Seizure‐onset regions demonstrate high inward directed connectivity during resting‐state: An SEEG study in focal epilepsy

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. How to cite this article: Narasimhan S, Kundassery KB, Gupta K, et al. Seizure-onset regions demonstrate high inward directed connectivity during resting-state:

Source-sink connectivity: A novel interictal EEG marker for seizure localization

“…The source-sink hypothesis is supported by clinical evidence based on the levels of glutamate and glutamate receptors in the brain [55][56][57][58] and iEEG studies that have demonstrated strong inward (inhibitory) connectivity to the EZ regions during rest. 59,60,63,64 We evaluated the predictive value of the SSI by a) rating the correspondence between the hypothesized CA-EZ and regions with high SSIs and b) building a random forest to model the probability of a successful surgery as a function of the source-sink metrics, and compared the performance to that of HFOs, a commonly used interictal iEEG feature. The analysis was performed on data from 65 patients treated across 6 clinical centers.…”

“…Several studies have demonstrated a high inward directed influence to the EZ at rest. 55,[67][68][69] In a recent study Narasimhan et al 67 stated that high inward connectivity may reflect inhibitory input from other regions to prevent the onset and spread of seizure activity, but the direction of these signals may flip when seizure activity begins. This is supported by iEEG studies in neocortical epilepsy demonstrating functional isolation of epileptogenic areas at rest 70,71 and that increased synchronization in seizure-onset regions may be suggestive of an inhibitory surround.…”

Interictal SEEG resting-state connectivity localizes seizure onset zone and predicts seizure outcome

“…The golden standard of localization of epileptogenic brain regions in clinical practice typically depends on capturing multiple seizures during the intracranial monitoring process, that may take multiple days or even weeks to complete (14). As such, a method which can estimate SOZ and predict prognosis outcome from analysis of brief, resting-state data segments would have tremendous clinical values to identify epileptogenic networks without requiring prolonged intracranial recordings, which would vastly improve patient care and reduce medical cost (15)(16)(17).…”

“…The underpinnings of seizure generation involve abnormal brain structures and aberrant functional connections among these regions, leading to large-scale network instability ( 30, 31 ). Resting-state network connectivity studies have suggested predominantly increased functional connectivity involving the EZ and surrounding structures ( 15 ), and stronger inward directional connectivity toward EZ ( 16 ). Furthermore, decreased interictal network synchrony and local heterogeneity was found to correlate with improved seizure outcome ( 32 ).…”

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“…However, the correct localization of the EZ to achieve seizure freedom after surgery, is still an unsolved and open question, as indicated by the high rate of failure of seizure control (30-40%) after surgery (Spencer and Huh, 2008;Bulacio et al, 2012). The advanced signal processing approaches, especially those based on the connectivity analysis, have been largely applied to stereoelectroencephalography (SEEG) from the patients with epilepsy to better pinpoint the location of the EZ (Varotto et al, 2013;Bartolomei et al, 2017;Adkinson et al, 2019;Narasimhan et al, 2020).…”