Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach.
Neurol Sci
; 42(6): 2379-2390, 2021 Jun.
Article
en En
| MEDLINE
| ID: mdl-33052576
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Epilepsia del Lóbulo Temporal
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Neurol Sci
Asunto de la revista:
NEUROLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Irán