Graph theory application with functional connectivity to distinguish left from right temporal lobe epilepsy

Area of Science:

• Neuroscience
• Graph Theory
• Medical Imaging

Background:

• Temporal lobe epilepsy (TLE) is a common neurological disorder.
• Determining seizure laterality is crucial for effective presurgical planning.
• Current methods for lateralization may require further refinement.

Purpose of the Study:

• To apply graph theory and functional connectivity to differentiate left from right TLE.
• To explore brain network alterations in TLE patients.
• To assess the potential of network analysis as a biomarker for TLE laterality.

Main Methods:

• Resting-state functional MRI (rs-fMRI) was used to examine functional connectivity in default mode (DMN), attention (AN), limbic (LN), sensorimotor (SN), and visual (VN) networks.
• Local nodal degree, a graph theory metric, was calculated for each network.
• Multivariate logistic regression analyzed the accuracy of seizure laterality identification.

Main Results:

• Left and right TLE patients exhibited distinct functional connectivity patterns compared to controls.
• The limbic network (LN) achieved 82.9% accuracy in determining TLE laterality.
• Combining network attributes improved accuracy to 94.3% for seizure lateralization.

Conclusions:

• Graph theory applied to brain network analysis is a valuable tool for TLE laterality determination.
• This approach can serve as a potential biomarker to enhance presurgical decision-making in TLE.
• Functional connectivity analysis offers a promising avenue for improving diagnostic confidence in TLE.