Extratemporal surface EEG features do not preclude successful surgical outcomes in drug-resistant epilepsy patients with unitemporal MRI lesions

2012 ◽  
Vol 14 (3) ◽  
pp. 275-289 ◽  
Author(s):  
Kyriakos Garganis ◽  
Vasileios Kokkinos ◽  
Basilios Zountsas
2011 ◽  
Vol 13 (3) ◽  
pp. 259-262
Author(s):  
Gabriella Di Rosa ◽  
Sonia Messina ◽  
Adele D’Amico ◽  
Enrico Bertini ◽  
Giuseppina Pustorino ◽  
...  

Seizure ◽  
2016 ◽  
Vol 41 ◽  
pp. 56-61 ◽  
Author(s):  
Ravindra Arya ◽  
James L. Leach ◽  
Paul S. Horn ◽  
Hansel M. Greiner ◽  
Michael Gelfand ◽  
...  

2019 ◽  
Author(s):  
Adam Li ◽  
Chester Huynh ◽  
Zachary Fitzgerald ◽  
Iahn Cajigas ◽  
Damian Brusko ◽  
...  

AbstractOver 15 million epilepsy patients worldwide do not respond to drugs. Successful surgical treatment requires complete removal, or disconnection of the seizure onset zone (SOZ), brain region(s) where seizures originate. Unfortunately, surgical success rates vary between 30%-70% because no clinically validated biological marker of the SOZ exists. We develop and retrospectively validate a new EEG marker - neural fragility. We validate this new marker in a retrospective analysis of 91 patients by using neural fragility of the annotated SOZ as a metric to predict surgical outcomes. Fragility predicts 43/47 surgical failures with an overall prediction accuracy of 76%, compared to the accuracy of clinicians being 48% (successful outcomes). In failed outcomes, we identify fragile regions that were untreated. When compared to 20 EEG features proposed as SOZ markers, fragility outperformed in predictive power and interpretability suggesting neural fragility as an EEG fingerprint of the SOZ.One Sentence SummaryNeural fragility, an intracranial EEG biomarker for the seizure onset zone in drug-resistant epilepsy, predicts surgical outcomes with high accuracy.


Author(s):  
Abdullah S. Bdaiwi ◽  
Hansel M. Greiner ◽  
James Leach ◽  
Francesco T. Mangano ◽  
Mark W. DiFrancesco

OBJECTIVE Focal cortical dysplasia (FCD) is often associated with drug-resistant epilepsy, leading to a recommendation to surgically remove the seizure focus. Predicting outcome for resection of FCD is challenging, requiring a new approach. Lesion-symptom mapping is a powerful and broadly applicable method for linking neurological symptoms or outcomes to damage to particular brain regions. In this work, the authors applied lesion network mapping, an expansion of the traditional approach, to search for the association of lesion network connectivity with surgical outcomes. They hypothesized that connectivity of lesion volumes, preoperatively identified by MRI, would associate with seizure outcomes after surgery in a pediatric cohort with FCD. METHODS This retrospective study included 21 patients spanning the ages of 3 months to 17.7 years with FCD lesions who underwent surgery for drug-resistant epilepsy. The mean brain-wide functional connectivity map of each lesion volume was assessed across a database of resting-state functional MRI data from healthy children (spanning approximately 2.9 to 18.9 years old) compiled at the authors’ institution. Lesion connectivity maps were averaged across age and sex groupings from the database and matched to each patient. The authors sought to associate voxel-wise differences in these maps with subject-specific surgical outcome (seizure free vs persistent seizures). RESULTS Lesion volumes with persistent seizures after surgery tended to have stronger connectivity to attention and motor networks and weaker connectivity to the default mode network compared with lesion volumes with seizure-free surgical outcome. CONCLUSIONS Network connectivity–based lesion-outcome mapping may offer new insight for determining the impact of lesion volumes discerned according to both size and specific location. The results of this pilot study could be validated with a larger set of data, with the ultimate goal of allowing examination of lesions in patients with FCD and predicting their surgical outcomes.


Epilepsia ◽  
2019 ◽  
Vol 60 (5) ◽  
pp. 948-957
Author(s):  
Xinghui He ◽  
Feng Zhai ◽  
Yuguang Guan ◽  
Jian Zhou ◽  
Tianfu Li ◽  
...  

2018 ◽  
Vol 26 (2) ◽  
pp. 13-18
Author(s):  
Yu.M. Zabrodskaya ◽  
◽  
D.A. Sitovskaya ◽  
S.M. Malyshev ◽  
T.V. Sokolova ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document