Saramati Narasimhan1,2,3, Keshav B Kundassery2, Kanupriya Gupta1,2, Graham W Johnson2,3, Kristin E Wills1,2, Sarah E Goodale2,3, Kevin Haas4, John D Rolston5, Robert P Naftel1, Victoria L Morgan1,2,3,6, Benoit M Dawant3,7, Hernán F J González2,3, Dario J Englot1,2,3,6,7. 1. Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 2. Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 3. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA. 4. Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 5. Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA. 6. Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 7. Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA.
Abstract
OBJECTIVE: In patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity. METHODS: In 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity. RESULTS: Ictogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes. SIGNIFICANCE: Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation.
OBJECTIVE: In patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity. METHODS: In 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity. RESULTS: Ictogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes. SIGNIFICANCE: Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation.
Authors: Saramati Narasimhan; Hernán F J González; Graham W Johnson; Kristin E Wills; Danika L Paulo; Victoria L Morgan; Dario J Englot Journal: J Neurosurg Date: 2022-04-01 Impact factor: 5.408
Authors: Danika L Paulo; Kristin E Wills; Graham W Johnson; Hernan F J Gonzalez; John D Rolston; Robert P Naftel; Shilpa B Reddy; Victoria L Morgan; Hakmook Kang; Shawniqua Williams Roberson; Saramati Narasimhan; Dario J Englot Journal: Neurology Date: 2022-03-25 Impact factor: 11.800
Authors: Peter N Taylor; Christoforos A Papasavvas; Thomas W Owen; Gabrielle M Schroeder; Frances E Hutchings; Fahmida A Chowdhury; Beate Diehl; John S Duncan; Andrew W McEvoy; Anna Miserocchi; Jane de Tisi; Sjoerd B Vos; Matthew C Walker; Yujiang Wang Journal: Brain Date: 2022-04-29 Impact factor: 15.255