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

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Abstract

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.

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Acknowledgments

Authors must recognize and show graduate towards the prodigious contribution of Iranian National Brain Mapping Laboratory (NBML), Tehran, Iran, for data acquisition service for the entire project.

Funding

This work was partially funded and supported by Iran’s National Elites Foundation, National Institute for Medical Research Development (Grant No. 971683), and Cognitive Sciences & Technologies Council (Grant No. 6431), between 2017 and 2019.

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Correspondence to Mohammad-Reza Nazem-Zadeh.

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Fallahi, A., Pooyan, M., Lotfi, N. et al. Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach. Neurol Sci 42, 2379–2390 (2021). https://doi.org/10.1007/s10072-020-04759-x

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