Stroke remains a leading cause of human disability (Feigin et al., 2025). Stroke-related deficits commonly include sensorimotor and non-motor cognitive impairments, with varying severity, and overlapping syndromes (Gittins et al., 2021; Mullick et al., 2015). Upper extremity (UE) motor impairments were reported to occur in 75.5% (Rathore et al., 2002), and cognitive impairments in 60% of stroke survivors (El Husseini et al., 2023). Such deficits have been linked to damage in specific brain regions (Bisogno et al., 2021).

Neuroimaging techniques have linked stroke-related motor impairments to injury in several brain regions. For example, the motor system is described as having “elegant” organization, and motor deficits are related to the integrity of specific structures such as the corticospinal tract (CST). Measures of injury to the CST are related to hand impairment, and its recovery and have been recommended for use as a biomarker in clinical trials (Lin et al., 2019; Boyd et al., 2017). Furthermore, it was recently established that structural integrity of the CST had the highest accuracy in terms of sensitivity and specificity for discriminating between stroke subjects with and without arm weakness, showing an AUC of 0.9 (Yoo et al., 2023). Similarly, finger proprioception, (Ingemanson et al., 2019a), and its improvement with therapy, (Ingemanson et al., 2019b) have been related to sensory system injury using a measure that includes damage to the thalamocortical sensory tract (TST).

More complex motor tasks, such as those that require sustained attention, also rely on additional cognitive processes, including motor planning, initiation, attention, and working memory (Roby-Brami et al., 2021; Mullick et al., 2015). These cognitive processes rely on interactions with additional brain networks, and so deficit severity for motor tasks with higher cognitive demand may be expected to correlate with degree of injury to multiple neural systems, motor and non-motor. Indeed, several studies have reported on the overlap between motor and cognitive functions after stroke (Gittins et al., 2021; Einstad et al., 2021; Lin et al., 2021). Neuroanatomical correlates of cognitive impairments, such as attentional deficits, involve large-scale multimodal attention networks distinct from sensorimotor networks (Murakami et al., 2014). Few studies have examined multiple behaviors and neural systems in parallel, though such an approach may be useful to understand the basis for post-stroke deficits in cognitively demanding sensorimotor tasks.

One prior study found distinct sites of brain injury when comparing grip strength, an elementary task low in attentional demand, to the Box and Blocks Test (BBT), a complex task high in attentional demand. Grip strength was associated with injury to sensorimotor structures, such as the peri—Rolandic cortex and CST, whereas BBT performance was associated with injury to more diffuse brain regions beyond the sensorimotor cortex, including orbitofrontal cortex and the dorsal anterior insula, a region known to be important for complex executive function (Lin et al., 2021).

Together, these studies suggest that deficits in elementary behaviors are related to injury to a restricted set of regional sensorimotor brain structures, while deficits in more complex behaviors that require motor and cognitive activity are related to more diffuse forms of brain injury that cause structural disconnection between networks. The current study therefore hypothesized that impairments in elementary UE sensorimotor tasks are correlated with injury to specific brain sensorimotor structures such as the CST, while deficits in complex motor and cognitive tasks are additionally related to structural disconnection between brain networks.

Adults with stroke onset in the past 30 days were recruited during admission to a single inpatient rehabilitation facility. Exclusion criteria included a major active disease that affected UE function (apart from the index stroke) or deficits in cognition or communication that would interfere with study procedures. All subjects signed informed consent, and the study was planned in adherence with the Declaration of Helsinki and approved by the University of California, Los Angeles Institutional Review Board (IRB#21-000936). Age, sex, days after stroke, and the paretic side were collected for each study participant as baseline demographics. All outcomes in this study were restricted to baseline assessments to allow a cross-sectional analysis with a minimal temporal gap between impairment measures and neuroimaging data.

A total of 60 patients with subacute stroke were enrolled from January 2022 to December 2024. Lesion-related neuroimaging measures were available for 55 of these; in five subjects, the stroke lesion could not be identified or processed due to low resolution of the neuroimages or timing of the scan. Participants had mean age 70.36 and were 42% females, with other characteristics appearing in Table 1. The mean lesion volume was 29.45 ml (range 0.096–354.6 ml). An overlap map of the 55 stroke lesions, all projected to the right hemisphere for the VLSM, appears in Figure 1A. Injury to the CST emanating from M1 was present in 70.7%; CST from PMd, 60%; and CST from PMv, in 65.5% of participants. Injury to the TST occurred in 56.4% of patients. The mean and standard deviations of network disconnections as measured by ChaCo scores are presented in Table 2, with the most extensive disconnection found between VN-SMN, VN-VAN, and SMN-FPN.

The present study examined associations that behavioral measures have with brain regional injury and structural network disconnection in patients with subacute ischemic stroke. Consistent with the existing evidence, (Lin et al., 2019; Feng et al., 2015; Yoo et al., 2023; Boyd et al., 2017) measures of elementary UE sensorimotor behavior, such as strength and impairment, were significantly related to extent of CST injury. On the other hand, measures of complex motor and cognitive behaviors (BBT and TMT-A) were explained by the extent of structural disconnections between neural networks. In the current patient cohort, lesion volume did not significantly explain any of the behaviors (after FDR correction). Exploratory LASSO regression examining all clinical and neuroimaging measures found complex patterns of structural network disconnection across behaviors. Taken together, the results support the idea that the integrity of distributed connections across multiple large-scale brain networks is more important to complex motor and cognitive behaviors as compared to elementary sensorimotor behaviors.

For elementary sensorimotor behaviors, injury to specific white matter tracts was associated with deficits in corresponding behaviors. For example, in VLSM analyses, lower FMUE and BBT scores were significantly associated with lesion clusters in the posterior limb of the internal capsule. These findings were replicated when testing for associations between behavioral outcomes, and the percentage of M1CST injury. These results are consistent with a growing body of literature that identifies the CST as a critical neuroimaging biomarker for predicting motor recovery following stroke (Lin et al., 2019; Feng et al., 2015), and such measures of CST lesion load have been found to have high sensitivity, specificity, and positive predictive value compared to other neuroimaging or neurophysiological measures (Feng et al., 2015). These convergent observations underscore the utility of CST injury as a reliable indicator of motor status post-stroke, here validated in a cohort of patients who were an average of 10 days after stroke onset. No such associations were found between CST injury and cognitive or mood measures, underscoring that such biomarkers of sensorimotor system injury are specific to UE sensorimotor status.

On the other hand, complex motor and cognitive deficits were related to structural disconnection between brain networks. Deficits in BBT and TMT-A were significantly related to many network disconnections (Figures 2, 3). Some deficits in complex motor and cognitive behaviors were also related to injury to sensorimotor structures such as CST. The BBT assesses gross hand dexterity across an extended period of time and the TMT-A depends on psychomotor speed and visuospatial attention. Both tests are time-constrained and require the sequential execution of repetitive activities (e.g., grasping and releasing blocks, connecting numbers by lines). Current findings indicate that successful performance on these two tasks depends on the integrity of structural connectivity between multiple distributed brain networks related to attention, visuospatial and motor processing speed, planning, as well as sensorimotor function. For individuals' post-stroke, such tasks often require identifying effective compensatory strategies within the constraints of their motor and/or cognitive status. Moreover, although these tasks do not constitute dual-task paradigms in the classical sense, their demands may better approximate the complexity of everyday activities as compared to tests of elementary sensorimotor behavior.

Deficits in performing complex motor and cognitive tasks thus correspond to disconnection syndromes (Geschwind, 1965a,b) given that they correspond to substantial structural disconnectivity in brain networks. Some differences were seen between structural disconnection patterns, with BBT performance more related to reduced network connectivity between SMN, DAN, VAN, and FPN; and with TMT-A performance more related to reduced network connectivity between DMN, FPN, and LMN. Clinically, it is evident that some individuals can adapt to achieve task goals (through wrist positioning, use of momentum, head movements, or other compensations) while others appear less able to formulate or execute such strategies. For a given patient, this emphasizes the importance of considering the cognitive demands for sensorimotor tasks that they may find difficult. Overall, such results emphasize the importance of cognitive-motor interactions in post-stroke functional performance.

The large number and overlap of network disconnections (e.g., between FPN, DAN, VAN, LMN and DMN) associated with BBT and TMT-A performance are well supported by existing evidence (Lin et al., 2021; Varjacic et al., 2018). Brain areas for which structural integrity has been associated with BBT performance post-stroke include the dorsal anterior insula, which is part of the salience network (VAN) and is important for complex cognitive function (Lin et al., 2021). Reviewing studies that examined neural injury or functional connectivity in healthy subjects, Varjacic et al. found that large-scale networks in prefrontal and parietal cortices mediate TMT performance, consistent with current findings (Figures 2, 3) (Varjacic et al., 2018). Current results support the suggestion that integrity of structural connectivity across large scale neural networks may be a useful biomarker of TMT-A performance that could be examined as a therapeutic target for non-invasive brain stimulation (Dallasta and Marsh, 2024) and here suggest that this remains true in the early subacute phase post-stroke.

The importance of intact connectivity across large neural networks as a means to understand inter-subject differences in stroke-related deficits is supported by a growing body of research. For example, the DMN is inactive during task performance, in contrast with activity in DAN and VAN, and is active during certain cognitive processes such as self-reflection or mind wandering (Menon, 2023). Reduced DMN connectivity and activity after stroke has been linked with both cognitive and motor dysfunction (Tuladhar et al., 2013; Dahms et al., 2024; Lariviere et al., 2018). Wu et al. (2020) reported that synchrony between DMN, and SMN, specifically between the posterior cingulate cortex and the supplementary motor area, and between posterior cingulate cortex and M1, correlated with improvements in arm motor status (FMUE) over the course of rehabilitation and follow up.

Understanding the impact that structural network disconnection has on complex behavioral tasks is critical for a comprehensive understanding of post-stroke recovery, particularly concerning a patient's capacity for activities of daily living. The FPN, for example is important for grasping and prospective action judgments (Geers et al., 2021; Mutha et al., 2014) and showed increased activation after stroke that correlates with motor recovery especially when the CST was injured (Olafson et al., 2022). In this study, most of the FPN disconnections were consistently associated with poorer gross manual dexterity as tested with the BBT and visuospatial processing speed as assessed with the TMT-A. Similarly, the successful execution of complex goal-oriented tasks, such as those assessed by the BBT and TMT-A, critically relies on the coordination of multiple neural networks. The DAN provides top-down control for sustained visuospatial attention, movement planning, and suppression of irrelevant information, while the VAN, or salience network, facilitates the dynamic reorienting of attention, handling of unexpected events and integrates critical sensory feedback, such as the tactile sensation of grasping a block or the sound of a block dropping. The models presented herein suggest that disconnections between VN and VAN might be associated with better FMUE score, while disconnections between VN and FPN explain longer times needed in the TMT-A, and VAN-VAN disconnections were related with worse MoCA scores. Although the current study is only cross-sectional, the results support the value of considering more distributed neural network measures in stroke research, where direct lesion correlates face limitations. A recent study reported significant associations between FMUE changes and DAN functional connectivity during fMRI that were modulated by tDCS, (Salazar et al., 2024) providing evidence of how activity within brain networks can be modulated, and how this is related to behavioral changes. Identifying relevant brain networks or functional nodes that are related to certain behavioral phenotypes (and their recovery over time) may suggest new treatment targets for which the aim is to favorably modify connectivity within, and between networks. Such network connectivity measures might additionally inform stroke clinicians and researchers regarding the basis for specific behavioral deficits, potential for recovery over time, as well as facilitators and barriers to benefit from certain therapies. However, longitudinal and interventional studies are needed to confirm, and validate these findings, which could then inform the development of personalized, optimized rehabilitation therapies.

The current study examined structural brain injury (lesion overlap with canonical brain structures plus VLSM) and structural network disconnections, revealing that structural network disconnections best explained performance on complex motor and cognitive tasks for which performance requires both substantial motor execution and cognitive activity. Thus, it is important to consider the indirect effects of the neural injury on network connectivity when aiming to understand and treat deficits in more complex behaviors that imply a higher ecological validity to real-world activities. Future research may expand on the association between structural disconnections and changes in behavioral deficits after stroke as well as on investigating the association between network disconnections and task complexity by considering experimental designs that allow the gradual comparison between simple- and dual-task constructs.

Several strengths and limitations must be considered when interpreting current findings. The availability of neuroimaging measures such as regional injury measures and structural disconnection measures that are derived from acute clinical stroke imaging as well as a comprehensive battery of behavioral outcome measures, is a strength. The behavioral scales were assessed on average 10 days post-stroke, which can be considered a reasonable time frame for correlations with acute neuroimaging measures. Hence future studies should additionally look at association between neural correlates and behavior with respect to changes in deficits over time and with respect to different treatment effects. Despite enrollment of patients meeting entry criteria, one key limitation of this study is the high overlap of stroke lesions in deep subcortical regions with limited overlap in frontal and occipital cortical regions, which reduces power to probe the latter areas for injury-behavior relationships and so might have influenced the neutral results for cognition and mood. Note that the current subcortical distribution of infarct locations is consistent with prior research (Corbetta et al., 2015; Schwarz et al., 2025). Furthermore, the fact that many higher order cognitive deficits are not specifically related to a certain brain region and rather to more indirect network disconnections supports the utility of our methodological approach even with the unbalanced distribution of lesion locations. Finally, most associations between behavioral deficits and neural network disconnection were moderate in strength, as the deficit profile of enrolled patients affected the statistical power to examine certain relationships. Similarly, the strength and robustness of the LASSO regression models were low to modest, warranting caution in interpreting the findings. Validating the results in independent study cohorts with larger sample sizes and longitudinally over time will be important next steps.

In sum, this study confirmed the relevance of injury to specific sensorimotor brain areas, such as CST, for understanding elementary sensorimotor deficits after stroke. The results also indicate that more complex motor and cognitive behaviors rely on intact connections between multiple brain networks. Together, the results emphasize the importance of understanding stroke as a network disorder. Such findings may help explain the variability and complexity in stroke-related deficits and in the long term may foster novel approaches to examining patients after stroke toward the goal of personalized, optimized rehabilitation therapies.