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.
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.
目的 觀察蜂蜇傷致橫紋肌溶解的MRI表現，探討MRI對蜂蜇傷致橫紋肌溶解癥的診斷價值。 方法 收集2008年9月-2009年12月急診科及腎內科蜂蜇傷患者4例。對其行蜇傷部位MR增強掃描，對其中1例患者行遠離部位肢體掃描。總結MRI征象，評價MRI在蜂蜇傷所致橫紋肌溶解臨床診治中的作用。 結果 蜇傷部位顯示T1WI稍低，T2WI高信號影像，在T2WI加壓脂影像中顯示最為清晰，橫紋肌損傷有局部隨肌間隙擴散趨勢，但遠端無蜇傷肌肉受累。 結論 蜂蜇傷導致的橫紋肌溶解可在MRI影像上得到直觀反映。MRI具有良好的軟組織對比度，能及時反映橫紋肌受累范圍及程度、治療后恢復情況等，可為其臨床診治評估提供有利信息。
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.
目的 對邊緣性腦炎患者磁共振（MR）影像學表現進行探討，以明確急性邊緣性腦炎的特異性磁共振影像學征象，了解磁共振成像（MRI）在急性邊緣性腦炎患者診斷以及病情評價中的應用價值。 方法 通過對2008年12月-2010年1月間臨床收集的8例邊緣性腦炎患者進行MRI檢查，并回顧性分析不同序列磁共振影像學表現，總結MRI征象，評價MRI檢查在急性邊緣性腦炎的臨床診治中的作用。 結果 邊緣性腦炎患者顯示特異性的雙側邊緣系統腫脹及信號異常，呈T1WI低信號影；T2WI及FLAIR成像為高信號影像；增強掃描未見確切異常強化；FLAIR成像是檢測病變最敏感的序列。部分患者可見累及邊緣系統外結構。隨訪病例影像學改變可有明顯好轉。 結論 邊緣性腦炎特異性損傷邊緣系統，以雙側海馬為主，MRI影像可直觀反映邊緣性腦炎早期及隨訪期改變，能直接了解邊緣性腦炎顱內受累范圍、程度及治療后恢復情況等，可為其臨床及時診斷及治療評估提供有利信息。