The risk factors and pattern of cerebral microbleeds in Parkinson's disease.

BACKGROUND AND PURPOSE: Cerebral microbleeds (CMBs) in Parkinson's disease (PD) have been reported recently and concerned increasingly. Our aim was to investigate the risk factors and pattern of CMBs in patients with PD, as well as the influence of risk factors on the pattern of CMBs. METHODS: We retrospectively collected medical and imaging data of 247 patients who underwent brain susceptibility-weighted imaging. Logistic regression analyses were performed to determine the risk factors of CMBs. The frequency and amount of CMBs in different locations between patients with and without risk factors were analyzed. RESULTS: Of the 247 patients with PD, 39 (15.79%) had CMBs, 27 (69.23%) had lobar CMBs, 20 (51.28%) had deep CMBs and 17 (43.59%) had infratentorial CMBs. A history of cerebral ischemic events was independently associated with the presence of CMBs (odds ratio (OR) 4.485 [95% CI 2.150-9.356]; p = 0.000), especially with lobar and deep CMBs. Hypertension and Hoehn and Yahr score were also associated with the presence of deep CMBs. Only white matter hyperintensities were independently associated with the presence of infratentorial CMBs. Compared to patients without risk factors, the frequency of deep CMBs was greater in those with a history of cerebral ischemic events (p = 0.013), while the amount of deep CMBs was higher in those with hypertension (p = 0.035). CONCLUSION: CMBs in PD seem to present a lobe-dominant pattern. A history of cerebral ischemic events and hypertension may be two strong risk factors which preferentially influences the pattern of deep CMBs in PD.

Risk factors of cerebral microbleeds in young and middle-aged patients with hypertension.

Objective This study aimed to investigate the incidence and related risk factors of cerebral microbleeds (CMBs) in young and middle-aged patients with hypertension. Methods The study included 232 young and middle-aged (18-59 years-old) patients with hypertension from September 2014 to December 2016 in the Department of Neurology, Affiliated Hospital of Guizhou Medical University, China. The data were recorded which included demographics, vascular risk factors, medication history, and imaging data of patients. CMBs were evaluated based on the microbleeds anatomical rating scale. Results Of the enrolled participants, 115 were CMB positive, accounting for 49.6%. CMBs were more prone to occur in deep regions than in others (39.13%). Multiple cerebral microbleeds were associated with white matter hyperintensities(WMH), dyslipidemia, hyperhomocysteine, and uric acid. Moreover, WMH, dyslipidemia, ever smoker, antiplatelets use, and hyperhomocysteine were found to be risk factors for deep or infratentorial CMBs in young and middle-aged patients with hypertension. However, the lobar CMBs only had an independent correlation with dyslipidemia in these participants. Conclusions The incidence of CMBs in patients with hypertension was relatively high. It mostly occurred in a deep or infratentorial area with more vascular-associated risk factors. However, in patients with lobar CMBs, factors associated with lipid metabolism, such as amyloid deposition and unidentified genotype variation, may be crucial. Screening and regular follow-ups of CMBs by Susceptibility Weighted Imaging and active prevention in young and middle-aged patients with hypertension have clinical significance for timely understanding and predicting the occurrence and development of related cerebrovascular disease events.

Reconstruction of Complex Network based on the Noise via QR Decomposition and Compressed Sensing.

It is generally known that the states of network nodes are stable and have strong correlations in a linear network system. We find that without the control input, the method of compressed sensing can not succeed in reconstructing complex networks in which the states of nodes are generated through the linear network system. However, noise can drive the dynamics between nodes to break the stability of the system state. Therefore, a new method integrating QR decomposition and compressed sensing is proposed to solve the reconstruction problem of complex networks under the assistance of the input noise. The state matrix of the system is decomposed by QR decomposition. We construct the measurement matrix with the aid of Gaussian noise so that the sparse input matrix can be reconstructed by compressed sensing. We also discover that noise can build a bridge between the dynamics and the topological structure. Experiments are presented to show that the proposed method is more accurate and more efficient to reconstruct four model networks and six real networks by the comparisons between the proposed method and only compressed sensing. In addition, the proposed method can reconstruct not only the sparse complex networks, but also the dense complex networks.