Objective:To investigate the imaging characteristics and diagnosis of thalamic dementia caused by medial tentorial dural arteriovenous fistula (TDAVF).Methods:The clinical and imaging data of 4 patients with medial TDAVF diagnosed at Nanjing Brain Hospital from August 2022 to September 2023 were retrospectively collected, summarized and analyzed.Results:Four patients were all males, with an average age of 67 years. The duration of cognitive impairment varies from 13 days to 2 months, with an average of about 1 month. The average score on the Mini-Mental State Examination (MMSE) was 13, while the average score on the Montreal Cognitive Assessment (MoCA) was 15. One patient was unable to complete these tests. MRI showed bilateral thalamic swelling, with scattered microbleeding lesions on diffusion-weighted imaging and susceptibility-weighted imaging ( n=3). Enhanced scans showed patchy or clumpy enhancement of the bilateral thalamus. Magnetic resonance angiography showed abnormal blood vessels along the straight sinus area ( n=4), while magnetic resonance venography showed no straight sinus development ( n=4). Magnetic resonance spectroscopy showed neuronal swelling and damage ( n=3), with no increase in choline peak. Arterial spin labeling perfusion imaging showed decreased perfusion in the lesions. Digital subtraction angiography confirmed TDAVF with straight sinus thrombosis. Two patients underwent embolization, with significant improvement in postoperative memory impairment. One patient underwent surgical clipping, while the other was transferred to other hospital for treatment. Conclusions:Patients with medial TDAVF often present with thalamic dementia. Imaging examinations show typical bilateral thalamic swelling with microbleeds, and early visualization of vascular shadows, without development of straight sinus. These features would be beneficial for the early diagnosis of TDAVF.

With the progress of population aging, cerebral small vessel disease is increasingly becoming a common and frequent disease threatening human health, which is a common reason leaing to cognitive function decline.The changes of white matter, especially white matter hyperintensities, are the most common and typical imaging marker of small cerebral vascular disease.In recent years, a number of studies has found that white matter hyperintensities are associated with cognitive decline.These studies mainly focused on the relationship between white matter hyperintensities and cognitive frailty, and the correlation between the size, location and dynamic evolution of white matter hyperintensities and cognitive impairment of small cerebral vascular disease.Functional magnetic resonance imaging studies also revealed that patients with cognitive impairment of cerebral small vessel disease had abnormal white matter changes and structural network connectivity.Structural network can be used for quantitative analysis because of its good stability.Diffusion tensor imaging and quantitative measurement of multi-dimensional structural network were used qualitatively.It was found that the structural network integrity was damaged, the network connection efficiency was reduced, and the connection was interrupted within the white matter hyperintensity.Big data and artificial intelligence research can make early prediction of white matter hyperintensity and structural networks in patients with cerebral small vessel disease with cognitive impairment.These studies provide a reliable basis for the discovery of abnormal microstructure and network changes in the early stage of white matter hyperintensities in cerebral small vessel disease with cognitive impairment.The paper reviews the research progress of white matter hyperintensity and brain structural network in patients with cognitive impairment of cerebral small vessel disease in recent years, and in order to improve the early diagnosis of the disease and promote the early prevention and treatment.

Mild cognitive impairment (MCI) is an unstable cognitive impairment state between normal aging and Alzheimer's disease (AD). The symptoms of MCI are mild and it has four different types of outcomes: reversing normal, maintaining stability, progression and death. However, 2/3 of MCI patients may still progress to dementia. Therefore, early identification of stable MCI (sMCI) and progressive MCI (pMCI) is beneficial for timely intervention, and delaying the progression of MCI, then improving patients' quality of life. Structural magnetic resonance imaging (sMRI) can predict dementia related neurodegeneration and cognitive decline. A large number of studies have found that, in addition to abnormalities in clinical scales, there are significant changes in sMRI during the progression of sMCI to pMCI, mainly including differences in cortical thickness and brain atrophy, hippocampal volume, and structural brain network connectivity. Especially, machine learning methods such as big data based neural convolutional networks are helpful in early prediction of sMCI and pMCI. These studies contribute to the discovery of early imaging markers for the conversion of sMCI to pMCI.