Publications by authors named "Jinwei Lang"
Article Synopsis
- Resting-state activity in the brain isn't completely free from tasks; it shows a hierarchical structure ready for cognitive functions and can predict brain activation during tasks through resting-state fMRI.
- The study introduces the Rest2Task model, which links resting-state connectivity to specific task-related brain activities using multivariate regression.
- This model successfully identifies task-specific components within resting-state networks and improves predictions for brain lateralization and psychiatric diagnoses, offering new insights into how brain networks adapt to different tasks.
The human brain is localized and distributed. On the one hand, each cognitive function tends to involve one hemisphere more than the other, also known as the principle of lateralization. On the other hand, interactions among brain regions in the form of functional connectivity (FC) are indispensable for intact function.
View Article and Find Full Text PDFNeuropsychiatric disorder (ND) is often accompanied by abnormal functional connectivity (FC) patterns in specific task contexts. The distinctive task-specific FC patterns can provide valuable features for ND classification models using deep learning. However, most previous studies rely solely on the whole-brain FC matrix without considering the prior knowledge of task-specific FC patterns.
View Article and Find Full Text PDFPurpose: Previous diffusion tensor imaging (DTI) studies have mainly focused on dose-dependent white matter (WM) alterations 1 month to 1 year after radiation therapy (RT) with a tract-average method. However, WM alterations immediately after RT are subtle, resulting in early WM alterations that cannot be detected by tract-average methods. Therefore, we performed a study with an along-tract method in patients with brain metastases to explore the early dose-response pattern of WM alterations after RT.
View Article and Find Full Text PDFObjective: To develop and validate a pretreatment magnetic resonance imaging (MRI)-based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.
Methods: We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs.