Myopia is a major problem of public health in China, and even in the world, and slowing down the progress of myopia has become a hot issue of concern. However, the effects of the current therapeutic and interventional modalities to myopia, including optical lenses, chemical drugs, and laser surgery, the effect of treatment and intervention is not very satisfactory, and these modalities may incur some side effects. This situation suggests that the pathogenic and regulatory mechanisms of myopia remain elusive, and the myopia treatments lack the accurate and effective targets to the etiology. A complete visual experience depends on the entire visual pathway from the retina to the visual cortex, in which any structural and functional defect can lead to visual abnormalities. In recent years, with the advances in the infrared spectroscopy and the magnetic resonance imaging technology, more and more evidence has shown that the progression of myopia is related to the visual cortex. Improving the functional connectivity and blood prefusion between different regions of the visual cortex may impede myopia profession. In-depth understanding of the interaction between myopia and the visual cortex is helpful to search for accurate and effective myopia treatment targets and novel intervention strategies.
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.
目的 应用脑功能磁共振探讨暴力人群对愉快和悲伤面部表情认知障碍的脑功能机制。 方法 2009年3月-8月，应用宾夕法尼亚大学三维彩色愉快和悲伤情绪面部表情图片作为情绪刺激，对男性暴力行为组(n=20)和与之相匹配的正常男性(n=21)，进行功能磁共振扫描，并采用SPM2对数据进行分析。 结果 愉快情绪图片刺激下，正常组比暴力组激活增加的脑区有左额中回、左前扣带回、左楔前叶、左颞中回、右中央后回和右侧小脑。悲伤情绪图片刺激下，正常组比暴力组激活增加的脑区有左额中回、左后扣带回、左楔前叶、右小脑、左颞中回及颞上回。 结论 暴力行为者对愉快和悲伤情绪的脑激活减低，主要表现在前额叶-颞叶-边缘脑区。
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.
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.
目的 利用局部一致性（ReHo）方法探测创伤后应激障碍（PTSD）患者在静息状态下是否存在着大脑功能异常。 方法 2010年5月－7月对18例未经治疗的地震PTSD患者和19例同样经历地震但未患PTSD的对照者进行了静息态功能磁共振成像（Rs-fMRI） 扫描。应用ReHo方法处理Rs-fMRI数据，得出PTSD患者的异常脑区，并将患者存在组间差异的脑区ReHo值与临床用PTSD诊断量表（CAPS）、汉密尔顿抑郁量表（HAMD）和汉密尔顿焦虑量表（HAMA）分别进行相关分析。 结果 ① PTSD组ReHo显著增加的脑区包括右侧颞下回、楔前叶、顶下叶、中扣带回，左侧枕中回以及左/右侧后扣带回；ReHo显著降低的脑区包括左侧海马和左/右侧腹侧前扣带回。② 异常脑区中后扣带回和右侧中扣带回ReHo与HAMD呈负相关（中扣带回r=?0.575，P=0.012；右侧后扣带回：r=?0.507，P=0.032），其余脑区ReHo与临床指标无明显相关性（P＞0.05），左侧海马与CAPS的相关性相对其他脑区较大（r=?0.430，P=0.075）。 结论 PTSD患者在静息状态下即存在着局部脑功能活动的降低和增加，ReHo方法可能有助于研究PTSD患者静息状态脑活动。
ObjectiveTo reveal impairments in the perceptual networks in tuberous sclerosis complex (TSC) with epilepsy by functional connectivity MRI (fcMRI). MethodsThe fcMRI-based independent component analysis (ICA) was used to measure the resting state functional connectivity in nine TSC patients with epilepsy recruited from June 2010 to June 2012 and perceptual networks including the sensorimotor network (SMN), visual network (VN), and auditory network (AN) were investigated. The correlation between Z values in regions of interest (ROIs) and age of seizure onset or duration of epilepsy were analyzed. ResultsCompared with the controls, the TSC patients with epilepsy presented decreased functional connectivity in primary visual cortex within the VN networks and there were no increased connectivity. Increased connectivity in left middle temporal gyrus and inferior temporal gyrus was found and decreased connectivity was detected in right inferior frontal gyrus within AN networks. Decreased connectivity was detected at the right inferior frontal gyrus and the increase in connectivity was found in right thalamus within SMN netwoks. No significant correlations were found between Z values in ROIs including the primary visual cortex within the VN, right thalamus and inferior frontal gyrus within SMN, left temporal lobe and right inferior frontal gyrus within AN and the duration of the disease or the age of onset. ConclusionFhere is altered (both increased and decreased) functional connectivity in the perceptual networks of TSC patients with epilepsy. The decreased functional connectivity may reflect the dysfunction of correlative perceptual networks in TSC patients, and the increased functional connectivity may indicate the compensatory mechanism or reorganization of cortical networks. Our fcMRI study may contribute to the understanding of neuropathophysiological mechanisms underlying perceptual impairments in TSC patients with epilepsy.