How to extract high discriminative features that help classification from complex resting-state fMRI (rs-fMRI) data is the key to improving the accuracy of brain disease recognition such as schizophrenia. In this work, we use a weighted sparse model for brain network construction, and utilize the Kendall correlation coefficient (KCC) to extract the discriminative connectivity features for schizophrenia classification, which is conducted with the linear support vector machine. Experimental results based on the rs-fMRI of 57 schizophrenia patients and 64 healthy controls show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 81.82%) than other competing methods. Specifically, compared with the traditional network construction methods (Pearson’s correlation and sparse representation) and the commonly used feature selection methods (two-sample t-test and Least absolute shrinkage and selection operator (Lasso)), the algorithm proposed in this paper can more effectively extract the discriminative connectivity features between the schizophrenia patients and the healthy controls, and further improve the classification accuracy. At the same time, the discriminative connectivity features extracted in the work could be used as the potential clinical biomarkers to assist the identification of schizophrenia.
Entropy model is widely used in epileptic electroencephalogram (EEG) analysis, but there are few reports on how to objectively select the parameters to compute the entropy model in the analysis of resting-state functional magnetic resonance imaging (rfMRI). Therefore, an optimization algorithm to confirm the parameters in multi-scale entropy (MSE) model was proposed, and the location of epileptogenic hemisphere was taken as an example to test the optimization effect by supervised machine learning. The rfMRI data of 20 temporal lobe epilepsy (TLE) patients with hippocampal sclerosis, positive on structural magnetic resonance imaging, were divided into left and right groups. Then, the parameters in MSE model were optimized by the receiver operating characteristic curves (ROC) and area under ROC curve (AUC) values in sensitivity analysis, and the entropy value of the brain regions with statistically significant difference between the groups were taken as sensitive features to epileptogenic hemisphere lateral. The optimized entropy values of these bio-marker brain areas were considered as feature vectors input into the support vector machine (SVM). Finally, combining optimized MSE model with SVM could accurately distinguish epileptogenic hemisphere in TLE at an average accuracy rate of 95%, which was higher than the current level. The results show that the MSE model parameter optimization algorithm can accurately extract the functional imaging markers sensitive to the epileptogenic hemisphere, and achieve the purpose of objectively selecting the parameters for MSE in rfMRI, which provides the basis for the application of entropy in advanced technology detection.
ObjectiveTo explore performances of functional magnetic resonance imaging (MRI) in evaluation of hepatic warm ischemia-reperfusion injury.MethodThe relative references about the principle of functional MRI and its application in the assessment of hepatic warm ischemia-reperfusion injury were reviewed and summarized.ResultsThe main functional MRI techniques for the assessment of hepatic warm ischemia-reperfusion injury included the diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), blood oxygen level dependent (BOLD), dynamic contrast enhancement MRI (DCE-MRI), and T2 mapping, etc.. These techniques mainly used in the animal model with hepatic warm ischemia-reperfusion injury currently.ConclusionsFrom current results of researches of animal models, functional MRI is a non-invasive tool to accurately and quantitatively evaluate microscopic information changes of liver tissue in vivo. It can provide a useful information on further understanding of mechanism and prognosis of hepatic warm ischemia-reperfusion injury. With development of donation after cardiac death, functional MRI will play a more important role in evaluation of hepatic warm ischemia-reperfusion injury.
The aim of this paper is to reveal the change of the brain function for nicotine addicts after smoking cessation, and explore the basis of neural physiology for the nicotine addicts in the process of smoking cessation. Fourteen subjects, who have a strong dependence on nicotine, have agreed to give up smoking and insist on completing the test, and 11 volunteers were recruited as the controls. The resting state functional magnetic resonance imaging and the regional homogeneity (ReHo) algorithm have been used to study the neural activity before and after smoking cessation. A two factors mixed design was used to investigate within-group effects and between-group effects. After 2 weeks’ smoking cessation, the increased ReHo value were exhibited in the brain area of supplementary motor area, paracentral lobule, calcarine, cuneus and lingual gyrus. It suggested that the synchronization of neural activity was enhanced in these brain areas. And between-group interaction effects were appeared in supplementary motor area, paracentral lobule, precentral gyrus, postcentral gyrus, and superior frontal gyrus. The results indicate that the brain function in supplementary motor area of smoking addicts would be enhanced significantly after 2 weeks’ smoking cessation.
White matter lesion (WML) of presumed vascular origin is one of the common imaging manifestations of cerebral small vessel diseases, which is the main reason of cognitive impairment and even vascular dementia in the elderly. However, there is a lack of early and effective diagnostic methods currently. In recent years, studies of diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) have shown that cognitive impairment in patients with WMLs is associated with disrupted white matter microstructural and brain network connectivity. Therefore, it’s speculated that DTI and rs-fMRI can be effective in early imaging diagnosis of WMLs-related cognitive impairment. This article reviews the role and significance of DTI and rs-fMRI in WMLs-related cognitive impairment.
Brain aging can affect the strength of functional connectivity between brain regions. In recent years, studies have shown that functional connectivity is fluctuant over time, and can reflect more physiological and pathological information. Therefore, in the study resting state functional magnetic resonance imaging (fMRI) data of 32 elderly subjects and 36 younger subjects were selected, and the sliding window technique was used to estimate dynamic functional connectivity network. Then, the dependency of fluctuating energy difference on frequency band was studied using wavelet packet analysis, conducting the linear regression with age at the same time. Results showed that the fluctuating energy in older group was significantly higher than that in the young group in low frequency, and it was significantly lower than that in the young people in high frequency. These results suggested that the dynamic functional connectivity between networks in the elderly exist slow wave phenomenon, which may be related to the decreased reaction rate of the elderly. This article provides new ideas and methods for the research about brain aging, and promotes a theoretical basis for further understanding of the physiological significance of brain dynamic functional connectivity.
Early diagnosis and accurate stage of liver fibrosis are important for conducting the clinic therapy and assessing the therapeutic outcome. Functional magnetic resonance imaging (fMRI), as a noninvasive and effective method, plays an important role in diagnosis and stage of liver fibrosis. This review focuses on the advances in fMRI evaluation of liver fibrosis.
We investigated the baseline brain activity level in patients with major depressive disorder (MDD) by amplitude of low-frequency fluctuation (ALFF) based on resting-state functional MRI (fMRI). We examined 13 patients in the MDD group and 14 healthy volunteers in the control group by resting-state fMRI on GE Signa 3.0T. We calculated and compared the ALFF values of the two groups. In the MDD group, ALFF values in the right medial prefrontal were higher than those in control group, with statistically significant differences (P<0.001). ALFF values in the left parietal in the MDD group were lower than those in control group with statistically significant differences (P<0.001). This resting-state fMRI study suggested that the alteration brain activity in the right medial prefrontal and left parietal ALFF contributed to the understanding of the pathophysiological mechanism of MDD patients.
Post-traumatic stress disorder (PTSD) is a mental disorder causing great distress to individuals, families and even society, and there is not yet effective way of unified prevention and treatment up till now. Lots of neuroimaging techniques, however, such as the magnetic resonance imaging, are widely used to the study of the pathogenesis of PTSD with the development of medical imaging. Functional magnetic resonance imaging (fMRI) can be applied to detect the abnormalities not only of the brain morphology but also of the function of various cerebral areas and neural circuit, and plays an important role in studying the pathogenesis of psychiatric diseases. In this paper, we mainly review the task-related and resting-state functional magnetic resonance imaging studies of the PTSD, and finally suggest possible directions for future research.
Objective To identify the most consistent and replicable characteristics of altered spontaneous brain activity in mesial temporal lobe epilepsy patients with unilateral hippocampal sclerosis (MTLE-HS). Methods A systematic literature search was performed in PubMed, Embase, The Cochrane Library, China National Knowledge Infrastructure, Wanfang, and CQVIP databases, to identify eligible whole-brain resting state functional magnetic resonance imaging studies that had measured differences in amplitude of low-frequency fluctuations or fractional amplitude of low-frequency fluctuations between patients with MTLE-HS and healthy controls from January 2000 to January 2019. After literature screening and data extraction, Anisotropic Effect-Size Signed Differential Mapping software was used for voxel based pooled meta-analysis. Results Nine datasets from six studies were finally included, which contained 207 MTLE-HS patients and 239 healthy controls. The results demonstrated that, compared with the healthy controls, the MTLE-HS patients showed increased spontaneous brain activity in right hippocampus and parahippocampal gyrus, right superior temporal gyrus, left cingulate gyrus, right fusiform gyrus, and right inferior temporal gyrus; while decreased spontaneous brain activity in left superior frontal gyrus, right angular gyrus, right middle frontal gyrus, left inferior parietal lobule, left precuneus, and right cerebellum (P<0.005, cluster extent≥10). Conclusion The current meta-analysis demonstrates that patients with MTLE-HS show increased spontaneous brain activity in lateral and mesial temporal regions and decreased spontaneous brain activity in default mode network, which preliminarily clarifies the characteristics of altered spontaneous brain activity in patients with MTLE-HS.