Stroke is a leading cause of death and disability worldwide, affecting a quarter of the population who are expected to experience at least one episode in their lifetime [1]. Common disabilities include sensorimotor and cognitive impairments, which need to be addressed through rehabilitation therapy [2]. While clinical evidence supports certain parameters, such as National Institutes of Health Stroke Scale (NIHSS) scores and infarct volume, in predicting functional outcomes after an acute ischemic stroke [3], the uncertainty in the response of some patients, especially those with intermediate clinical deficits after reperfusion treatment, highlights the need for complex analytical methodologies that consider structural and functional connectivity parameters. These methodologies shift the focus toward a more comprehensive perspective of the brain as a complex and interconnected network system. Structural and functional connectivity play a crucial role in determining the severity of brain damage, which can affect a patient’s response to treatment and outcomes in the first few months following a stroke. Brain lesions often cause nonlocal effects, and several studies suggest that post-stroke deficits result from physiological dysfunction in distributed brain networks rather than being limited to the lesion site alone.

Variability in stroke lesion location and size complicates spatial normalization [4] and impedes voxel-based statistical models, rendering traditional methods ineffective. Nevertheless, the indirect estimation of structural connectivity damage has been shown to accurately predict behavioral deficits after stroke [5].

In this study, we applied a probabilistic methodology [6] to quantify brain damage and structural disconnections in the white matter (WM) that affect cortical areas, using lesion masking on a tractography atlas and parcellation of gray matter into functional network nodes. We aimed to determine whether disconnection markers can predict functional outcomes after acute ischemic stroke. Furthermore, we validated the results using individual diffusion tensor imaging (DTI) measurements.

Ischemic lesions in 43 patients were segmented and included in the analysis. The sociodemographic variables, lesion features, and clinical scores are presented in Table 1. Significant differences between functional outcome groups were observed for lesion volume, lesion location, and subacute NIHSS score. The probabilistic distributions of lesions for all cohorts and groups are shown in Figure 1.

No significant differences in lesion mapping were observed between groups. Average dysconnectivity maps, plotted against the probability of lesions for each group, are presented for illustrative purposes in Figure 2. None of the parcellation damage or net-to-net disconnection variables met the criteria for data dimensionality reduction. We found no statistically significant differences in overall disconnection, between-parcellation disconnections, or in comparison between the SFI and NSFC groups. However, significant probabilistic structural disconnection was found within the mid-posterior (median SFI, 0.206; median NSFC, 8.502; Wilcoxon rank-sum test, P=0.013) and central (median SFI, 0.665; median NSFC, 10.314; Wilcoxon rank-sum test, P=0.022) regions of the corpus callosum in the NSFC group (Figure 3 and Table 2). Additionally, increased disconnection severity was observed within the somatomotor network (SMN) in patients with NSFC 3 months after discharge (SFI mean, 0.114; NSFC median, 0.176; Wilcoxon rank-sum test, pFDR=0.039).

Logistic regression analysis was performed to predict 3-month functional outcomes based on changes in the mRS score from discharge. The results indicated that age and NIHSS score were significant predictors, whereas sex and lesion volume were not. Additionally, probabilistic structural disconnection severity of the central and mid-posterior parts of the corpus callosum and of the SMN was statistically significant after controlling for baseline NIHSS score and age. The first dimension of PCA, which combined the identified key disconnection measures, accounted for 88% of the total variance and showed a fairly equal contribution from each variable. The prediction accuracy for SFI and NSFC outcomes was calculated by dividing the dataset into training (67%, 30 samples) and test subsets and adjusting the logistic regression model with age, baseline NIHSS score, and the first PCA component as predictors. The outcome prediction of the null model, which included age and baseline NIHSS scores, showed a prediction accuracy of 53.8% (95% CI 25.1-80.8). This result was further corroborated by bootstrap analysis, which estimated a stable accuracy of 74.4% (95% CI 48.8-83.7). The pseudo-R² (Nagelkerke) value of this model was 0.40. The odds ratio for NIHSS was 0.57 (95% CI 0.35-0.81), suggesting that for each one-point increase in NIHSS score, the odds of functional improvement decreased by 43%. Adding the PCA component significantly improved model performance, increasing the prediction accuracy to 76.9% (95% CI 46.2-95.0). Bootstrap analysis confirmed the model’s stability, with an estimated accuracy of 81.4% (95% CI 65.1-88.4). The pseudo-R² value for the enhanced model was 0.40. The odds ratio for the PCA component was 0.48 (95% CI 0.18-0.92), indicating that for each one-unit increase in this component, the odds of a positive outcome decreased by 52%. In this model, the odds ratio for NIHSS at baseline was 0.66 (95% CI 0.43-0.91), indicating a slightly weaker but still significant association with the outcome compared to the null model.

Of the initial 43 patients with segmented lesion masks, 38 (15 SFI and 23 NSFC) had DTI sequences available for processing. To ensure data reliability, a comprehensive quality control analysis was performed for all DTI acquisitions. None of the participants was identified as an outlier. Motion correction metrics revealed an average DWI correlation of 0.6683±0.0623, indicating minimal subject movement during scanning, while the number of scans with significant motion or artifacts was low (median=0, interquartile range=0, 0).

The ROI used to assess differences in FA, AD, RD, and MD values was obtained as the sum of bilateral projections of the central and mid-posterior regions of the corpus callosum, masked by the ICBM body of the corpus callosum region to avoid overlap with lesional sites, and placed in MNI space. As the variables were normally distributed and showed homoscedasticity between the subsamples, a t-test was applied. We found statistically significant differences in the FA and RD between the SFI and NSFC groups (Table 3), even after FDR correction. The FA decreased, whereas RD increased in the NSFC group, suggesting microstructural changes detected using the atlas-based lesion quantification approach. Furthermore, using a probabilistic approach, we identified bilateral projecting tracts of the corpus callosum with statistically lower QA in the NSFC group (FDRcorrected P<0.050), as shown in Figure 4, thus validating our previous findings.

Our study found that probabilistic structural disconnection mapping is effective in predicting long-term functional outcomes based on acute stroke disconnections. By incorporating atlasbased structural disconnection parameters, prediction accuracy increased from 53.8% to 76.9%. Although lesion volume is a potential biomarker for predicting stroke recovery outcomes, it was not a strong predictor in our sample. The PCA results suggest that the identified disconnection measures are highly correlated, with minimal differences in their contributions. We propose that each of these measures represents a predictive marker for poor outcomes at the 3-month follow-up, as they appear to be concurrent and reflect the same disruption pathway in our NSFC sample.

These findings highlight the importance of preserving connections between the two brain hemispheres, specifically within the SMN, through the corpus callosum. This suggests that these connections play a crucial role in the recovery process and are potentially compromised in the middle of the corpus callosum. This damage may impede the effectiveness of rehabilitation efforts by disrupting communication between the nodes of the SMN in the opposite hemisphere. Our findings are consistent with previous studies demonstrating a link between lower interhemispheric functional connectivity and poor outcomes in stroke patients [3,14-17], particularly within the SMN. Additionally, behavioral deficits have been associated with reduced functional connectivity in the corresponding networks, as observed in patients with a loss of interhemispheric connectivity in the motor network [18].

This finding is also consistent with the decreased FA and increased RD in the corpus callosum observed in patients with poor outcomes in our cohort, suggesting axonal loss within this region, which connects the interhemispheric SMN nodes, even when the ischemic lesion occurs at a distant site. Many previous studies have focused on microstructural damage in the corticospinal tract as a WM motor pathway, but few have included the corpus callosum in their analyses [19,20]. Similar to our study, Li et al. [19] previously found significantly decreased callosal FA and increased RD and AD in stroke patients compared to controls. Stewart et al. [20] also found a positive correlation between FA in the premotor section of the corpus callosum and reaching performance. However, to the best of our knowledge, this is the first study to link atlas-based probabilistic structural disconnections with deterministic DTI microstructure measures for predicting 3-month outcomes after acute stroke. Knowing the degree of structural disconnection in the first few days after a stroke can help in designing personalized rehabilitation programs. Therefore, patients with a more severe degree of disruption may benefit from a more intensive rehabilitation strategy.

Our analysis required only standard lesion mapping from routinely acquired DWI. However, this study has several limitations, including a small sample size and the use of a standardized structural connectome atlas instead of whole-brain tractography to infer structural disconnections. This approach was used because the reconstruction of WM fibers from diffusion tensor images is unreliable in the presence of brain lesions. Although we did not account for individual variability or discrepancies between our cohort and the population represented in the HCP atlas in the lesion quantification analysis, our findings were consistent with microstructural changes computed using the BRAIN-CONNECTS DTI sequence in a non-lesioned site, such as the corpus callosum, indicating that LQT disconnection metrics are sufficiently robust to be validated with a 32-direction DTI acquisition. However, 32-direction DTI images, as used in this study for validation, have lower angular resolution compared with the 90 directions of the atlas used, possibly underestimating brain damage in crossing-fiber regions accounted for by the atlas. Further research is required to validate this tool and determine whether it can replace expensive, time-consuming sequences for assessing the microstructural damage of interhemispheric connections. Patients with NIHSS scores between 5 and 13 face more uncertainty regarding outcomes than those with lower scores, who are more likely to have favorable outcomes at 3 months according to the Rankin Scale, and those with scores above 13, who usually have poor outcomes. Addressing this uncertainty is the main goal of BRAIN-CONNECTS. Therefore, one limitation of this study is that the results cannot be generalized to patients with other stroke severity levels or beyond 3 months post-onset. Future studies should examine how age-related WM changes and rehabilitation intensity affect outcomes in patients with moderate disease severity.

This study highlights the role of structural connectivity, particularly within the corpus callosum, in predicting stroke recovery. Probabilistic structural disconnection mapping provides valuable predictive markers for WM tract damage and poststroke outcomes. These findings emphasize the importance of preserving interhemispheric connections within the SMN and suggest that structural disconnections in the acute phase serve as significant indicators of mid- and long-term functional outcomes.