ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2021
Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach.
Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Dynamic Analysis of Gray Matter and White Matter Functional Network Connectivity in Individuals with Temporal Lobe Epilepsy.Research square · 2026Article
- Machine learning-based identification and brain network characterization of flight cadets compared with air traffic control (ATC) students using static and dynamic functional connectivity.Frontiers in human neuroscience · 2026Article
- Detection of brain network abnormalities by graph invariants in Alzheimer's disease using MRI images.Scientific reports · 2025Article
- Language and Memory Network Alterations in Temporal Lobe Epilepsy: A Functional and Structural Connectivity Study.AJNR. American journal of neuroradiology · 2025Article
- Disrupted modular and hub topology in right temporal lobe epilepsy: a multimodal MRI network analysis.Frontiers in neurology · 2025Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network.Frontiers in neuroscience · 2023Article
- Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis.Frontiers in neuroscience · 2023Article
- Cortical thickness analysis in temporal lobe epilepsy using fully Bayesian spectral method in magnetic resonance imaging.BMC medical imaging · 2022Article
- Comparison of multimodal findings on epileptogenic side in temporal lobe epilepsy using self-organizing maps.Magma (New York, N.Y.) · 2022Article
- Common functional connectivity alterations in focal epilepsies identified by machine learning.Epilepsia · 2022Article
- Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models.Entropy (Basel, Switzerland) · 2022Article
- Classification of partial seizures based on functional connectivity: A MEG study with support vector machine.Frontiers in neuroinformatics · 2022Article
- Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network.Brain sciences · 2021Article
- Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy.Frontiers in neuroscience · 2021Review
- Altered Functional Topological Organization in Type-2 Diabetes Mellitus With and Without Microvascular Complications.Frontiers in neuroscience · 2021Article
- Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review.Frontiers in neuroscience · 2021Review
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeFunctional 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).
methodsSix 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.
resultsOur 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%.
conclusionAccounting 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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