ArticleJournal of magnetic resonance imaging : JMRI2015
Computer-automated focus lateralization of temporal lobe epilepsy using fMRI.
Article in Journal of magnetic resonance imaging : JMRI, 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 43 citations in OpenAlex.
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Abnormal functional connectivity density involvement in freezing of gait and its application for subtyping Parkinson's disease.Brain imaging and behavior · 2023Article
- Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery.Frontiers in surgery · 2023Review
- Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Article
- Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review.Frontiers in neuroscience · 2021Review
- Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study.Frontiers in neuroscience · 2021Article
- Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy.Frontiers in neuroscience · 2021Review
- Neurosurgery and artificial intelligence.AIMS neuroscience · 2021Review
- Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy.European radiology · 2019Article
- Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes.NeuroImage. Clinical · 2018Article
- Article
- Functional connectivity homogeneity correlates with duration of temporal lobe epilepsy.Epilepsy & behavior : E&B · 2015Article
- Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study.NeuroImage. Clinical · 2015Article
- Functional network alterations and their structural substrate in drug-resistant epilepsy.Frontiers in neuroscience · 2014Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo compare the performance of computer-automated diagnosis using functional magnetic resonance imaging (fMRI) interictal graph theory (CADFIG) to that achieved in standard clinical practice with MRI, for lateralizing the affected hemisphere in temporal lobe epilepsy (TLE). MATERIALS AND
methodsInterictal resting state fMRI and high-resolution MRI were performed on 14 left and 10 right TLE patients. Functional topology measures were calculated from fMRI using graph theory, and used to lateralize the epileptogenic hemisphere using quadratic discriminant analysis. Leave-one-out cross-validation prediction accuracy of CADFIG was compared to performance based on expert manual analysis (MA) of MRI, using video EEG as the "gold standard" for focus lateralization.
resultsCADFIG correctly lateralized 95.8% (23/24) of cases, compared to 66.7% (16/24) with expert MA of MRI. Combining MA with CADFIG allowed all cases (24/24) to be correctly lateralized. CADFIG correctly identified the affected hemisphere for all patients (8/8) where MRI failed to lateralize.
conclusionCADFIG based on fMRI lateralized the affected hemisphere in TLE with superior performance compared to expert MA of MRI. These results demonstrate that functional patterns in fMRI can be used with automated machine learning for diagnostic lateralization in TLE. Addition of fMRI-based tests to existing protocols for identifying the affected hemisphere in presurgical assessment can improve diagnostic accuracy and surgical outcome in TLE.
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Registered trials
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.