Isolated effective coherence analysis of epileptogenic networks in temporal lobe epilepsy using stereo-electroencephalography / 生物医学工程学杂志

Stereo-electroencephalography (SEEG) is widely used to record the electrical activity of patients' brain in clinical. The SEEG-based epileptogenic network can better describe the origin and the spreading of seizures, which makes it an important measure to localize epileptogenic zone (EZ). SEEG data from six patients with refractory epilepsy are used in this study. Five of them are with temporal lobe epilepsy, and the other is with extratemporal lobe epilepsy. The node outflow (out-degree) and inflow (in-degree) of information are calculated in each node of epileptic network, and the overlay between selected nodes and resected nodes is analyzed. In this study, SEEG data is transformed to bipolar montage, and then the epileptic network is established by using independent effective coherence (iCoh) method. The SEEG segments at onset, middle and termination of seizures in Delta, Theta, Alpha, Beta, and Gamma rhythms are used respectively. Finally, the K-means clustering algorithm is applied on the node values of out-degree and in-degree respectively. The nodes in the cluster with high value are compared with the resected regions. The final results show that the accuracy of selected nodes in resected region in the Delta, Alpha and Beta rhythm are 0.90, 0.88 and 0.89 based on out-degree values in temporal lobe epilepsy patients respectively, while the in-degree values cannot differentiate them. In contrast, the out-degree values are higher outside the temporal lobe in the patient with extratemporal lobe epilepsy. Based on the out-degree feature in low-frequency epileptic network, this study provides a potential quantitative measure for identifying patients with temporal lobe epilepsy in clinical.

Social network analysis and modeling of cellphone-based syndromic surveillance data for Ebola in Sierra Leone

OBJECTIVE@#To explore and visualize the connectivity of suspected Ebola cases and surveillance callers who used cellphone technology in Moyamba District in Sierra Leone for Ebola surveillance, and to examine the demographic differences and characteristics of Ebola surveillance callers who make more calls as well as those callers who are more likely to make at least one positive Ebola call.@*METHODS@#Surveillance data for 393 suspected Ebola cases (192 males, 201 females) were collected from October 23, 2014 to June 28, 2015 using cellphone technology. UCINET and NetDraw software were used to explore and visualize the social connectivity between callers and suspected Ebola cases. Poisson and logistic regression analyses were used to do multivariable analysis.@*RESULTS@#The entire social network was comprised of 393 ties and 745 nodes. Women (AOR = 0.33, 95% CI [0.14, 0.81]) were associated with decreased odds of making at least one positive Ebola surveillance call compared to men. Women (IR = 0.63, 95% CI [0.49, 0.82]) were also associated with making fewer Ebola surveillance calls compared to men.@*CONCLUSION@#Social network visualization can analyze syndromic surveillance data for Ebola collected by cellphone technology with unique insights.

Social network analysis and modeling of cellphone-based syndromic surveillance data for Ebola in Sierra Leone

Objective To explore and visualize the connectivity of suspected Ebola cases and surveillance callers who used cellphone technology in Moyamba District in Sierra Leone for Ebola surveillance, and to examine the demographic differences and characteristics of Ebola surveillance callers who make more calls as well as those callers who are more likely to make at least one positive Ebola call. Methods Surveillance data for 393 suspected Ebola cases (192 males, 201 females) were collected from October 23, 2014 to June 28, 2015 using cellphone technology. UCINET and NetDraw software were used to explore and visualize the social connectivity between callers and suspected Ebola cases. Poisson and logistic regression analyses were used to do multivariable analysis. Results The entire social network was comprised of 393 ties and 745 nodes. Women (AOR = 0.33, 95% CI [0.14, 0.81]) were associated with decreased odds of making at least one positive Ebola surveillance call compared to men. Women (IR = 0.63, 95% CI [0.49, 0.82]) were also associated with making fewer Ebola surveillance calls compared to men. Conclusion Social network visualization can analyze syndromic surveillance data for Ebola collected by cellphone technology with unique insights.