ObjectiveTo investigate the feasibility of proton magnetic resonance spectroscopy (1H-MRS) imaging, by which to quantitatively analyze liver fat content for therapeutic evaluation of fatty liver at 3.0 T MRI. MethodsTwenty-six patients who diagnosed with fatty liver were examined with proton MRS at Siemens Trio Tim 3.0 T MRI before treatment and 3, 6 months after treatment, respectively. The water peak, fat peak, water peak area, and fat peak area were detected, and the relative lipid content 1 (RLC1) and relative lipid content 2 (RLC2)were calculated. Fatty liver index (FLI) was referred to the standard which was calculated from triglycerides (TG), gammaglutamyl-transferase (GGT), waist circumference, and body mass index. ResultsThere were significantly different differences of RLC1 and RLC2 among before treatment and 3, 6 months after treatment (Plt;0.05). Compared with before treatment, the RLC1 and RLC2 values significantly decreased on month 3 or 6 after treatment (Plt;0.05). There were positive correlation between RLC1 or RLC2 and FLI (r=0.476, Plt;0.00; r=0.475, Plt;0.001). The intraclass correlation coefficient was more than 0.75 before treatment, the repeatability was better. ConclusionsProton MRS can quantitatively measure liver fat content. It can be reliably used for dynamic monitoring the therapeutic effects for fatty liver. Proton MRS is accurate, and has a good clinical application in dynamically monitoring the progression of fatty liver and evaluating the therapeutic effects of various treatments.
Nowadays, an increasing number of researches have shown that epilepsy, as a kind of neural network disease, not only affects the brain region of seizure onset, but also remote regions at which the brain network structures are damaged or dysfunctional. These changes are associated with abnormal network of epilepsy. Resting-state network is closely related to human cognitive function and plays an important role in cognitive process. Cognitive dysfunction, a common comorbidity of epilepsy, has adverse impacts on life quality of patients with epilepsy. The mechanism of cognitive dysfunction in epileptic patients is still incomprehensible, but the change of resting-state brain network may be associated with their cognitive impairment. In order to further understand the changes of resting-state network associated with the cognitive function and explore the brain network mechanism of the occurrence of cognitive dysfunction in patients with epilepsy, we review the related researches in recent years.
Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the “evolutional” and “structural” properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.
Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that damages patients’ memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.