ArticlePloS one2015
Functional Connectivity Alterations in Epilepsy from Resting-State Functional MRI.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Thalamic connectivity mirrors spatial maps of network dysfunction in nonlesional focal epilepsy.Epilepsia · 2026Article
- Altered Effective Connectivity of the Attentional Network in Temporal Lobe Epilepsy with EEG Data.Bioengineering (Basel, Switzerland) · 2025Article
- Brain connectivity correlates of breathing and cardiac patterns in epilepsy: A study including SUDEP cases.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Applications of generative adversarial networks in neuroimaging and clinical neuroscience.NeuroImage · 2023Review
- Classification of partial seizures based on functional connectivity: A MEG study with support vector machine.Frontiers in neuroinformatics · 2022Article
- A Novel Knowledge Distillation-Based Feature Selection for the Classification of ADHD.Biomolecules · 2021Article
- Regional abnormality of functional connectivity is associated with clinical manifestations in individuals with intractable focal epilepsy.Scientific reports · 2021Article
- Localization of epileptic seizure focus by computerized analysis of fMRI recordings.Brain informatics · 2020Article
- Multi-Head Self-Attention Model for Classification of Temporal Lobe Epilepsy Subtypes.Frontiers in physiology · 2020Article
- Engineering brain activity patterns by neuromodulator polytherapy for treatment of disorders.Nature communications · 2019Article
- Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning.Brain connectivity · 2019Article
- Graph-to-signal transformation based classification of functional connectivity brain networks.PloS one · 2019Article
- Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks.The neuroradiology journal · 2017Review
- A MACHINE LEARNING MODEL TO PREDICT SEIZURE SUSCEPTIBILITY FROM RESTING-STATE FMRI CONNECTIVITY.Spring simulation conference (SpringSim)Article
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Authors and funding
4 authors.
Funding
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Abstract
The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.
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