@article{RN269,
   author = {Drenthen, G. S. and Jansen, J. F. A. and Gommer, E. and Gupta, L. and Hofman, P. A. M. and van Kranen-Mastenbroek, V. H. and Hilkman, D. M. and Vlooswijk, M. C. G. and Rouhl, R. P. W. and Backes, W. H.},
   title = {Predictive value of functional MRI and EEG in epilepsy diagnosis after a first seizure},
   journal = {Epilepsy Behav},
   volume = {115},
   pages = {107651},
   note = {Drenthen, Gerhard S
Jansen, Jacobus F A
Gommer, Erik
Gupta, Lalit
Hofman, Paul A M
van Kranen-Mastenbroek, Vivianne H
Hilkman, Danny M
Vlooswijk, Marielle C G
Rouhl, Rob P W
Backes, Walter H
eng
Epilepsy Behav. 2021 Feb;115:107651. doi: 10.1016/j.yebeh.2020.107651. Epub 2020 Dec 11.},
   abstract = {It is often difficult to predict seizure recurrence in subjects who have suffered a first-ever epileptic seizure. In this study, the predictive value of physiological signals measured using Electroencephalography (EEG) and functional MRI (fMRI) is assessed. In particular those patients developing epilepsy (i.e. a second unprovoked seizure) that were initially evaluated as having a low risk of seizure recurrence are of interest. In total, 26 epilepsy patients, of which 8 were initially evaluated as having a low risk of seizure recurrence (i.e. converters), and 17 subjects with only a single seizure were included. All subjects underwent routine EEG as well as fMRI measurements. For diagnostic classification, features related to the temporal dynamics were determined for both the processed EEG and fMRI data. Subsequently, a logistic regression classifier was trained on epilepsy and first-seizure subjects. The trained model was tested using the clinically relevant converters group. The sensitivity, specificity, and AUC (mean+/-SD) of the regression model including metrics from both modalities were 74+/-19%, 82+/-18%, and 0.75+/-0.12, respectively. Positive and negative predictive values (mean+/-SD) of the regression model with both EEG and fMRI features are 84+/-14% and 78+/-12%. Moreover, this EEG/fMRI model showed significant improvements compared to the clinical diagnosis, whereas the models using metrics from either EEG or fMRI do not reach significance (p>0.05). Temporal metrics computationally derived from EEG and fMRI time signals may clinically aid and synergistically improve the predictive value in a first-seizure sample.},
   keywords = {*Electroencephalography
*Epilepsy/diagnostic imaging
Humans
Magnetic Resonance Imaging
Predictive Value of Tests
Seizures/diagnostic imaging
*First fit
*New-onset epilepsy
*fMRI
competing financial interests or personal relationships that could have appeared
to influence the work reported in this paper.},
   ISSN = {1525-5069 (Electronic)
1525-5050 (Linking)},
   DOI = {10.1016/j.yebeh.2020.107651},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/33309424},
   year = {2021},
   type = {Journal Article}
}

{"_id":"3SMSB2udeMTcFrJxX","bibbaseid":"drenthen-jansen-gommer-gupta-hofman-vankranenmastenbroek-hilkman-vlooswijk-etal-predictivevalueoffunctionalmriandeeginepilepsydiagnosisafterafirstseizure-2021","author_short":["Drenthen, G. S.","Jansen, J. F. A.","Gommer, E.","Gupta, L.","Hofman, P. A. M.","van Kranen-Mastenbroek, V. H.","Hilkman, D. M.","Vlooswijk, M. C. G.","Rouhl, R. P. W.","Backes, W. H."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Drenthen"],"firstnames":["G.","S."],"suffixes":[]},{"propositions":[],"lastnames":["Jansen"],"firstnames":["J.","F.","A."],"suffixes":[]},{"propositions":[],"lastnames":["Gommer"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Gupta"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Hofman"],"firstnames":["P.","A.","M."],"suffixes":[]},{"propositions":["van"],"lastnames":["Kranen-Mastenbroek"],"firstnames":["V.","H."],"suffixes":[]},{"propositions":[],"lastnames":["Hilkman"],"firstnames":["D.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Vlooswijk"],"firstnames":["M.","C.","G."],"suffixes":[]},{"propositions":[],"lastnames":["Rouhl"],"firstnames":["R.","P.","W."],"suffixes":[]},{"propositions":[],"lastnames":["Backes"],"firstnames":["W.","H."],"suffixes":[]}],"title":"Predictive value of functional MRI and EEG in epilepsy diagnosis after a first seizure","journal":"Epilepsy Behav","volume":"115","pages":"107651","note":"Drenthen, Gerhard S Jansen, Jacobus F A Gommer, Erik Gupta, Lalit Hofman, Paul A M van Kranen-Mastenbroek, Vivianne H Hilkman, Danny M Vlooswijk, Marielle C G Rouhl, Rob P W Backes, Walter H eng Epilepsy Behav. 2021 Feb;115:107651. doi: 10.1016/j.yebeh.2020.107651. Epub 2020 Dec 11.","abstract":"It is often difficult to predict seizure recurrence in subjects who have suffered a first-ever epileptic seizure. In this study, the predictive value of physiological signals measured using Electroencephalography (EEG) and functional MRI (fMRI) is assessed. In particular those patients developing epilepsy (i.e. a second unprovoked seizure) that were initially evaluated as having a low risk of seizure recurrence are of interest. In total, 26 epilepsy patients, of which 8 were initially evaluated as having a low risk of seizure recurrence (i.e. converters), and 17 subjects with only a single seizure were included. All subjects underwent routine EEG as well as fMRI measurements. For diagnostic classification, features related to the temporal dynamics were determined for both the processed EEG and fMRI data. Subsequently, a logistic regression classifier was trained on epilepsy and first-seizure subjects. The trained model was tested using the clinically relevant converters group. The sensitivity, specificity, and AUC (mean+/-SD) of the regression model including metrics from both modalities were 74+/-19%, 82+/-18%, and 0.75+/-0.12, respectively. Positive and negative predictive values (mean+/-SD) of the regression model with both EEG and fMRI features are 84+/-14% and 78+/-12%. Moreover, this EEG/fMRI model showed significant improvements compared to the clinical diagnosis, whereas the models using metrics from either EEG or fMRI do not reach significance (p>0.05). Temporal metrics computationally derived from EEG and fMRI time signals may clinically aid and synergistically improve the predictive value in a first-seizure sample.","keywords":"*Electroencephalography *Epilepsy/diagnostic imaging Humans Magnetic Resonance Imaging Predictive Value of Tests Seizures/diagnostic imaging *First fit *New-onset epilepsy *fMRI competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","issn":"1525-5069 (Electronic) 1525-5050 (Linking)","doi":"10.1016/j.yebeh.2020.107651","url":"https://www.ncbi.nlm.nih.gov/pubmed/33309424","year":"2021","bibtex":"@article{RN269,\n author = {Drenthen, G. S. and Jansen, J. F. A. and Gommer, E. and Gupta, L. and Hofman, P. A. M. and van Kranen-Mastenbroek, V. H. and Hilkman, D. M. and Vlooswijk, M. C. G. and Rouhl, R. P. W. and Backes, W. H.},\n title = {Predictive value of functional MRI and EEG in epilepsy diagnosis after a first seizure},\n journal = {Epilepsy Behav},\n volume = {115},\n pages = {107651},\n note = {Drenthen, Gerhard S\nJansen, Jacobus F A\nGommer, Erik\nGupta, Lalit\nHofman, Paul A M\nvan Kranen-Mastenbroek, Vivianne H\nHilkman, Danny M\nVlooswijk, Marielle C G\nRouhl, Rob P W\nBackes, Walter H\neng\nEpilepsy Behav. 2021 Feb;115:107651. doi: 10.1016/j.yebeh.2020.107651. Epub 2020 Dec 11.},\n abstract = {It is often difficult to predict seizure recurrence in subjects who have suffered a first-ever epileptic seizure. In this study, the predictive value of physiological signals measured using Electroencephalography (EEG) and functional MRI (fMRI) is assessed. In particular those patients developing epilepsy (i.e. a second unprovoked seizure) that were initially evaluated as having a low risk of seizure recurrence are of interest. In total, 26 epilepsy patients, of which 8 were initially evaluated as having a low risk of seizure recurrence (i.e. converters), and 17 subjects with only a single seizure were included. All subjects underwent routine EEG as well as fMRI measurements. For diagnostic classification, features related to the temporal dynamics were determined for both the processed EEG and fMRI data. Subsequently, a logistic regression classifier was trained on epilepsy and first-seizure subjects. The trained model was tested using the clinically relevant converters group. The sensitivity, specificity, and AUC (mean+/-SD) of the regression model including metrics from both modalities were 74+/-19%, 82+/-18%, and 0.75+/-0.12, respectively. Positive and negative predictive values (mean+/-SD) of the regression model with both EEG and fMRI features are 84+/-14% and 78+/-12%. Moreover, this EEG/fMRI model showed significant improvements compared to the clinical diagnosis, whereas the models using metrics from either EEG or fMRI do not reach significance (p>0.05). Temporal metrics computationally derived from EEG and fMRI time signals may clinically aid and synergistically improve the predictive value in a first-seizure sample.},\n keywords = {*Electroencephalography\n*Epilepsy/diagnostic imaging\nHumans\nMagnetic Resonance Imaging\nPredictive Value of Tests\nSeizures/diagnostic imaging\n*First fit\n*New-onset epilepsy\n*fMRI\ncompeting financial interests or personal relationships that could have appeared\nto influence the work reported in this paper.},\n ISSN = {1525-5069 (Electronic)\n1525-5050 (Linking)},\n DOI = {10.1016/j.yebeh.2020.107651},\n url = {https://www.ncbi.nlm.nih.gov/pubmed/33309424},\n year = {2021},\n type = {Journal Article}\n}\n\n","author_short":["Drenthen, G. S.","Jansen, J. F. A.","Gommer, E.","Gupta, L.","Hofman, P. A. 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