FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2916415046> ?p ?o ?g. }

• W2916415046 endingPage "193" @default.
• W2916415046 startingPage "184" @default.
• W2916415046 abstract "The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01–0.027 Hz) and Slow-4 (0.027–0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01–0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder." @default.
• W2916415046 created "2019-03-02" @default.
• W2916415046 creator A5000579235 @default.
• W2916415046 creator A5000936837 @default.
• W2916415046 creator A5003751009 @default.
• W2916415046 creator A5011757473 @default.
• W2916415046 creator A5012962437 @default.
• W2916415046 creator A5016369937 @default.
• W2916415046 creator A5020454494 @default.
• W2916415046 creator A5021076382 @default.
• W2916415046 creator A5024280709 @default.
• W2916415046 creator A5024357271 @default.
• W2916415046 creator A5029597528 @default.
• W2916415046 creator A5030109109 @default.
• W2916415046 creator A5032054525 @default.
• W2916415046 creator A5038554111 @default.
• W2916415046 creator A5043743232 @default.
• W2916415046 creator A5046953642 @default.
• W2916415046 creator A5050110257 @default.
• W2916415046 creator A5050772700 @default.
• W2916415046 creator A5054814428 @default.
• W2916415046 creator A5062052975 @default.
• W2916415046 creator A5065156623 @default.
• W2916415046 creator A5066330351 @default.
• W2916415046 creator A5067812949 @default.
• W2916415046 creator A5075148309 @default.
• W2916415046 creator A5085317223 @default.
• W2916415046 creator A5091196272 @default.
• W2916415046 creator A5091228230 @default.
• W2916415046 creator A5091669306 @default.
• W2916415046 date "2019-03-01" @default.
• W2916415046 modified "2023-09-27" @default.
• W2916415046 title "Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning" @default.
• W2916415046 cites W1499705617 @default.
• W2916415046 cites W1817561967 @default.
• W2916415046 cites W1824984390 @default.
• W2916415046 cites W1931883112 @default.
• W2916415046 cites W1968263405 @default.
• W2916415046 cites W1970807601 @default.
• W2916415046 cites W1973855259 @default.
• W2916415046 cites W1974508089 @default.
• W2916415046 cites W1979138969 @default.
• W2916415046 cites W1982127093 @default.
• W2916415046 cites W1983208069 @default.
• W2916415046 cites W1984053826 @default.
• W2916415046 cites W1988332547 @default.
• W2916415046 cites W1990134753 @default.
• W2916415046 cites W1992318477 @default.
• W2916415046 cites W1993804466 @default.
• W2916415046 cites W2004293194 @default.
• W2916415046 cites W2009494091 @default.
• W2916415046 cites W2011968953 @default.
• W2916415046 cites W2024665862 @default.
• W2916415046 cites W2024729467 @default.
• W2916415046 cites W2031167046 @default.
• W2916415046 cites W2038068904 @default.
• W2916415046 cites W2057312656 @default.
• W2916415046 cites W2062704956 @default.
• W2916415046 cites W2077608427 @default.
• W2916415046 cites W2078563124 @default.
• W2916415046 cites W2091290379 @default.
• W2916415046 cites W2093174553 @default.
• W2916415046 cites W2096352448 @default.
• W2916415046 cites W2100634972 @default.
• W2916415046 cites W2117140276 @default.
• W2916415046 cites W2130513423 @default.
• W2916415046 cites W2136487516 @default.
• W2916415046 cites W2142164352 @default.
• W2916415046 cites W2142875089 @default.
• W2916415046 cites W2143426320 @default.
• W2916415046 cites W2151721316 @default.
• W2916415046 cites W2161502085 @default.
• W2916415046 cites W2161819003 @default.
• W2916415046 cites W2320482265 @default.
• W2916415046 cites W2473436919 @default.
• W2916415046 cites W2492307518 @default.
• W2916415046 cites W2499800833 @default.
• W2916415046 cites W2524909356 @default.
• W2916415046 cites W2731789640 @default.
• W2916415046 cites W2743692336 @default.
• W2916415046 cites W2753404107 @default.
• W2916415046 cites W2765754703 @default.
• W2916415046 cites W2899900491 @default.
• W2916415046 cites W2977883299 @default.
• W2916415046 cites W4239510810 @default.
• W2916415046 cites W4243274381 @default.
• W2916415046 cites W581674602 @default.
• W2916415046 doi "https://doi.org/10.1089/brain.2018.0601" @default.
• W2916415046 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6484357" @default.
• W2916415046 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30803273" @default.
• W2916415046 hasPublicationYear "2019" @default.
• W2916415046 type Work @default.
• W2916415046 sameAs 2916415046 @default.
• W2916415046 citedByCount "14" @default.
• W2916415046 countsByYear W29164150462019 @default.
• W2916415046 countsByYear W29164150462020 @default.
• W2916415046 countsByYear W29164150462021 @default.
• W2916415046 countsByYear W29164150462022 @default.

• next