Lateralization-specific motor network reorganization in pontine infarction revealed by resting-state functional connectivity magnetic resonance imaging

Introduction

Accounting for 7% of all ischemic strokes, pontine infarction (PI) is the most frequently encountered type of posterior circulation infarction (1-3). Isolated PI accounts for approximately 15% of posterior circulation cerebral infarction cases (4). Brainstem infarction presents with acute onset and is marked by severe clinical manifestations and a high incidence of disability. Most patients develop varying degrees of persistent functional impairment, including both motor and cognitive deficits (5,6), representing a substantial health burden in middle-aged and older adult populations. The most common initial clinical manifestation of PI is typically isolated motor hemiplegia, resulting from corticospinal tract involvement within the pontine region (7-9). Despite comparable lesion topography and volume across cases of PI, significant variability exists in motor recovery outcomes among patients, with the pathophysiological mechanisms remaining poorly understood. Recent studies into post–brainstem infarction neural reorganization have predominantly examined clinical symptomatology evaluation and structural neuroimaging features (10-15). Emerging evidence from contemporary neuroscience research has indicated that aberrant neural activity constitutes a fundamental pathophysiological mechanism underlying poststroke cerebral reorganization, with functional disruptions manifesting not only within the ischemic core and penumbral regions but also extending to the remote brain networks exhibiting functional connectivity (FC) with the primary lesion site (16-18). Similar structural damage has been demonstrated to lead to varying degrees of functional impairment. Patients may exhibit multiple deficits during the acute phase, which cannot be readily attributed solely to the direct effects of the focal lesion (19,20). Consequently, elucidating the neurophysiological mechanisms underlying motor recovery is critical to clarifying the development and optimization of poststroke rehabilitation paradigms.

The brain comprises an intricate web of regions that are linked by both functional interactions and structural pathways (21). These subnetworks operate both independently and interdependently, collectively sustaining brain activity. Over the past decade, advanced neuroimaging modalities, particularly resting-state FC (rsFC) analysis, have emerged as powerful tools for investigating temporal synchronization patterns across distributed neural networks (22). These approaches have been proven particularly valuable for analyzing functional brain networks (23). Using rsFC analysis based on regions of interest (ROIs), studies have demonstrated that focal brain lesions can induce connectivity-based alterations in structurally intact brain regions distal to the lesion site. Empirical support for this phenomenon has been generated from research on motor (24-26) and attentional networks (27,28). Furthermore, there is a correlation between connectivity alterations and behavioral recovery in the poststroke rehabilitation phase (24,26-28). In the specific context of pontine and brainstem stroke, several resting-state functional magnetic resonance imaging (rs-fMRI) studies have reported FC alterations within the default mode network (DMN), sensorimotor network (SMN), and thalamo-cortical circuits. For instance, Chen et al. identified distinct patterns of FC damage in the DMN and SMN in patients with pontine stroke during the early chronic phase (29). Zhang et al. and Geng et al. further demonstrated widespread static and dynamic functional network connectivity impairments in patients with acute brainstem ischemic stroke, with alterations spanning the DMN, executive control network (ECN), and cerebellum network (30,31). Additionally, Wang et al. reported longitudinal changes in sensorimotor FC that could differentiate between thalamic and PI, providing insights into the location-specific neural reorganization following subcortical stroke (32). However, the research on other neural networks, particularly those implicated in motor execution, remains relatively underdeveloped compared to other domains of brain network research. Therefore, we conducted a study focused on patients with PI—the most prevalent subtype of subcortical stroke affecting motor pathways (33). Using ROI-based rs-fMRI, we analyzed functional alterations within the motor execution network associated with pontine infarcts exhibiting comparable lesion topography and volume, all involving motor pathway disruption. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0340/rc).

Methods

Participants

The cross-sectional study enrolled 31 untreated patients with magnetic resonance imaging (MRI)-confirmed isolated PI [19 patients with left PI (LPI) and 12 patients with right PI (RPI)] who presented with unilateral limb weakness during their initial visit to the Emergency Department of Beijing Friendship Hospital, Capital Medical University between July 2018 and January 2020. The sample size was determined by the availability of eligible patients during the study period. A matched control group (n=31) of healthy, right-handed individuals was randomly selected from the local community, with matching based on age, gender, and education. No complex sampling methods (e.g., stratification or clustering) were used. The inclusion criteria were as follows: (I) first-onset PI as confirmed by MRI and unilateral limb weakness; (II) PI characterized by a single pontine lesion affecting the corticospinal tract; (III) right-handedness; and (IV) stable clinical conditions and good compliance, willingness to undergo brain fMRI scanning, and provision of written informed consent. The exclusion criteria were the following: (I) a history of left-handedness prior to stroke; (II) recurrent stroke confirmed through clinical history and MRI evaluation; (III) extension of the lesion to encompass the cerebral cortex; (IV) intellectual disability; (V) any other brain abnormalities on MR images; (VI) any previous head trauma, neurologic disorders or psychosurgery and any substantial physical illness; (VII) modified Fazekas scale score for white-matter hyperintensities greater than 1 (34); (VIII) poor imaging quality; and (IX) head motion exceeding 3 mm in translation or 3° in rotation during rs-fMRI acquisition, failure to complete the MRI scanning procedure, or inability to undergo the National Institute of Health Stroke Scale (NIHSS) assessment. Fulfillment of any one exclusion criterion was sufficient for a patient to be excluded from the study. Based on these criteria, two participants were removed from the study due to the following reasons: (I) two or more stroke lesions and lack of corticospinal tract involvement and (II) significant head movement (with translation >3 mm or rotation >3°) throughout MRI acquisition. Initially, 33 patients with acute PI were screened for eligibility; 2 were excluded as described, and the remaining 31 patients were included in the final analysis. For these 31 participants, no other data were missing for any variable of interest. All patients with PI were right-handed; however, stroke lesions varied in laterality, occurring in either the left or the right pons. To control for potential hemispheric lateralization effects, study participants were stratified and analyzed separately based on lesion laterality (left vs. right pons involvement). No history of psychiatric illness was reported in the first-degree relatives of the healthy control participants, who were screened with the same set of exclusion criteria as that applied for the study participants. The NIHSS was used to evaluate motor function in the patients with stroke. All participants completed the NIHSS assessment after the scale was explained in detail by healthcare professionals. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (No. 2020-P2-063-01). Informed consent was provided by all participants. The stroke lesion volumes of patients with PI during the acute phase were manually delineated via MRIcron software (www.nitrc.org/projects/mricron) based on acute diffusion-weighted imaging (DWI). All lesion masks were visually inspected and validated by two experienced radiologists (including the first author, X.C.) to ensure accuracy and consistency. Any discrepancies were resolved through discussion. The lesion masks of all stroke patients were normalized to the Montreal Neurological Institute (MNI) space and subsequently overlaid onto a standardized template to create lesion distribution probability maps.

MRI data acquisition

MRI scans were acquired with a MAGNETOM Prisma 3.0-Tesla scanner (Siemens Healthineers, Erlangen, Germany) equipped with a 64-channel head coil. For the PI group, imaging was conducted at 1-week postinfarction. The normal control (NC) participants underwent a single scanning session under identical parameters. All participants were instructed to remain motionless, keep their eyes closed, and avoid focusing on any specific thoughts. To minimize head movement and reduce scanner noise, comfortable foam padding and earplugs were provided. rs-fMRI was conducted with a gradient-echo echo-planar imaging (EPI) sequence with the following parameters: repetition time/echo time (TR/TE) =2,000/30 ms, field of view (FOV) =224×224 mm², flip angle =90°, matrix =64×64, slice thickness =3.5 mm, slice number =33, voxel size =3.5×3.5×3.5 mm³, and number of time points =240. DWI sequences were obtained in the axial plane with the following settings: TR/TE =4,800/81.7 ms, FOV = 256×256 mm², slice thickness =6.0 mm, and number of slices =20.

Clinical evaluation

The NIHSS, a 15-item assessment with a possible score range of 0 to 42, was employed to assess clinical stroke severity. Higher NIHSS scores indicate greater functional impairment (35).

Data analysis

The preprocessing and analysis of rs-fMRI data were carried out with a combination of Statistical Parametric Mapping 8 (http://www.fl.ion.ucl.ac.uk/spm; Wellcome Trust Centre for Neuroimaging, University College London, London, UK) and the Data Processing Assistant for Resting-State Functional MR Imaging software version 2.2 (DPARSF; http://www.restfmri.net; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China). The preprocessing pipelines included the following steps: (I) The first 10 volumes were discarded to both allow for scanner signal stabilization and participant acclimatization. (II) Slice-timing correction was performed on the remaining 230 volumes to adjust for temporal differences in slice acquisition. (III) Realignment was conducted to correct for head motion, with exclusion thresholds of 3-mm translation and 3° rotation being applied. One participant exceeding these thresholds was excluded. The mean framewise displacement was calculated to quantify average head motion (36). (IV) The images were spatially normalized to the MNI template, with resampling to a 3×3×3 mm³ voxel size. (V) Spatial smoothing was applied with a Gaussian kernel at a 6-mm full-width at half maximum (FWHM). (VI) Temporal band-pass filtering (0.01< f <0.08 Hz) was implemented to attenuate low-frequency drift and high-frequency physiological noise (37). (VII) The linear trend was then removed. We employed a seed-based FC approach to examine temporal variations in FC values among patients with PI. Seed regions for the subsequent analysis were defined according to the motor execution network-associated ROIs presented in Table 1 (17). DPARSF software was used to generate spherical ROIs with a 6-mm radius around each seed point. Pearson correlation coefficients were computed between the mean time series of each ROI and every voxel in the whole-brain gray matter, and these coefficients were then used to generate the seed-based FC maps. Subsequently, these FC maps were converted to Fisher’s z-scores for group-level statistical analysis.

Statistical analysis

The Shapiro-Wilk normality test was applied to examine clinical data distributions. We performed a single-factor analysis of variance (ANOVA) with SPSS 25.0 software (IBM Corp., Armonk, NY, USA) to analyze group differences in clinical parameters, such as age, educational attainment, and head motion. Gender comparison among three groups was conducted via the chi-squared test (χ²) analysis. A two-sample t-test was used to compare acute-phase differences in NIHSS scores and lesion volumes between the PI subgroups. All analyses employed two-tailed tests with a significance threshold of P<0.05. As no significant differences emerged for any of the demographic and clinical variables assessed (age, years of education, head motion, gender, acute-phase lesion volume, or NIHSS scores), they were included as covariates of no interest in the subsequent FC analyses. This step ensured that the observed effects were not confounded by variability in these parameters. Differences in FC among the LPI, RPI, and NC groups were assessed via one-way ANOVA, which was followed by post hoc pairwise comparisons with Bonferroni correction [cluster-level family-wise error (FWE)-corrected P<0.05]. Pearson correlation analyses were performed to assess the relationships between: (I) FC values of abnormal regions and NIHSS scores; and (II) FC values of abnormal regions and acute-phase lesion volumes, in the LPI and RPI subgroups (P<0.05). (The abnormal regions were extracted separately using the Resting State fMRI Data Analysis Toolkit; http://resting-fmri.sourceforge.net). To clarify the relationship FC and motor deficit, we also determined the NIHSS motor subscores (upper and lower extremity items) for each patient in the subgroups (LPI and RPI groups). The Pearson correlation coefficients between the FC values of the abnormal regions and these motor subscores (P<0.05) were then calculated. No sensitivity analyses (e.g., varying of statistical thresholds or exclusion of extreme values) were performed.

Results

Demographic and clinical evaluation

The demographic and clinical characteristics of the participants in the LPI, RPI, and NC groups are summarized in Table 2. All groups were comparable in terms of age (F=0.075; P’=0.93), years of education (F=0.018; P’=0.98), head motion (F=0.124; P’=0.88), and gender distribution (χ²=2.22; P’=0.33). Furthermore, between the LPI and RPI groups, no significant differences were found in acute-phase lesion volume (P=0.99) or NIHSS scores (P=0.47). The acute-phase lesion probability maps for these two patient groups are compared in Figure 1. Given the relatively small sample size, we did not further stratify patients based on detailed topographic features such as axial topography (ventral vs. tegmental pons) or rostrocaudal level. All included patients had DWI-confirmed corticospinal tract involvement, which was the primary focus of our study.

Figure 1 Lesion distribution maps in the LPI and RPI groups. The color bar represents the lesion probability. LH, left hemisphere; LPI, left pontine infarction; RH, right hemisphere; RPI, right pontine infarction.

Two patients were excluded from the study (one with multiple pontine lesions and lack of corticospinal tract involvement and one with excessive head motion). For the remaining 31 participants included in the analysis, there were no missing data for any variable of interest.

FC of PI subgroups and the NC group

Using the motor execution network as the seed ROI (17), we further examined the FC alterations between the PI subgroups and the NC group.

Increased FC

In the LPI group, with the left thalamus (LTh) serving as the seed ROI, patients exhibited significantly increased FC in the left inferior temporal gyrus (Temporal_Inf_L), right dorsolateral superior frontal gyrus (Frontal_Sup_R), and right precuneus (Precuneus_R). Significant differences in FC were observed between the right basal ganglia (RBG) and the right supplementary motor area (Supp_Motor_Area_R), right dorsolateral superior frontal gyrus (Frontal_Sup_R), and right precentral gyrus (Precentral_R). In addition, elevated FC was found between the cerebellum and the right thalamus (RTh) [thresholded at voxel-level P<0.005 (uncorrected) with cluster-level FWE-corrected P<0.05] (Table 3 and Figure 2A).

Figure 2 Comparison of functional connectivity alterations between left and right pontine infarction groups. The yellow balls represent the seed ROI, and the red balls represent brain regions with FC changes with the seed ROI. The color of the line indicates the z-scored FC values. (A) FC changes in the LPI group. (B) FC changes in the RPI group. Cingulum_Post_L, left posterior cingulate gyrus; FC, functional connectivity; LAICb, left anterior inferior cerebellum; LH, left hemisphere; Lingual_R, right lingual gyrus; LPI, left pontine infarction; LTh, left thalamus; Occipital_Mid_L, left middle occipital gyrus; Precentral_R, right precentral gyrus; Precuneus_L, left precuneus; RBG, right basal ganglia; RH, right hemisphere; ROI, region of interest; RPCG, right postcentral gyrus; RPI, right pontine infarction; RTh, right thalamus; Supp_Motor_Area_L, left supplementary motor area.

In the RPI group, increased FC was observed between the left precuneus (Precuneus_L) and LTh. With the RBG serving as the seed ROI, elevated FC was found with the Precentral_R and left supplementary motor area (Supp_Motor_Area_L). Additionally, enhanced FC was observed between the left posterior cingulate cortex (Cingulum_Post_L) and RTh [thresholded at voxel-level P<0.005 (uncorrected) with cluster-level FWE-corrected P<0.05] (Table 4 and Figure 2B).

Decreased FC

In the LPI group, reduced FC was observed between the Precentral_R and Supp_Motor_Area_R. Using the right ventral premotor cortex (RPMv) as the seed ROI, we observed lower FC in the right orbital part of the inferior frontal gyrus (Frontal_Inf_Orb_R), left middle occipital gyrus (Occipital_Mid_L), and left postcentral gyrus (Postcentral_L). Concomitantly, significant FC differences were identified between the right superior occipital gyrus (Occipital_Sup_R), and right postcentral gyrus (Postcentral_R) (thresholded at voxel-level P<0.005 [uncorrected with cluster-level FWE-corrected P<0.05) (Table 3 and Figure 2A).

Meanwhile, in the RPI group, decreased FC was observed between the right lingual gyrus (Lingual_R) and Postcentral_R. Concurrently, divergent FC patterns were observed between the Occipital_Mid_L and left anterior inferior cerebellum [thresholded at voxel-level P<0.005 (uncorrected) with cluster-level FWE-corrected P<0.05] (Table 4 and Figure 2B).

Correlation with behavioral function

The relationships between acute-phase lesion volumes, NIHSS scores, and FC changes are depicted in Figure 3. A statistically significant positive correlation was observed between the z-scored FC of the Frontal_Sup_R and acute lesion volumes in patients with LPI (r=0.575; P=0.012). In both the LPI and RPI groups, we found no significant correlation between FC values and NIHSS total scores (P>0.05). Furthermore, when we examined the NIHSS motor subscores (upper and lower extremity items) separately, no significant correlations with FC values were identified in either group (all P values >0.05).

Figure 3 Correlation between FC values and acute‑phase lesion volumes in the LPI group. (A) The yellow ball represents the seed ROI (LTh), and the red ball represents a brain region showing significantly altered FC with the seed (Frontal_Sup_R). (B) A statistically significant positive correlation was observed between the z‑scored FC of the Frontal_Sup_R and acute lesion volumes (r=0.575; P=0.012). FC, functional connectivity; Frontal_Sup_R, right dorsolateral superior frontal gyrus; LH, left hemisphere; LPI, left pontine infarction; LTh, left thalamus; RH, right hemisphere; ROI, region of interest.

All correlation analyses reported above were unadjusted, and no confounder-adjusted correlation coefficients were calculated.

Discussion

In our study, ROI-based rs-fMRI was used to examine poststroke alterations in the functionality of the motor execution network in patients with subcortical motor pathway involvement. Our findings are broadly consistent with and extend prior rs-fMRI studies in pontine and brainstem stroke. Specifically, Chen et al. reported that patients with pontine stroke exhibited decreased FC in the left medial prefrontal gyrus of the anterior DMN, the Precuneus_R and right posterior cingulate cortex (PCC) of the posterior DMN, and left middle cingulate gyrus of the SMN (29). Meanwhile, Zhang et al. found widespread static and dynamic functional network connectivity impairment in patients with acute ischemic stroke of the brainstem, particularly involving the DMN-ECN and DMN-visual network connections (30). Geng et al. further discovered altered FC within the DMN, ECN, salience network, auditory network, and cerebellar network, with correlations between functional disconnection and upper limb dysfunction (31). Our study complements these findings by focusing specifically on lateralization-specific reorganization within the motor execution network—an aspect that has received relatively less research attention. Our findings suggest that the neural mechanism underlying motor dysfunction after PI may represent a dynamic imbalance in multinetwork early adaptive changes. Crucially, lesion laterality may differentially shape distinct neurocompensatory pathways. In patients with comparable infarct volumes, heterogeneous motor outcomes may be associated with differential patterns of functional alterations within the motor execution network, DMN, and frontoparietal control network (FPN). Specifically, lesion laterality might drive divergent neural resource allocation strategies: LPIs primarily show enhanced recruitment of resources from the right dorsolateral prefrontal cortex (DLPFC), whereas right-sided lesions appear to rely more on compensatory resources from the DMN.

Lateralized early network alterations of motor networks

In patients with LPI (right hemiparesis), enhanced FC between the right DLPFC and thalamus was observed, coupled with strengthening of the ipsilateral basal ganglia-supplementary motor area (SMA) pathways. Conversely, connectivity attenuation primarily occurred within the ipsilateral motor cortex (e.g., between Precentral_R and SMA) and between the PMv and sensory cortices. These observations may indicate that left hemispheric damage leads to preferential recruitment of right prefrontal cognitive resources (e.g., for motor sequence planning), with cortico–basal ganglia automated motor execution pathways potentially serving early compensatory responses (38).

Patients with RPI (left hemiparesis) exhibited increased precuneus-thalamic connectivity but deficient FPN activation, suggesting possible compensatory spatial remapping via the DMN due to impaired attentional control (39-41).

Thalamo-basal ganglia hub function

The thalamus serves as a pivotal integrative hub for sensorimotor and cognitive processing, dynamically reconstituting neural pathways postinjury. Its dorsomedial, ventral anterior, and pulvinar nuclei form multimodal relay networks that mediate cross-hemispheric coordination and motivation maintenance (42). The cortico-thalamic motor loop integrates thalamic and cortical activity through a defined neural pathway. This circuit originates from projections of the primary motor cortex (M1) and somatosensory cortex (S1) to the basal ganglia. Processed motor information is then relayed from the basal ganglia to the motor thalamus (ventral anterior/ventral lateral nuclei), ultimately projecting back to the M1/S1, thus forming a closed-loop regulatory system (43). Our findings suggest that the thalamus may serve as a pivotal hub: Left-sided lesions may enhance thalamo-bilateral cerebellar connections to facilitate interhemispheric coordination, a mechanism neuroanatomically consistent with the crossed cerebellar innervation principle. Meanwhile, right-sided lesions may strengthen thalamo-contralateral PCC coupling, potentially recruiting the limbic-motivational circuit to sustain goal-directed behavior persistence (44).

The basal ganglia appear to demonstrate robust cross-hemispheric early adaptive capabilities following PI. Specifically, left pontine lesions may activate ipsilateral SMA pathways through cortico-striato-pallido-thalamic loops, thereby potentially optimizing motor sequence planning and initiation. Conversely, right pontine lesions may recruit contralateral SMA resources via transcallosal anterior commissure fibers, which may enhance spatial motor coordination and error correction. Taken together, these findings suggest a potential plastic role of basal ganglia circuitry in functional motor recovery (45).

Correlation of altered FC across groups

In patients with LPI, the FC between the right DLPFC and LTh exhibits a significant positive correlation with infarct volume (r=0.575; P=0.012). This finding may reflect two fundamental neuroadaptive mechanisms: larger lesions may cause greater disruption of white-matter fibers, potentially necessitating enhanced recruitment of the right DLPFC-thalamic pathway to compensate for impaired left hemispheric motor planning (46). Its strengthened connectivity with the thalamus may help optimize motor sequence integration, suggesting this region could serve as a “cognitive-motor hub” in unilateral injury compensation (47). We exclusively observed this phenomenon in the LPI group, which is consistent with the notion that left hemispheric damage preferentially recruits contralateral prefrontal resources (47). We note that this correlation should be interpreted with caution given the cross-sectional design, and whether it represents a true compensatory capacity or merely an acute epiphenomenon remains to be determined by longitudinal validation.

Notably, in both the LPI and RPI groups, we did not detect significant correlations between FC values and NIHSS total scores or motor subscores. Several factors may explain this absence of correlation. First, the NIHSS was originally designed for anterior circulation stroke and has limited sensitivity for detecting pure motor deficits in brainstem stroke, particularly when cortical functions remain intact. Second, the cross-sectional design of our study captured only acute-phase FC alterations, which may serve as early neural signals of recovery potential rather than direct correlates of concurrent clinical severity. Future longitudinal studies with more sensitive motor scales (e.g., Fugl-Meyer Assessment) are needed to examine these possibilities.

Comparison with previous studies

In the comparison of our findings with the literature, several similarities and differences deserve mention. Similar to Chen et al.’s observation that pontine stroke affects both DMN and SMN connectivity (29), we also found alterations involving the DMN, particularly in patients with RPI. However, our study further suggests that motor execution network alterations in PI may be lateralization-specific—a distinction not explicitly examined in previous research. Consistent with Wang et al.’s finding that sensorimotor functional reorganization differs between thalamic and PI (32), our results suggest the possibility that even within PI, lesion laterality may be associated with distinct patterns of network changes. Additionally, in line with Geng et al.’s report of correlations between functional disconnection and motor impairment in brainstem stroke (31), we observed that FC alterations correlated with lesion volume but not with NIHSS scores—which may reflect the limited sensitivity of NIHSS for patients with brainstem stroke. Taken together, these comparisons suggest that our findings both corroborate and extend the existing literature by highlighting a potential role of lesion laterality in influencing network alterations after PI. However, given the cross-sectional design of our study, these interpretations remain preliminary and require longitudinal validation.

This study involved several limitations that should be acknowledged. First, the stringent inclusion criteria resulted in a relatively small sample size, which reduced statistical power. Moreover, due to this, we could not produce a detailed neuroradiological characterization of pontine lesions beyond lesion volume and laterality. Our study specifically focused on patients with PI involving the corticospinal tract, as confirmed by DWI. However, we were unable to further stratify these lesions according to axial topography (ventral vs. tegmental pons) or rostrocaudal level (upper vs. lower pons), primarily because the sample size was too small to allow for meaningful subgroup analyses based on these finer anatomical subdivisions. Consequently, these unmeasured anatomical variables may introduce heterogeneity that could influence FC patterns independently of lesion laterality. Previous studies have demonstrated that these topographic features significantly influence clinical presentation and recovery patterns (13,33,48,49). As an alternative, we presented lesion distribution as probability maps (Figure 1) to visually convey the overall topographic pattern within the constraints of our sample size. Therefore, future investigations should enroll PI cases with lesions at different axial levels to further elucidate the mechanisms of motor recovery. Second, functional recovery is a long-term process, and the lack of longitudinal follow-up data may obscure true recovery trajectories. This is a particularly important limitation because our cross-sectional design captures only a single time point (~1 week poststroke); therefore, all conclusions related to reorganization or compensatory processes remain speculative. Additionally, extending the follow-up duration is needed to validate the relationships between FC and long-term outcomes. Third, while we employed ROI-based rs-fMRI to analyze motor execution network alterations, we did not incorporate diffusion-based structural data, such as in diffusion tensor imaging (DTI). Given that PI primarily disrupts white-matter tracts (especially the corticospinal tract), the absence of DTI limits our ability to directly link structural disconnection to the observed FC changes. Previous studies have shown that DTI-derived metrics such as fractional anisotropy can detect Wallerian degeneration along the pyramidal tract after brainstem stroke and that the degree of tract damage correlates with motor outcomes (10,50). However, DTI provides microstructural information but cannot capture functional network dynamics. Our findings therefore primarily reflect network-level functional alterations. Future studies should adopt multimodal neuroimaging approaches—for example, by combining DTI tractography with rs-fMRI—to comprehensively characterize how structural disconnection may drive functional reorganization after PI. This represents an important direction for our future research. Fourth, due to the spatial resolution constraints of conventional rs-fMRI (3-Tesla MRI with a voxel size of 3.5×3.5×3.5 mm³), we were unable to reliably differentiate individual thalamic subnuclei. Consequently, the thalamus was included as a single ROI within the motor execution network. We acknowledge that the thalamus is functionally heterogeneous, and future studies incorporating high-field MRI (e.g., at 7 Tesla) or diffusion-based thalamic parcellation are needed to examine the specific contributions of distinct thalamic subnuclei (e.g., ventral lateral and ventral anterior) to poststroke motor reorganization. Given the relatively small sample size and the recruitment of patients from a single center in China, the generalizability of our findings to other populations (e.g., different ethnic groups or patients with more severe neurological deficits) may be limited. Future studies with larger, more diverse cohorts are needed to confirm the external validity of these results.

Conclusions

Our findings suggest that motor functional outcomes following PI may be influenced by a dynamic imbalance in the early alterations of multiple brain networks and potentially driven by lesion laterality. Specifically, patients with left-sided lesions primarily exhibited recruitment of cognitive resources from the right prefrontal cortex, while those with right-sided lesions tended to exhibit early spatial changes within the DMN for compensation. The thalamus and basal ganglia may act as a central hub, coordinating motor and motivational functions through transhemispheric pathway adaptations. Altered FC—such as that within the DLPFC-thalamic pathway—might reflect neural compensatory potential and thus serve as a target for individualized rehabilitation. However, longitudinal studies are required to determine whether these early network alterations actually translate into long-term functional recovery.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0340/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0340/dss

Funding: This work was supported by the Beijing Natural Science Foundation (No. 7242043 to P.Q.) and the Beijing Scholars Program (No. [2015] 160 to Z.W.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0340/coif). All authors report that this work was supported by the Beijing Natural Science Foundation (No. 7242043 to P.Q.) and the Beijing Scholars Program (No. [2015] 160 to Z.W.). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (No. 2020-P2-063-01) and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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