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.
Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD ［age: (8.27 ± 2.77) years］ and 23 normal children ［age: (8.70 ± 2.58) years］ were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children. In this study, visual brain function network features were used for classification, with a classification accuracy of up to 96%. Compared with the traditional method of constructing a whole brain network, the accuracy was improved by about 10%. The test results show that the use of visual area brain function network analysis can better distinguish ADHD children from normal children. This method has certain help to distinguish the brain network between ADHD children and normal children, and is helpful for the auxiliary diagnosis of ADHD children.
目的 观察蜂蜇伤致横纹肌溶解的MRI表现，探讨MRI对蜂蜇伤致横纹肌溶解症的诊断价值。 方法 收集2008年9月-2009年12月急诊科及肾内科蜂蜇伤患者4例。对其行蜇伤部位MR增强扫描，对其中1例患者行远离部位肢体扫描。总结MRI征象，评价MRI在蜂蜇伤所致横纹肌溶解临床诊治中的作用。 结果 蜇伤部位显示T1WI稍低，T2WI高信号影像，在T2WI加压脂影像中显示最为清晰，横纹肌损伤有局部随肌间隙扩散趋势，但远端无蜇伤肌肉受累。 结论 蜂蜇伤导致的横纹肌溶解可在MRI影像上得到直观反映。MRI具有良好的软组织对比度，能及时反映横纹肌受累范围及程度、治疗后恢复情况等，可为其临床诊治评估提供有利信息。
目的 对边缘性脑炎患者磁共振（MR）影像学表现进行探讨，以明确急性边缘性脑炎的特异性磁共振影像学征象，了解磁共振成像（MRI）在急性边缘性脑炎患者诊断以及病情评价中的应用价值。 方法 通过对2008年12月-2010年1月间临床收集的8例边缘性脑炎患者进行MRI检查，并回顾性分析不同序列磁共振影像学表现，总结MRI征象，评价MRI检查在急性边缘性脑炎的临床诊治中的作用。 结果 边缘性脑炎患者显示特异性的双侧边缘系统肿胀及信号异常，呈T1WI低信号影；T2WI及FLAIR成像为高信号影像；增强扫描未见确切异常强化；FLAIR成像是检测病变最敏感的序列。部分患者可见累及边缘系统外结构。随访病例影像学改变可有明显好转。 结论 边缘性脑炎特异性损伤边缘系统，以双侧海马为主，MRI影像可直观反映边缘性脑炎早期及随访期改变，能直接了解边缘性脑炎颅内受累范围、程度及治疗后恢复情况等，可为其临床及时诊断及治疗评估提供有利信息。
Anesthesia consciousness monitoring is an important issue in basic neuroscience and clinical applications, which has received extensive attention. In this study, in order to find the indicators for monitoring the state of clinical anesthesia, a total of 14 patients undergoing general anesthesia were collected for 5 minutes resting electroencephalogram data under three states of consciousness (awake, moderate and deep anesthesia). Sparse partial least squares (SPLS) and traditional synchronized likelihood (SL) are used to calculate brain functional connectivity, and the three conscious states before and after anesthesia were distinguished by the connection features. The results show that through the whole brain network analysis, SPLS and traditional SL method have the same trend of network parameters in different states of consciousness, and the results obtained by SPLS method are statistically significant (P<0.05). The connection features obtained by the SPLS method are classified by the support vector machine, and the classification accuracy is 87.93%, which is 7.69% higher than that of the connection feature classification obtained by SL method. The results of this study show that the functional connectivity based on the SPLS method has better performance in distinguishing three kinds of consciousness states, and may provides a new idea for clinical anesthesia monitoring.
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.