Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

Purpose

Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE).

Methods

Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE.

Results

Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%.

Conclusion

Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

     

COMPARISON OF ELISA AND lEA FOR ESTIMATION OF ANTIBODY LEVELS OF CATTLE TO THEILERIA ANNULATA VACCINE

Bovine tropical Theileriosis caused by Theileria annulata is an economically important disease of cattle. An enzyme linked immunosorbent assay (ELISA) was used to determine antibody levels in vaccinated and unvaccinated cattle, using cellular schizont as antigen and its results were compared with immunofluorescent assay (IFA). For this test 126 sera collected (105 vaccinated, 31 not vaccinated) from cows and assayed with ELISA which among them 104 sera were positive and 32 sera were negative. Same sample assayed with IFA in which 99 were positive sera and 37 were negative sera. Thereby the sensitivity and specificity of this ELISA on comparsion with lEA were 95.5% and 66.6% respectively. This study revealed that ELISA could be successfully used for both differentiating vaccinated and not vaccinated cattle and obtaining the titer of vaccinated cattle.     

Temporal and extratemporal atrophic manifestation of temporal lobe epilepsy using voxel-based morphometry and corticometry: clinical application in lateralization of epileptogenic zone

Neurological Sciences - Advances in MRI acquisition and data processing have become important for revealing brain structural changes. Previous studies have reported widespread structural brain...