. Additionally, we found extensive abnormal functional connectivity (FC) in the PSA cerebellar network. Notably, FC within the FPN (FPN-FPN) and between FPN and CON, SN, SMN (dorsal), and DMN was significantly reduced in the PSA group, as was DMN-DMN and DMN-SMN (dorsal) connectivity. Mean FC in the PSA cerebellar network was significantly related to sigma, Cp, Lp, Eg, and Eloc, as well as nodal centrality in the DMN, SMN (dorsal), FPN, and CON. These findings suggest disrupted functional connectivity within the cerebellum, leading to impairments in functional segregation and local information transmission in specific cerebellar subregions. Such changes may underlie dysfunction in cerebellar areas involved in cognition and language processing. Small-World Properties of the Cerebellar Network in PSA Our results showed that the cerebellar functional network in PSA retained a small-world organization, but displayed significantly decreased values of σ, γ, λ, Cp, and Eloc. The parameter σ reflects the balance between local clustering and global connectivity [ 27 ] . A decline in σ suggests that local and global integration in the PSA cerebellar network is compromised. Likewise, the significant decrease in γ supports diminished integration capacity in local neural circuits, potentially affecting efficient coordination of language-related neural pathways [ 28 ] . A lower λ indicates reduced connectivity efficiency between the cerebellum and other cognitive regions, especially those involved in language processing and executive function [ 29 ] . These alterations imply impaired information integration and reduced ability to perform cognitive tasks in PSA patients. Furthermore, Cp and Eloc were also significantly lower in the PSA group, indicating weaker local information integration that may affect local coordination and executive control during language generation [ 27 ] . Decreased Eloc suggests diminished local information transfer efficiency and reduced fault tolerance [ 28 , 30 ] , which can degrade overall performance on complex cognitive tasks [ 30 ] . Importantly, Cp and Eloc correlated significantly with multiple language and cognitive functions (including auditory comprehension, memory, reasoning, visuospatial ability, attention, executive function, and total cognitive score). This corroborates evidence that the cerebellum is not only critical for motor control but also for higher-order cognitive processes, aligning with Buckner et al. [ 31 ] , who emphasized the cerebellum’s pivotal role in cognition. These findings differ somewhat from the study by Shi et al., [ 32 ] who examined the topological characteristics of the whole-brain functional connectome in patients with acute ischemic stroke in the brainstem and noted significant increases in Cp and Eloc but decreased γ and Eglobal, suggesting the network topology shifted toward a more regular structure. By contrast, our PSA findings highlight diminished local and global integration capacity, culminating in impaired language and cognitive function—possibly indicative of different compensation mechanisms in brainstem lesions. Consistent with our previous research [ 2 ] and other studies [ 6 , 3 ] [ 31 , 33 ] , such changes in small-world properties and local/global efficiencies are frequently associated with language deficits, underscoring the utility of graph theory for identifying potential biomarkers (e.g., local efficiency) in aphasia assessment. Taken together, the pronounced reductions in Cp and Eloc reflect crucial changes in the PSA cerebellar functional network, implying that cerebellar involvement is essential for language and cognition. These measures may serve as potential neurophysiological markers of cognitive impairment in PSA, further validating the application of graph-theoretical analysis in elucidating brain dysfunction in aphasia. Altered Node Properties and Functional Connectivity in the Cerebellar Network of PSA Patients Traditional research and treatment approaches for aphasia primarily adopt a symptom-lesion perspective, positing that specific aphasic symptoms and clinical manifestations correspond directly to localized brain damage. Consequently, language and speech impairments can be partially predicted based on lesion sites. Following this rationale, the present study investigated topological alterations within the cerebellar network by examining node properties of various ROIs, as well as intrinsic cerebellar functional connectivity. When any one of the node parameters (i.e., betweenness centrality, degree, clustering coefficient, node efficiency, or local efficiency) exhibited a significant abnormality, we inferred that the centrality of the corresponding brain region might be disrupted. Through an analysis of node properties in PSA patients’ cerebellar functional networks, we observed a notable decrease in node centrality in several key brain networks, including the DMN, FPN, CON, and SMN (Dor). The DMN is closely associated with internally oriented thought processes, emotional regulation, semantic processing, and memory retrieval, and it plays a crucial role in semantic retrieval and integration during complex language tasks [ 34 ] . Previous studies suggest that the DMN is vital in motor aphasia caused by cerebral infarction, potentially enhancing language recovery through compensatory mechanisms [ 35 ] . In this study, the DMN in PSA patients showed significantly reduced betweenness centrality, indicating weakened integrative capacity and reduced mediation in language production, emotion regulation, and self-awareness [31]. As a metabolically active hub region, the posterior DMN (pDMN) connects extensively with various brain areas, particularly the FPN [ 36 ] . Zhang et al. [ 37 ] also demonstrated weakened coupling between the pDMN and left FPN (lFPN) in PSA patients, supporting these findings. In our study, functional connectivity in DMN-DMN, DMN-FPN, and DMN-SMN (Dor) was significantly reduced among PSA patients, suggesting diminished coordination among semantic processing, introspective thinking, and motor execution. Such disruption in network interactions likely impairs cognitive flexibility and semantic integration in language production. Collectively, impaired node centrality and functional connectivity in the DMN suggest a decline in semantic retrieval capacity during language production, particularly in complex semantic tasks, leading to deficient language output. The FPN primarily comprises prefrontal and parietal regions responsible for cognitive control, task switching, executive functions, and attention regulation. A core function of the FPN is to dynamically integrate both internal and external information streams, facilitating problem-solving, planning, and task execution. Our findings revealed that PSA patients exhibit significantly reduced FPN node centrality, reflecting compromised executive control and attentional regulation during language tasks. This deficit likely exacerbates language impairment and hampers rehabilitation outcomes. Further analysis showed a marked decrease in FPN-FPN functional connectivity, implying reduced synergy among network nodes and, consequently, diminished efficiency in language control, working memory, and task execution. Moreover, a significant decline in FPN-CON connectivity indicated weakened coordination between executive and cognitive control functions. Similarly, reduced connectivity between the FPN and the SN, as well as between the FPN and the SMN, suggests diminished attentional modulation and motor control during abrupt language tasks (e.g., articulation), impairing language fluency and accuracy. In line with these findings, Zhu et al. [ 35 ] showed substantially weakened intra-FPN connectivity in PSA patients, along with reduced left FPN–right frontal connectivity, correlating with language comprehension deficits. Zhang et al. [ 37 ] further reported significant alterations in cross-network FC in subacute PSA, especially reduced FC between the left and right FPN, closely associated with language deficits. Consistent with those observations, our resting-state FC analyses confirm that PSA patients display decreased FPN node centrality and functional connectivity, underscoring functional impairments in executive control, attentional modulation, motor coordination, and language production—phenomena that align with their suboptimal performance in language and executive tasks. The CON plays a crucial role in cognitive control and sustained attention, maintaining task-related attention and monitoring during language production and comprehension. It is also vital for self-regulatory behaviors and error responses [ 38 ] . In the present study, PSA patients exhibited significantly reduced node degree and node efficiency in the CON, implying reduced local and global information processing efficiency. This limitation in sustained attention and executive function can manifest as difficulty maintaining attention during lengthy language production or comprehension tasks, leading to lower efficiency when performing cognitively demanding language tasks. The SMN —particularly its dorsal segment—is primarily responsible for sensorimotor integration, including motor regulation in speech production, and is specifically linked to articulation and oral-motor coordination [ 39 ] . Here, a marked decline in node degree and local efficiency was noted in the dorsal SMN among PSA patients, suggesting insufficient functionality in tasks requiring motor coordination for language production, particularly articulation and motor control. Xu et al. [ 40 ] investigated the functional connectivity of brain networks in PSA patients, demonstrating significantly decreased connectivity in the SMN, salience network, and language network, accompanied by a broader reduction in both interhemispheric and intrahemispheric connections. Notably, stronger connections were still observed between the language network and cerebellar network, possibly reflecting a compensatory mechanism. Limitations Several limitations should be acknowledged. First, the sample size was relatively small, and we did not further stratify PSA patients by aphasia subtype, which may limit our understanding of subtype-specific cerebellar network characteristics. Second, this study focused exclusively on cerebellar functional connectivity and topological features and did not investigate dynamic or structural connections between the cerebellum and the cerebrum. Future studies can integrate structural MRI to construct cerebellar structural networks, enabling in-depth exploration of structure-function relationships and their links to language and cognitive deficits. Finally, although we collected imaging and clinical data before and after rTMS treatment, we did not perform a longitudinal analysis. Future longitudinal studies are warranted to track dynamic changes in network properties during therapy and their relationship with clinical outcomes, potentially optimizing treatment strategies. Conclusion Using resting-state fMRI and graph-theoretical analysis, this study constructed and evaluated the topological organization of the cerebellar functional network in PSA. Despite retaining small-world properties, the PSA group showed significantly decreased σ, γ, λ, Cp, and Eloc, indicating compromised organizational structure and information processing efficiency. Moreover, Cp and Eloc were strongly associated with cognitive and language performance, suggesting they may serve as potential indicators for assessing PSA. Concomitantly, node centrality in the DMN, FPN, CON, and SMN (dorsal) declined, and intranetwork functional connectivity was significantly reduced—highlighting impaired information integration and transfer that could affect both language and cognition. These findings underscore the pivotal role of the cerebellum in PSA and emphasize the link between topological disturbances, weakened functional connectivity, and aphasia. They thus provide novel perspectives for understanding the neural mechanisms of PSA and improving clinical interventions. Declarations Acknowledgment We thank the participants for their contribution and the research team for their support. Funding Weacknowledgefinancialsupportfrom the National Key Research and Development Program of China (Grant number: 2023YFF1204600); The National Natural Science Foundation of China (82227802, 82302306, 82302336); The Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (No. JNU1AF-CFTP-2022-a01201); The Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); The Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); The Postdoctoral Science Foundation of China (2022M721349); The Medical Science and Technology Research Fund of Guangdong Province, China (A2023144). Declaration of conflicting interests Competing financial interests:The authors declare no competing financial interests. Authors’ contributions L.C. and Z.L drafted the initial manuscript and performed language editing. Z.L.and M.W conducted data processing and statistical analysis. 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00:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6379167/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6379167/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12035-026-05781-4","type":"published","date":"2026-03-20T15:58:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83199631,"identity":"a8f7b874-c356-44fa-af2a-21efcd523b99","added_by":"auto","created_at":"2025-05-21 06:08:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71575,"visible":true,"origin":"","legend":"\u003cp\u003eThe small-world indices σ (sigma), γ (gamma), and λ (lambda) of the PSA and HC groups across sparsity thresholds ranging from 0.05 to 0.40.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eWithin the sparsity range of 0.05 to 0.40, both the PSA and HC groups exhibited γ values significantly greater than 1, λ values approximately equal to 1, and σ values significantly greater than 1, indicating that both groups demonstrated typical small-world network properties. Aphasia after Stroke，PSA；Healthy controls，HC； Small-world index, \u003cem\u003eσ\u003c/em\u003e (sigma)； Clustering coefficient index, \u003cem\u003eγ\u003c/em\u003e(gamma)； Characteristic path length index, \u003cem\u003eλ\u003c/em\u003e (lambda).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6379167/v1/b702cc26dcc5e3cfb5afd41b.png"},{"id":83199637,"identity":"503e23b5-d6a1-43af-9f0b-059c556ded6e","added_by":"auto","created_at":"2025-05-21 06:08:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmall-world network metrics of the cerebellar network in the PSA and HC groups. *\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u0026lt; 0.05was considered statistically significant, **\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u0026lt; 0.005.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e Aphasia after Stroke，PSA； Healthy controls，HC； Small-world index, \u003cem\u003eσ\u003c/em\u003e (sigma)； Clustering coefficient index, \u003cem\u003eγ\u003c/em\u003e (gamma)； Characteristic path length index, \u003cem\u003eλ\u003c/em\u003e (lambda)； Average clustering coefficient, \u003cem\u003eCp\u003c/em\u003e； Average characteristic path length, \u003cem\u003eLp\u003c/em\u003e； Global efficiency, \u003cem\u003eEglob\u003c/em\u003e； Local efficiency, \u003cem\u003eEloc\u003c/em\u003e。\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6379167/v1/259790a9e3a6c86bedfb6f77.png"},{"id":83199632,"identity":"d7f37996-960a-43fe-8235-6e2d8584112f","added_by":"auto","created_at":"2025-05-21 06:08:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":178707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroup Differences in Nodal Properties of the Cerebellar Network Between PSA and HC Group.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The larger the T-value, the larger the sphere diameter. T-values greater than 0 are shown in red, while T-values less than 0 are shown in blue. Abbreviations: DMN, default mode network; FPN, frontoparietal network; CON, cingulo-opercular network; SMN (Dorsal), dorsal somatomotor network.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6379167/v1/1f82b20423db192b7ef89f18.png"},{"id":83199627,"identity":"34fc3158-c52f-4037-9155-5f01ccda8846","added_by":"auto","created_at":"2025-05-21 06:08:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAltered functional connectivity within the cerebellar functional network in the PSA group.\u003c/strong\u003e\u003cbr\u003e\n \u003cstrong\u003eNote:\u003c/strong\u003e Compared to the HC group, the PSA group exhibited widespread reductions in functional connectivity within the cerebellar network. Significant decreases were primarily observed in the following connections: FPN–FPN, FPN–CON, FPN–SN, FPN–SMN (Dorsal), FPN–DMN, DMN–DMN, and DMN–SMN (Dorsal). Group comparisons of ROI-to-ROI functional connectivity (FC) within the cerebellar network were performed using network-based statistics (NBS), with a significance threshold of \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.005. A permutation test with 5,000 iterations was conducted to confirm the robustness of the findings. \u003cstrong\u003eAbbreviations:\u003c/strong\u003eFPN, frontoparietal network; DMN, default mode network; SN, salience network; CON, cingulo-opercular network; SMN (Dorsal), dorsal somatomotor network; PSA, post-stroke aphasia; HC, healthy controls.\u003cbr\u003e\nRed lines indicate positive \u003cem\u003et\u003c/em\u003e-values; blue lines indicate negative \u003cem\u003et\u003c/em\u003e-values.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6379167/v1/a4979d6a45a8e91beece1098.png"},{"id":83199641,"identity":"e00d4e9c-fa67-44ff-958a-de81979a421b","added_by":"auto","created_at":"2025-05-21 06:08:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96649,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations Between Cerebellar Network Metrics and Language-Cognitive Functions in PSA Patients (r value). \u003cstrong\u003eNotes:\u003c/strong\u003e \u003cstrong\u003ep \u0026lt; 0.05\u003c/strong\u003ewas considered statistically significant. PSA, Post-stroke Aphasia; σ (sigma), Small-world index; Cp, Average clustering coefficient; Lp, Average characteristic path length; Eg, Global efficiency; Eloc, Local efficiency; DMN, Default Mode Network; FPN, Fronto-Parietal Network; SN, Salience Network; CON, Cingulo-Opercular Network; SMN (Dor), Somatomotor Network-Dorsal; Bc, Betweenness centrality; DC, Degree centrality; aNe, Nodal efficiency; aNLe, Nodal local efficiency.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6379167/v1/55555c3f68d4e2fa24ca81df.png"},{"id":105223301,"identity":"e813b9b9-e03b-4066-97de-60ded7e5665b","added_by":"auto","created_at":"2026-03-23 16:03:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2295730,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6379167/v1/2de4f380-af79-4a87-a117-ed836f06f740.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Reorganization of Cerebellar Functional Network Topology in Post-Stroke Aphasia: A Resting-State fMRI Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePost-stroke aphasia (PSA) is an acquired language disorder generally caused by ischemic stroke involving occlusion of the left middle cerebral artery, which primarily impairs various language functions such as auditory comprehension, speech production, naming, reading, and writing. PSA is one of the most common and severe complications of stroke \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, as it not only exacerbates motor, cognitive, and social impairments but also increases mortality risk and reduces quality of life. In addition, PSA substantially influences multiple cognitive networks in the brain, particularly those regions engaged in language processing. Recent findings have emphasized the critical role of the cerebellum in language function, indicating that it may play a significant role in the reorganization of neural networks in PSA.\u003c/p\u003e \u003cp\u003eGraph-theoretical analyses provide effective tools for the investigation of complex brain networks. By employing such methods, researchers gain profound insights into the topological principles of these networks, thereby offering valuable information on brain function as well as its alterations under pathological conditions. Previous work constructed whole-brain functional connectivity networks in PSA based on fMRI and employed graph theory to evaluate the topological properties of the PSA brain, revealing that the global functional network in PSA exhibits a \u0026ldquo;small-world\u0026rdquo; organization. Furthermore, both global and local topological attributes were disrupted, while certain core nodes of the dual-stream model showed abnormal centrality, suggesting that network topological impairment may be a potential marker of speech dysfunction in PSA\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In a related study, Cao et al.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e reported that individuals with PSA demonstrated gut microbiota imbalance, increased inflammation, and impaired language function; graph-theoretical analyses showed compensatory activation in right-hemisphere language areas, whereas functional decline appeared near the left-hemisphere lesion. Tao and Rapp et al. \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e explored functional network characteristics in PSA and noted that networks with higher local integration (i.e., stronger modularity) were associated with superior therapeutic responses and milder deficits. Notably, both global modularity and local integration improved following therapy in intact ventral occipitotemporal regions related to spelling. Johnson et al.\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e also adopted a graph-theoretical approach to analyze semantic-processing networks in aphasia, finding that individuals with higher pre-treatment network metrics achieved better naming outcomes. These results suggest that greater global efficiency and network strength in semantic regions may serve as favorable prognostic factors for naming recovery. Baliki et al. \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e using rs-fMRI combined with graph theory, discovered that resting-state functional connectivity (rsFC) in PSA was closely related to treatment outcomes; for instance, global efficiency and interhemispheric connectivity were positively associated with improvements in language and visual attention. Moreover, connectivity between the default mode network (DMN) and auditory areas was strongly linked to language recovery, whereas connectivity between the salience network and visual regions correlated with enhanced visual attention. Gleichgerrcht et al.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e investigated white matter structural networks via DTI, focusing on \u0026ldquo;rich club\u0026rdquo; regions, and showed that a greater number of intact left-hemisphere rich-club hubs was negatively correlated with aphasia severity. Taken together, these studies suggest that network topological metrics are reliable predictors of therapeutic efficacy in comprehensive language rehabilitation for aphasia.\u003c/p\u003e \u003cp\u003eAlthough many studies have addressed the relationship between cortical network topology and aphasia, research on the role of the cerebellum\u0026mdash;particularly its topological organization\u0026mdash;remains limited. This gap constrains a more thorough understanding of cerebellar function, especially regarding its involvement in advanced cognitive processes\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Resting-state functional connectivity studies indicate that, aside from its participation in motor control networks, the cerebellum also takes part in \u0026ldquo;cognitive\u0026rdquo; networks, forming functional interactions with prefrontal and parietal association cortices. Task-dependent activation patterns further support the idea that the cerebellum can be functionally divided in accordance with its anatomical connectivity with sensorimotor and association areas of the cerebral cortex\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccording to the internal model theory of cerebellar function, the cerebellum plays a pivotal role in motor and speech planning by predicting forthcoming linguistic information and adjusting speech production as necessary\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Due to the close structural connections between the cerebellum and the cerebrum, cerebellar regions associated with language receive inputs from frontal-temporal areas\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e and relay predictive information to the prefrontal cortex to ensure accurate language output\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. One diffusion tensor imaging (DTI) study showed that the cerebellum and the cerebral cortex are interconnected via the thalamus in a contralateral manner\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e; similarly, the dentate nucleus is structurally linked to multiple regions of the contralateral frontal lobe\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Functioning as part of a subcortical circuit, the cerebellum receives input from Broca\u0026rsquo;s area through the left anterior insula, then transmits this information to the left ventral premotor cortex, which eventually projects to the left primary motor cortex. This model positions the cerebellum below left frontal motor planning centers in the speech-processing hierarchy\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, which reinforces evidence of cerebellar-cortical interactions during language processing. Keser et al.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e investigated cerebellar white matter integrity and its connections with the cortex in PSA using DTI. The findings indicated that lower fractional anisotropy (FA) and higher mean diffusivity (MD) in the right cerebellum and its left-hemisphere pathways were associated with poorer performance in picture naming, underlining the importance of cerebellar-cortical circuits and white matter integrity in post-stroke language recovery. Satoer et al.\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e reported that individuals with cerebellar stroke demonstrated severe language deficits, including challenges in lexical retrieval, phonology, semantics, and syntax, and neither lesion side nor lesion volume significantly influenced outcomes. These results imply that \u0026ldquo;cerebellar aphasia\u0026rdquo; is not strictly lateralized and entails extensive interactions with cortical and subcortical language areas, highlighting the cerebellum\u0026rsquo;s role in both motor regulation and cognitive-linguistic functions.\u003c/p\u003e \u003cp\u003eA graph-theoretical investigation by Chen et al.\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e in combination with hidden Markov model analyses showed that Crus I and Crus II exhibit high degree centrality in the cerebellar functional connectome. The cerebellar connectome displays small-world, modular, and hierarchical organization, comprising three main modules (attention/executive, default mode, and task-positive networks), reflecting pronounced functional interactions between the cerebrum and cerebellum. Seitzman et al.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e designed a refined cerebellar ROI atlas, which enhanced the precision of network analyses and demonstrated that these cerebellar ROIs have a highly specific functional organization. Building on these developments, the present study employed the Seitzman-27 cerebellar atlas to construct cerebellar functional networks in PSA, with the goal of investigating topological changes in the network and their relationship with language and cognitive abilities.\u003c/p\u003e \u003cp\u003eConsidering the importance of the cerebellum in language function and its extensive connections with the cerebral cortex, analyzing the topological characteristics of the cerebellar functional network in PSA through graph-theoretical approaches is of high significance. Based on the above research, this study collected resting-state fMRI data from individuals with PSA. According to the Seitzman atlas, 27 cerebellar cortical and subcortical regions were extracted as nodes to form a 27\u0026times;27 cerebellar functional connectivity matrix. Subsequently, functional connectivity methods and graph theory were used to examine the small-world properties, global and nodal metrics, and intrinsic connectivity patterns of the cerebellum. In addition, the study assessed how these aberrant FC values and network topological indices correlate with language and cognitive functions in PSA, in order to enhance our understanding of cerebellar involvement in post-stroke aphasia.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eFrom October 2019 to June 2024, we recruited 73 PSA at the Department of Rehabilitation Medicine (outpatient and inpatient) of the First Affiliated Hospital of Jinan University. Additionally, 75 age-, sex-, and education-matched healthy controls (HC) were recruited, all of whom met the head motion criteria. All participants were right-handed native Chinese speakers. The inclusion criteria for PSA were: 1. First-ever stroke, confirmed by MRI or CT, primarily involving the left middle cerebral artery territory. 2. Aphasia diagnosed using the Chinese version of the Western Aphasia Battery (WAB), with no prior language intervention. 3. Clear consciousness, able to cooperate with interviews and examinations. 4. Age 25\u0026ndash;80 years, at least elementary education, native Chinese speaker, and right-handed (based on the Edinburgh Handedness Inventory). 5. Stroke onset 2\u0026ndash;12 months before enrollment. The exclusion criteria for both PSA patients and HC were: Intracranial lesions on routine MRI, history of psychiatric or organic brain disease, head trauma, or unconsciousness lasting over 5 minutes. Language, reading, or writing difficulties due to severe sensory or motor impairments. Congenital or childhood learning disabilities affecting language development. Severe physical illness, drug side effects, or substance/alcohol abuse. Severe depression, anxiety, suicidal behavior, or agitation. Contraindications to MRI or inability to cooperate during scanning. Pregnancy or breastfeeding. This study was approved by the Ethics Committee of the First Affiliated Hospital of Jinan University (Approval No. KY-2024-096). Clinical trial number: not applicable. All procedures were conducted in accordance with the principles of the Declaration of Helsinki. All participants received a full explanation of the study and signed written informed consent forms prior to enrollment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003efMRI Data Acquisition\u003c/h3\u003e\n\u003cp\u003eResting-state fMRI data were collected from all PSA patients before any language therapy or rTMS treatment. Data for all participants were acquired using a GE Discovery MR 750 3.0T superconducting MRI scanner equipped with an 8-channel phased-array surface head coil. During scanning, participants lay supine with eyes closed, remained awake and relaxed, and were instructed to minimize active thinking. Foam padding and earplugs were used to reduce head motion and scanner noise, respectively.\u003c/p\u003e \u003cp\u003eA high-resolution T1-weighted structural MRI was obtained via a 3D-BRAVO sequence with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;4,500 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;3.22 ms, slice thickness\u0026thinsp;=\u0026thinsp;1.0 mm, interslice gap\u0026thinsp;=\u0026thinsp;0.5 mm, field of view (FOV)\u0026thinsp;=\u0026thinsp;240 mm \u0026times; 240 mm, flip angle\u0026thinsp;=\u0026thinsp;15\u0026deg;, voxel size\u0026thinsp;=\u0026thinsp;0.47 \u0026times; 0.47 \u0026times; 1.0 mm\u0026sup3;, and 164 slices. Resting-state functional images were acquired using a gradient-recalled echo-planar imaging (GRE-EPI) sequence: TR\u0026thinsp;=\u0026thinsp;2,100 ms, TE\u0026thinsp;=\u0026thinsp;30 ms, slice thickness\u0026thinsp;=\u0026thinsp;3.0 mm, interslice gap\u0026thinsp;=\u0026thinsp;0.6 mm, voxel size\u0026thinsp;=\u0026thinsp;3.125 \u0026times; 3.125 \u0026times; 3.6 mm\u0026sup3;, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, matrix size\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, number of volumes\u0026thinsp;=\u0026thinsp;160, and 42 slices. Conventional T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) were also performed to exclude any additional lesions or acute infarctions.\u003c/p\u003e\n\u003ch3\u003eData Preprocessing\u003c/h3\u003e\n\u003cp\u003eRoutine MRI images were first examined to exclude individuals with other intracranial lesions. Subsequently, both resting-state fMRI (rs-fMRI) and high-resolution T1-weighted images were checked using MRIcro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.MRIcro.com\" target=\"_blank\"\u003ewww.MRIcro.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.MRIcro.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for incomplete coverage or significant head motion/artifacts.\u003c/p\u003e \u003cp\u003ePreprocessing of rs-fMRI data was conducted using DPARSF 6.0 (Data Processing Assistant for Resting-State fMRI; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.restfmri.net\u003c/span\u003e\u003cspan address=\"http://www.restfmri.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in MATLAB 2016b (MathWorks, Natick, MA, USA) and SPM8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk/spm\u003c/span\u003e\u003cspan address=\"http://www.fil.ion.ucl.ac.uk/spm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The first 10 volumes of each rs-fMRI dataset were discarded to allow for signal stabilization. Slice timing correction was then performed, followed by 3D head motion correction using a 24-parameter rigid-body transformation. Participants with head translation\u0026thinsp;\u0026gt;\u0026thinsp;2.5 mm or rotation\u0026thinsp;\u0026gt;\u0026thinsp;2.5\u0026deg; were excluded. Lesion masks for PSA patients were generated from their high-resolution T1-weighted images using ITK-SNAP, employing semi-automatic segmentation and manual correction by two experienced imaging physicians. The masks were later used for normalization. T1 image segmentation was performed to separate gray matter, white matter, and cerebrospinal fluid. For patients, segmentation was carried out with the Clinical Toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/clinicaltbx/\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/clinicaltbx/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in SPM12 using enantiomorphic normalization with a six-tissue parameter model, shown to be more effective than traditional lesion-masked normalization[14,15]. For healthy controls, the standard SPM New Segment method was used. All resting-state functional images were co-registered to the corresponding T1-weighted images and normalized to the Montreal Neurological Institute (MNI) space with a voxel size of 3 \u0026times; 3 \u0026times; 3 mm\u0026sup3;. To further reduce confounding influences on the BOLD signal, the global signal, white matter signal, cerebrospinal fluid signal, and Friston-24 head motion parameters were regressed out. Finally, a temporal band-pass filter (0.01\u0026ndash;0.08 Hz) was applied to mitigate high-frequency physiological noise and low-frequency drift.\u003c/p\u003e\n\u003ch3\u003eClinical Scale Assessments\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e(1) Chinese Version of the Western Aphasia Battery (WAB)\u003c/h2\u003e \u003cp\u003eThe WAB \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e is an assessment tool adapted from the original WAB through translation and cultural modification. It is specifically designed to evaluate language impairments and classify aphasia types in Chinese-speaking patients. The original WAB was developed by Kertesz in 1982 and provides a systematic evaluation of core language abilities, including spontaneous speech, auditory comprehension, repetition, and naming. The primary outcome measure, the Aphasia Quotient (AQ), ranges from 0 to 100; higher scores indicate better language function. AQ scores are categorized as follows: 93.8\u0026ndash;100 (normal), 75\u0026ndash;93.7 (mild aphasia), 50\u0026ndash;74.9 (moderate aphasia), and 0\u0026ndash;49.9 (severe aphasia). The Chinese version underwent rigorous localization and validation to ensure its suitability for clinical and research applications in Mandarin-speaking populations. It is widely used in aphasia diagnosis, rehabilitation assessment, and related research in China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e(2) Non-Language Cognitive Assessment (NLCA)\u003c/h2\u003e \u003cp\u003eThe NLCA \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e is a specialized instrument for evaluating nonverbal cognitive functions, particularly useful for individuals with speech impairments or language disorders. It provides an objective measure of cognition independent of language skills, covering multiple domains such as memory, attention, reasoning, executive function, and visuospatial ability. By incorporating five non-language tasks, the NLCA avoids potential bias introduced by speech or language deficits, making it especially suitable for patients with post-stroke aphasia (PSA) and other populations with language impairment. This tool offers reliable support for clinical diagnosis and cognitive rehabilitation, allowing more accurate insights into patients\u0026rsquo; cognitive status and guiding personalized therapeutic strategies\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction of the Cerebellar Functional Connectivity Network Based on ROIs\u003c/h3\u003e\n\u003cp\u003eIn this study, we employed the Seitzman-27 cerebellar atlas\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to construct a cerebellar functional network. This atlas comprises 27 cerebellar cortical and subcortical regions, defined as network nodes. Using the GRETNA toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nitrc.org/projects/gretna/\u003c/span\u003e\u003cspan address=\"http://www.nitrc.org/projects/gretna/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, we performed network analysis by first averaging the time series of all voxels within each node and extracting the mean time series for each region of interest (ROI). Pearson\u0026rsquo;s correlation coefficients were then computed between every pair of ROIs for each participant to generate a 27 \u0026times; 27 functional connectivity matrix. To improve the normality of these correlations, we applied Fisher\u0026rsquo;s r-to-z transformation\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\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\u003e\u003cb\u003eSeitzman-27 cerebellar atlas\u003c/b\u003es\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e\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=\"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=\"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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBrain Region, Abbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eROI Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMNI Coordinates\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDorsal Attention Network, DAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDefault Mode Network, DMN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-28.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-40.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisual Network, VN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\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-74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eFronto Parietal Network, FPN\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\u003e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-72.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-62.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCingulo Opercular Network, CON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSalience Network, SN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSomatomotor Network-Dorsal, SMN (Dor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSomatomotor Network-Lateral, SMN (Lat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe mean time series of each ROI was extracted using a 4-mm radius spherical mask. Abbreviations, MNI: Montreal Neurological Institute; +: right hemisphere; \u0026minus;: left hemisphere.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eFunctional Network Analysis\u003c/h3\u003e\n\u003cp\u003eBecause no consensus exists on the optimal functional network threshold, we applied a commonly used range of sparsity thresholds (0.05 to 0.40, in increments of 0.01) to the correlation matrices. A lower sparsity limit of 0.05 ensured that the average node degree was greater than the natural logarithm of the node count, while the upper limit of 0.40 did not exceed 50% network density. Within this 0.05\u0026ndash;0.40 range, we calculated the small-world index, global network metrics, and nodal metrics of the cerebellar functional network for each participant in both the PSA and HC groups\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll demographic and clinical data from the PSA and HC groups were analyzed using IBM SPSS 27.0 (IBM SPSS Inc., Chicago, IL, USA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.spss.com\u003c/span\u003e\u003cspan address=\"http://www.spss.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). First, skewness and kurtosis values were checked and fell within an acceptable range (\u0026minus;\u0026thinsp;2 to +\u0026thinsp;2). An independent-samples t-test was used to compare age, WAB, and NLCA scores between the two groups, whereas sex distribution was assessed by the chi-square test. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. To explore group differences in global and local network measures, we conducted an independent-samples t-test on the area under the curve (AUC) for each measure, applying 5,000 permutations to ensure robust results. For local network metrics (such as nodal betweenness, nodal degree, and nodal clustering coefficient), Bonferroni correction was used (p\u0026thinsp;\u0026lt;\u0026thinsp;0.00185); we also reported trends of potential biological significance at a more lenient threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.037). Group comparisons of ROI-ROI FC within the cerebellar network were performed using network-based statistics (NBS), with a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.005. A permutation test with 5,000 iterations further confirmed the robustness of the findings. Age, head motion, and sex were included as covariates. To investigate the relationships among cerebellar functional network topological measures, cerebellar average FC, and language/cognitive function in PSA patients, we employed Pearson\u0026rsquo;s correlation analysis. Specifically, we examined whether global measures, local measures, and mean FC values correlated with language and cognitive test scores. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Data\u003c/h2\u003e \u003cp\u003eDemographic and clinical characteristics of the PSA group (n\u0026thinsp;=\u0026thinsp;73) and the HC group (n\u0026thinsp;=\u0026thinsp;75) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. No significant difference in age was observed between the two groups. The lesion types included cerebral infarction (n\u0026thinsp;=\u0026thinsp;65), infarction with hemorrhagic transformation (n\u0026thinsp;=\u0026thinsp;7), and intracerebral hemorrhage (n\u0026thinsp;=\u0026thinsp;1), all located in the left cerebral hemisphere.\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\u003eDemographic and Clinical Data of PSA and HC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSA Group (n\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHC Group (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003et\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.59\u0026thinsp;\u0026plusmn;\u0026thinsp;10.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.16\u0026thinsp;\u0026plusmn;\u0026thinsp;11.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.184\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender (Male: Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59:14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55:20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.279\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDuration of Illness (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eWestern Aphasia Battery (WAB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpontaneous Speech\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuditory Comprehension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.55\u0026thinsp;\u0026plusmn;\u0026thinsp;48.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepetition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.82\u0026thinsp;\u0026plusmn;\u0026thinsp;23.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.78\u0026thinsp;\u0026plusmn;\u0026thinsp;14.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAphasia Quotient (AQ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.76\u0026thinsp;\u0026plusmn;\u0026thinsp;17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eNon-Linguistic Cognitive Assessment Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMemory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogical Reasoning Ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisuospatial Ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExecutive Function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Cognitive Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.14\u0026thinsp;\u0026plusmn;\u0026thinsp;15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: 1. Comparison using independent sample t-test; 2. Gender comparison using chi-square test. Abbreviations: Western Aphasia Battery (WAB), Aphasia Quotient (AQ), Aphasia after Stroke (PSA), Healthy Controls (HC).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGlobal Topological Measures of the Cerebellar Network in PSA\u003c/h2\u003e \u003cp\u003eWithin the predefined sparsity range (0.05 to 0.40, step\u0026thinsp;=\u0026thinsp;0.01), both the PSA and HC groups showed γ\u0026thinsp;\u0026gt;\u0026thinsp;1 and λ\u0026thinsp;\u0026asymp;\u0026thinsp;1, resulting in a small-world index σ (σ\u0026thinsp;=\u0026thinsp;γ/λ)\u0026thinsp;\u0026gt;\u0026thinsp;1. These findings indicate that both groups retained the characteristic high-efficiency, low-cost \u0026ldquo;small-world\u0026rdquo; organization in the cerebellar network. However, the PSA group displayed lower σ, γ, and λ than the HC group, suggesting impairments in small-world topological organization of the cerebellar functional network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we compared the AUC values of these global indices between groups. Compared with the HC group, the PSA group exhibited significant decreases in sigma (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.596, p\u0026thinsp;=\u0026thinsp;0.010), gamma (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.299, p\u0026thinsp;=\u0026thinsp;0.001), lambda (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.061, p\u0026thinsp;=\u0026thinsp;0.003), Cp (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.132, p\u0026thinsp;=\u0026thinsp;0.035), and Eloc (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.760, p\u0026thinsp;=\u0026thinsp;0.007). No significant differences were observed in Lp (t\u0026thinsp;=\u0026thinsp;0.110, p\u0026thinsp;=\u0026thinsp;0.913) or Eglob (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.374, p\u0026thinsp;=\u0026thinsp;0.709) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLocal Topological Measures of the Cerebellar Network\u003c/h2\u003e \u003cp\u003eCompared with HCs, the PSA group showed significantly reduced nodal betweenness in the DMN, reduced nodal degree in the FPN, CON, and SMN (dorsal) networks, reduced nodal clustering coefficient in the CON, and reduced nodal efficiency in the FPN and CON, as well as lower local efficiency in the FPN, CON, and SMN (dorsal) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eGroup Differences in Nodal Properties of the Cerebellar Network Between PSA and HC Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eROI Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMNI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNodal Betweenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNodal Degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eNodal Clustering Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eNodal Efficiency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eNodal Local Efficiency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\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\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDMN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDMN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-28.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFPN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFPN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFPN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-62.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFPN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCON\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCON\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSMN (Dor)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eNote: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant (uncorrected). # indicates uncorrected results. Bonferroni correction was applied, setting the significance level for each node at **p\u0026thinsp;\u0026lt;\u0026thinsp;0.00185 (i.e., 0.05/27). Additionally, node-level results at a more lenient threshold of *p\u0026thinsp;\u0026lt;\u0026thinsp;0.037 (i.e., 1/27) are also reported for reference. Abbreviations: MNI, Montreal Neurological Institute; DMN, default mode network; FPN, frontoparietal network; CON, cingulo-opercular network; SMN (Dorsal), dorsal somatomotor network.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAlterations in Intranetwork FC Within the Cerebellar Functional Network in PSA\u003c/h2\u003e \u003cp\u003eCompared with HCs, the PSA group showed significantly decreased FC primarily in the FPN, DMN, and SMN (dorsal). In particular, FC within the FPN-FPN was markedly reduced (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.47, p\u0026thinsp;=\u0026thinsp;0.001; t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.648, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.465, p\u0026thinsp;=\u0026thinsp;0.001), as well as FPN-CON (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.388, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FPN-SN (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.819, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FPN-SMN (dorsal) (t\u0026thinsp;=\u0026thinsp;3.733, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FPN-DMN (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DMN-DMN (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.976, p\u0026thinsp;=\u0026thinsp;0.002), and DMN-SMN (dorsal) (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.527, p\u0026thinsp;=\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eAlterations in Intra-Cerebellar FC Between the PSA and HC Groups.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROI Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROI Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPN 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFPN 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPN 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFPN 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPN 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFPN 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPN 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFPN 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPN 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCON 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPN 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPN 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCON 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPN 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMN (Dor)23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPN 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMN 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMN 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMN 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMN 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMN (Dor) 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Differences were considered statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, corrected using network-based statistics (NBS) with 5,000 non-parametric permutations. Abbreviations: FPN, frontoparietal network; DMN, default mode network; SN, salience network; CON, cingulo-opercular network; SMN (Dorsal), dorsal somatomotor network.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations Between Cerebellar Network Metrics and Clinical Scales in PSA\u003c/h2\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analysis showed that the mean FC in PSA patients was significantly related to sigma, Cp, Lp, Eg, and Eloc, as well as nodal betweenness in the DMN, nodal degree in the SMN (dorsal), nodal efficiency in the FPN/CON, and local efficiency in the FPN. Cp was significantly associated with auditory comprehension, memory, reasoning, visuospatial ability, attention, executive function, and total cognitive scores. Eloc was similarly correlated with auditory comprehension, memory, reasoning, visuospatial ability, attention, executive function, and total cognitive scores. Locally, DMN nodal betweenness correlated with speech fluency and repetition, while FPN nodal efficiency correlated with speech fluency and course of disease. Local efficiency in the FPN was also significantly correlated with disease duration (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\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\u003eCorrelations Between Cerebellar Network Metrics and Language-Cognitive Functions in PSA Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical Measure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eGlobal Metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eaNe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eaNe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eaNLe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esigma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEloc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDMN ROI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMN (Dor) ROI23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFPN ROI12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCON ROI20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFPN ROI12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.262\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.234\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAuditory Comprehension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.320\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.339\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRepetition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.262\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMemory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.332\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.376\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLogical Reasoning Ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.305\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.336\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVisuospatial Ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.254\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.276\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAttention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.328\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.343\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExecutive Function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.361\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.382\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Cognitive Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.335\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.364\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDisease Duration (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-0.366\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e-0.277\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePSA Average FC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.241\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.617\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.554\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.742\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.473\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.386\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.246\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-0.386\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.409\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eNotes: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant (*), and p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 was highly significant (**). PSA, Post-stroke Aphasia; σ (sigma), Small-world index; Cp, Average clustering coefficient; Lp, Average characteristic path length; Eg, Global efficiency; Eloc, Local efficiency; DMN, Default Mode Network; FPN, Fronto-Parietal Network; SN, Salience Network; CON, Cingulo-Opercular Network; SMN (Dor), Somatomotor Network-Dorsal; Bc, Betweenness centrality; DC, Degree centrality; aNe, Nodal efficiency; aNLe, Nodal local efficiency.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, based on resting-state fMRI and the Seitzman-27 cerebellar atlas, we constructed a cerebellar functional connectivity network and employed graph-theoretical methods to systematically analyze the global and local topological properties of the cerebellar functional network in PSA. We also examined the relationships between these network metrics and clinical variables. The results demonstrated that the cerebellar functional network in PSA retained \u0026ldquo;small-world\u0026rdquo; characteristics; however, σ, γ, λ, Cp, and Eloc were significantly decreased. Moreover, both Cp and Eloc were closely associated with language and cognitive functions. At the nodal level, the node centrality of the DMN, FPN, CON, and SMN (dorsal) networks was significantly reduced. Specifically, DMN nodal betweenness showed significant correlations with fluency and repetition, while FPN nodal efficiency and local efficiency correlated with fluency and disease duration. Additionally, we found extensive abnormal functional connectivity (FC) in the PSA cerebellar network. Notably, FC within the FPN (FPN-FPN) and between FPN and CON, SN, SMN (dorsal), and DMN was significantly reduced in the PSA group, as was DMN-DMN and DMN-SMN (dorsal) connectivity. Mean FC in the PSA cerebellar network was significantly related to sigma, Cp, Lp, Eg, and Eloc, as well as nodal centrality in the DMN, SMN (dorsal), FPN, and CON. These findings suggest disrupted functional connectivity within the cerebellum, leading to impairments in functional segregation and local information transmission in specific cerebellar subregions. Such changes may underlie dysfunction in cerebellar areas involved in cognition and language processing.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSmall-World Properties of the Cerebellar Network in PSA\u003c/h2\u003e \u003cp\u003eOur results showed that the cerebellar functional network in PSA retained a small-world organization, but displayed significantly decreased values of σ, γ, λ, Cp, and Eloc. The parameter σ reflects the balance between local clustering and global connectivity\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. A decline in σ suggests that local and global integration in the PSA cerebellar network is compromised. Likewise, the significant decrease in γ supports diminished integration capacity in local neural circuits, potentially affecting efficient coordination of language-related neural pathways\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. A lower λ indicates reduced connectivity efficiency between the cerebellum and other cognitive regions, especially those involved in language processing and executive function\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese alterations imply impaired information integration and reduced ability to perform cognitive tasks in PSA patients. Furthermore, Cp and Eloc were also significantly lower in the PSA group, indicating weaker local information integration that may affect local coordination and executive control during language generation\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Decreased Eloc suggests diminished local information transfer efficiency and reduced fault tolerance\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, which can degrade overall performance on complex cognitive tasks\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Importantly, Cp and Eloc correlated significantly with multiple language and cognitive functions (including auditory comprehension, memory, reasoning, visuospatial ability, attention, executive function, and total cognitive score). This corroborates evidence that the cerebellum is not only critical for motor control but also for higher-order cognitive processes, aligning with Buckner et al.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, who emphasized the cerebellum\u0026rsquo;s pivotal role in cognition.\u003c/p\u003e \u003cp\u003eThese findings differ somewhat from the study by Shi et al.,\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e who examined the topological characteristics of the whole-brain functional connectome in patients with acute ischemic stroke in the brainstem and noted significant increases in Cp and Eloc but decreased γ and Eglobal, suggesting the network topology shifted toward a more regular structure. By contrast, our PSA findings highlight diminished local and global integration capacity, culminating in impaired language and cognitive function\u0026mdash;possibly indicative of different compensation mechanisms in brainstem lesions. Consistent with our previous research\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e and other studies\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, such changes in small-world properties and local/global efficiencies are frequently associated with language deficits, underscoring the utility of graph theory for identifying potential biomarkers (e.g., local efficiency) in aphasia assessment.\u003c/p\u003e \u003cp\u003eTaken together, the pronounced reductions in Cp and Eloc reflect crucial changes in the PSA cerebellar functional network, implying that cerebellar involvement is essential for language and cognition. These measures may serve as potential neurophysiological markers of cognitive impairment in PSA, further validating the application of graph-theoretical analysis in elucidating brain dysfunction in aphasia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAltered Node Properties and Functional Connectivity in the Cerebellar Network of PSA Patients\u003c/h2\u003e \u003cp\u003e Traditional research and treatment approaches for aphasia primarily adopt a symptom-lesion perspective, positing that specific aphasic symptoms and clinical manifestations correspond directly to localized brain damage. Consequently, language and speech impairments can be partially predicted based on lesion sites. Following this rationale, the present study investigated topological alterations within the cerebellar network by examining node properties of various ROIs, as well as intrinsic cerebellar functional connectivity. When any one of the node parameters (i.e., betweenness centrality, degree, clustering coefficient, node efficiency, or local efficiency) exhibited a significant abnormality, we inferred that the centrality of the corresponding brain region might be disrupted. Through an analysis of node properties in PSA patients\u0026rsquo; cerebellar functional networks, we observed a notable decrease in node centrality in several key brain networks, including the DMN, FPN, CON, and SMN (Dor).\u003c/p\u003e \u003cp\u003eThe DMN is closely associated with internally oriented thought processes, emotional regulation, semantic processing, and memory retrieval, and it plays a crucial role in semantic retrieval and integration during complex language tasks\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Previous studies suggest that the DMN is vital in motor aphasia caused by cerebral infarction, potentially enhancing language recovery through compensatory mechanisms \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In this study, the DMN in PSA patients showed significantly reduced betweenness centrality, indicating weakened integrative capacity and reduced mediation in language production, emotion regulation, and self-awareness [31]. As a metabolically active hub region, the posterior DMN (pDMN) connects extensively with various brain areas, particularly the FPN \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Zhang et al. \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e also demonstrated weakened coupling between the pDMN and left FPN (lFPN) in PSA patients, supporting these findings. In our study, functional connectivity in DMN-DMN, DMN-FPN, and DMN-SMN (Dor) was significantly reduced among PSA patients, suggesting diminished coordination among semantic processing, introspective thinking, and motor execution. Such disruption in network interactions likely impairs cognitive flexibility and semantic integration in language production. Collectively, impaired node centrality and functional connectivity in the DMN suggest a decline in semantic retrieval capacity during language production, particularly in complex semantic tasks, leading to deficient language output.\u003c/p\u003e \u003cp\u003eThe FPN primarily comprises prefrontal and parietal regions responsible for cognitive control, task switching, executive functions, and attention regulation. A core function of the FPN is to dynamically integrate both internal and external information streams, facilitating problem-solving, planning, and task execution. Our findings revealed that PSA patients exhibit significantly reduced FPN node centrality, reflecting compromised executive control and attentional regulation during language tasks. This deficit likely exacerbates language impairment and hampers rehabilitation outcomes. Further analysis showed a marked decrease in FPN-FPN functional connectivity, implying reduced synergy among network nodes and, consequently, diminished efficiency in language control, working memory, and task execution. Moreover, a significant decline in FPN-CON connectivity indicated weakened coordination between executive and cognitive control functions. Similarly, reduced connectivity between the FPN and the SN, as well as between the FPN and the SMN, suggests diminished attentional modulation and motor control during abrupt language tasks (e.g., articulation), impairing language fluency and accuracy. In line with these findings, Zhu et al.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e showed substantially weakened intra-FPN connectivity in PSA patients, along with reduced left FPN\u0026ndash;right frontal connectivity, correlating with language comprehension deficits. Zhang et al. \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e further reported significant alterations in cross-network FC in subacute PSA, especially reduced FC between the left and right FPN, closely associated with language deficits. Consistent with those observations, our resting-state FC analyses confirm that PSA patients display decreased FPN node centrality and functional connectivity, underscoring functional impairments in executive control, attentional modulation, motor coordination, and language production\u0026mdash;phenomena that align with their suboptimal performance in language and executive tasks.\u003c/p\u003e \u003cp\u003eThe CON plays a crucial role in cognitive control and sustained attention, maintaining task-related attention and monitoring during language production and comprehension. It is also vital for self-regulatory behaviors and error responses\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. In the present study, PSA patients exhibited significantly reduced node degree and node efficiency in the CON, implying reduced local and global information processing efficiency. This limitation in sustained attention and executive function can manifest as difficulty maintaining attention during lengthy language production or comprehension tasks, leading to lower efficiency when performing cognitively demanding language tasks.\u003c/p\u003e \u003cp\u003eThe SMN \u0026mdash;particularly its dorsal segment\u0026mdash;is primarily responsible for sensorimotor integration, including motor regulation in speech production, and is specifically linked to articulation and oral-motor coordination\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Here, a marked decline in node degree and local efficiency was noted in the dorsal SMN among PSA patients, suggesting insufficient functionality in tasks requiring motor coordination for language production, particularly articulation and motor control. Xu et al.\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e investigated the functional connectivity of brain networks in PSA patients, demonstrating significantly decreased connectivity in the SMN, salience network, and language network, accompanied by a broader reduction in both interhemispheric and intrahemispheric connections. Notably, stronger connections were still observed between the language network and cerebellar network, possibly reflecting a compensatory mechanism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the sample size was relatively small, and we did not further stratify PSA patients by aphasia subtype, which may limit our understanding of subtype-specific cerebellar network characteristics. Second, this study focused exclusively on cerebellar functional connectivity and topological features and did not investigate dynamic or structural connections between the cerebellum and the cerebrum. Future studies can integrate structural MRI to construct cerebellar structural networks, enabling in-depth exploration of structure-function relationships and their links to language and cognitive deficits. Finally, although we collected imaging and clinical data before and after rTMS treatment, we did not perform a longitudinal analysis. Future longitudinal studies are warranted to track dynamic changes in network properties during therapy and their relationship with clinical outcomes, potentially optimizing treatment strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing resting-state fMRI and graph-theoretical analysis, this study constructed and evaluated the topological organization of the cerebellar functional network in PSA. Despite retaining small-world properties, the PSA group showed significantly decreased σ, γ, λ, Cp, and Eloc, indicating compromised organizational structure and information processing efficiency. Moreover, Cp and Eloc were strongly associated with cognitive and language performance, suggesting they may serve as potential indicators for assessing PSA. Concomitantly, node centrality in the DMN, FPN, CON, and SMN (dorsal) declined, and intranetwork functional connectivity was significantly reduced\u0026mdash;highlighting impaired information integration and transfer that could affect both language and cognition. These findings underscore the pivotal role of the cerebellum in PSA and emphasize the link between topological disturbances, weakened functional connectivity, and aphasia. They thus provide novel perspectives for understanding the neural mechanisms of PSA and improving clinical interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants for their contribution and the research team for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeacknowledgefinancialsupportfrom the National Key Research and Development Program of China (Grant number: 2023YFF1204600); The National Natural Science Foundation of China (82227802, 82302306, 82302336); The Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (No. JNU1AF-CFTP-2022-a01201); The Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); The Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); The Postdoctoral Science Foundation of China (2022M721349); The Medical Science and Technology Research Fund of Guangdong Province, China (A2023144).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting financial interests:The authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.C. and Z.L drafted the initial manuscript and performed language editing. Z.L.and M.W conducted data processing and statistical analysis. Y.D. and X.F collected data and performed clinical data analysis. Y.S. and L.L. contributed to data processing. X.C. and Q.F. were responsible for data collection. Z.C.and W.H supervised the project. L.C and Y.D. contributed to data processing, performed language editing, and provided key revisions. S.Z. supervised the project, provided critical revisions, and approved the final version of the manuscript. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEngelter S T, Gostynski M, Papa S, et al. Epidemiology of aphasia attributable to first ischemic stroke: incidence, severity, fluency, etiology, and thrombolysis[J]. Stroke, 2006, 37(6): 1379-1384.\u003c/li\u003e\n\u003cli\u003eChen X, Chen L, Zheng S, et al. Disrupted Brain Connectivity Networks in Aphasia Revealed by Resting-State fMRI[J]. 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Disrupted brain connectome in semantic variant of primary progressive aphasia[J]. Neurobiology of Aging, 2014, 35(11): 2646-2655.\u003c/li\u003e\n\u003cli\u003eRaichle M E. The brain\u0026rsquo;s default mode network[J]. Annual Review of Neuroscience, 2015, 38: 433-447.\u003c/li\u003e\n\u003cli\u003eBalaev V, Petrushevsky A, Martynova O. Changes in Functional Connectivity of Default Mode Network with Auditory and Right Frontoparietal Networks in Poststroke Aphasia[J]. Brain Connectivity, 2016, 6(9): 714-723.\u003c/li\u003e\n\u003cli\u003eLeech R, Braga R, Sharp D J. Echoes of the Brain within the Posterior Cingulate Cortex[J]. The Journal of Neuroscience, 2012, 32(1): 215-222.\u003c/li\u003e\n\u003cli\u003eZhang C, Xia Y, Feng T, et al. Disrupted Functional Connectivity Within and Between Resting-State Networks in the Subacute Stage of Post-stroke Aphasia.[J]. Frontiers in neuroscience, 2021, 15: 746264.\u003c/li\u003e\n\u003cli\u003eSadaghiani S, D\u0026rsquo;Esposito M. Functional Characterization of the Cingulo-Opercular Network in the Maintenance of Tonic Alertness[J]. Cerebral Cortex, 2015, 25(9): 2763-2773.\u003c/li\u003e\n\u003cli\u003eFox M D, Raichle M E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging[J]. Nature Reviews Neuroscience, 2007, 8(9): 700-711.\u003c/li\u003e\n\u003cli\u003eXu X, Ren C, Fang H, et al. Exploring the functional connectivity characteristics of brain networks in post-stroke patients with global aphasia: a healthy control based resting-state fMRI study.[J]. Annals of palliative medicine, 2021, 10(12): 12113-12128.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Aphasia, Stroke, Cerebellum, Graph Theory, Functional Magnetic Resonance Imaging","lastPublishedDoi":"10.21203/rs.3.rs-6379167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6379167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eThis study investigated cerebellar functional network topology and connectivity in post-stroke aphasia (PSA) using resting-state fMRI and graph theory. We further explored associations between these alterations and language/cognitive functions to clarify the cerebellum\u0026rsquo;s role in PSA.\u003c/p\u003e\u003ch2\u003eMaterials and Methods:\u003c/h2\u003e \u003cp\u003eSeventy-three right-handed PSA patients and 75 matched healthy controls underwent 3T rs-fMRI. A cerebellar functional network was constructed using the Seitzman-27 atlas. Graph theory was applied to assess global/local topological properties and functional connectivity (FC). Correlations with language and cognitive performance were analyzed.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAlthough the cerebellar network in PSA preserved a small-world organization (σ\u0026thinsp;\u0026gt;\u0026thinsp;1), key metrics (σ, γ, λ, clustering coefficient [Cp], local efficiency [Eloc]) were significantly reduced (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating impaired network integration and local processing. Cp and Eloc correlated with multiple cognitive and language domains. Nodal centrality was diminished in the default mode network (DMN), frontoparietal network (FPN), cingulo-opercular network (CON), and dorsal sensorimotor network (SMN [Dor]). Specific nodal metrics correlated with fluency, repetition, and disease duration. FC analysis revealed widespread reductions in intra- and inter-network connectivity, primarily involving FPN and DMN.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003ePSA is characterized by cerebellar network disorganization and extensive FC alterations that are closely linked to language and cognitive impairments. Topological metrics such as Cp and Eloc may serve as biomarkers for assessing functional deficits. These findings highlight the cerebellum\u0026rsquo;s integrative role in higher-order functions beyond motor control and provide a neurobiological basis for targeted neuromodulation or rehabilitation strategies aimed at restoring cerebellar-cortical connectivity in PSA.\u003c/p\u003e","manuscriptTitle":"The Reorganization of Cerebellar Functional Network Topology in Post-Stroke Aphasia: A Resting-State fMRI Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 06:08:09","doi":"10.21203/rs.3.rs-6379167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-08T08:33:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-07T17:59:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330365613724539073708891833284883555794","date":"2025-07-01T09:01:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T09:24:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16459731002481519249868815584010402477","date":"2025-05-21T15:54:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121587935890010880905406734921960418671","date":"2025-05-18T05:35:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-16T13:33:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-07T08:06:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-07T08:03:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2025-04-05T00:35:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"197337aa-2585-44d3-bb05-40f987f6c426","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:00:53+00:00","versionOfRecord":{"articleIdentity":"rs-6379167","link":"https://doi.org/10.1007/s12035-026-05781-4","journal":{"identity":"molecular-neurobiology","isVorOnly":false,"title":"Molecular Neurobiology"},"publishedOn":"2026-03-20 15:58:20","publishedOnDateReadable":"March 20th, 2026"},"versionCreatedAt":"2025-05-21 06:08:09","video":"","vorDoi":"10.1007/s12035-026-05781-4","vorDoiUrl":"https://doi.org/10.1007/s12035-026-05781-4","workflowStages":[]},"version":"v1","identity":"rs-6379167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6379167","identity":"rs-6379167","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}