Kang, Hakmook

Abstract How do brain networks limit seizure activity? In the interictal suppression hypothesis, we recently postulated that high inward connectivity to seizure onset zones (SOZs) from non-involved zones (NIZs) is a sign of broader network suppression. If broad networks appear to be responsible for interictal SOZ suppression, what changes during seizure initiation, spread and termination? For patients with drug-resistant epilepsy, intracranial monitoring offers a view into the electrographic networks that organize around and in response to the SOZ. In this manuscript, we investigate network dynamics in the peri-ictal periods to assess possible mechanisms of seizure suppression and the consequences of this suppression being overwhelmed. Peri-ictal network dynamics were derived from stereo electroencephalography recordings from 75 patients with drug-resistant epilepsy undergoing presurgical evaluation at Vanderbilt University Medical Center. We computed directed connectivity from 5 s windows in the periods between, immediately before, during and after 668 seizures. We aligned connectivity matrices across seizures and patients, then calculated net connectivity changes from the SOZ, propagative zone and NIZ. Across all seizure types, we observed two phases: a rapid increase in directed communication towards the SOZ, followed by a collapse in network connectivity. During the first phase, SOZs could be distinguished from all other regions (one-way ANOVA, P = 8.32 × 10−19–2.22 × 10−7). In the second phase and post-ictal period, SOZ inward connectivity decreased yet remained distinct (one-way ANOVA, P = 2.58 × 10−10–1.66 × 10−2). NIZs appeared to drive increased SOZ connectivity while intra-NIZ connectivity decreased concordantly. Stratifying by seizure subtype, we found that consciousness-impairing seizures decrease inward connectivity from the NIZ earlier than consciousness-sparing seizures (one-way ANOVA, P < 0.01 after false discovery correction). Tracking network reorganization against a surrogate for seizure involvement highlighted a possible antagonism between seizure propagation and the ability of the NIZ to maintain high connectivity to the SOZ. Finally, we found that inclusion of peri-ictal connectivity improved the accuracy of SOZ classification from previous models to a combined area under the curve of 93%. Overall, NIZs appear actively to increase inhibitory signaling towards the SOZ during seizure onset, possibly to thwart seizure activity. Although inhibition appears insufficient to prevent seizure onset, the inability to restrict seizure propagation might contribute to loss of consciousness during larger seizures. Dynamic connectivity patterns uncovered in this work might: (i) facilitate accurate delineation of surgical targets in focal epilepsy; (ii) reveal why interictal suppression of SOZs might be insufficient to prevent all seizures; and (iii) provide insight into mechanisms of loss of consciousness during certain seizures.

Purpose Diffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but variability due to session and scanner biases is a challenge. Methods To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm 2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de‐identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi‐compartment neurite orientation dispersion and density model, (3) white‐matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region‐wise fractional anisotropy, mean diffusivity, and principal eigenvector; region‐wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle‐wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. Results We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. Conclusions This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.