BACKGROUND Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. OBJECTIVE To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.
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:
OBJECTIVESeizure outcome after mesial temporal lobe epilepsy (mTLE) surgery is complex and diverse, even across patients with homogeneous presurgical clinical profiles. The authors hypothesized that this is due in part to variations in network connectivity across the brain before and after surgery. Although presurgical network connectivity has been previously characterized in these patients, the objective of this study was to characterize presurgical to postsurgical functional network connectivity changes across the brain after mTLE surgery.METHODSTwenty patients with drug-refractory unilateral mTLE (5 left side, 10 female, age 39.3 ± 13.5 years) who underwent either selective amygdalohippocampectomy (n = 13) or temporal lobectomy (n = 7) were included in the study. Presurgical and postsurgical (36.6 ± 14.3 months after surgery) functional connectivity (FC) was measured with 3-T MRI and compared with findings in age-matched healthy controls (n = 44, 21 female, age 39.3 ± 14.3 years). Postsurgical connectivity changes were then related to seizure outcome, type of surgery, and presurgical disease parameters.RESULTSThe results demonstrated significant decreases of FC from control group values across the brain after surgery that were not present before surgery, including many contralateral hippocampal connections distal to the surgical site. Postsurgical impairment of contralateral precuneus to ipsilateral occipital connectivity was associated with seizure recurrence. Presurgical impairment of the contralateral precuneus to contralateral temporal lobe connectivity was associated with those who underwent selective amygdalohippocampectomy compared to those who had temporal lobectomy. Finally, changes in thalamic connectivity after surgery were linearly related to duration of epilepsy and frequency of consciousness-impairing seizures prior to surgery.CONCLUSIONSThe widespread contralateral hippocampal FC changes after surgery may be a reflection of an ongoing epileptogenic progression that has been altered by the surgery, rather than a direct result of the surgery itself. This network evolution may contribute to long-term seizure outcome. Therefore, the combination of presurgical network mapping with the understanding of the dynamic effects of surgery on the networks may ultimately be used to create predictors of the likelihood of long-term seizure recurrence in individual patients after mTLE surgery.
Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.