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:

ObjectiveWe sought to augment the presurgical workup of medically refractory temporal lobe epilepsy by creating a supervised machine learning technique that uses diffusion-weighted imaging to classify patient-specific seizure onset laterality and surgical outcome.Methods151 subjects were included in this analysis: 62 patients (aged 18–68 years, 36 women) and 89 healthy controls (aged 18–71 years, 47 women). We created a supervised machine learning technique that uses diffusion-weighted metrics to classify subject groups. Specifically, we sought to classify patients versus healthy controls, unilateral versus bilateral temporal lobe epilepsy, left versus right temporal lobe epilepsy and seizure-free versus not seizure-free surgical outcome. We then reduced the dimensionality of derived features with community detection for ease of interpretation.ResultsWe classified the subject groups in withheld testing data sets with a cross-fold average testing areas under the receiver operating characteristic curve of 0.745 for patients versus healthy controls, 1.000 for unilateral versus bilateral seizure onset, 0.662 for left versus right seizure onset, 0.800 for left-sided seizure-free vsersu not seizure-free surgical outcome and 0.775 for right-sided seizure-free versus not seizure-free surgical outcome.ConclusionsThis technique classifies important clinical decisions in the presurgical workup of temporal lobe epilepsy by generating discerning white-matter features. We believe that this work augments existing network connectivity findings in the field by further elucidating important white-matter pathology in temporal lobe epilepsy. We hope that this work contributes to recent efforts aimed at using diffusion imaging as an augmentation to the presurgical workup of this devastating neurological disorder.

ObjectiveTo determine whether the nucleus basalis of Meynert (NBM) may be a key network structure of altered functional connectivity in temporal lobe epilepsy (TLE), we examined fMRI with network-based analyses.MethodsWe acquired resting-state fMRI in 40 adults with TLE and 40 matched healthy control participants. We calculated functional connectivity of NBM and used multiple complementary network-based analyses to explore the importance of NBM in TLE networks without biasing our results by our approach. We compared patients to controls and examined associations of network properties with disease metrics and neurocognitive testing.ResultsWe observed marked decreases in connectivity between NBM and the rest of the brain in patients with TLE (0.91 ± 0.88, mean ± SD) vs controls (1.96 ± 1.13, p < 0.001, t test). Larger decreases in connectivity between NBM and fronto-parietal-insular regions were associated with higher frequency of consciousness-impairing seizures (r = −0.41, p = 0.008, Pearson). A core network of altered nodes in TLE included NBM ipsilateral to the epileptogenic side and bilateral limbic structures. Furthermore, normal community affiliation of ipsilateral NBM was lost in patients, and this structure displayed the most altered clustering coefficient of any node examined (3.46 ± 1.17 in controls vs 2.23 ± 0.93 in patients). Abnormal connectivity between NBM and subcortical arousal community was associated with modest neurocognitive deficits. Finally, a logistic regression model incorporating connectivity properties of ipsilateral NBM successfully distinguished patients from control datasets with moderately high accuracy (78%).ConclusionsThese results suggest that while NBM is rarely studied in epilepsy, it may be one of the most perturbed network nodes in TLE, contributing to widespread neural effects in this disabling disorder.