Neurotransmitter Systems Underlie Structure-Function Decoupling and Recovery in Post- Stroke Aphasia

Neurotransmitter Systems Underlie Structure-Function Decoupling and Recovery in Post- Stroke Aphasia | 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 Neurotransmitter Systems Underlie Structure-Function Decoupling and Recovery in Post- Stroke Aphasia Daoran Wang, Dongdong Jiang, Tongyan Zhang, Kai Zheng, Guilan Huang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8131643/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objectives : Although alterations in brain structure and function have been implicated in both post-stroke aphasia (PSA) and motor deficits, how structural-functional coupling (SFC) is affected in stroke patients with and without aphasia (nonPSA) remains unclear. This study aimed to characterize SFC alterations in PSA and examine their associations with neurotransmitter systems. Methods: Fifty-two patients with left-hemisphere stroke (PSA: n = 29; nonPSA: n = 23) and 19 demographically matched healthy controls were enrolled. Language function in PSA patients was assessed approximately 28 days and 3 months post-stroke using the Western Aphasia Battery (WAB). All participants underwent T1-weighted and resting-state functional MRI at baseline. Region-wise SFC was computed as the correlation between gray matter volume and the fractional amplitude of low-frequency fluctuations (fALFF). Group differences were assessed using one-way analyses of covariance. Relationships among SFC alterations, language outcomes, and lesion-derived neurotransmitter-informed network damage were further evaluated. Results: Group comparisons revealed distinct SFC alterations associated with motor and language deficits in PSA. Language-specific decoupling was observed in the contralesional putamen, middle temporal pole, and posterior cerebellum, whereasmotor-specific decoupling occurred in the contralesional prefrontal cortex, superior parietal lobule, precuneus, and inferior/superior temporal cortices, extending into cerebellar regions. Both domains shared a common decoupling pattern in the ipsilesional posterior cerebellum. Reduced SFC in the contralesional middle temporal pole correlated with poorer spontaneous speech performance. Compared with nonPSA, PSA patients exhibited greater lesion load, network disconnections, and pre-/post-synaptic disruption ratio associated with poorer aphasia recovery relative to nonPSA in several neurotransmitter systems, especially serotonergic system. Mediation analysis further indicated that SFC in the contralesional caudate partially mediated the relationship between neurotransmitter disruption and aphasia severity. Conclusions: Our findings suggest distinct and shared structural-functional decoupling for language and motor dysfunctions in the patients with aphasia after stroke, which was associated with specific neurotransmitter systems. post-stroke aphasia magnetic resonance imaging structural-functional coupling neurotransmitter language recovery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Stroke often leads to multiple neurological deficits. A left middle cerebral artery infarction involving the inferior frontal and precentral cortices can cause right-sided hemiparesis and aphasia 1 . Evidence indicates that stroke patients with more severe language dysfunction tend to experience poorer motor recovery, suggesting a close relationship between language and motor outcomes 2 . This interaction implies that the two functions may share overlapping neural mechanisms of recovery 3 . Although early post-stroke functional reorganization contributes to behavioral improvement 4 , this process is frequently incomplete and varies considerably among individuals 5 . Therefore, elucidating the neural basis linking motor and language deficits is essential for optimizing rehabilitation strategies in patients with post-stroke aphasia (PSA). Patients with PSA typically exhibit gray matter damage associated with language deficits 6 , 7 , leading to neural dysfunction not only within the lesion site 8 but also in remote, functionally connected regions 9 —for example, the contralesional cingulate cortex and ipsilesional precentral gyrus after subcortical stroke 10 . Structural impairment is often accompanied by cortical functional alterations 11 , 12 . Post-stroke neuroimaging studies have reported increased amplitude of low-frequency fluctuations (ALFF) in the contralesional mesial temporal lobe and ipsilesional hippocampus 13 , which correlate with behavioral measures of language function 14 , 15 . Collectively, these findings demonstrate widespread structural and functional reorganization following stroke 16 . Recent advances in neuroimaging have highlighted structural–functional coupling (SFC)—the degree of correspondence between structural integrity and functional activity—as a sensitive marker of brain organization 17 , 18 . Compared with unimodal indices, SFC provides a more integrative and sensitive measure for detecting subtle brain alterations 19 , 20 . Disrupted SFC has been identified as a neural substrate underlying brain injury 21 , behavioral impairment 22 , and cognitive dysfunction 23 . In stroke, reduced SFC in sensorimotor networks has been linked to motor deficits 24 – 26 . However, the characteristics of SFC alterations in PSA remain largely unknown, and it is unclear how language- and motor-related SFC changes differ or overlap. Beyond structural and functional alterations, stroke also disrupts neurotransmitter systems, which have been implicated in post-stroke sequelae such as depression 27 , cognitive impairment 28 , and aphasia 29 . Pharmacological interventions targeting serotonergic 30 and dopaminergic 31 , 32 systems have shown promise in promoting post-stroke functional recovery 33 . Functional imaging studies further demonstrate that ALFF abnormalities in the striatum are associated with 5-HT and D2 receptor densities in depression 34 . Similarly, altered ALFF following stroke has been linked to disruptions in monoaminergic systems—particularly serotonergic and dopaminergic pathways—and to motor recovery of the paretic hand 35 . Moreover, the spatial distribution of neurotransmitter receptors (e.g., serotonin, dopamine, acetylcholine) closely aligns with large-scale resting-state networks in stroke patients 36 . Recent methodological advances now allow integration of lesion data with normative PET-based maps of neurotransmitter receptors and transporters, enabling inference of neurotransmitter-specific damage after stroke 37 , 38 . Nevertheless, the relationship between neurotransmitter network disruption, SFC alterations, and language outcomes in PSA remains largely unexplored. In the present study, we aimed to elucidate how PSA-related alterations in SFC relate to both neurotransmitter network damage and language performance. We hypothesized that: (1) PSA would exhibit region-specific disruptions in SFC linked to language and motor deficits; (2) these SFC changes would be linked to specific neurotransmitter systems; and (3) SFC alterations would mediate the relationship between neurotransmitter damages and post-stroke language impairment. 2 Materials and Methods 2.1 Participants We recruited 29 patients with post-stroke aphasia (PSA; mean age = 59.4 ± 21.6 years; 41.4% male) from Wuxi Central Rehabilitation Hospital. Inclusion criteria were as follows: (1) age between 30 and 80 years; (2) first-ever unilateral left-hemisphere stroke; (3) right-handed native Chinese speakers with normal premorbid language abilities, no professional vocal or instrumental training, and a WAB Aphasia Quotient (AQ) < 93.8; (4) education level of primary school or above; and (5) normal vision and hearing. Exclusion criteria included: (1) history of drug or alcohol abuse, seizures, or neuropsychiatric disorders; (2) contraindications for MRI (e.g., cranial defects, skin lesions at stimulation sites, intracranial implants, pacemakers, or medication pumps); (3) history of neurosurgery; (4) severe cognitive impairment precluding participation in evaluation or therapy; and (5) presence of other neurological or systemic conditions affecting language or cognition. Additionally, 23 stroke patients without aphasia (nonPSA; mean age = 68.7 ± 8.7 years; 43.5% male) were recruited using the same criteria, except for the absence of language deficits (AQ ≥ 98.4). Nineteen right-handed healthy controls (HCs; mean age = 58.4 ± 11.7 years; 36.8% male), fluent in Chinese and matched to the patient groups for age, sex, and education, were also enrolled. 2.2 Language Assessment Aphasia diagnosis and severity were determined using the Western Aphasia Battery–Revised (WAB-R) 39 , which yields an Aphasia Quotient (AQ) as a summary index. The WAB-R comprises four main subtests: spontaneous speech, auditory verbal comprehension, repetition, and naming. In the spontaneous speech subtest, patients answered questions from the examiner and described picture scenes. In the auditory verbal comprehension subtest, patients responded to yes/no questions and identified real objects, written words, numbers, colors, and body parts based on verbal instructions. The repetition subtest requires patients repeated words and sentences of varying lengths and complexity. In the naming subtest, patients named visually presented objects, completed unfinished sentences, and performed category fluency tasks (e.g., naming as many animals as possible within one minute). The subtests contribute 20, 10, 10, and 10 points, respectively, to the total AQ score. Each PSA patient completed the WAB-R twice: approximately 28 days post-stroke (WAB0) and again at 3 months post-stroke (WAB1). Changes in language performance were calculated as ΔAQ = AQ1 – AQ0. Individual assessment results are presented in Table S1 . 2.3 MRI Data Acquisition MRI scanning was performed approximately 28 days after stroke onset using a 3.0 Tesla GE SIGNA Architect scanner. Sagittal three-dimensional (3D) T1-weighted images were acquired using a brain volume (BRAVO) sequence with the following parameters: repetition time (TR)/echo time (TE) = 7.7/3.1 ms, field of view (FOV) = 240 × 240 mm², matrix = 256 × 256, slice thickness = 1.0 mm, interslice gap = 1.0 mm, and 176 slices. Resting-state functional MRI (rs-fMRI) data were collected using an echo-planar imaging (EPI) sequence with the following parameters: TR/TE = 2500/30 ms, slice thickness = 3.5 mm, interslice gap = 1.0 mm, matrix = 64 × 64, FOV = 240 × 240 mm², 50 axial slices, and 200 volumes. During the scan, participants were instructed to keep their eyes closed, remain still, stay awake, and avoid engaging in any specific thoughts. T2-weighted images were obtained using a turbo spin-echo sequence (TR/TE = 5000/120 ms, FOV = 220 × 220 mm², matrix = 320 × 320, slice thickness = 5 mm, no interslice gap; 30 slices) to identify lesion locations. All images were visually inspected for artifacts, structural abnormalities, and excessive head motion prior to data preprocessing and analysis. 2.4 Lesion Delineation Stroke lesions were manually delineated on T2-weighted images for each patient, slice by slice, using MRIcron software ( https://www.nitrc.org/projects/mricron ). A lesion overlap map illustrating the spatial distribution across all stroke patients is shown in Fig. 1 . The resulting lesion masks were used to calculate individual lesion volumes. Detailed lesion characteristics for each participant are provided in Supplementary Figure S1 and Table S2. 2.5 Lesion-derived neurotransmitter-informed metrics To quantify neurotransmitter-specific damage, each patient’s binary lesion map was overlaid onto normative neurotransmitter maps derived from PET tracer atlases 37 . These atlases include receptor and transporter density distributions for key neurotransmitter systems such as serotonin, dopamine, and acetylcholine. The normative receptor and transporter density maps were co-registered to a standard structural connectome template using both affine and nonlinear transformations implemented in Advanced Normalization Tools (ANTs). Streamlines from the normative tractogram were weighted by the receptor or transporter density at their terminal voxels, thereby generating neurotransmitter-specific streamline weights, consistent with previous methodologies 38 . Based on this framework, we computed three lesion-derived neurotransmitter-informed metrics for each patient: (1) Lesion loaddefined as the cumulative receptor/transporter density within lesioned voxels; (2) Disconnection index, defined as the total neurotransmitter-weighted streamline burden intersecting with the lesion; and (3) Pre-/post-synaptic disruption ratio, calculated as the ratio between presynaptic (transporter-based) and postsynaptic (receptor-based) disruptions, reflecting differential impacts on neurotransmission pathways. 2.6 MRI Data Processing Resting-state fMRI data were preprocessed using Statistical Parametric Mapping (SPM8; http://www.fil.ion.ucl.ac.uk/spm ) and the Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox 40 . The preprocessing pipeline included the following steps: (1) the first 10 volumes were discarded to allow for magnetization equilibrium and participant adaptation, leaving 190 usable volumes; (2) temporal differences between slices were corrected, and head motion was realigned, with no participant exhibited translational displacement > 2 mm or rotation > 2°; (3) each participant’s T1-weighted image was co-registered to their mean functional image and segmented into gray matter, white matter, and cerebrospinal fluid; (4) spurious variance from head motion parameters and blood-oxygen-level-dependent (BOLD) signals in WM and CSF was regressed out; (5) functional images were normalized to the Montreal Neurological Institute (MNI) space using parameters estimated from segmentation 41 and resampled to 3 mm isotropic voxels; (6) normalized images were spatially smoothed using a Gaussian kernel with 6 mm full-width at half-maximum (FWHM). After preprocessing, each voxel’s time series was transformed into the frequency domain using a fast Fourier transform (FFT). The square root of the power spectrum within the 0.01–0.08 Hz frequency range was calculated to obtain the ALFF 42 . The fALFF was computed as the ratio of ALFF to the total amplitude across the entire frequency range 43 . All T1-weighted structural images were visually inspected to ensure adequate quality and absence of artifacts. The image origin was manually aligned to the anterior commissure. Subsequently, images were segmented into GM, WM, and CSF using the unified segmentation algorithm in SPM12, with bias correction and intensity normalization applied to improve classification accuracy. The DARTEL algorithm was then employed to generate a study-specific template for high-dimensional normalization of GM images to MNI space. During normalization, GM images were modulated to preserve total tissue volume, producing gray matter volume (GMV) maps. These GMV maps were resampled to a voxel size of 1.5 × 1.5 × 1.5 mm³ and smoothed using an 8 mm FWHM Gaussian kernel. 2.7 Structure–function coupling analysis SFC analysis was performed by integrating GMV maps derived from T1-weighted MRI with fractional fALFF maps obtained from resting-state fMRI. A region-wise approach was employed using the Automated Anatomical Labeling (AAL) atlas, which divides the brain into 116 predefined regions. For each participant and each AAL region, the Pearson correlation coefficient between GMV and fALFF values across all voxels within the region. Thus, each participant obtained a 1×116 SFC matrix 44 . To improve normality and facilitate statistical comparison, correlation coefficients were transformed using Fisher’s r-to-z transformation prior to group-level analyses. 2.8 Statistical analysis Intergroup differences in clinical data, language assessments, and SFC were examined. Prior to group comparisons, the normality of each variable was assessed using the Shapiro–Wilk test. For variables with a normal distribution, one-way analysis of variance (ANOVA) was applied, followed by post hoc pairwise comparisons with Bonferroni correction. For non-normally distributed variables, the Kruskal–Wallis test was used, with post hoc pairwise comparisons conducted using Dun’s test. Categorical variables (e.g., sex) were analyzed using the chi-square test. To evaluate longitudinal changes in WAB scores within the PSA group, paired-samples t-tests or Wilcoxon signed-rank tests were performed, depending on data distribution. All statistical analyses were performed using R version 4.4.2 (R Core Team, 2024). Descriptive statistics were computed to summarize the characteristics of the data. To identify regional SFC differences among HC, nonPSA, and PSA groups, region-wise ANCOVA were performed on Fisher z–transformed SFC values, with age, sex, and years of education as covariates. Post hoc two-sample t-tests were conducted for regions exhibiting significant main effects. Comparisons of lesion-derived neurotransmitter-informed metrics between PSA and nonPSA groups were also performed using two-sample t-tests, controlling for the same covariates. To correct for multiple comparisons across the 116 AAL regions, false discovery rate (FDR) correction was applied. A threshold of p < 0.05 (two-tailed) was considered statistically significant. To explore the associations among regional SFC values, neurotransmitter-informed metrics, and WAB scores in the PSA group, multivariate linear regression analyses were conducted for each AAL region, with age, sex, education, and lesion volume included as covariates: WAB score ~ SFC or lesion-derived neurotransmitter-informed metrics + Age + Sex + Education + Lesion volume. We further assessed the relationship between neurotransmitter-informed metrics and SFC: SFC ~ lesion-derived neurotransmitter-informed metrics + Age + Sex + Education + Lesion volume. P < 0.05 was considered statistically significant. Finally, mediation analyses were performed using the mediation package in R to determine whether SFC mediated the relationship between lesion-derived neurotransmitter-informed metrics and the WAB scores. The bootstrap method (5,000 iterations) with 95% confidence intervals (CIs) was used to estimate indirect effects. In the mediation model, SFC values served as the mediator variable (M), lesion-derived neurotransmitter-informed metrics as the independent variable (X), and WAB score as the dependent variable (Y). All models were adjusted for age, sex, and years of education. Mediation was considered significant when the 95% CI of the indirect effect did not include zero. 3 Results 3.1 Demographic and Clinical Comparisons As summarized in Table 1 , the PSA group exhibited significantly lower baseline scores in spontaneous speech, auditory comprehension, repetition, naming, and overall language ability compared with both the nonPSA and healthy control (HC) groups. No significant group differences were observed in age (p = 0.79), sex (p = 0.11), or years of education (p = 0.27). Within the PSA group, significant improvements were observed in Aphasia Quotient (AQ) (p = 0.008), auditory comprehension (p = 0.049), and spontaneous speech (p = 0.017) scores of the WAB) at the 3-month follow-up compared to baseline. In contrast, naming (p = 0.058) and repetition (p = 0.321) scores did not show significant longitudinal changes. Table 1 Demographic and Clinical Information of All Participants. Characteristic PSA (n = 29) nonPSA (n = 23) NC (n = 19) F/χ 2 /t-value p-value Sex (M/F) 12 / 17 10 / 13 7 / 12 2.09 p = 0.15 Age (years) 59.35 ± 13.61 68.70 ± 8.70 58.40 ± 11.66 2.44 p = 0.10 Education (years) 9.58 ± 1.43 9.98 ± 2.43 10.36 ± 3.09 1.14 p = 0.27 Lesion site left (F/P/T/I/B) 28/26/6/1/13 0/1/1/2/18 — — — AQ-0 17.32 ± 15.87 98.53 ± 1.12 — — — Auditory Comprehension-0 2.91 ± 2.46 97.95 ± 0.23 99.95 ± 0.23 20.77 p < 0.001 Spontaneous speech-0 2.65 ± 3.00 96.89 ± 1.12 98.53 ± 1.12 24.19 p < 0.001 Naming-0 1.15 ± 1.74 97.89 ± 1.12 99.89 ± 1.12 17.75 p < 0.001 Repetition-0 1.96 ± 2.64 97.95 ± 0.23 99.89 ± 1.12 16.69 p < 0.001 AQ-1* 23.27 ± 21.25 — — Auditory Comprehension-1* 3.65 ± 3.21 — — Spontaneous speech-1* 3.74 ± 3.27 — — Naming-1 1.74 ± 2.53 — — Repetition-1 2.51 ± 3.08 — — ΔAQ 5.95 ± 8.07 — — *p < 0.05, the difference is statistically significant. F = Frontal, P = Parietal, T = Temporal, I = Insula B = Basal Ganglia, 0 and 1 represent clinical assessments at baseline and at three month, respectively. 3.2 Regional SFC Comparison and Relationship with Language Function Group-averaged SFC maps are shown for HC, PSA, and nonPSA groups (Fig. 2 A–C) respectively, providing an overview of the spatial distribution patterns. The ANCOVA revealed significant group effects on SFC across multiple regions (F = 4.63–28.38, all adjusted p < 0.05). Post hoc analyses identified three distinct patterns of SFC alterations (Fig. 2 D–F; Table S3). Language-specific regions (significantly altered in the PSA group compared with both HC and nonPSA, but not between nonPSA and HC) included the contralesional putamen, middle temporal pole, and bilateral posterior cerebellar lobules. Motor-specific regions (significantly altered in both PSA and nonPSA groups relative to HC, but not between PSA and nonPSA) were mainly involved in the contralesional prefrontal cortex, insula, superior parietal cortex, precuneus, caudate inferior/superior temporal cortices, extending to cerebellar regions. Shared regions (significantly altered in both PSA and nonPSA groups relative to HC, and also differed between PSA and nonPSA) included the ipsilesional cerebellar lobule IX. These findings suggest that stroke-related language and motor impairments are associated with distinct yet partially overlapping SFC alterations, with the cerebellum potentially acting as a convergence hub for both functions. Furthermore, regression analyses showed that SFC in the contralesional middle temporal pole was significantly correlated with spontaneous speech performance in the early phase (β = − 0.39, p = 0.022, Cohen’s f² = 0.25), indicating that lower SFC in this region was associated with poorer spontaneous speech outcomes (Fig. 2 G). 3.3 Alterations in neurotransmitter-informed metrics and their relationships with language function Group comparisons revealed significant differences between the PSA and nonPSA groups in lesion load and disconnection indices across multiple neurotransmitter systems, including dopamine (D1, D2, DAT), serotonin (5-HT1a, 5-HT1b, 5-HT2a, 5-HT4, 5-HT6, 5-HTT), noradrenergic, and cholinergic (VAChT, M1, A4B2) pathways (Fig. 3 A–B; Table S4). In contrast, the pre-/post-synaptic disruption ratio differed between groups primarily in the presynaptic D2, 5-HT1a, and 5-HT2a systems (Fig. 3 C). Furthermore, significant negative correlations were observed between lesion load within several neurotransmitter systems and ΔAQ scores (uncorrected, p < 0.05), indicating higher lesion load in M1, noradrenaline, 5-HT1a, 5-HT1b, and 5-HT2a receptors might be associated with poorer aphasia recovery (Fig. 3 D and Table S5). 3.4 Correlation between SFC and lesion-derived neurotransmitter-informed Metrics Regression analyses revealed significant associations between SFC in the contralesional middle temporal pole and multiple lesion-derived neurotransmitter-informed metrics within the PSA group (Fig. 4 , Table S6). Specifically, SFC showed positive correlations with lesion load in the M1 (β = 2.89, p = 0.049), NAT (β = 3.28, p = 0.040), and 5-HT1b (β = 3.65, p = 0.033) systems. Similarly, SFC was positively correlated with disconnection indices in cholinergic (VAChT), dopaminergic (D1, D2, DAT), noradrenergic, and serotonergic (5-HT1a, 5-HT1b, 5-HT2a, 5-HT4, 5-HT6, 5-HTT) pathways (all β ≈ 2.5–3.3, all p < 0.05). Outside this region, only one significant association emerged: SFC in the contralesional cerebellar lobule IX correlated with the presynaptic D1 ratio (β = 0.24, p = 0.043). Although these effects did not remain significant after FDR correction, these exploratory results suggest that altered SFC in specific contralesional regions may capture neurotransmitter-related network disruption and potential compensatory plasticity following stroke. 3.5 Mediation Analysis Exploratory mediation analyses suggested that SFC in the contralesional caudate may partially mediate the relationship between lesion-derived neurotransmitter-informed network disruption and language performance. Although no mediation effects survived FDR correction, several indirect paths reached nominal significance (p < 0.05), primarily involving serotonergic and dopaminergic systems (Fig. 5 ). The strongest nominal mediation effect was observed for 5-HTT disconnection, where the indirect effect through contralesional caudate SFC reached β = 2.352 (95% CI [0.114, 5.689], p = 0.028), despite a non-significant total effect (β = − 8.433, p = 0.110). Similar patterns were noted across other serotonergic and dopaminergic targets. For instance, lesion load in D2 receptor availability exhibited an indirect effect of β = 1.547 (95% CI [0.176, 3.616], p = 0.030), alongside significant total (β = − 7.807, p = 0.008) and direct (β = − 9.355, p = 0.010) effects, indicating partial mediation. Comparable mediation trends were also observed for lesion load 5-HT4 (β = 1.608, p = 0.028) and disconnection 5-HT4 (β = 1.943, p = 0.018), both demonstrating significant total or direct effects. 4 Discussion Our findings support the initial hypothesis that PSA involves region-specific structural-functional decoupling associated with multiple neurotransmitter systems. Specifically, three distinct SFC alteration patterns—language-specific, motor-specific, and shared regions—were identified in the PSA group, reflecting the multifaceted nature of post-stroke neural reorganization. Reduced SFC in the contralesional middle temporal pole was linked to poorer spontaneous speech, while SFC in contralesional temporal and subcortical regions correlated with lesion-derived neurotransmitter-informed network disruptions. Mediation analyses further suggested that caudate SFC may partially mediate the relationship between lesion-derived neurotransmitter-informed network damages and language outcomes. Collectively, these results highlight the intertwined roles of network-level and neurochemical alterations in shaping post-stroke language recovery. 4.1 Language-specific regions: contralesional temporal and subcortical decoupling as a marker of linguistic impairment In the PSA group, language-specific decoupling was observed in the contralesional putamen, middle temporal pole, and bilateral posterior cerebellar lobules, suggesting these alterations are more closely related to aphasia than to motor deficits. Importantly, reduced SFC in the contralesional middle temporal pole predicted poorer spontaneous speech, emphasizing its importance in early recovery. The resting-state language network encompasses both temporal and subcortical regions, including the putamen 45 . Previous studies have linked post-stroke cognitive impairment to reduced SFC strength in the basal ganglia and bilateral medial temporal lobes 46 , 47 . Our findings align with evidence identifying the temporal pole as a multimodal semantic hub 48 , 49 and support prior reports implicating the contralesional temporal cortex in compensatory language processing following left-hemisphere stroke 50 , 51 . The basal ganglia, particularly the putamen, are involved in phonological and articulatory control 52 , while neuronal activity within the temporal lobes has been associated with language and speech impairments in PSA 51 , 53 . Moreover, contralesional temporal activation has been linked to compensatory word generation in cognitively impaired individuals 54 . Similarly, cerebellar hyperactivation has been proposed as a compensatory mechanism in PSA 55 . This observation is consistent with previous work showing bilateral cerebellar engagement in linguistic control and error monitoring 56 , 57 . Taken together, our findings suggest that decoupling in the basal ganglia, contralesional temporal, and cerebellar regions may represent disrupted semantic–phonological integration and compensatory reorganization processes that collectively shape language recovery trajectories in PSA. 4.2 Motor-specific regions: reorganization of frontoparietal and subcortical coupling Motor-specific regions included the contralesional prefrontal cortex, insula, superior parietal cortex, precuneus, caudate, inferior/superior temporal cortices, and cerebellar regions, suggesting that these abnormalities primarily relate to motor dysfunction. Consistent with prior research, reduced SFC within the prefrontal and parietal cortices has been linked to motor impairment following stroke 26 , 58 , 59 .. The insula acts as a connector hub between the frontal and temporal cortices 60 and exhibits dynamic reorganization during post-stroke recovery 61 . The caudate, a key component of the basal ganglia–thalamocortical circuit, supports both motor control and cognitive flexibility 62 . Furthermore, enhanced cerebellar activation has been interpreted as a compensatory mechanism that facilitates motor recovery 63 , 64 . Our findings extend this evidence by demonstrating that motor-related SFC alterations in PSA reflect not only the direct disconnection of sensorimotor circuits but also adaptive reorganization involving higher-order cortical and subcortical regions. These changes likely represent the brain’s compensatory attempt to restore functional integration within disrupted motor networks. 4.3 Shared regions: cerebellar convergence of language and motor dysfunction The shared region identified in the ipsilesional cerebellar lobule IX suggests that this structure serves as a convergence zone where language and motor impairments intersect in PSA. This finding supports the growing view of the cerebellum as an integrative hub involved in both articulatory coordination and cognitive–linguistic control 65 , 66 . Prior studies have demonstrated cerebellar engagement in speech and limb motor recovery following stroke 56 , 57 , while behavioral evidence indicates that improvements in motor function often parallel or predict language recovery 67 . Beyond stroke, converging evidence from other neurological disorders also underscores the cerebellum’s integrative role across motor and speech domains. In Parkinson’s disease and multiple sclerosis, the cerebellum is increasingly recognized as a central node in both tremor regulation and gait coordination 68 , 69 . Similarly, cerebellar anterior lobe activity has been associated with speech control in epilepsy and autism spectrum disorder 70 , 71 . Thus, the shared cerebellar decoupling pattern observed here may represent a common substrate of adaptive reorganization underlying both movement and speech-related recovery processes. Notably, although both language- and motor-specific SFC patterns involved cerebellar regions, the shared cerebellar hub (the ipsilesional lobule IX) likely represents a distinct integrative zone. Motor-specific abnormalities encompassed both anterior-lobe sensorimotor modules (lobules IV–V and VI), which mediate fine motor execution and timing 72 , and posterior motor-associative regions (Crus I/II and vermis VIII), which support executive–motor coordination 73 , 74 . In contrast, language-specific decoupling was confined to posterior-lobe cognitive–linguistic regions, including the contralesional lobules IX and X, consistent with their established roles in semantic integration and higher-order language control 75 , 76 . Importantly, lobule IX is functionally unique: its rostral and caudal subdivisions participate in both the default mode network (DMN) and the dorsal attention network (DAN) 77 This dual-network embedding has been suggested to support visuospatial integration and higher-order cognitive coordination via interactions between DMN and DAN 78 . Such cross-network interactions may offer a plausible explanation for why the ipsilesional lobule IX emerged as a shared region in PSA. It may function as a high-level “co-processor” that is recruited whenever task demands (linguistic, motor, or combined) require complex integration, refined coordination 79 . 4.4 Lesion-derived neurotransmitter-informed network damages and language recovery Beyond the regional SFC alterations, we observed widespread disruptions across dopaminergic, serotonergic, noradrenergic, and cholinergic systems, with PSA patients exhibiting greater lesion load and disconnection indices compared with nonPSA patients. Moreover, higher lesion burden within these neurotransmitter systems was associated with poorer aphasia recovery, underscoring their critical contribution to post-stroke language plasticity. Previous studies have demonstrated that damage to cortical cholinergic pathways is strongly associated with cognitive impairment following subacute ischemic stroke, and that greater disruption predicts poorer functional outcomes in the acute phase 80 , 81 . Pharmacological enhancement of acetylcholine transmission—such as through donepezil—has been shown to improve WAB scores in PSA patients 82 . Consistent with these findings, our study revealed that lesion load in the M1 and VAChT pathways was negatively correlated with ΔAQ, suggesting that preserved cholinergic integrity supports better language recovery. The dopaminergic system plays a pivotal role in rehabilitation-induced neuroplasticity 83 , 84 . In our data, SFC in the contralesional temporal pole and cerebellum correlated with dopaminergic lesion metrics, in line with dopamine’s established role in reinforcement learning, motivation, and cortical plasticity 85 . Similarly, higher NAT lesion load predicted poorer ΔAQ, consistent with the noradrenergic system’s involvement in attention, arousal, and cognitive readiness for therapy 86 . Augmenting noradrenergic tone—such as via pharmacological agents combined with behavioral training—has been shown to improve naming performance in aphasic patients 87 . Serotonergic mechanisms also emerged as key modulators of post-stroke language recovery. Selective serotonin reuptake inhibitors (SSRIs), which enhance synaptic serotonin availability, have been proposed to promote neural plasticity and improve subacute aphasia outcomes 88 . In our study, better language recovery was linked to spared serotonergic receptor regions (5-HT1a, 5-HT1b, and 5-HT2a), consistent with evidence that serotonin facilitates experience-dependent reorganization in stroke recovery 89 . Importantly, the contralesional middle temporal pole and cerebellar lobule IX emerged as integrative hubs where these neuromodulatory effects converged. These regions are known to integrate semantic, attentional, and affective signals 90 , 91 and also demonstrated dopaminergic associations, reinforcing their roles as sensorimotor–linguistic integrators and potential targets for dopaminergic modulation during recovery 92 . Collectively, these findings suggest that lesion-derived neurotransmitter-informed network damages provide valuable insight into the neurochemical substrates of recovery potential. The contralesional temporal and cerebellar hubs, in particular, appear to mediate adaptive reorganization through neuromodulator-sensitive plasticity mechanisms that may guide future pharmacological and neuromodulatory interventions. 4.5 Caudate coupling as a mediator between lesion-derived neurotransmitter-informed network damages and language outcome The caudate nucleus, a central component of the cortico–striatal circuitry, plays a critical role in cognitive control, action selection, and motor–language integration 93 In the present study, altered SFC in the contralesional caudate was associated with dopaminergic and serotonergic lesion metrics as well as with language performance, suggesting that this region may mediate the neurochemical influence of stroke lesions on language recovery 94 , 95 . Previous studies have highlighted the contribution of cortico–striatal loops to speech initiation, lexical selection, and language monitoring 96 , with dopaminergic transmission serving as a crucial modulator of these processes 97 . Our mediation analysis further suggests that coupling strength within the contralesional caudate may serve as a neural bridge, linking lesion-derived neurotransmitter-informed network damages to adaptive reorganization within the language network 98 , 99 . This finding provides a theoretical foundation for pharmacological and neuromodulatory interventions that target dopaminergic and serotonergic pathways to facilitate post-stroke language recovery 100 . Specifically, modulation of striatal function—through dopaminergic agonists, selective serotonin reuptake inhibitors, or targeted brain stimulation (e.g., tDCS, rTMS)—may enhance cortico–striatal–language coupling and promote functional restoration. 4.6 Limitations Several limitations should be acknowledged in the present study. First, the relatively small sample size may limit the statistical power and generalizability of the findings; future multicenter studies with larger, demographically balanced cohorts are warranted to validate these results. Second, the method used to construct SFC—based on voxelwise correlations between ALFF and GMV—captures fundamental structure–function relationships but may overlook higher-order or dynamic interactions. Future research should apply advanced analytical frameworks, such as graph signal processing or multimodal fusion approaches, to characterize nonlinear or time-varying coupling patterns more comprehensively 101 , 102 . Finally, the neurotransmitter templates were derived from a population-based PET atlas rather than PSA-specific PET imaging. Consequently, the observed correlations between neurotransmitter-informed metrics, SFC, and behavioral outcomes should be interpreted cautiously. Direct validation using PET imaging in PSA patients will be essential to confirm the neurochemical basis of the observed structure–function–behavior relationships. 5 Conclusion This study underscores the critical role of structural–functional coupling (SFC) in post-stroke aphasia recovery. We identified significant decoupling in contralesional regions, including the putamen, middle temporal pole, and bilateral cerebellar lobules. These SFC alterations were associated with both neurotransmitter-system disruptions and language performance. Collectively, our findings suggest that contralesional SFC supports compensatory neural reorganization and may serve as a promising target for neuromodulation-based interventions to enhance language recovery after stroke. Declarations Ethics approval and consent to participate The studies involving human participants were reviewed and approved by the Ethics Committee of Wuxi Mental Health Center (Wuxi Central Rehabilitation Hospital; No. WXMHCIRB2023LLky055). The patients/participants provided their written informed consent to participate in this study. Consent for publication Not applicable. Availability of data and materials All data used to support the findings of this study are included within the article, and raw data are available from the corresponding author. Competing interests The authors declare that they have no competing interests. Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Social Development Project of Jiangsu Province (No. BE2022700), the Research Foundation of Jiangsu provincial commission of Health (No. LKM2022044), the Top Talent Support Program for Young and Middle-aged People of Wuxi Health Committee. Authors’ contributions Caili Ren, Zhiyong Zhao, and Xinlei Xu designed the study. Kai Zheng, Guilan Huang conducted the data collection. Dongdong Jiang, and Tongyan Zhang performed the data analysis. Daoran Wang and Zhiyong Zhao prepared the manuscript draft, including the figures. All authors reviewed, edited, and approved the final manuscript. Acknowledgment Not applicable. References Ramsey LE, Siegel JS, Lang CE, et al. Behavioural clusters and predictors of performance during recovery from stroke. Nat Hum Behav. 2017;1(3):0038. Gialanella B. Aphasia assessment and functional outcome prediction in patients with aphasia after stroke. J Neurol. 2011;258(2):343–9. Ginex V, Gilardone G, Viganò M, et al. Interaction Between Recovery of Motor and Language Abilities After Stroke. Arch Phys Med Rehabil. 2020;101(8):1367–76. Hope TMH, Leff AP, Prejawa S, et al. Right hemisphere structural adaptation and changing language skills years after left hemisphere stroke. Brain. 2017;140(6):1718–28. Wilson SM, Entrup JL, Schneck SM, et al. Recovery from aphasia in the first year after stroke. Brain. 2023;146(3):1021–39. Yang M, Li Y, Li J, et al. Beyond the Arcuate Fasciculus: Damage to Ventral and Dorsal Language Pathways in Aphasia. Brain Topogr. 2017;30(2):249–56. Geva S, Baron J-C, Jones PS, et al. A comparison of VLSM and VBM in a cohort of patients with post-stroke aphasia. NeuroImage: Clin. 2012;1(1):37–47. Brodtmann A, Pardoe H, Li Q, et al. Changes in regional brain volume three months after stroke. J Neurol Sci. 2012;322(1–2):122–8. Westlake KP, Nagarajan SS. Functional Connectivity in Relation to Motor Performance and Recovery After Stroke [Internet]. Front. Syst. Neurosci. 2011;5[cited 2025 Nov 10 ] Available from: http://journal.frontiersin.org/article/ 10.3389/fnsys.2011.00008/abstract Fan F, Zhu C, Chen H, et al. Dynamic brain structural changes after left hemisphere subcortical stroke. Hum Brain Mapp. 2013;34(8):1872–81. Jin K, Xu D, Shen Z, et al. Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis. Acta Neuropsychiatr. 2021;33(4):182–90. Zhang J, Meng L, Qin W, et al. Structural Damage and Functional Reorganization in Ipsilesional M1 in Well-Recovered Patients With Subcortical Stroke. Stroke. 2014;45(3):788–93. Yang M, Li J, Li Y, et al. Altered Intrinsic Regional Activity and Interregional Functional Connectivity in Post-stroke Aphasia. Sci Rep. 2016;6(1):24803. Deng X, Wang M, Zhang Y, et al. Resting-state functional alterations in patients with brain arteriovenous malformations involving language areas. Hum Brain Mapp. 2023;44(7):2790–801. Kyeong S, Kang H, Kim DH. Structural Integrity and Functional Neural Activity Associated with Oral Language Function after Stroke. JCM. 2022;11(11):3028. Han Y, Jing Y, Shi Y, et al. The role of language-related functional brain regions and white matter tracts in network plasticity of post-stroke aphasia. J Neurol. 2024;271(6):3095–115. Popp JL, Thiele JA, Faskowitz J, et al. Structural-functional brain network coupling predicts human cognitive ability. NeuroImage. 2024;290:120563. Schlemm E, Ingwersen T, Königsberg A, et al. Preserved structural connectivity mediates the clinical effect of thrombolysis in patients with anterior-circulation stroke. Nat Commun. 2021;12(1):2590. Ma J, Liu F, Yang B, et al. Selective Aberrant Functional-Structural Coupling of Multiscale Brain Networks in Subcortical Vascular Mild Cognitive Impairment. Neurosci Bull. 2021;37(3):287–97. Baum GL, Cui Z, Roalf DR, et al. Development of structure-function coupling in human brain networks during youth. Proc Natl Acad Sci U S A. 2020;117(1):771–8. Wang S, Gan S, Yang X, et al. Decoupling of Structural and Functional Connectivity in Hubs and Cognitive Impairment After Mild Traumatic Brain Injury. Brain Connect. 2021;11(9):745–58. Hirsiger S, Koppelmans V, Mérillat S, et al. Structural and functional connectivity in healthy aging: Associations for cognition and motor behavior. Hum Brain Mapp. 2016;37(3):855–67. Litwińczuk MC, Muhlert N, Cloutman L, et al. Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function. NeuroImage. 2022;262:119531. Liu X, Qiu S, Wang X, et al. Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction. NeuroImage: Clin. 2023;37:103332. Chen H, Geng W, Shang S, et al. Alterations of brain network topology and structural connectivity-functional connectivity coupling in capsular versus pontine stroke. Euro J Neurol. 2021;28(6):1967–76. Zhang, Zhang Y, Wang L, et al. Disrupted structural and functional connectivity networks in ischemic stroke patients. Neuroscience. 2017;364:212–25. Loubinoux I, Kronenberg G, Endres M, et al. Post-stroke depression: mechanisms, translation and therapy. J Cell Mol Med. 2012;16(9):1961–9. O’Sullivan MJ, Oestreich LKL, Wright P, Clarkson AN. Cholinergic and hippocampal systems facilitate cross-domain cognitive recovery after stroke. Brain. 2022;145(5):1698–710. Brzosko Z, Mierau SB, Paulsen O. Neuromodulation of Spike-Timing-Dependent Plasticity: Past, Present, and Future. Neuron. 2019;103(4):563–81. Chollet F, Tardy J, Albucher J-F, et al. Fluoxetine for motor recovery after acute ischaemic stroke (FLAME): a randomised placebo-controlled trial. Lancet Neurol. 2011;10(2):123–30. Ford GA, Bhakta BB, Cozens A, et al. Dopamine Augmented Rehabilitation in Stroke (DARS): a multicentre double-blind, randomised controlled trial of co-careldopa compared with placebo, in addition to routine NHS occupational and physical therapy, delivered early after stroke on functional recovery. Efficacy Mech Eval. 2019;6(5):1–138. Scheidtmann K, Fries W, Müller F, Koenig E. Effect of levodopa in combination with physiotherapy on functional motor recovery after stroke: a prospective, randomised, double-blind study. Lancet. 2001;358(9284):787–90. Cramer SC. Treatments to Promote Neural Repair after Stroke. J Stroke. 2018;20(1):57–70. Zang Z, Qiao K, Chen X, et al. Divergent Longitudinal Trajectories and Neurotransmitter Associations of Striatal Subparcellation Spontaneous Activity in Major Depressive Disorder. Biol Psychiatry Global Open Sci. 2025;5(5):100523. Wang Z, Zheng M, Yuan B, et al. Neuroimaging and biological markers of different paretic hand outcomes after stroke. J Neuroeng Rehabil. 2025;22(1):150. Koch PJ, Frey BM, Backhaus W, et al. Neurotransmitter-informed connectivity maps and their application for outcome inference after stroke. Brain. 2025;148(11):3935–45. Hansen JY, Shafiei G, Markello RD, et al. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat Neurosci. 2022;25(11):1569–81. Alves PN, Nozais V, Hansen JY, et al. Neurotransmitters’ white matter mapping unveils the neurochemical fingerprints of stroke. Nat Commun. 2025;16(1):2555. Kertesz A. Western Aphasia Battery–Revised [Internet]. 2012;[cited 2025 Nov 10 ] Available from: https://doi.apa.org/doi/ 10.1037/t15168-000 Yan DPARSF. a MATLAB toolbox for pipeline data analysis of resting-state fMRI [Internet]. Front. Syst. Neurosci. 2010;[cited 2025 Nov 10 ] Available from: http://journal.frontiersin.org/article/ 10.3389/fnsys.2010.00013/abstract Ashburner J, Friston KJ. Unified segmentation. NeuroImage. 2005;26(3):839–51. Zang Y-F, Yong H, Chao-Zhe Z, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Develop. 2007;29(2):83–91. Zou Q-H, Zhu C-Z, Yang Y, et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods. 2008;172(1):137–41. Feng M, Wen H, Li J, et al. Editorial: Neuroimaging of brain structure-function coupling mechanism in neuropsychiatric disorders. Front Neurosci. 2023;17:1270645. Tomasi D, Volkow ND. Resting functional connectivity of language networks: characterization and reproducibility. Mol Psychiatry. 2012;17(8):841–54. Zhu H, Zuo L, Zhu W, et al. The distinct disrupted plasticity in structural and functional network in mild stroke with basal ganglia region infarcts. Brain Imaging Behav. 2022;16(5):2199–219. Wang N, Liang C, Zhang X, et al. Brain structure–function coupling associated with cognitive impairment in cerebral small vessel disease. Front Neurosci. 2023;17:1163274. Patterson K, Nestor PJ, Rogers TT. Where do you know what you know? The representation of semantic knowledge in the human brain. Nat Rev Neurosci. 2007;8(12):976–87. Ralph MAL, Jefferies E, Patterson K, Rogers TT. The neural and computational bases of semantic cognition. Nat Rev Neurosci. 2017;18(1):42–55. Tsapkini K, Frangakis CE, Hillis AE. The function of the left anterior temporal pole: evidence from acute stroke and infarct volume. Brain. 2011;134(10):3094–105. Stockert A, Wawrzyniak M, Klingbeil J, et al. Dynamics of language reorganization after left temporo-parietal and frontal stroke. Brain. 2020;143(3):844–61. Soghomonian J-J, editor. The Basal Ganglia: Novel Perspectives on Motor and Cognitive Functions [Internet]. Cham: Springer International Publishing; 2016.[cited 2025 Sept 2 ] Available from: http://link.springer.com/ 10.1007/978-3-319-42743-0 Grönholm EO, Roll MC, Horne MA, et al. Predominance of caudate nucleus lesions in acute ischaemic stroke patients with impairment in language and speech. Euro J Neurol. 2016;23(1):148–53. Kemik K, Ada E, Çavuşoğlu B, et al. Detecting language network alterations in mild cognitive impairment using task-based fMRI and resting‐state fMRI: A comparative study. Brain Behav. 2024;14(5):e3518. Du Y, Lee Y, He C, et al. The changed functional status of the brain was involved in patients with poststroke aphasia: Coordinate-based (activation likelihood estimation) meta‐analysis. Brain Behav. 2020;10(12):e01867. Yuan Q, Li H, Du B, et al. The cerebellum and cognition: further evidence for its role in language control. Cereb Cortex. 2022;33(1):35–49. Keser Z, Sebastian R, Hasan KM, Hillis AE. Right Hemispheric Homologous Language Pathways Negatively Predicts Poststroke Naming Recovery. Stroke. 2020;51(3):1002–5. Zuo L, Li Z, Jing J, et al. Brain structural–functional coupling mechanism in mild subcortical stroke and its relationship with cognition. Brain Res. 2024;1845:149167. Zhao Z, Wu J, Fan M, et al. Altered intra- and inter‐network functional coupling of resting‐state networks associated with motor dysfunction in stroke. Hum Brain Mapp. 2018;39(8):3388–97. Cauda F, Costa T, Torta DME, et al. Meta-analytic clustering of the insular cortex. NeuroImage. 2012;62(1):343–55. De Bruyn N, Saenen L, Thijs L, et al. Brain connectivity alterations after additional sensorimotor or motor therapy for the upper limb in the early-phase post stroke: a randomized controlled trial. Brain Commun. 2021;3(2):fcab074. Radanovic M, Almeida VN. Subcortical Aphasia. Curr Neurol Neurosci Rep. 2021;21(12):73. Wu P, Zeng F, Li Y, et al. Changes of resting cerebral activities in subacute ischemic stroke patients. Neural Regen Res. 2015;10(5):760. Park C, Chang WH, Ohn SH, et al. Longitudinal Changes of Resting-State Functional Connectivity During Motor Recovery After Stroke. Stroke. 2011;42(5):1357–62. Newman-Norlund RD, Gibson M, Johnson L, et al. Cerebellar Atrophy and Language Processing in Chronic Left-Hemisphere Stroke. Neurobiol Lang. 2024;5(3):722–35. Hanakawa T, Hotta F, Nakamura T, et al. Macrostructural Cerebellar Neuroplasticity Correlates With Motor Recovery After Stroke. Neurorehabil Neural Repair. 2023;37(11–12):775–85. Ginex V, Veronelli L, Vanacore N, et al. Motor recovery in post-stroke patients with aphasia: the role of specific linguistic abilities. Top Stroke Rehabil. 2017;24(6):428–34. Wu T, Hallett M. The cerebellum in Parkinson’s disease. Brain. 2013;136(3):696–709. Gera G, Fling BW, Horak FB. Cerebellar White Matter Damage Is Associated With Postural Sway Deficits in People With Multiple Sclerosis. Arch Phys Med Rehabil. 2020;101(2):258–64. Pang L, Fan B, Chen Z, et al. Disruption of Cerebellar–Cerebral Functional Connectivity in Temporal Lobe Epilepsy and the Connection to Language and Cognitive Functions. Front Neurosci. 2022;16:871128. Becker EBE, Stoodley CJ. Autism Spectrum Disorder and the Cerebellum [Internet]. In: International Review of Neurobiology. Elsevier; 2013 p. 1–34.[cited 2025 Nov 10 ] Available from: https://linkinghub.elsevier.com/retrieve/pii/B9780124187009000010 Mottolese C, Richard N, Harquel S, et al. Mapping motor representations in the human cerebellum. Brain. 2013;136(1):330–42. Stoodley C, Schmahmann J. Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies. NeuroImage. 2009;44(2):489–501. Coffman KA, Dum RP, Strick PL. Cerebellar vermis is a target of projections from the motor areas in the cerebral cortex. Proc. Natl. Acad. Sci. U.S.A. 2011;108(38):16068–16073. Jobson KR, Hoffman LJ, Metoki A, et al. Language and the Cerebellum: Structural Connectivity to the Eloquent Brain. Neurobiol Lang. 2024;5(3):652–75. Guell X, Gabrieli JDE, Schmahmann JD. Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort. NeuroImage. 2018;172:437–49. Ramanoel S, York E, Habas C. Participation of the caudal cerebellar lobule IX to the dorsal attentional network. cerebellum ataxias. 2018;5(1):9. Zhang P, Duan L, Ou Y, et al. The cerebellum and cognitive neural networks. Front Hum Neurosci. 2023;17:1197459. Schmahmann JD. The cerebellum and cognition. Neurosci Lett. 2019;688:62–75. Zhong H-H, Qu J, Xiao W-M, et al. Severity of Lesions Involving the Cortical Cholinergic Pathways May Be Associated With Cognitive Impairment in Subacute Ischemic Stroke. Front Neurol. 2021;12:606897. Qu J-F, Chen Y-K, Luo G-P et al. Severe Lesions Involving Cortical Cholinergic Pathways Predict Poorer Functional Outcome in Acute Ischemic Stroke. Stroke. 2018;49(12):2983–2989. Berthier ML, Green C, Higueras C, et al. A randomized, placebo-controlled study of donepezil in poststroke aphasia. Neurology. 2006;67(9):1687–9. Frase S, Löffler F, Hosp JA. Enhancing Post-Stroke Rehabilitation and Preventing Exo-Focal Dopaminergic Degeneration in Rats—A Role for Substance P. IJMS. 2022;23(7):3848. Villa M, Martínez-Vega M, Del Pozo A, et al. The Role of the Dopamine System in Post-Stroke Mood Disorders in Newborn Rats. IJMS. 2023;24(4):3229. Song M-K, Cramer SC. Dopaminergic Pathways in Neuroplasticity After Stroke and Vagus Nerve Stimulation. Stroke. 2025;56(6):1625–30. Saxena S, Hillis AE. An update on medications and noninvasive brain stimulation to augment language rehabilitation in post-stroke aphasia. Expert Rev Neurother. 2017;17(11):1091–107. Delaina W-B, Curtis S, Natarajan R, et al. A Double-Blind, Placebo-Controlled Study of the Use of Amphetamine in the Treatment of Aphasia. Stroke. 2001;32(9):2093–8. Stockbridge MD, Fridriksson J, Sen S, et al. Protocol for Escitalopram and Language Intervention for Subacute Aphasia (ELISA): A randomized, double blind, placebo-controlled trial. PLoS ONE. 2021;16(12):e0261474. Hillis AE, Beh YY, Sebastian R, et al. Predicting recovery in acute poststroke aphasia. Ann Neurol. 2018;83(3):612–22. Carhart-Harris RL, Leech R, Erritzoe D, et al. Functional Connectivity Measures After Psilocybin Inform a Novel Hypothesis of Early Psychosis. Schizophr Bull. 2013;39(6):1343–51. Andrews-Hanna JR, Reidler JS, Sepulcre J, et al. Functional-Anatomic Fractionation of the Brain’s Default Network. Neuron. 2010;65(4):550–62. Wang Z, Wang L, Gao F, et al. Exploring cerebellar transcranial magnetic stimulation in post-stroke limb dysfunction rehabilitation: a narrative review. Front Neurosci. 2025;19:1405637. Copland DA, Brownsett S, Iyer K, Angwin AJ. Corticostriatal Regulation of Language Functions. Neuropsychol Rev. 2021;31(3):472–94. Premi E, Dukart J, Mattioli I, et al. Unravelling neurotransmitters impairment in primary progressive aphasias. Hum Brain Mapp. 2023;44(6):2245–53. Gil-Navarro S, Lomeña F, Cot A, et al. Decreased striatal dopamine transporter uptake in the non‐fluent/agrammatic variant of primary progressive aphasia. Euro J Neurol. 2013;20(11):1459. Simonyan K, Herscovitch P, Horwitz B. Speech-induced striatal dopamine release is left lateralized and coupled to functional striatal circuits in healthy humans: A combined PET, fMRI and DTI study. NeuroImage. 2013;70:21–32. Berthier ML, Pulvermüller F, Dávila G, et al. Drug Therapy of Post-Stroke Aphasia: A Review of Current Evidence. Neuropsychol Rev. 2011;21(3):302–17. Kiran S, Thompson CK. Neuroplasticity of Language Networks in Aphasia: Advances, Updates, and Future Challenges. Front Neurol. 2019;10:295. Grefkes C, Fink GR. Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain. 2011;134(5):1264–76. Vitti E, Hillis AE. Treatment of post-stroke aphasia: A narrative review for stroke neurologists. Int J Stroke. 2021;16(9):1002–8. Herzog R, Rosas FE, Whelan R, et al. Genuine high-order interactions in brain networks and neurodegeneration. Neurobiol Dis. 2022;175:105918. Cao P, Wen G, Liu X, et al. Modeling the dynamic brain network representation for autism spectrum disorder diagnosis. Med Biol Eng Comput. 2022;60(7):1897–913. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialforReview.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 May, 2026 Reviews received at journal 06 Feb, 2026 Reviewers agreed at journal 24 Dec, 2025 Reviewers agreed at journal 24 Dec, 2025 Reviewers invited by journal 22 Dec, 2025 Editor assigned by journal 18 Nov, 2025 Submission checks completed at journal 18 Nov, 2025 First submitted to journal 17 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8131643","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565367105,"identity":"4a963408-532d-4cec-aa11-37d6ee437851","order_by":0,"name":"Daoran Wang","email":"","orcid":"","institution":"Department of Rehabilitation Medicine, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation HospitalW","correspondingAuthor":false,"prefix":"","firstName":"Daoran","middleName":"","lastName":"Wang","suffix":""},{"id":565367106,"identity":"2a1cfabf-b1c1-41a8-a2aa-ccffca3061cf","order_by":1,"name":"Dongdong Jiang","email":"","orcid":"","institution":"Department of Rehabilitation Medicine, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation HospitalW","correspondingAuthor":false,"prefix":"","firstName":"Dongdong","middleName":"","lastName":"Jiang","suffix":""},{"id":565367109,"identity":"a9ae41d5-82f7-4393-819f-c6083c76d857","order_by":2,"name":"Tongyan Zhang","email":"","orcid":"","institution":"Department of Rehabilitation Medicine, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation HospitalW","correspondingAuthor":false,"prefix":"","firstName":"Tongyan","middleName":"","lastName":"Zhang","suffix":""},{"id":565367111,"identity":"36d15e42-1189-4224-af36-17c8a072f1ea","order_by":3,"name":"Kai Zheng","email":"","orcid":"","institution":"Department of Rehabilitation Medicine, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation HospitalW","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zheng","suffix":""},{"id":565367112,"identity":"f10e7b7f-4474-488d-9372-00ffc734bb7e","order_by":4,"name":"Guilan Huang","email":"","orcid":"","institution":"Department of Rehabilitation Medicine, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation HospitalW","correspondingAuthor":false,"prefix":"","firstName":"Guilan","middleName":"","lastName":"Huang","suffix":""},{"id":565367113,"identity":"f208f538-aae6-4475-ba14-10d7532141d5","order_by":5,"name":"Xinlei Xu","email":"","orcid":"","institution":"Department of Rehabilitation Medicine, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation HospitalW","correspondingAuthor":false,"prefix":"","firstName":"Xinlei","middleName":"","lastName":"Xu","suffix":""},{"id":565367114,"identity":"5797c367-5c0f-45b2-954a-96bfc2ae628d","order_by":6,"name":"Zhiyong Zhao","email":"","orcid":"","institution":"Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health","correspondingAuthor":false,"prefix":"","firstName":"Zhiyong","middleName":"","lastName":"Zhao","suffix":""},{"id":565367115,"identity":"04bb4ec5-1c9b-45dc-9c2c-3cbe009a969a","order_by":7,"name":"Caili Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYNACAwsefgaGBBCTsYFILRI8kg2kaWGQYDA4AGER1mLe3nvww48CCRnj2w1PN/Mw2MhuOMD87AE+LTJnziVL9gAdZnbnQNptHoY04w0H2MwN8LpIIseMgQek5UYCSMvhxA0HeNgk8GqRf2PG+AeoxXgGWMt/IrQAzWcG2WIgAdZygAgtPDnG0jJALRJAh92cY5BsPPMwmxl+LexnDD+++WNjzz8jJ+3Gmwo72b7jzc/wakECPAnAOAXSzESqBwL2A8SrHQWjYBSMghEFAKQDQLas+ZFcAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Rehabilitation Medicine, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation HospitalW","correspondingAuthor":true,"prefix":"","firstName":"Caili","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2025-11-17 06:08:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8131643/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8131643/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99193816,"identity":"15f3a4d2-ade9-4161-86b0-7f70dc264ec2","added_by":"auto","created_at":"2025-12-30 01:28:00","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1893842,"visible":true,"origin":"","legend":"","description":"","filename":"fig1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/a260f54359d9bc7ca673bb87.tif"},{"id":99316624,"identity":"893694cb-c191-4815-9305-f865a6ed055a","added_by":"auto","created_at":"2025-12-31 16:28:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3754318,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/a6fcae250db975bf88c04402.docx"},{"id":99193814,"identity":"383eebfa-3992-4443-8a6f-64b070ff4887","added_by":"auto","created_at":"2025-12-30 01:27:55","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2469814,"visible":true,"origin":"","legend":"","description":"","filename":"fig2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/29b7d1c5e2510b4dcd850901.tif"},{"id":99316165,"identity":"93ebb2be-1105-45e2-8467-ba7a25528949","added_by":"auto","created_at":"2025-12-31 16:27:48","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":701982,"visible":true,"origin":"","legend":"","description":"","filename":"fig3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/65ffe6a22bedc32380ecdefb.tif"},{"id":99193802,"identity":"6ac6efbf-a956-41bf-9aed-487a1c00a85c","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":949448,"visible":true,"origin":"","legend":"","description":"","filename":"fig4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/c61f897e93fb53a70f7bf409.tif"},{"id":99193784,"identity":"451ce884-2a46-4ce2-ac24-fd2038c03ddc","added_by":"auto","created_at":"2025-12-30 01:27:43","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":667030,"visible":true,"origin":"","legend":"","description":"","filename":"fig5.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/c3fbc33fe2192d948ce46d6d.tif"},{"id":99193780,"identity":"b8779705-fd78-45bb-a14d-f014f90fbb9c","added_by":"auto","created_at":"2025-12-30 01:27:42","extension":"json","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9748,"visible":true,"origin":"","legend":"","description":"","filename":"e26337c80833408a88f20ad4301aadf9.json","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/4f8d8ee0ce76f237f652e0fe.json"},{"id":99193811,"identity":"6a636cd6-0f3b-40dc-8719-f5097b7db470","added_by":"auto","created_at":"2025-12-30 01:27:46","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":770978,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialforReview.docx","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/41ca6556ef3e6fd4fc3a69b5.docx"},{"id":99193786,"identity":"45380359-f969-4ba4-9894-5a6be6b8b064","added_by":"auto","created_at":"2025-12-30 01:27:43","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":186867,"visible":true,"origin":"","legend":"","description":"","filename":"e26337c80833408a88f20ad4301aadf91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/a2edfa65fd9f55c032b6ea7f.xml"},{"id":99193803,"identity":"71770725-a542-478e-ada8-8d7521ad7069","added_by":"auto","created_at":"2025-12-30 01:27:45","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1893842,"visible":true,"origin":"","legend":"","description":"","filename":"fig1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/b472b2bfa82fbd0186a24c87.tif"},{"id":99193797,"identity":"a05b490f-1f81-4200-ae08-947edb70ce8e","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2469814,"visible":true,"origin":"","legend":"","description":"","filename":"fig2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/441a072c257348c6e8da8853.tif"},{"id":99317353,"identity":"4f98cb50-e3d7-4bf8-a210-2a038fa29a1d","added_by":"auto","created_at":"2025-12-31 16:30:03","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":701982,"visible":true,"origin":"","legend":"","description":"","filename":"fig3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/f3ee70686d17a1409e9e6f63.tif"},{"id":99193810,"identity":"0246bd96-3800-44a7-bfd7-f849eb065f6a","added_by":"auto","created_at":"2025-12-30 01:27:46","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":949448,"visible":true,"origin":"","legend":"","description":"","filename":"fig4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/fa14c210912efd95385ceb93.tif"},{"id":99193793,"identity":"f8585959-1358-4be7-a562-eeec3a0c11ac","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"tif","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":667030,"visible":true,"origin":"","legend":"","description":"","filename":"fig5.tif","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/41a5aee44413ef338159d31e.tif"},{"id":99316112,"identity":"8c06ff02-5490-4c95-88b3-3ffd9ed4d5eb","added_by":"auto","created_at":"2025-12-31 16:27:45","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":959641,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/d7124d9221901a6d08e603f3.png"},{"id":99193790,"identity":"b1179fde-315f-4884-bd56-e8c09ead486f","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":780730,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/46dbd93b8c37ddcdd431d9db.png"},{"id":99193804,"identity":"a5d37ef9-ea97-48ea-8a96-c25f3bc091b7","added_by":"auto","created_at":"2025-12-30 01:27:45","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":654639,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/62a9c5589b4ddfecffe9a389.png"},{"id":99193783,"identity":"3fd4751f-979d-494d-9b9c-7249d4089660","added_by":"auto","created_at":"2025-12-30 01:27:43","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":604170,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/da299b620adb78ddf55c5141.jpeg"},{"id":99193801,"identity":"88b2ecea-4a51-4302-8661-1afe66528727","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":374069,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/9c29b373651f7c4fcffef33b.png"},{"id":99193798,"identity":"6ae15515-f61c-416c-9d1f-84fcf3a5485e","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":245173,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/9f8fdc30bf771d6ec7cba1cf.png"},{"id":99193809,"identity":"c75becce-d70d-484e-b8fe-4d76132dda4f","added_by":"auto","created_at":"2025-12-30 01:27:45","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267443,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/46e3d39d4b70c79493ca7775.png"},{"id":99193788,"identity":"a8696ccc-658d-4337-b4d1-6d14bcd0b070","added_by":"auto","created_at":"2025-12-30 01:27:43","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105994,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/62e3966a84e772efc705af1c.png"},{"id":99193799,"identity":"3f064588-4c5a-4e8e-88c4-20ba3310d31f","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148024,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/0a8ca716b59607f7a099d859.png"},{"id":99193791,"identity":"e9914ee9-d60b-4ef4-9689-0a2bd683a666","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90857,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/2b52ea3ee23b769e1dd59e05.png"},{"id":99193796,"identity":"0b0a46a3-bb73-45b0-ae10-b4d5c94fbed9","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176972,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/b45303530e4fa289d4c4c783.png"},{"id":99193800,"identity":"b6536d26-5608-451f-9d4e-69656e8a45df","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143399,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/ce0abba78d9083d468faee9f.png"},{"id":99193789,"identity":"7e89f859-7f99-422f-82bb-31905b8708dd","added_by":"auto","created_at":"2025-12-30 01:27:43","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116834,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/e31f667b3d7f5e59c01039a2.png"},{"id":99193805,"identity":"ac48c60a-3e41-48a7-94b2-11aa07c91ca1","added_by":"auto","created_at":"2025-12-30 01:27:45","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143471,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/92f6746f5171ed2cccdd7c5b.png"},{"id":99193808,"identity":"7a072396-2b69-4a9a-a828-8275cf84607b","added_by":"auto","created_at":"2025-12-30 01:27:45","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91747,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/c8362cff7d5bacf3460c8eda.png"},{"id":99193812,"identity":"3e1700da-d476-4e60-bf31-7f8600b8eaae","added_by":"auto","created_at":"2025-12-30 01:27:46","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":184570,"visible":true,"origin":"","legend":"","description":"","filename":"e26337c80833408a88f20ad4301aadf91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/c35fa4873f785522dab496de.xml"},{"id":99193794,"identity":"270b2f5d-1c80-4a6a-a2dd-aac28df8ad7c","added_by":"auto","created_at":"2025-12-30 01:27:44","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200767,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/87ed341993dbc390a870c5e5.html"},{"id":99193785,"identity":"75cdfc78-7593-4f05-9075-0817909eba78","added_by":"auto","created_at":"2025-12-30 01:27:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":959641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLesion overlap maps for (A) 29 patients with post-stroke aphasia (PSA) and (B) 23 patients without aphasia (nonPSA). The color bar represents the number of patients with lesions in each voxel. \u003c/strong\u003eThe Z-axis ranges from Z = 0 to Z = 48 in Montreal Neurological Institute (MNI) space. R denotes the right hemisphere.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/688d322fe0a173955ef1ddbb.png"},{"id":99193782,"identity":"82523b8e-fc18-4b1a-a980-b0c8c80fbf90","added_by":"auto","created_at":"2025-12-30 01:27:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":780730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) Mean SFC map of HCs.\u003c/strong\u003e \u003cstrong\u003e(B) Mean SFC map of patients with PSA.\u003c/strong\u003e \u003cstrong\u003e(C) Mean SFC map of patients with\u003c/strong\u003e \u003cstrong\u003enonPSA\u003c/strong\u003e. In panels A-C, the color bar indicates Pearson’s correlation coefficient (r) between GMV and fALFF within each anatomical region.\u003cstrong\u003e \u003c/strong\u003eThe regions showing significant differences between groups were categorized into language-specific (D), motor-specific (E), and shared (F) patterns based on post hoc comparisons. \u003cstrong\u003eLanguage-specific regions\u003c/strong\u003e exhibited significant SFC reductions in PSA compared with both HC and nonPSA, indicating aphasia-related decoupling. \u003cstrong\u003eMotor-specific regions\u003c/strong\u003e showed SFC alterations in both PSA and nonPSA relative to HC, reflecting stroke-related motor decoupling. \u003cstrong\u003eShared regions\u003c/strong\u003e demonstrated SFC alterations in both patient groups compared to HC and also differ between PSA and nonPSA. Brain maps display ANOVA results (colorbars = F values).\u003cstrong\u003e (G) \u003c/strong\u003eRegression plot showing the relationship between SFC in the contralesional middle temporal pole and spontaneous speech scores in the early phase, controlling for age, sex, education, and lesion volume. L, left; R, right; PSA, post-stroke aphasia; HC, healthy controls; SFC, structural–functional coupling; TPO_mid, Middle Temporal Pole; *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001; ns, not significant.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/365f5d251efe6d39e4a2a523.png"},{"id":99193813,"identity":"c76b563b-eb3c-4ca6-9229-2a45db6d8743","added_by":"auto","created_at":"2025-12-30 01:27:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":654639,"visible":true,"origin":"","legend":"\u003cp\u003eRadar plots illustrating group differences between PSA and nonPSA patients in (A) lesion load, (B) disconnection indices, and (C) pre-/post-synaptic disruption ratios. Values represent –log10(p FDR). Red dots indicate PSA \u0026gt; nonPSA, blue dots indicate PSA \u0026lt; nonPSA, and grey denotes non-significant results. (D) Regression plots illustrating the associations between lesion-derived neurotransmitter-informed metrics and changes in aphasia severity (ΔAQ). D1/D2, dopamine receptors; DAT, dopamine transporter; 5-HT1a, 5-HT1b, 5-HT2a, 5-HT4, and 5-HT6, serotonin receptors; 5-HTT, serotonin transporter; NAT, noradrenaline transporter; VAChT, vesicular acetylcholine transporter; M1, muscarinic acetylcholine receptor subtype M1; A4B2, nicotinic acetylcholine receptor subtype α4β2. *p \u0026lt; 0.05; **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/30fe54c4f6095d441efa2e30.png"},{"id":99317351,"identity":"a2d332ad-a54f-4bc7-9ae5-c06f5e356627","added_by":"auto","created_at":"2025-12-31 16:30:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":849833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRadar and scatter plots illustrating associations between SFC and lesion-derived neurotransmitter-informed metrics. \u003c/strong\u003eThe radar plot presents standardized regression β coefficients depicting the strength of associations between SFC in language-specific regions and various neurotransmitter-related indices. The accompanying scatter plots show representative correlations between SFC values (x-axis) and lesion-derived neurotransmitter-informed metrics (y-axis), with each dot representing an individual patient. TPO_mid, Middle Temporal Pole; Cere9, Cerebellum Lobule 9; D1/D2, dopamine receptors; DAT, dopamine transporter; 5-HT1a, 5-HT1b, 5-HT2a, 5-HT4, and 5-HT6, serotonin receptors; 5-HTT, serotonin transporter; NAT, noradrenaline transporter; VAChT, vesicular acetylcholine transporter; M1, muscarinic acetylcholine receptor subtype M1; A4B2, nicotinic acetylcholine receptor subtype α4β2.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/2bed5d5a99eac7d4399a5ed6.png"},{"id":99193807,"identity":"97d79bf7-2bf5-43f6-926d-e673e9f59b78","added_by":"auto","created_at":"2025-12-30 01:27:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":374069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation models illustrating the relationships among lesion-derived neurotransmitter-informed metrics, SFC in the contralesional caudate, and aphasia severity at the 3-month post-stroke follow-up (AQ-1).\u003c/strong\u003e Each panel presents the standardized path coefficients for the indirect, direct, and total effects. Several indirect (a×b) paths reached nominal significance (p \u0026lt; 0.05); however, none survived FDR correction. These findings should therefore be interpreted cautiously and considered preliminary. AQ, Aphasia Quotient; 5-HTT, serotonin transporter; 5-HT4, serotonin receptor 4; D2, dopamine D2 receptor; ACME, average causal mediation effect; ADE, average direct effect. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/6b693323d48c52d26d6210e9.png"},{"id":99193815,"identity":"9f3d7256-7151-4157-8270-3906f97ae8d7","added_by":"auto","created_at":"2025-12-30 01:28:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":770978,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialforReview.docx","url":"https://assets-eu.researchsquare.com/files/rs-8131643/v1/7c7d80a87778a562c62b5de7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neurotransmitter Systems Underlie Structure-Function Decoupling and Recovery in Post- Stroke Aphasia","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eStroke often leads to multiple neurological deficits. A left middle cerebral artery infarction involving the inferior frontal and precentral cortices can cause right-sided hemiparesis and aphasia \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Evidence indicates that stroke patients with more severe language dysfunction tend to experience poorer motor recovery, suggesting a close relationship between language and motor outcomes \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This interaction implies that the two functions may share overlapping neural mechanisms of recovery \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Although early post-stroke functional reorganization contributes to behavioral improvement \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, this process is frequently incomplete and varies considerably among individuals \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, elucidating the neural basis linking motor and language deficits is essential for optimizing rehabilitation strategies in patients with post-stroke aphasia (PSA).\u003c/p\u003e \u003cp\u003ePatients with PSA typically exhibit gray matter damage associated with language deficits \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, leading to neural dysfunction not only within the lesion site \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e but also in remote, functionally connected regions \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u0026mdash;for example, the contralesional cingulate cortex and ipsilesional precentral gyrus after subcortical stroke \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Structural impairment is often accompanied by cortical functional alterations \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Post-stroke neuroimaging studies have reported increased amplitude of low-frequency fluctuations (ALFF) in the contralesional mesial temporal lobe and ipsilesional hippocampus \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, which correlate with behavioral measures of language function \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings demonstrate widespread structural and functional reorganization following stroke \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Recent advances in neuroimaging have highlighted structural\u0026ndash;functional coupling (SFC)\u0026mdash;the degree of correspondence between structural integrity and functional activity\u0026mdash;as a sensitive marker of brain organization \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Compared with unimodal indices, SFC provides a more integrative and sensitive measure for detecting subtle brain alterations \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Disrupted SFC has been identified as a neural substrate underlying brain injury \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, behavioral impairment \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and cognitive dysfunction \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In stroke, reduced SFC in sensorimotor networks has been linked to motor deficits \u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, the characteristics of SFC alterations in PSA remain largely unknown, and it is unclear how language- and motor-related SFC changes differ or overlap.\u003c/p\u003e \u003cp\u003eBeyond structural and functional alterations, stroke also disrupts neurotransmitter systems, which have been implicated in post-stroke sequelae such as depression \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, cognitive impairment \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and aphasia \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Pharmacological interventions targeting serotonergic \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and dopaminergic \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e systems have shown promise in promoting post-stroke functional recovery \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Functional imaging studies further demonstrate that ALFF abnormalities in the striatum are associated with 5-HT and D2 receptor densities in depression \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Similarly, altered ALFF following stroke has been linked to disruptions in monoaminergic systems\u0026mdash;particularly serotonergic and dopaminergic pathways\u0026mdash;and to motor recovery of the paretic hand \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Moreover, the spatial distribution of neurotransmitter receptors (e.g., serotonin, dopamine, acetylcholine) closely aligns with large-scale resting-state networks in stroke patients \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Recent methodological advances now allow integration of lesion data with normative PET-based maps of neurotransmitter receptors and transporters, enabling inference of neurotransmitter-specific damage after stroke \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the relationship between neurotransmitter network disruption, SFC alterations, and language outcomes in PSA remains largely unexplored.\u003c/p\u003e \u003cp\u003eIn the present study, we aimed to elucidate how PSA-related alterations in SFC relate to both neurotransmitter network damage and language performance. We hypothesized that: (1) PSA would exhibit region-specific disruptions in SFC linked to language and motor deficits; (2) these SFC changes would be linked to specific neurotransmitter systems; and (3) SFC alterations would mediate the relationship between neurotransmitter damages and post-stroke language impairment.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eWe recruited 29 patients with post-stroke aphasia (PSA; mean age\u0026thinsp;=\u0026thinsp;59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.6 years; 41.4% male) from Wuxi Central Rehabilitation Hospital. Inclusion criteria were as follows: (1) age between 30 and 80 years; (2) first-ever unilateral left-hemisphere stroke; (3) right-handed native Chinese speakers with normal premorbid language abilities, no professional vocal or instrumental training, and a WAB Aphasia Quotient (AQ)\u0026thinsp;\u0026lt;\u0026thinsp;93.8; (4) education level of primary school or above; and (5) normal vision and hearing. Exclusion criteria included: (1) history of drug or alcohol abuse, seizures, or neuropsychiatric disorders; (2) contraindications for MRI (e.g., cranial defects, skin lesions at stimulation sites, intracranial implants, pacemakers, or medication pumps); (3) history of neurosurgery; (4) severe cognitive impairment precluding participation in evaluation or therapy; and (5) presence of other neurological or systemic conditions affecting language or cognition. Additionally, 23 stroke patients without aphasia (nonPSA; mean age\u0026thinsp;=\u0026thinsp;68.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 years; 43.5% male) were recruited using the same criteria, except for the absence of language deficits (AQ\u0026thinsp;\u0026ge;\u0026thinsp;98.4). Nineteen right-handed healthy controls (HCs; mean age\u0026thinsp;=\u0026thinsp;58.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 years; 36.8% male), fluent in Chinese and matched to the patient groups for age, sex, and education, were also enrolled.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Language Assessment\u003c/h2\u003e \u003cp\u003eAphasia diagnosis and severity were determined using the Western Aphasia Battery\u0026ndash;Revised (WAB-R) \u003csup\u003e39\u003c/sup\u003e, which yields an Aphasia Quotient (AQ) as a summary index. The WAB-R comprises four main subtests: spontaneous speech, auditory verbal comprehension, repetition, and naming. In the spontaneous speech subtest, patients answered questions from the examiner and described picture scenes. In the auditory verbal comprehension subtest, patients responded to yes/no questions and identified real objects, written words, numbers, colors, and body parts based on verbal instructions. The repetition subtest requires patients repeated words and sentences of varying lengths and complexity. In the naming subtest, patients named visually presented objects, completed unfinished sentences, and performed category fluency tasks (e.g., naming as many animals as possible within one minute). The subtests contribute 20, 10, 10, and 10 points, respectively, to the total AQ score. Each PSA patient completed the WAB-R twice: approximately 28 days post-stroke (WAB0) and again at 3 months post-stroke (WAB1). Changes in language performance were calculated as ΔAQ\u0026thinsp;=\u0026thinsp;AQ1 \u0026ndash; AQ0. Individual assessment results are presented in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MRI Data Acquisition\u003c/h2\u003e \u003cp\u003eMRI scanning was performed approximately 28 days after stroke onset using a 3.0 Tesla GE SIGNA Architect scanner. Sagittal three-dimensional (3D) T1-weighted images were acquired using a brain volume (BRAVO) sequence with the following parameters: repetition time (TR)/echo time (TE)\u0026thinsp;=\u0026thinsp;7.7/3.1 ms, field of view (FOV)\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 mm\u0026sup2;, matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256, slice thickness\u0026thinsp;=\u0026thinsp;1.0 mm, interslice gap\u0026thinsp;=\u0026thinsp;1.0 mm, and 176 slices. Resting-state functional MRI (rs-fMRI) data were collected using an echo-planar imaging (EPI) sequence with the following parameters: TR/TE\u0026thinsp;=\u0026thinsp;2500/30 ms, slice thickness\u0026thinsp;=\u0026thinsp;3.5 mm, interslice gap\u0026thinsp;=\u0026thinsp;1.0 mm, matrix\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, FOV\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 mm\u0026sup2;, 50 axial slices, and 200 volumes. During the scan, participants were instructed to keep their eyes closed, remain still, stay awake, and avoid engaging in any specific thoughts. T2-weighted images were obtained using a turbo spin-echo sequence (TR/TE\u0026thinsp;=\u0026thinsp;5000/120 ms, FOV\u0026thinsp;=\u0026thinsp;220 \u0026times; 220 mm\u0026sup2;, matrix\u0026thinsp;=\u0026thinsp;320 \u0026times; 320, slice thickness\u0026thinsp;=\u0026thinsp;5 mm, no interslice gap; 30 slices) to identify lesion locations. All images were visually inspected for artifacts, structural abnormalities, and excessive head motion prior to data preprocessing and analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Lesion Delineation\u003c/h2\u003e \u003cp\u003eStroke lesions were manually delineated on T2-weighted images for each patient, slice by slice, using MRIcron software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/mricron\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/mricron\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A lesion overlap map illustrating the spatial distribution across all stroke patients is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The resulting lesion masks were used to calculate individual lesion volumes. Detailed lesion characteristics for each participant are provided in Supplementary \u003cb\u003eFigure S1\u003c/b\u003e and \u003cb\u003eTable S2.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Lesion-derived neurotransmitter-informed metrics\u003c/h2\u003e \u003cp\u003eTo quantify neurotransmitter-specific damage, each patient\u0026rsquo;s binary lesion map was overlaid onto normative neurotransmitter maps derived from PET tracer atlases \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These atlases include receptor and transporter density distributions for key neurotransmitter systems such as serotonin, dopamine, and acetylcholine. The normative receptor and transporter density maps were co-registered to a standard structural connectome template using both affine and nonlinear transformations implemented in Advanced Normalization Tools (ANTs). Streamlines from the normative tractogram were weighted by the receptor or transporter density at their terminal voxels, thereby generating neurotransmitter-specific streamline weights, consistent with previous methodologies \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Based on this framework, we computed three lesion-derived neurotransmitter-informed metrics for each patient: (1) Lesion loaddefined as the cumulative receptor/transporter density within lesioned voxels; (2) Disconnection index, defined as the total neurotransmitter-weighted streamline burden intersecting with the lesion; and (3) Pre-/post-synaptic disruption ratio, calculated as the ratio between presynaptic (transporter-based) and postsynaptic (receptor-based) disruptions, reflecting differential impacts on neurotransmission pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 MRI Data Processing\u003c/h2\u003e \u003cp\u003eResting-state fMRI data were preprocessed using Statistical Parametric Mapping (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) and the Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The preprocessing pipeline included the following steps: (1) the first 10 volumes were discarded to allow for magnetization equilibrium and participant adaptation, leaving 190 usable volumes; (2) temporal differences between slices were corrected, and head motion was realigned, with no participant exhibited translational displacement\u0026thinsp;\u0026gt;\u0026thinsp;2 mm or rotation\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026deg;; (3) each participant\u0026rsquo;s T1-weighted image was co-registered to their mean functional image and segmented into gray matter, white matter, and cerebrospinal fluid; (4) spurious variance from head motion parameters and blood-oxygen-level-dependent (BOLD) signals in WM and CSF was regressed out; (5) functional images were normalized to the Montreal Neurological Institute (MNI) space using parameters estimated from segmentation \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and resampled to 3 mm isotropic voxels; (6) normalized images were spatially smoothed using a Gaussian kernel with 6 mm full-width at half-maximum (FWHM). After preprocessing, each voxel\u0026rsquo;s time series was transformed into the frequency domain using a fast Fourier transform (FFT). The square root of the power spectrum within the 0.01\u0026ndash;0.08 Hz frequency range was calculated to obtain the ALFF \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The fALFF was computed as the ratio of ALFF to the total amplitude across the entire frequency range \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll T1-weighted structural images were visually inspected to ensure adequate quality and absence of artifacts. The image origin was manually aligned to the anterior commissure. Subsequently, images were segmented into GM, WM, and CSF using the unified segmentation algorithm in SPM12, with bias correction and intensity normalization applied to improve classification accuracy. The DARTEL algorithm was then employed to generate a study-specific template for high-dimensional normalization of GM images to MNI space. During normalization, GM images were modulated to preserve total tissue volume, producing gray matter volume (GMV) maps. These GMV maps were resampled to a voxel size of 1.5 \u0026times; 1.5 \u0026times; 1.5 mm\u0026sup3; and smoothed using an 8 mm FWHM Gaussian kernel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Structure\u0026ndash;function coupling analysis\u003c/h2\u003e \u003cp\u003eSFC analysis was performed by integrating GMV maps derived from T1-weighted MRI with fractional fALFF maps obtained from resting-state fMRI. A region-wise approach was employed using the Automated Anatomical Labeling (AAL) atlas, which divides the brain into 116 predefined regions. For each participant and each AAL region, the Pearson correlation coefficient between GMV and fALFF values across all voxels within the region. Thus, each participant obtained a 1\u0026times;116 SFC matrix \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. To improve normality and facilitate statistical comparison, correlation coefficients were transformed using Fisher\u0026rsquo;s r-to-z transformation prior to group-level analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eIntergroup differences in clinical data, language assessments, and SFC were examined. Prior to group comparisons, the normality of each variable was assessed using the Shapiro\u0026ndash;Wilk test. For variables with a normal distribution, one-way analysis of variance (ANOVA) was applied, followed by post hoc pairwise comparisons with Bonferroni correction. For non-normally distributed variables, the Kruskal\u0026ndash;Wallis test was used, with post hoc pairwise comparisons conducted using Dun\u0026rsquo;s test. Categorical variables (e.g., sex) were analyzed using the chi-square test. To evaluate longitudinal changes in WAB scores within the PSA group, paired-samples t-tests or Wilcoxon signed-rank tests were performed, depending on data distribution. All statistical analyses were performed using R version 4.4.2 (R Core Team, 2024). Descriptive statistics were computed to summarize the characteristics of the data.\u003c/p\u003e \u003cp\u003eTo identify regional SFC differences among HC, nonPSA, and PSA groups, region-wise ANCOVA were performed on Fisher z\u0026ndash;transformed SFC values, with age, sex, and years of education as covariates. Post hoc two-sample t-tests were conducted for regions exhibiting significant main effects. Comparisons of lesion-derived neurotransmitter-informed metrics between PSA and nonPSA groups were also performed using two-sample t-tests, controlling for the same covariates. To correct for multiple comparisons across the 116 AAL regions, false discovery rate (FDR) correction was applied. A threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed) was considered statistically significant.\u003c/p\u003e \u003cp\u003e To explore the associations among regional SFC values, neurotransmitter-informed metrics, and WAB scores in the PSA group, multivariate linear regression analyses were conducted for each AAL region, with age, sex, education, and lesion volume included as covariates: WAB score\u0026thinsp;~\u0026thinsp;SFC or lesion-derived neurotransmitter-informed metrics\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Education\u0026thinsp;+\u0026thinsp;Lesion volume. We further assessed the relationship between neurotransmitter-informed metrics and SFC: SFC\u0026thinsp;~\u0026thinsp;lesion-derived neurotransmitter-informed metrics\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Education\u0026thinsp;+\u0026thinsp;Lesion volume. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eFinally, mediation analyses were performed using the mediation package in R to determine whether SFC mediated the relationship between lesion-derived neurotransmitter-informed metrics and the WAB scores. The bootstrap method (5,000 iterations) with 95% confidence intervals (CIs) was used to estimate indirect effects. In the mediation model, SFC values served as the mediator variable (M), lesion-derived neurotransmitter-informed metrics as the independent variable (X), and WAB score as the dependent variable (Y). All models were adjusted for age, sex, and years of education. Mediation was considered significant when the 95% CI of the indirect effect did not include zero.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic and Clinical Comparisons\u003c/h2\u003e \u003cp\u003eAs summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the PSA group exhibited significantly lower baseline scores in spontaneous speech, auditory comprehension, repetition, naming, and overall language ability compared with both the nonPSA and healthy control (HC) groups. No significant group differences were observed in age (p\u0026thinsp;=\u0026thinsp;0.79), sex (p\u0026thinsp;=\u0026thinsp;0.11), or years of education (p\u0026thinsp;=\u0026thinsp;0.27). Within the PSA group, significant improvements were observed in Aphasia Quotient (AQ) (p\u0026thinsp;=\u0026thinsp;0.008), auditory comprehension (p\u0026thinsp;=\u0026thinsp;0.049), and spontaneous speech (p\u0026thinsp;=\u0026thinsp;0.017) scores of the WAB) at the 3-month follow-up compared to baseline. In contrast, naming (p\u0026thinsp;=\u0026thinsp;0.058) and repetition (p\u0026thinsp;=\u0026thinsp;0.321) scores did not show significant longitudinal changes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Clinical Information of All Participants.\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\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enonPSA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF/χ\u003csup\u003e2\u003c/sup\u003e/t-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eSex (M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 / 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 / 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 / 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.35\u0026thinsp;\u0026plusmn;\u0026thinsp;13.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.40\u0026thinsp;\u0026plusmn;\u0026thinsp;11.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesion site left (F/P/T/I/B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28/26/6/1/13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/1/1/2/18\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\"\u003e \u003cp\u003eAQ-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.32\u0026thinsp;\u0026plusmn;\u0026thinsp;15.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\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=\"c1\"\u003e \u003cp\u003eAuditory Comprehension-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpontaneous speech-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaming-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepetition-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ-1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.27\u0026thinsp;\u0026plusmn;\u0026thinsp;21.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuditory Comprehension-1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpontaneous speech-1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaming-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepetition-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔAQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the difference is statistically significant. F\u0026thinsp;=\u0026thinsp;Frontal, P\u0026thinsp;=\u0026thinsp;Parietal, T\u0026thinsp;=\u0026thinsp;Temporal, I\u0026thinsp;=\u0026thinsp;Insula B\u0026thinsp;=\u0026thinsp;Basal Ganglia, 0 and 1 represent clinical assessments at baseline and at three month, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Regional SFC Comparison and Relationship with Language Function\u003c/h2\u003e \u003cp\u003eGroup-averaged SFC maps are shown for HC, PSA, and nonPSA groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C) respectively, providing an overview of the spatial distribution patterns.\u003c/p\u003e \u003cp\u003eThe ANCOVA revealed significant group effects on SFC across multiple regions (F\u0026thinsp;=\u0026thinsp;4.63\u0026ndash;28.38, all adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Post hoc analyses identified three distinct patterns of SFC alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u0026ndash;F; Table S3). Language-specific regions (significantly altered in the PSA group compared with both HC and nonPSA, but not between nonPSA and HC) included the contralesional putamen, middle temporal pole, and bilateral posterior cerebellar lobules. Motor-specific regions (significantly altered in both PSA and nonPSA groups relative to HC, but not between PSA and nonPSA) were mainly involved in the contralesional prefrontal cortex, insula, superior parietal cortex, precuneus, caudate inferior/superior temporal cortices, extending to cerebellar regions. Shared regions (significantly altered in both PSA and nonPSA groups relative to HC, and also differed between PSA and nonPSA) included the ipsilesional cerebellar lobule IX. These findings suggest that stroke-related language and motor impairments are associated with distinct yet partially overlapping SFC alterations, with the cerebellum potentially acting as a convergence hub for both functions.\u003c/p\u003e \u003cp\u003eFurthermore, regression analyses showed that SFC in the contralesional middle temporal pole was significantly correlated with spontaneous speech performance in the early phase (β = \u0026minus;\u0026thinsp;0.39, p\u0026thinsp;=\u0026thinsp;0.022, Cohen\u0026rsquo;s f\u0026sup2; = 0.25), indicating that lower SFC in this region was associated with poorer spontaneous speech outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Alterations in neurotransmitter-informed metrics and their relationships with language function\u003c/h2\u003e \u003cp\u003eGroup comparisons revealed significant differences between the PSA and nonPSA groups in lesion load and disconnection indices across multiple neurotransmitter systems, including dopamine (D1, D2, DAT), serotonin (5-HT1a, 5-HT1b, 5-HT2a, 5-HT4, 5-HT6, 5-HTT), noradrenergic, and cholinergic (VAChT, M1, A4B2) pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B; Table S4). In contrast, the pre-/post-synaptic disruption ratio differed between groups primarily in the presynaptic D2, 5-HT1a, and 5-HT2a systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eFurthermore, significant negative correlations were observed between lesion load within several neurotransmitter systems and ΔAQ scores (uncorrected, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating higher lesion load in M1, noradrenaline, 5-HT1a, 5-HT1b, and 5-HT2a receptors might be associated with poorer aphasia recovery (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation between SFC and lesion-derived neurotransmitter-informed Metrics\u003c/h2\u003e \u003cp\u003eRegression analyses revealed significant associations between SFC in the contralesional middle temporal pole and multiple lesion-derived neurotransmitter-informed metrics within the PSA group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table S6). Specifically, SFC showed positive correlations with lesion load in the M1 (β\u0026thinsp;=\u0026thinsp;2.89, p\u0026thinsp;=\u0026thinsp;0.049), NAT (β\u0026thinsp;=\u0026thinsp;3.28, p\u0026thinsp;=\u0026thinsp;0.040), and 5-HT1b (β\u0026thinsp;=\u0026thinsp;3.65, p\u0026thinsp;=\u0026thinsp;0.033) systems. Similarly, SFC was positively correlated with disconnection indices in cholinergic (VAChT), dopaminergic (D1, D2, DAT), noradrenergic, and serotonergic (5-HT1a, 5-HT1b, 5-HT2a, 5-HT4, 5-HT6, 5-HTT) pathways (all β\u0026thinsp;\u0026asymp;\u0026thinsp;2.5\u0026ndash;3.3, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Outside this region, only one significant association emerged: SFC in the contralesional cerebellar lobule IX correlated with the presynaptic D1 ratio (β\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;=\u0026thinsp;0.043). Although these effects did not remain significant after FDR correction, these exploratory results suggest that altered SFC in specific contralesional regions may capture neurotransmitter-related network disruption and potential compensatory plasticity following stroke.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mediation Analysis\u003c/h2\u003e \u003cp\u003eExploratory mediation analyses suggested that SFC in the contralesional caudate may partially mediate the relationship between lesion-derived neurotransmitter-informed network disruption and language performance. Although no mediation effects survived FDR correction, several indirect paths reached nominal significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), primarily involving serotonergic and dopaminergic systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The strongest nominal mediation effect was observed for 5-HTT disconnection, where the indirect effect through contralesional caudate SFC reached β\u0026thinsp;=\u0026thinsp;2.352 (95% CI [0.114, 5.689], p\u0026thinsp;=\u0026thinsp;0.028), despite a non-significant total effect (β = \u0026minus;\u0026thinsp;8.433, p\u0026thinsp;=\u0026thinsp;0.110). Similar patterns were noted across other serotonergic and dopaminergic targets. For instance, lesion load in D2 receptor availability exhibited an indirect effect of β\u0026thinsp;=\u0026thinsp;1.547 (95% CI [0.176, 3.616], p\u0026thinsp;=\u0026thinsp;0.030), alongside significant total (β = \u0026minus;\u0026thinsp;7.807, p\u0026thinsp;=\u0026thinsp;0.008) and direct (β = \u0026minus;\u0026thinsp;9.355, p\u0026thinsp;=\u0026thinsp;0.010) effects, indicating partial mediation. Comparable mediation trends were also observed for lesion load 5-HT4 (β\u0026thinsp;=\u0026thinsp;1.608, p\u0026thinsp;=\u0026thinsp;0.028) and disconnection 5-HT4 (β\u0026thinsp;=\u0026thinsp;1.943, p\u0026thinsp;=\u0026thinsp;0.018), both demonstrating significant total or direct effects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOur findings support the initial hypothesis that PSA involves region-specific structural-functional decoupling associated with multiple neurotransmitter systems. Specifically, three distinct SFC alteration patterns\u0026mdash;language-specific, motor-specific, and shared regions\u0026mdash;were identified in the PSA group, reflecting the multifaceted nature of post-stroke neural reorganization. Reduced SFC in the contralesional middle temporal pole was linked to poorer spontaneous speech, while SFC in contralesional temporal and subcortical regions correlated with lesion-derived neurotransmitter-informed network disruptions. Mediation analyses further suggested that caudate SFC may partially mediate the relationship between lesion-derived neurotransmitter-informed network damages and language outcomes. Collectively, these results highlight the intertwined roles of network-level and neurochemical alterations in shaping post-stroke language recovery.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Language-specific regions: contralesional temporal and subcortical decoupling as a marker of linguistic impairment\u003c/h2\u003e \u003cp\u003eIn the PSA group, language-specific decoupling was observed in the contralesional putamen, middle temporal pole, and bilateral posterior cerebellar lobules, suggesting these alterations are more closely related to aphasia than to motor deficits. Importantly, reduced SFC in the contralesional middle temporal pole predicted poorer spontaneous speech, emphasizing its importance in early recovery.\u003c/p\u003e \u003cp\u003eThe resting-state language network encompasses both temporal and subcortical regions, including the putamen \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Previous studies have linked post-stroke cognitive impairment to reduced SFC strength in the basal ganglia and bilateral medial temporal lobes \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Our findings align with evidence identifying the temporal pole as a multimodal semantic hub \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and support prior reports implicating the contralesional temporal cortex in compensatory language processing following left-hemisphere stroke \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The basal ganglia, particularly the putamen, are involved in phonological and articulatory control \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, while neuronal activity within the temporal lobes has been associated with language and speech impairments in PSA \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Moreover, contralesional temporal activation has been linked to compensatory word generation in cognitively impaired individuals \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilarly, cerebellar hyperactivation has been proposed as a compensatory mechanism in PSA \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. This observation is consistent with previous work showing bilateral cerebellar engagement in linguistic control and error monitoring \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Taken together, our findings suggest that decoupling in the basal ganglia, contralesional temporal, and cerebellar regions may represent disrupted semantic\u0026ndash;phonological integration and compensatory reorganization processes that collectively shape language recovery trajectories in PSA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Motor-specific regions: reorganization of frontoparietal and subcortical coupling\u003c/h2\u003e \u003cp\u003eMotor-specific regions included the contralesional prefrontal cortex, insula, superior parietal cortex, precuneus, caudate, inferior/superior temporal cortices, and cerebellar regions, suggesting that these abnormalities primarily relate to motor dysfunction. Consistent with prior research, reduced SFC within the prefrontal and parietal cortices has been linked to motor impairment following stroke \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.. The insula acts as a connector hub between the frontal and temporal cortices \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e and exhibits dynamic reorganization during post-stroke recovery \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. The caudate, a key component of the basal ganglia\u0026ndash;thalamocortical circuit, supports both motor control and cognitive flexibility \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Furthermore, enhanced cerebellar activation has been interpreted as a compensatory mechanism that facilitates motor recovery \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Our findings extend this evidence by demonstrating that motor-related SFC alterations in PSA reflect not only the direct disconnection of sensorimotor circuits but also adaptive reorganization involving higher-order cortical and subcortical regions. These changes likely represent the brain\u0026rsquo;s compensatory attempt to restore functional integration within disrupted motor networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Shared regions: cerebellar convergence of language and motor dysfunction\u003c/h2\u003e \u003cp\u003eThe shared region identified in the ipsilesional cerebellar lobule IX suggests that this structure serves as a convergence zone where language and motor impairments intersect in PSA. This finding supports the growing view of the cerebellum as an integrative hub involved in both articulatory coordination and cognitive\u0026ndash;linguistic control \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Prior studies have demonstrated cerebellar engagement in speech and limb motor recovery following stroke \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, while behavioral evidence indicates that improvements in motor function often parallel or predict language recovery \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Beyond stroke, converging evidence from other neurological disorders also underscores the cerebellum\u0026rsquo;s integrative role across motor and speech domains. In Parkinson\u0026rsquo;s disease and multiple sclerosis, the cerebellum is increasingly recognized as a central node in both tremor regulation and gait coordination \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Similarly, cerebellar anterior lobe activity has been associated with speech control in epilepsy and autism spectrum disorder \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Thus, the shared cerebellar decoupling pattern observed here may represent a common substrate of adaptive reorganization underlying both movement and speech-related recovery processes.\u003c/p\u003e \u003cp\u003eNotably, although both language- and motor-specific SFC patterns involved cerebellar regions, the shared cerebellar hub (the ipsilesional lobule IX) likely represents a distinct integrative zone. Motor-specific abnormalities encompassed both anterior-lobe sensorimotor modules (lobules IV\u0026ndash;V and VI), which mediate fine motor execution and timing \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, and posterior motor-associative regions (Crus I/II and vermis VIII), which support executive\u0026ndash;motor coordination \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. In contrast, language-specific decoupling was confined to posterior-lobe cognitive\u0026ndash;linguistic regions, including the contralesional lobules IX and X, consistent with their established roles in semantic integration and higher-order language control \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Importantly, lobule IX is functionally unique: its rostral and caudal subdivisions participate in both the default mode network (DMN) and the dorsal attention network (DAN) \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e This dual-network embedding has been suggested to support visuospatial integration and higher-order cognitive coordination via interactions between DMN and DAN \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Such cross-network interactions may offer a plausible explanation for why the ipsilesional lobule IX emerged as a shared region in PSA. It may function as a high-level \u0026ldquo;co-processor\u0026rdquo; that is recruited whenever task demands (linguistic, motor, or combined) require complex integration, refined coordination \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Lesion-derived neurotransmitter-informed network damages and language recovery\u003c/h2\u003e \u003cp\u003eBeyond the regional SFC alterations, we observed widespread disruptions across dopaminergic, serotonergic, noradrenergic, and cholinergic systems, with PSA patients exhibiting greater lesion load and disconnection indices compared with nonPSA patients. Moreover, higher lesion burden within these neurotransmitter systems was associated with poorer aphasia recovery, underscoring their critical contribution to post-stroke language plasticity. Previous studies have demonstrated that damage to cortical cholinergic pathways is strongly associated with cognitive impairment following subacute ischemic stroke, and that greater disruption predicts poorer functional outcomes in the acute phase \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Pharmacological enhancement of acetylcholine transmission\u0026mdash;such as through donepezil\u0026mdash;has been shown to improve WAB scores in PSA patients \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Consistent with these findings, our study revealed that lesion load in the M1 and VAChT pathways was negatively correlated with ΔAQ, suggesting that preserved cholinergic integrity supports better language recovery. The dopaminergic system plays a pivotal role in rehabilitation-induced neuroplasticity \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. In our data, SFC in the contralesional temporal pole and cerebellum correlated with dopaminergic lesion metrics, in line with dopamine\u0026rsquo;s established role in reinforcement learning, motivation, and cortical plasticity \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Similarly, higher NAT lesion load predicted poorer ΔAQ, consistent with the noradrenergic system\u0026rsquo;s involvement in attention, arousal, and cognitive readiness for therapy \u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Augmenting noradrenergic tone\u0026mdash;such as via pharmacological agents combined with behavioral training\u0026mdash;has been shown to improve naming performance in aphasic patients \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. Serotonergic mechanisms also emerged as key modulators of post-stroke language recovery. Selective serotonin reuptake inhibitors (SSRIs), which enhance synaptic serotonin availability, have been proposed to promote neural plasticity and improve subacute aphasia outcomes \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. In our study, better language recovery was linked to spared serotonergic receptor regions (5-HT1a, 5-HT1b, and 5-HT2a), consistent with evidence that serotonin facilitates experience-dependent reorganization in stroke recovery \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImportantly, the contralesional middle temporal pole and cerebellar lobule IX emerged as integrative hubs where these neuromodulatory effects converged. These regions are known to integrate semantic, attentional, and affective signals \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e and also demonstrated dopaminergic associations, reinforcing their roles as sensorimotor\u0026ndash;linguistic integrators and potential targets for dopaminergic modulation during recovery \u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings suggest that lesion-derived neurotransmitter-informed network damages provide valuable insight into the neurochemical substrates of recovery potential. The contralesional temporal and cerebellar hubs, in particular, appear to mediate adaptive reorganization through neuromodulator-sensitive plasticity mechanisms that may guide future pharmacological and neuromodulatory interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Caudate coupling as a mediator between lesion-derived neurotransmitter-informed network damages and language outcome\u003c/h2\u003e \u003cp\u003eThe caudate nucleus, a central component of the cortico\u0026ndash;striatal circuitry, plays a critical role in cognitive control, action selection, and motor\u0026ndash;language integration \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e In the present study, altered SFC in the contralesional caudate was associated with dopaminergic and serotonergic lesion metrics as well as with language performance, suggesting that this region may mediate the neurochemical influence of stroke lesions on language recovery \u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. Previous studies have highlighted the contribution of cortico\u0026ndash;striatal loops to speech initiation, lexical selection, and language monitoring \u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e, with dopaminergic transmission serving as a crucial modulator of these processes \u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. Our mediation analysis further suggests that coupling strength within the contralesional caudate may serve as a neural bridge, linking lesion-derived neurotransmitter-informed network damages to adaptive reorganization within the language network \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. This finding provides a theoretical foundation for pharmacological and neuromodulatory interventions that target dopaminergic and serotonergic pathways to facilitate post-stroke language recovery \u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. Specifically, modulation of striatal function\u0026mdash;through dopaminergic agonists, selective serotonin reuptake inhibitors, or targeted brain stimulation (e.g., tDCS, rTMS)\u0026mdash;may enhance cortico\u0026ndash;striatal\u0026ndash;language coupling and promote functional restoration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged in the present study. First, the relatively small sample size may limit the statistical power and generalizability of the findings; future multicenter studies with larger, demographically balanced cohorts are warranted to validate these results. Second, the method used to construct SFC\u0026mdash;based on voxelwise correlations between ALFF and GMV\u0026mdash;captures fundamental structure\u0026ndash;function relationships but may overlook higher-order or dynamic interactions. Future research should apply advanced analytical frameworks, such as graph signal processing or multimodal fusion approaches, to characterize nonlinear or time-varying coupling patterns more comprehensively \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e,\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. Finally, the neurotransmitter templates were derived from a population-based PET atlas rather than PSA-specific PET imaging. Consequently, the observed correlations between neurotransmitter-informed metrics, SFC, and behavioral outcomes should be interpreted cautiously. Direct validation using PET imaging in PSA patients will be essential to confirm the neurochemical basis of the observed structure\u0026ndash;function\u0026ndash;behavior relationships.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study underscores the critical role of structural\u0026ndash;functional coupling (SFC) in post-stroke aphasia recovery. We identified significant decoupling in contralesional regions, including the putamen, middle temporal pole, and bilateral cerebellar lobules. These SFC alterations were associated with both neurotransmitter-system disruptions and language performance. Collectively, our findings suggest that contralesional SFC supports compensatory neural reorganization and may serve as a promising target for neuromodulation-based interventions to enhance language recovery after stroke.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the Ethics Committee of Wuxi Mental Health Center (Wuxi Central Rehabilitation Hospital; No. WXMHCIRB2023LLky055). The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used to support the findings of this study are included within the article,\u0026nbsp;and raw data are available from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Social Development Project of Jiangsu Province (No. BE2022700), the Research Foundation of Jiangsu provincial commission of Health (No. LKM2022044), the Top Talent Support Program for Young and Middle-aged People of Wuxi Health Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCaili Ren, Zhiyong Zhao, and Xinlei Xu designed the study. Kai Zheng, Guilan Huang conducted the data collection. Dongdong Jiang, and Tongyan Zhang performed the data analysis. Daoran Wang and Zhiyong Zhao prepared the manuscript draft, including the figures. All authors reviewed, edited, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRamsey LE, Siegel JS, Lang CE, et al. Behavioural clusters and predictors of performance during recovery from stroke. Nat Hum Behav. 2017;1(3):0038.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGialanella B. Aphasia assessment and functional outcome prediction in patients with aphasia after stroke. J Neurol. 2011;258(2):343\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGinex V, Gilardone G, Vigan\u0026ograve; M, et al. Interaction Between Recovery of Motor and Language Abilities After Stroke. Arch Phys Med Rehabil. 2020;101(8):1367\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHope TMH, Leff AP, Prejawa S, et al. Right hemisphere structural adaptation and changing language skills years after left hemisphere stroke. Brain. 2017;140(6):1718\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson SM, Entrup JL, Schneck SM, et al. Recovery from aphasia in the first year after stroke. Brain. 2023;146(3):1021\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Li Y, Li J, et al. Beyond the Arcuate Fasciculus: Damage to Ventral and Dorsal Language Pathways in Aphasia. Brain Topogr. 2017;30(2):249\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeva S, Baron J-C, Jones PS, et al. A comparison of VLSM and VBM in a cohort of patients with post-stroke aphasia. NeuroImage: Clin. 2012;1(1):37\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrodtmann A, Pardoe H, Li Q, et al. Changes in regional brain volume three months after stroke. J Neurol Sci. 2012;322(1\u0026ndash;2):122\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWestlake KP, Nagarajan SS. Functional Connectivity in Relation to Motor Performance and Recovery After Stroke [Internet]. Front. Syst. Neurosci. 2011;5[cited 2025 Nov 10 ] Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://journal.frontiersin.org/article/\u003c/span\u003e\u003cspan address=\"http://journal.frontiersin.org/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnsys.2011.00008/abstract\u003c/span\u003e\u003cspan address=\"10.3389/fnsys.2011.00008/abstract\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan F, Zhu C, Chen H, et al. Dynamic brain structural changes after left hemisphere subcortical stroke. Hum Brain Mapp. 2013;34(8):1872\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin K, Xu D, Shen Z, et al. Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis. Acta Neuropsychiatr. 2021;33(4):182\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Meng L, Qin W, et al. Structural Damage and Functional Reorganization in Ipsilesional M1 in Well-Recovered Patients With Subcortical Stroke. Stroke. 2014;45(3):788\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Li J, Li Y, et al. Altered Intrinsic Regional Activity and Interregional Functional Connectivity in Post-stroke Aphasia. Sci Rep. 2016;6(1):24803.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng X, Wang M, Zhang Y, et al. Resting-state functional alterations in patients with brain arteriovenous malformations involving language areas. Hum Brain Mapp. 2023;44(7):2790\u0026ndash;801.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyeong S, Kang H, Kim DH. Structural Integrity and Functional Neural Activity Associated with Oral Language Function after Stroke. JCM. 2022;11(11):3028.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Jing Y, Shi Y, et al. The role of language-related functional brain regions and white matter tracts in network plasticity of post-stroke aphasia. J Neurol. 2024;271(6):3095\u0026ndash;115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopp JL, Thiele JA, Faskowitz J, et al. Structural-functional brain network coupling predicts human cognitive ability. NeuroImage. 2024;290:120563.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlemm E, Ingwersen T, K\u0026ouml;nigsberg A, et al. Preserved structural connectivity mediates the clinical effect of thrombolysis in patients with anterior-circulation stroke. Nat Commun. 2021;12(1):2590.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa J, Liu F, Yang B, et al. Selective Aberrant Functional-Structural Coupling of Multiscale Brain Networks in Subcortical Vascular Mild Cognitive Impairment. Neurosci Bull. 2021;37(3):287\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaum GL, Cui Z, Roalf DR, et al. Development of structure-function coupling in human brain networks during youth. Proc Natl Acad Sci U S A. 2020;117(1):771\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Gan S, Yang X, et al. Decoupling of Structural and Functional Connectivity in Hubs and Cognitive Impairment After Mild Traumatic Brain Injury. Brain Connect. 2021;11(9):745\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirsiger S, Koppelmans V, M\u0026eacute;rillat S, et al. Structural and functional connectivity in healthy aging: Associations for cognition and motor behavior. Hum Brain Mapp. 2016;37(3):855\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLitwińczuk MC, Muhlert N, Cloutman L, et al. Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function. NeuroImage. 2022;262:119531.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Qiu S, Wang X, et al. Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction. NeuroImage: Clin. 2023;37:103332.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Geng W, Shang S, et al. Alterations of brain network topology and structural connectivity-functional connectivity coupling in capsular versus pontine stroke. Euro J Neurol. 2021;28(6):1967\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Zhang Y, Wang L, et al. Disrupted structural and functional connectivity networks in ischemic stroke patients. Neuroscience. 2017;364:212\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoubinoux I, Kronenberg G, Endres M, et al. Post-stroke depression: mechanisms, translation and therapy. J Cell Mol Med. 2012;16(9):1961\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Sullivan MJ, Oestreich LKL, Wright P, Clarkson AN. Cholinergic and hippocampal systems facilitate cross-domain cognitive recovery after stroke. Brain. 2022;145(5):1698\u0026ndash;710.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrzosko Z, Mierau SB, Paulsen O. Neuromodulation of Spike-Timing-Dependent Plasticity: Past, Present, and Future. Neuron. 2019;103(4):563\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChollet F, Tardy J, Albucher J-F, et al. Fluoxetine for motor recovery after acute ischaemic stroke (FLAME): a randomised placebo-controlled trial. Lancet Neurol. 2011;10(2):123\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFord GA, Bhakta BB, Cozens A, et al. Dopamine Augmented Rehabilitation in Stroke (DARS): a multicentre double-blind, randomised controlled trial of co-careldopa compared with placebo, in addition to routine NHS occupational and physical therapy, delivered early after stroke on functional recovery. Efficacy Mech Eval. 2019;6(5):1\u0026ndash;138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheidtmann K, Fries W, M\u0026uuml;ller F, Koenig E. Effect of levodopa in combination with physiotherapy on functional motor recovery after stroke: a prospective, randomised, double-blind study. Lancet. 2001;358(9284):787\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCramer SC. Treatments to Promote Neural Repair after Stroke. J Stroke. 2018;20(1):57\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZang Z, Qiao K, Chen X, et al. Divergent Longitudinal Trajectories and Neurotransmitter Associations of Striatal Subparcellation Spontaneous Activity in Major Depressive Disorder. Biol Psychiatry Global Open Sci. 2025;5(5):100523.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Zheng M, Yuan B, et al. Neuroimaging and biological markers of different paretic hand outcomes after stroke. J Neuroeng Rehabil. 2025;22(1):150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoch PJ, Frey BM, Backhaus W, et al. Neurotransmitter-informed connectivity maps and their application for outcome inference after stroke. Brain. 2025;148(11):3935\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen JY, Shafiei G, Markello RD, et al. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat Neurosci. 2022;25(11):1569\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlves PN, Nozais V, Hansen JY, et al. Neurotransmitters\u0026rsquo; white matter mapping unveils the neurochemical fingerprints of stroke. Nat Commun. 2025;16(1):2555.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKertesz A. Western Aphasia Battery\u0026ndash;Revised [Internet]. 2012;[cited 2025 Nov 10 ] Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.apa.org/doi/\u003c/span\u003e\u003cspan address=\"https://doi.apa.org/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/t15168-000\u003c/span\u003e\u003cspan address=\"10.1037/t15168-000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan DPARSF. a MATLAB toolbox for pipeline data analysis of resting-state fMRI [Internet]. Front. Syst. Neurosci. 2010;[cited 2025 Nov 10 ] Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://journal.frontiersin.org/article/\u003c/span\u003e\u003cspan address=\"http://journal.frontiersin.org/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnsys.2010.00013/abstract\u003c/span\u003e\u003cspan address=\"10.3389/fnsys.2010.00013/abstract\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshburner J, Friston KJ. Unified segmentation. NeuroImage. 2005;26(3):839\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZang Y-F, Yong H, Chao-Zhe Z, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Develop. 2007;29(2):83\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou Q-H, Zhu C-Z, Yang Y, et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods. 2008;172(1):137\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng M, Wen H, Li J, et al. Editorial: Neuroimaging of brain structure-function coupling mechanism in neuropsychiatric disorders. Front Neurosci. 2023;17:1270645.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomasi D, Volkow ND. Resting functional connectivity of language networks: characterization and reproducibility. Mol Psychiatry. 2012;17(8):841\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu H, Zuo L, Zhu W, et al. The distinct disrupted plasticity in structural and functional network in mild stroke with basal ganglia region infarcts. Brain Imaging Behav. 2022;16(5):2199\u0026ndash;219.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang N, Liang C, Zhang X, et al. Brain structure\u0026ndash;function coupling associated with cognitive impairment in cerebral small vessel disease. Front Neurosci. 2023;17:1163274.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatterson K, Nestor PJ, Rogers TT. Where do you know what you know? The representation of semantic knowledge in the human brain. Nat Rev Neurosci. 2007;8(12):976\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRalph MAL, Jefferies E, Patterson K, Rogers TT. The neural and computational bases of semantic cognition. Nat Rev Neurosci. 2017;18(1):42\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsapkini K, Frangakis CE, Hillis AE. The function of the left anterior temporal pole: evidence from acute stroke and infarct volume. Brain. 2011;134(10):3094\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStockert A, Wawrzyniak M, Klingbeil J, et al. Dynamics of language reorganization after left temporo-parietal and frontal stroke. Brain. 2020;143(3):844\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoghomonian J-J, editor. The Basal Ganglia: Novel Perspectives on Motor and Cognitive Functions [Internet]. Cham: Springer International Publishing; 2016.[cited 2025 Sept 2 ] Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://link.springer.com/\u003c/span\u003e\u003cspan address=\"http://link.springer.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-319-42743-0\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-42743-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGr\u0026ouml;nholm EO, Roll MC, Horne MA, et al. Predominance of caudate nucleus lesions in acute ischaemic stroke patients with impairment in language and speech. Euro J Neurol. 2016;23(1):148\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKemik K, Ada E, \u0026Ccedil;avuşoğlu B, et al. Detecting language network alterations in mild cognitive impairment using task-based fMRI and resting‐state fMRI: A comparative study. Brain Behav. 2024;14(5):e3518.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Y, Lee Y, He C, et al. The changed functional status of the brain was involved in patients with poststroke aphasia: Coordinate-based (activation likelihood estimation) meta‐analysis. Brain Behav. 2020;10(12):e01867.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Q, Li H, Du B, et al. The cerebellum and cognition: further evidence for its role in language control. Cereb Cortex. 2022;33(1):35\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeser Z, Sebastian R, Hasan KM, Hillis AE. Right Hemispheric Homologous Language Pathways Negatively Predicts Poststroke Naming Recovery. Stroke. 2020;51(3):1002\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo L, Li Z, Jing J, et al. Brain structural\u0026ndash;functional coupling mechanism in mild subcortical stroke and its relationship with cognition. Brain Res. 2024;1845:149167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Z, Wu J, Fan M, et al. Altered intra- and inter‐network functional coupling of resting‐state networks associated with motor dysfunction in stroke. Hum Brain Mapp. 2018;39(8):3388\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCauda F, Costa T, Torta DME, et al. Meta-analytic clustering of the insular cortex. NeuroImage. 2012;62(1):343\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Bruyn N, Saenen L, Thijs L, et al. Brain connectivity alterations after additional sensorimotor or motor therapy for the upper limb in the early-phase post stroke: a randomized controlled trial. Brain Commun. 2021;3(2):fcab074.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadanovic M, Almeida VN. Subcortical Aphasia. Curr Neurol Neurosci Rep. 2021;21(12):73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu P, Zeng F, Li Y, et al. Changes of resting cerebral activities in subacute ischemic stroke patients. Neural Regen Res. 2015;10(5):760.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark C, Chang WH, Ohn SH, et al. Longitudinal Changes of Resting-State Functional Connectivity During Motor Recovery After Stroke. Stroke. 2011;42(5):1357\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman-Norlund RD, Gibson M, Johnson L, et al. Cerebellar Atrophy and Language Processing in Chronic Left-Hemisphere Stroke. Neurobiol Lang. 2024;5(3):722\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanakawa T, Hotta F, Nakamura T, et al. Macrostructural Cerebellar Neuroplasticity Correlates With Motor Recovery After Stroke. Neurorehabil Neural Repair. 2023;37(11\u0026ndash;12):775\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGinex V, Veronelli L, Vanacore N, et al. Motor recovery in post-stroke patients with aphasia: the role of specific linguistic abilities. Top Stroke Rehabil. 2017;24(6):428\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu T, Hallett M. The cerebellum in Parkinson\u0026rsquo;s disease. Brain. 2013;136(3):696\u0026ndash;709.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGera G, Fling BW, Horak FB. Cerebellar White Matter Damage Is Associated With Postural Sway Deficits in People With Multiple Sclerosis. Arch Phys Med Rehabil. 2020;101(2):258\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePang L, Fan B, Chen Z, et al. Disruption of Cerebellar\u0026ndash;Cerebral Functional Connectivity in Temporal Lobe Epilepsy and the Connection to Language and Cognitive Functions. Front Neurosci. 2022;16:871128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker EBE, Stoodley CJ. Autism Spectrum Disorder and the Cerebellum [Internet]. In: International Review of Neurobiology. Elsevier; 2013 p. 1\u0026ndash;34.[cited 2025 Nov 10 ] Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/B9780124187009000010\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/B9780124187009000010\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMottolese C, Richard N, Harquel S, et al. Mapping motor representations in the human cerebellum. Brain. 2013;136(1):330\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoodley C, Schmahmann J. Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies. NeuroImage. 2009;44(2):489\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoffman KA, Dum RP, Strick PL. Cerebellar vermis is a target of projections from the motor areas in the cerebral cortex. Proc. Natl. Acad. Sci. U.S.A. 2011;108(38):16068\u0026ndash;16073.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJobson KR, Hoffman LJ, Metoki A, et al. Language and the Cerebellum: Structural Connectivity to the Eloquent Brain. Neurobiol Lang. 2024;5(3):652\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuell X, Gabrieli JDE, Schmahmann JD. Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort. NeuroImage. 2018;172:437\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamanoel S, York E, Habas C. Participation of the caudal cerebellar lobule IX to the dorsal attentional network. cerebellum ataxias. 2018;5(1):9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang P, Duan L, Ou Y, et al. The cerebellum and cognitive neural networks. Front Hum Neurosci. 2023;17:1197459.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmahmann JD. The cerebellum and cognition. Neurosci Lett. 2019;688:62\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong H-H, Qu J, Xiao W-M, et al. Severity of Lesions Involving the Cortical Cholinergic Pathways May Be Associated With Cognitive Impairment in Subacute Ischemic Stroke. Front Neurol. 2021;12:606897.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQu J-F, Chen Y-K, Luo G-P et al. Severe Lesions Involving Cortical Cholinergic Pathways Predict Poorer Functional Outcome in Acute Ischemic Stroke. Stroke. 2018;49(12):2983\u0026ndash;2989.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerthier ML, Green C, Higueras C, et al. A randomized, placebo-controlled study of donepezil in poststroke aphasia. Neurology. 2006;67(9):1687\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrase S, L\u0026ouml;ffler F, Hosp JA. Enhancing Post-Stroke Rehabilitation and Preventing Exo-Focal Dopaminergic Degeneration in Rats\u0026mdash;A Role for Substance P. IJMS. 2022;23(7):3848.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVilla M, Mart\u0026iacute;nez-Vega M, Del Pozo A, et al. The Role of the Dopamine System in Post-Stroke Mood Disorders in Newborn Rats. IJMS. 2023;24(4):3229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong M-K, Cramer SC. Dopaminergic Pathways in Neuroplasticity After Stroke and Vagus Nerve Stimulation. Stroke. 2025;56(6):1625\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaxena S, Hillis AE. An update on medications and noninvasive brain stimulation to augment language rehabilitation in post-stroke aphasia. Expert Rev Neurother. 2017;17(11):1091\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelaina W-B, Curtis S, Natarajan R, et al. A Double-Blind, Placebo-Controlled Study of the Use of Amphetamine in the Treatment of Aphasia. Stroke. 2001;32(9):2093\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStockbridge MD, Fridriksson J, Sen S, et al. Protocol for Escitalopram and Language Intervention for Subacute Aphasia (ELISA): A randomized, double blind, placebo-controlled trial. PLoS ONE. 2021;16(12):e0261474.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHillis AE, Beh YY, Sebastian R, et al. Predicting recovery in acute poststroke aphasia. Ann Neurol. 2018;83(3):612\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarhart-Harris RL, Leech R, Erritzoe D, et al. Functional Connectivity Measures After Psilocybin Inform a Novel Hypothesis of Early Psychosis. Schizophr Bull. 2013;39(6):1343\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrews-Hanna JR, Reidler JS, Sepulcre J, et al. Functional-Anatomic Fractionation of the Brain\u0026rsquo;s Default Network. Neuron. 2010;65(4):550\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Wang L, Gao F, et al. Exploring cerebellar transcranial magnetic stimulation in post-stroke limb dysfunction rehabilitation: a narrative review. Front Neurosci. 2025;19:1405637.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCopland DA, Brownsett S, Iyer K, Angwin AJ. Corticostriatal Regulation of Language Functions. Neuropsychol Rev. 2021;31(3):472\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePremi E, Dukart J, Mattioli I, et al. Unravelling neurotransmitters impairment in primary progressive aphasias. Hum Brain Mapp. 2023;44(6):2245\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGil-Navarro S, Lome\u0026ntilde;a F, Cot A, et al. Decreased striatal dopamine transporter uptake in the non‐fluent/agrammatic variant of primary progressive aphasia. Euro J Neurol. 2013;20(11):1459.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimonyan K, Herscovitch P, Horwitz B. Speech-induced striatal dopamine release is left lateralized and coupled to functional striatal circuits in healthy humans: A combined PET, fMRI and DTI study. NeuroImage. 2013;70:21\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerthier ML, Pulverm\u0026uuml;ller F, D\u0026aacute;vila G, et al. Drug Therapy of Post-Stroke Aphasia: A Review of Current Evidence. Neuropsychol Rev. 2011;21(3):302\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiran S, Thompson CK. Neuroplasticity of Language Networks in Aphasia: Advances, Updates, and Future Challenges. Front Neurol. 2019;10:295.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrefkes C, Fink GR. Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain. 2011;134(5):1264\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitti E, Hillis AE. Treatment of post-stroke aphasia: A narrative review for stroke neurologists. Int J Stroke. 2021;16(9):1002\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerzog R, Rosas FE, Whelan R, et al. Genuine high-order interactions in brain networks and neurodegeneration. Neurobiol Dis. 2022;175:105918.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao P, Wen G, Liu X, et al. Modeling the dynamic brain network representation for autism spectrum disorder diagnosis. Med Biol Eng Comput. 2022;60(7):1897\u0026ndash;913.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"post-stroke aphasia, magnetic resonance imaging, structural-functional coupling, neurotransmitter, language recovery","lastPublishedDoi":"10.21203/rs.3.rs-8131643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8131643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: Although alterations in brain structure and function have been implicated in both post-stroke aphasia (PSA) and motor deficits, how structural-functional coupling (SFC) is affected in stroke patients with and without aphasia (nonPSA) remains unclear. This study aimed to characterize SFC alterations in PSA and examine their associations with neurotransmitter systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Fifty-two patients with left-hemisphere stroke (PSA: n = 29; nonPSA: n = 23) and 19 demographically matched healthy controls were enrolled. Language function in PSA patients was assessed approximately 28 days and 3 months post-stroke using the Western Aphasia Battery (WAB). All participants underwent T1-weighted and resting-state functional MRI at baseline. Region-wise SFC was computed as the correlation between gray matter volume and the fractional amplitude of low-frequency fluctuations (fALFF). Group differences were assessed using one-way analyses of covariance. Relationships among SFC alterations, language outcomes, and lesion-derived neurotransmitter-informed network damage were further evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Group comparisons revealed distinct SFC alterations associated with motor and language deficits in PSA. Language-specific decoupling was observed in the contralesional putamen, middle temporal pole, and posterior cerebellum, whereasmotor-specific decoupling occurred in the contralesional prefrontal cortex, superior parietal lobule, precuneus, and inferior/superior temporal cortices, extending into cerebellar regions. Both domains shared a common decoupling pattern in the ipsilesional posterior cerebellum. Reduced SFC in the contralesional middle temporal pole correlated with poorer spontaneous speech performance. Compared with nonPSA, PSA patients exhibited greater lesion load, network disconnections, and pre-/post-synaptic disruption ratio associated with poorer aphasia recovery relative to nonPSA in several neurotransmitter systems, especially serotonergic system. Mediation analysis further indicated that SFC in the contralesional caudate partially mediated the relationship between neurotransmitter disruption and aphasia severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur findings suggest distinct and shared structural-functional decoupling for language and motor dysfunctions in the patients with aphasia after stroke, which was associated with specific neurotransmitter systems.\u003c/p\u003e","manuscriptTitle":"Neurotransmitter Systems Underlie Structure-Function Decoupling and Recovery in Post- Stroke Aphasia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 01:27:06","doi":"10.21203/rs.3.rs-8131643/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"269275077872481455337660312805501096369","date":"2026-05-14T20:55:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T15:07:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164223474726199278993645916060524861039","date":"2025-12-25T02:38:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266783821649480344665835029425765839648","date":"2025-12-24T19:47:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-22T14:08:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T13:32:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-18T13:31:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2025-11-17T06:06:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e1f75e09-86da-4dc7-acc3-9b945e82e3c2","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"269275077872481455337660312805501096369","date":"2026-05-14T20:55:11+00:00","index":61,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T01:27:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 01:27:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8131643","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8131643","identity":"rs-8131643","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}