ArticleNeurology2020
Clinical utility of structural connectomics in predicting memory in temporal lobe epilepsy.
Article in Neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- Assessing the ecological quality of Rheum tanguticum based on machine learning models and cultivation verification.NPJ science of food · 2025Article
- Connectomes in Temporal Lobe and Idiopathic Generalized Epilepsies.Journal of clinical medicine · 2025Review
- Characterizing white matter connectome abnormalities in patients with temporal lobe epilepsy using threshold-free network-based statistics.Brain and behavior · 2024Article
- Neurobehavioral and Clinical Comorbidities in Epilepsy: The Role of White Matter Network Disruption.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2024Review
- Preoperative white matter network organization and memory decline after epilepsy surgery.Journal of neurosurgery · 2023Article
- Association of neighborhood deprivation with white matter connectome abnormalities in temporal lobe epilepsy.Epilepsia · 2023Article
- Brain structural connectivity sub typing in unilateral temporal lobe epilepsy.Brain imaging and behavior · 2022Article
- Multimodal connectome biomarkers of cognitive and affective dysfunction in the common epilepsies.Network neuroscience (Cambridge, Mass.) · 2022Article
- Advanced neuroimaging techniques in epilepsy.Current opinion in neurology · 2022Review
- Clinical Utility of Structural Connectomics in Predicting Memory in Temporal Lobe Epilepsy.Neurology · 2022Article
- Emerging Trends in Neuroimaging of Epilepsy.Epilepsy currents · 2021Article
- Artificial Intelligence Applications in the Imaging of Epilepsy and Its Comorbidities: Present and Future.Epilepsy currentsArticle
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo determine the predictive power of white matter neuronal networks (i.e., structural connectomes [SCs]) in discriminating memory-impaired patients with temporal lobe epilepsy (TLE) from those with normal memory.
methodsT1- and diffusion MRI (dMRI), clinical variables, and neuropsychological measures of verbal memory were available for 81 patients with TLE. Prediction of memory impairment was performed with a tree-based classifier (XGBoost) for 4 models: (1) a clinical model including demographic and clinical features, (2) a hippocampal volume (HCV) model, (3) a tract model including 5 temporal lobe white matter association tracts derived from a dMRI atlas, and (4) an SC model based on dMRI. SCs were derived by extracting cortical-cortical connections from a temporal lobe subnetwork with probabilistic tractography. Principal component (PC) analysis was then applied to reduce the dimensionality of the SC, yielding 10 PCs. Multimodal models were also tested combining SCs and tracts with HCV. Each model was trained on 48 patients from 1 epilepsy center and tested on 33 patients from a different center.
resultsMultimodal models that included the SC + HCV model yielded the highest classification accuracy (81%; 0.90 sensitivity; 0.67 specificity), outperforming the clinical model (61%;
conclusionSCs and tract-based models are stronger predictors of memory impairment in TLE than HCVs and clinical variables. However, SCs may provide additional information about local cortical-cortical connectivity contributing to memory that is not captured in large association tracts.
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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.