ArticleSpring simulation conference (SpringSim)
A MACHINE LEARNING MODEL TO PREDICT SEIZURE SUSCEPTIBILITY FROM RESTING-STATE FMRI CONNECTIVITY.
Article in Spring simulation conference (SpringSim). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
10 citing papers in PubMed, 19 citations in OpenAlex.
- Harnessing machine learning for functional connectivity-based feature discovery in post-traumatic epilepsy.Neuroimage. Reports · 2026Article
- Comprehensive review of machine learning and deep learning techniques for epileptic seizure detection and prediction based on neuroimaging modalities.Visual computing for industry, biomedicine, and art · 2025Review
- Changes in functional connectivity in relapsing-remitting multiple sclerosis spinal cord measured via region-based and data-driven analyses.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis.Brain sciences · 2024Article
- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
- An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data.Bioengineering (Basel, Switzerland) · 2023Article
- Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets.Brain connectivity · 2022Article
- Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients.Frontiers in neuroscience · 2020Article
- Imaging biomarkers of posttraumatic epileptogenesis.Epilepsia · 2019Review
- Default Mode Network Resting State Connectivity Derived From Task-Based fMRI: A Validation Study in People With Epilepsy.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Traumatic brain injury (TBI) is a leading cause of disability globally. Many patients develop post-traumatic epilepsy, or recurrent seizures following TBI. In recent years, significant efforts have been made to identify biomarkers of epileptogenesis that may assist in preventing seizure occurrence by identifying high-risk patients. We present a novel method of assessing seizure susceptibility using data from 49 patients enrolled in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We employ a machine learning paradigm that utilizes a Random Forest classifier trained with resting-state functional magnetic resonance imaging (fMRI) data to predict seizure outcomes. Following 100 rounds of stratified cross-validation with 70% of resting state fMRI scans as the training set and 30% as the testing set, our model was found to assess seizure outcome in the testing set with 69% accuracy. To validate the method, we compared our results with classification by Support Vector Machines and Neural Network classifiers.
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What OpenQuestion holds
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.