The corpus callosum (CC) is the largest commissural bridge of white-matter fibers between bilateral hemispheres (1), accompanied by a unique anterior and posterior double circulation system and abundant collateral arteries (2). Because of the sufficient blood supply, CC infarction is extremely rare and accounts for barely 2.3–8.0% of cerebral ischemic stroke (3–5). Because of its unique physiological structure and function, the manifestations of CC infarction are variable and lacking of specificity. Due to these special and complex characteristics, misdiagnosis and delayed treatment are not uncommon for CC infarction (6). Interestingly, we previously found that, compared to general basal ganglia infarction, patients with CC infarction had lower National Institutes of Health Stroke Scale scores and better recovery at the time of discharge, while the one-year mortality is higher with poorer long-term prognosis (5). Cognitive impairment is one of the main causes of poor long-term prognosis in patients with CC cerebral infarction. Unfortunately, due to it’s occult exacerbation process, patients often do not pay enough attention to it in the early stage, and miss the optimal intervention period, resulting in irreversible cognitive impairment.

Subjective cognitive decline (SCD) is an individual’s self-report of cognitive decline and is nowadays thought to be a precursor to various common cognitive disorders in clinic, such as mild cognitive impairment (MCI) (7) and Alzheimer’s disease (AD) (8). Recent researches have revealed that compared with age-matched healthy controls, patients with SCD suffer a 4.5–6 times higher risk of developing into MCI or AD (9, 10). Compared to universally-known post-stroke cognitive impairment (PSCI), SCD places more emphasis on the patient’s subjective perceptions and timely feedback from caregivers, making it easier to identify and intervene early. Meanwhile, our, as well as others’ previous studies have proved that, white matter lesions (WMLs) are important pathological mechanisms for cognitive dysfunctions (5, 11–14). As an extremely rare subtype of stroke with prominent WMLs, CC infarction is likely to become a potential driver of SCD and other symptomatic cognitive decline. Therefore, aiming to restore brain health and cognitive abilities as long as possible, this at-risk group is recognized as an eligible target population for early intervention strategies (15, 16).

The role of physicians has always been to synthesize the data available to them to identify prognosis patterns that guide early intervention. Machine learning (ML) is a new rising technical foundation of artificial intelligence, which enables the computer to learn the rules hidden in the data automatically (17). Several studies have revealed that ML-based models are promising in predicting the diagnosis, prognosis or recurrence of ischemic stroke, what’s more, those models are also widely used in the field of psychology, biomechanics and so on (18–23). Nevertheless, it still lacks of ML-based evidence on SCD prediction after cerebral infarction. What’s more, the “black-box” character of ML-technique hinders clinicians to have a good understand of the predictive decision, namely failure in accountability (24). To this end, we proposed an interpretable strategy combining ML algorithm with SHapley Additive exPlanations (SHAP) to provide consistent and locally accurate attribution values for each feature within each prediction model. It’s calculated by comparing the predicting discrepancy in all possible combinations containing and withholding each feature and provide a unique report individually (25).

Here, with the largest sample of CC infarction to date, this is an exploratory study that for the first time emphasizes the clinical feasibility to individually predict the occurrence of one-year SCD after CC infarction by using ML methods. We also attempt to apply SHAP-value for explaining the importance and influence of each predictor contributing to the optimal model’s outcome. We expect this ML-derived early warning system and SHAP-based framework of interpretation could help clinicians to better counsel patients, conduct targeted follow-up and determine personalized interventional measures.

The presence of SCD is known to be associated with a high risk for objective cognitive decline and even clinical progression to symptomatic disease stages (42, 43). Effective intervention to delay or prevent pathologic cognitive decline may best to targeted at the earliest symptomatice disease stage, such as SCD, in which cognitive function is still relatively preserved (44). This is an exploratory study that for the first time focuses on post-stroke SCD of rare CC infarction via an interpretable machine learning-derived early warning strategy.

After multivariate adjustment for potential confonders, we found that female, 3-month mRS scores, pure CC infarction and infarction subregion of CC independently correlated with the incidence of SCD. Interestingly, our previous study has reported that males had a higher incidence of CC infarction (5), while in the current cohort, we found females were more susceptible to SCD after this specific infarction. Reasons for this phenomenon may include: (1) females in the present study had an older onset-age of CC infarction than males (64 [58,71] vs. 62 [55,69]), (2) women are usually considered to have a lager corpus callosum volume (45–47), indicating that callosum may play a more important role in maintaining brain function of females, (3) women tend to have higher cortisol but lower estradiol levels in menopausal period (48). Indeed, scholars have well-clarified that higher serum cortisol is correlated with more severe microstructural WMLs, particularly in CC, while estrogen are thought to promote the remyelination, and the latter in turn is strongly associated with general cognitive capacity (49–51). Meanwhile, a strong interaction between serum cortisol and cerebral atrophy among females, but not males was also identified (52). Richa et al. (53) once reported that the MoCA scores (between 4–8 weeks post-infarct) were obviously correlated to mRS scores (at the same follow-up points) among the stroke patients. Then, our results showed that 3-month mRS scores were related to longer-time cognitive outcome due to CC infarction.

The structure of CC can be divided into four classical parts in the order from front to back: rostrum, genu, body and splenium (54). Consistent with previous reports (5), we found that the incidence of ‘pure’ CC infarction was rare, while the mental disturbance and cognitive dysfunction were more prominent than ‘complex’ CC infarction. The mechanisms of the discrepancy are still unclear, perhaps the atypical symptoms and insufficient distinguishment of MRI scan made it difficult to draw sufficient attention and appropriate prevention of ‘pure’ CC infarction. Meanwhile, we reported for the first time that acute infarction in the splenium had a higher tendency of cognitive decline than that in the genu. As the most vulnerable area of the CC infarction, the splenium is more vulnerable to insufficient blood supply, and the splenium lesions were known to be related with cognitive disorder, aphasia, homonymous hemianopsia, alien hand syndrome and so on (54). Therefore, we believe that the splenium plays an important role in the high incidence of SCD caused by CC infarction to some extent. What’s more, patients with at least two subregions of CC infarction were more susceptible to SCD than those only had lesions in the genu. This result is well understood given that the more structural damage CC is, the more disrupted the fiber connections and information transmission between the bilateral hemispheres. Besides, evidence showing that the infarction in body or splenium of CC could lead to disturbed executive capacity, attention and calculation (55), which may provide a side note for our viewpoint.

Besides multivariable analysis, LASSO analysis was also adopted to select potential risk predictors by eliminating irrelevant features. It is universally accepted that age was a risk factor of cognition damage after various types of ischemic stroke (56). Except of age, evidence linking high HCY(HHCY) and cognitive decline is profuse (57). It is known that, HHCY is not only associated with WMLs, but also the progression of WMLs (58). In the meantime, extensive intracranial vascular stenosis is another promotor for SCD after CC infarction. Cerebral angiostenosis/occlusion has already been proved to induce hypoperfusion and impaired executive dysfunctions, such as working memory, attention, cognitive flexibility, planning, thought organization and implementation (59). This phenomenon indicates that appropriate increase of cerebral blood flow may help prevent post-stroke SCD. Interestingly, NLR is often known as a risk factor for PSCI (60). However, we found that NLR is negatively associated with self-report cognitive decline, indicating that NLR is likely to act as a compensatory neuroprotective response in the early stage of CC infarction. Biological mechanisms between NLR and risk of post-stroke SCD have not been explored before and warrants further clarifications, especially in the condition of CC infarction.

In our study, the combination of LASSO regression and ML-based models was beneficial to identify the optimal configuration to predict whether it is vulnerable to develop SCD after CC infarction or not. Then, the seven ML algorithms were assessed by several metrics, comprising AUC-value, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), as well as F1 scores. Apart from global explanation, the well-accepted local explanation, SHAP was also implemented to interpret how a complex black-box ML model makes a prediction (61). By incorporating the individualized patient profile, the level of contribution and directionality of specific input features were visualized (62). As shown in the Table 1, the LR model seemed to be the best-performance classifier with the highest scores of AUC- value (77.1%), accuracy (70.3%), sensitivity (76.3%) and NPV (76.5%). In addition, acceptable values of specificity, PPV and F1 score (all above 65.0%) were achieved in the validation set. Taken together, we selected the LR model as the optimal algorithm with the best generalization ability. At the same time, we suggested that we should treat this problem dialectically and choose appropriate predictive classifier according to different clinical needs.

The strength of our research is that the cohort has the largest sample of CC infarction in the world, and the datasets are non-synthetic, which is more likely to be objective and effective as a screening tool. Unlike studies focused on each risk factor individually or its pathophysiological interpretation (63, 64), we aimed to encompass a large combination of variables from real-world clinical situations once. The variables we used, including demographics, laboratory and radiological findings were all easily accessible for clinicians, which could assist with the early prediction and prevention for suspected post-stroke SCD. Additionally, an interpretable and explainable ML model was created with the help of SHAP-explainer, promoting to make individualized clinical decisions.

There are some limitations that still needed to be ironed out in our study. Firstly, although this investigation had the maximal population of patients with acute CC infarction, the sample size was still needed to be added. Secondly, we did not exploit the different cognitive abilities separately, such as orientation, calculation, executive abilities, long-term and short-term memory and attention, etc. Thirdly, the follow-up period is not long enough to verify the proportion of patients with SCD who eventually converted to PSCI. Therefore, muti-center prospective cohorts with detailed cognitive domains impairment are needed in the future.

In conclusion, the present study screened out 9 key features associated with post-SCD and developed a LR-model which can improve the prediction accuracy of one-year SCD after CC infarction. What’s more, the individual report generated by SHAP facilitate the early implementation of primary prevention measures. Based on these techniques, we are even expected to continue to individually predict the long-term effects of different clinical drugs on cognitive impairment to shape a brighter future for patients with CC infarction.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

The studies involving human participants were reviewed and approved by Changhai Hospital Ethics Committee (No. CHEC2021-1021). The patients/participants provided their written informed consent to participate in this study.

YX, XS, and XB: conceived and designed the study. YX, XS, YL, YH, ML, RS, GY, CS, QD, BD, and XB: performed the study. YX, XS, YL, BD, and XB: revised the article for intellectual content. YX, XS, and BD: wrote the article. All authors contributed to the article and approved the submitted version.

The work was financially supported by the National Natural Science Foundation of China (81871040 and 82101563), the Clinical Research Plan of SHDC (no. SHDC2020CR1038B), Scientific research project of Shanghai Health Commission (20214Y0500), and the Youth Program of Naval Medical University (2021JCQN10).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.