Migraine is the most common primary headache clinically, with high disability rate and heavy burden. Functional MRI (fMRI) plays a significant role in the study of migraine. This article reviews the main advances of migraine without aura (MwoA) based on resting-state fMRI in recent years, including the exploration of the mechanism of fMRI in the occurrence and development of MwoA in terms of regional functional activities and functional network connections, as well as the research progress of the potential clinical application of fMRI in aiding diagnosis and assessing treatment effect for MwoA. At last, this article summarizes the current distresses and prospects of fMRI research on MwoA.
Simultaneous recording of electroencephalogram (EEG)-functional magnetic resonance imaging (fMRI) plays an important role in scientific research and clinical field due to its high spatial and temporal resolution. However, the fusion results are seriously influenced by ballistocardiogram (BCG) artifacts under MRI environment. In this paper, we improve the off-line constrained independent components analysis using real-time technique (rt-cICA), which is applied to the simulated and real resting-state EEG data. The results show that for simulated data analysis, the value of error in signal amplitude (Er) obtained by rt-cICA method was obviously lower than the traditional methods such as average artifact subtraction (P<0.005). In real EEG data analysis, the improvement of normalized power spectrum (INPS) calculated by rt-cICA method was much higher than other methods (P<0.005). In conclusion, the novel method proposed by this paper lays the technical foundation for further research on the fusion model of EEG-fMRI.
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.
Although a great number of studies have investigated the changes of resting-state functional connectivity (rsFC) in patients with mental disorders, such as depression and schizophrenia etc, little is known how stable the changes are, and whether temporal sad or happy mood can modulate the intrinsic rsFC. In our experiments, happy and sad video clips were used to induce temporally happy and sad mood states in 20 healthy young adults. We collected functional magnetic resonance imaging (fMRI) data while participants were watching happy or sad video clips, which were administrated in two consecutive days. Seed-based functional connectivity analyses were conducted using the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), and amygdala as seeds to investigate neural network related to executive function, attention, and emotion. We also investigated the association of the rsFC changes with emotional arousability level to understand individual differences. There is significantly stronger functional connectivity between the left DLPFC and posterior cingulate cortex (PCC) under sad mood than that under happy mood. The increased connectivity strength was positively correlated with subjects' emotional arousability. The increased positive correlation between the left DLPFC and PCC under sad relative to happy mood might reflect an increased processing of negative emotion-relevant stimuli. The easier one was induced by strong negative emotion (higher emotional arousability), the greater the left DLPFC-PCC connectivity was indicated, the greater the instability of the intrinsic rsFC was shown.
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.
This study sought to reveal the difference of brain functions at resting-state between subjects with sub-health and normal controls by using the functional magnetic resonance imaging (fMRI) technology. Resting-state fMRI scans were performed on 24 subjects of sub-health and on 24 healthy controls with gender, age and education matched with the sub-health persons. Compared to the healthy controls, the sub-health group showed significantly higher regional homogeneity (ReHo) in the left post-central gyrus and the right post-central gyrus. On the other hand, the sub-health group showed significantly lower ReHo in the left superior frontal gyrus, in the right anterior cingulated cortex and ventra anterior cingulate gyrus, in the left dorsolateral frontal gyrus, and in the right middle temporal gyrus. The Significant difference in ReHo suggests that thebsub-health persons have abnormalities in certain brain regions. It is proved that its specific action and meaning deserves further assessment.
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.
The emergence of real-time functional magnetic resonance imaging (rt-fMRI) has provided foundations for neurofeedback based on brain hemodynamics and has given the new opportunity and challenge to cognitive neuroscience research. Along with the study of advanced brain neural mechanisms, the regulation goal of rt-fMRI neurofeedback develops from the early specific brain region activity to the brain network connectivity more accordant with the brain functional activities, and the study of the latter may be a trend in the area. Firstly, this paper introduces basic principle and development of rt-fMRI neurofeedback. Then, it specifically discusses the current research status of brain connectivity neurofeedback technology, including research approaches, experimental methods, conclusions, and so on. Finally, it discusses the problems in this field in the future development.
ObjectiveSeizure-related respiratory or cardiac dysfunction was once thought to be the direct cause of sudden unexpected death in epilepsy (SUDEP), but both may be secondary to postictal cerebral inhibition. An important issue that has not been explored to date is the neural network basis of cerebral inhibition. Our aim was to investigate the features of neural networks in patients at high risk for SUDEP using a blood oxygen level-dependent (BOLD) resting-state functional MRI (Rs-fMRI) technique. MethodsRs-fMRI data were recorded from 13 patients at high risk for SUDEP and 12 patients at low risk for SUDEP. The amplitude of low-frequency fluctuations (ALFF) values were compared between the two groups to decipt the regional brain activities. ResultsCompared with patients at low risk for SUDEP, patients at high risk exhibited significant ALFF reductions in the right superior frontal gyrus, the left superior orbital frontal gyrus, the left insula and the left thalamus; and ALFF increase in the right middle cigulum gyrus, the right supplementary motor area and the left thalamus. ConclusionsThese findings highlight the need to understand the fundamental neural network dysfunction in SUDEP, which may fill the missing link between seizure-related cardiorespiratory dysfunction and SUDEP, and provide a promising neuroimaging biomarker for risk prediction of SUDEP.