Abstract
Objectives: To investigate the performance of substate classification of children with benign epilepsy with centrotemporal spikes (BECTS) by granger causality density (GCD) based support vector machine (SVM) model.Methods: Forty-two children with BECTS (21 females, 21 males; mean age, 8.6 ± 1.96 years) were classified into interictal epileptic discharges (IEDs; 11 females, 10 males) and non-IEDs (10 females, 11 males) substates depending on presence of central-temporal spikes or not. GCD was calculated on four metrics, including inflow, outflow, total-flow (inflow + outflow) and int-flow (inflow – outflow) connectivity. SVM classifier was applied to discriminate the two substates.Results: The Rolandic area, caudate, dorsal attention network, visual cortex, language networks, and cerebellum had discriminative effect on distinguishing the two substates. Relative to each of the four GCD metrics, using combined metrics could reach up the classification performance (best value; AUC, 0.928; accuracy rate, 90.83%; sensitivity, 90%; specificity, 95%), especially for the combinations with more than three GCD metrics. Specially, combined the inflow, outflow and int-flow metric received the best classification performance with the highest AUC value, classification accuracy and specificity. Furthermore, the GCD-SVM model received good and stable classification performance across 14 dimension reduced data sets.Conclusions: The GCD-SVM model could be used for BECTS substate classification, which might have the potential to provide a promising model for IEDs detection. This may help assist clinicians for administer drugs and prognosis evaluation.
Highlights
Functional neuroimaging methods have been widely used to describe functional network changes and the relationships among different brain networks in diseases
The combination with inflow, outflow and int-flow connectivity significantly reached up the classification accuracy and received the best classification performance with the highest accuracy rate (90.83%) and specificity (95%), as well as extremely high area under curve (AUC) value (0.928) and sensitivity (86%) (Figure 3C)
The granger causality density (GCD) metric of total-flow connectivity entered into the classification and the sensitivity could reach up to 90% (Figure 3D), but the AUC value, accuracy rate and specificity decreased (Table 3)
Summary
Functional neuroimaging methods have been widely used to describe functional network changes and the relationships among different brain networks in diseases. BECTS Classification by GCD-SVM Model over the whole brain, could reflect the communication amounts among brain regions [1] Both methods cannot reflect the directed information flow among different brain regions. The proposed GCD analysis could avoid redundancy and overfitting, which makes it suitable for neuroimaging data analysis, and even for high-dimensional and short dataset This method could provide an opportunity for unbiased searches abnormalities within the entire connectivity matrix without any priori hypotheses, and reflect the directed information flow among different brain networks from voxel levels. It has not been applied into any diseases
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