ArticleBrain connectivity2020
Graph Theory Analysis of Functional Connectivity Combined with Machine Learning Approaches Demonstrates Widespread Network Differences and Predicts Clinical Variables in Temporal Lobe Epilepsy.
Article in Brain connectivity, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 50 citations in OpenAlex.
- Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Mapping human thalamocortical connectivity with electrical stimulation and recording.Nature neuroscience · 2025Article
- Disrupted modular and hub topology in right temporal lobe epilepsy: a multimodal MRI network analysis.Frontiers in neurology · 2025Article
- Bundle-specific tractography approach for identifying white matter microstructural changes following traumatic brain injury in rats: An EpiBioS4Rx study.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped.Physical and engineering sciences in medicine · 2024Article
- Associations of Cerebral Blood Flow Patterns With Gray and White Matter Structure in Patients With Temporal Lobe Epilepsy.Neurology · 2024Article
- Causal Cortical and Thalamic Connections in the Human Brain.bioRxiv : the preprint server for biology · 2024Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy.ArXiv · 2024Article
- Hub overload and failure as a final common pathway in neurological brain network disorders.Network neuroscience (Cambridge, Mass.) · 2024Review
- Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance.NeuroImage · 2023Article
- Preoperative white matter network organization and memory decline after epilepsy surgery.Journal of neurosurgery · 2023Article
- Multi-shell connectome DWI-based graph theory measures for the prediction of temporal lobe epilepsy and cognition.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Functional connectivity alterations in traumatic brain injury patients with late seizures.Neurobiology of disease · 2023Article
- Article
- Can Artificial Intelligence Diagnose Transient Global Amnesia Using Electroencephalography Data?Journal of clinical neurology (Seoul, Korea) · 2023Article
- DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization From Resting-State fMRI Connectivity.IEEE transactions on bio-medical engineering · 2023Article
- Decreasing Shortest Path Length of the Sensorimotor Network Induces Frontal Glioma-Related Epilepsy.Frontiers in oncology · 2022Article
- Contralesional Sensorimotor Network Participates in Motor Functional Compensation in Glioma Patients.Frontiers in oncology · 2022Article
- Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments.PloS one · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Understanding how global brain networks are affected in epilepsy may elucidate the pathogenesis of seizures and its accompanying neurobehavioral comorbidities. We investigated functional changes within neural networks in temporal lobe epilepsy (TLE) using graph theory analysis of resting-state connectivity. Twenty-seven TLE presurgical patients (age 41.0 ± 12.3 years) and 85 age, gender, and handedness equivalent healthy controls (HCs; age 39.7 ± 16.9 years) were enrolled. Eyes-closed resting-state functional magnetic resonance image scans were analyzed to compare network properties and functional connectivity (FC) changes. TLE subjects showed significantly higher global efficiency, lower clustering coefficient ratio, and lower shortest path lengths ratio than HCs, as an indication of a more synchronized, yet less segregated network. A trend of functional reorganization with a shift of network hubs to the contralateral hemisphere was noted in TLE subjects. Support vector machine (SVM) with linear kernel was trained to separate between neural networks in TLE and HC subjects based on graph measurements. SVM analysis allowed separation between TLE and HC networks with 80.66% accuracy using eight features of graph measurements. Support vector regression (SVR) was used to predict neurocognitive performance from graph metrics. An SVR linear predictor showed discriminative prediction accuracy for four key neurocognitive variables in TLE (absolute
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.