Wearable sensors and machine learning fusion-based fall risk prediction in covert cerebral small vessel disease.

Background: Fall risk prediction is crucial for preventing falls in patients with cerebral small vessel disease (CSVD), especially for those with gait disturbances. However, research in this area is limited, particularly in the early, asymptomatic phase. Wearable sensors offer an objective method for gait assessment.

Advances in the study of apathy related to cerebral small vessel disease.

Cerebral small vessel disease (CSVD) is a complex clinical-imaging pathological syndrome caused by small vessel lesions in the brain, which is characterized by aging-related, insidious onset and slow progression. Apathy is a key component of the common neuropsychiatric symptoms among CSVD patients, severely affecting their daily lives and social functioning. Moreover, there are fewer studies on CSVD-related apathy, and greater attention should be paid to this condition in clinical practice.