A deep-learning model for predicting post-stroke cognitive impairment based on brain network damage

Post-stroke cognitive impairment (PSCI) is a common and severe complication following acute lacunar stroke (ALS). Due to the limitations of current assessment tools and imaging methods, the early diagnosis of PSCI within 3 months of ALS remaining challenging. This study aimed to develop an effective, reliable, and accurate deep-learning method to predict PSCI within 3 months of ALS.

In total, 100 ALS patients were enrolled in the study, of whom 39 were diagnosed with PSCI and 61 were non-PSCI. First, we quantified three-dimensional (3D) gray-matter damage and white-matter tract disconnection, providing both regional damage (RD) and structural disconnection (SDC) higher-dimensional insights into brain network disruption. Second, we developed a novel deep-learning model based on ResNet18, integrating 3D RD, SDC, and diffusion-weighted imaging (DWI) to provide a comprehensive analysis of brain network integrity and predict PSCI. Finally, we compared the performance of our method with other methods, and visualized brain network damage associated with PSCI.

Our model showed strong predictive performance and had a mean accuracy (ACC) of 0.820±0.024, an area under the curve (AUC) of 0.795±0.068, a sensitivity (SEN) of 0.746±0.121, a specificity (SPE) of 0.869±0.044, and a F1-score (F1) of 0.760±0.050 in the five-fold cross-validation, outperforming existing models. In the PSCI patients, brain network damage significantly affected the salience, default mode, and somatic motor networks.

This study not only established a model that can accurately predict PSCI, it also identified potential targets for symptom-based treatments, offering new insights into PSCI.