How small blood vessel damage affects the brain and causes symptoms

Monday 15 December 2025, 10:30 am
Mechanism behind clinical symptoms in cerebral small vessel disease: A neuroimaging perspective
PhD candidate
H. Li
Promotor(s)
prof. dr. H.F. de Leeuw
Co-promotor(s)
dr. A.M. Tuladhar
Location
Aula

Cerebral small vessel disease (SVD) damages the brain’s small blood vessels and is a leading cause of cognitive decline and dementia in older adults. Traditional MRI scans can reveal visible lesions such as “white matter hyperintensities,” which appear as bright spots on brain images. However, these visible changes do not fully explain the wide range of symptoms seen in people with SVD. This thesis uses advanced brain imaging to look deeper into how SVD affects the brain. It focuses on three main mechanisms: 1) strategic effects when key brain areas or communication pathways are damaged; 2) remote effects when damage spreads beyond the original lesion, leading to gradual loss of brain connections and tissue; 3) waste-clearance problems, where the brain’s natural cleaning systems stop working properly. By linking these imaging markers with clinical data, our work reveals how SVD disrupts the brain on multiple levels and offers clues for future prevention and treatment strategies.

Hao Li was born on 17 November 1995 in Hunan Province, China. He obtained his medical degree in psychiatry from Xiangya School of Medicine, Central South University, in 2018. He then completed his neurology residency and earned a Master’s degree at Sun Yat-sen University (China), where he developed a strong interest in neuroimaging and cerebral small vessel disease. In 2021, he started his PhD at the Department of Neurology, Radboud University Medical Center, the Netherlands, under the supervision of Dr. Anil M. Tuladhar and Prof. Frank-Erik de Leeuw. His research, titled “Mechanisms behind clinical symptoms in cerebral small vessel disease: a neuroimaging perspective,” focused on uncovering how small vessel damage leads to cognitive and behavioral symptoms using advanced MRI analyses in SVD population.