Dynamic tractography‐based localization of spike sources and animation of spike propagations

Abstract

Objective: This study was undertaken to build and validate a novel dynamic tractography-based model for localizing interictal spike sources and visualizing monosynaptic spike propagations through the white matter. Methods:This cross-sectional study investigated 1900 spike events recorded in 19 patients with drug-resistant temporal lobe epilepsy (TLE) who underwent extraoperative intracranial electroencephalography (iEEG) and resective surgery.Twelve patients had mesial TLE (mTLE) without a magnetic resonance imagingvisible mass lesion. The remaining seven had a mass lesion in the temporal lobe neocortex. We identified the leading and lagging sites, defined as those initially and subsequently (but within ≤50 ms) showing spike-related augmentation of broadband iEEG activity. In each patient, we estimated the sources of 100 spike discharges using the latencies at given electrode sites and diffusion-weighted imaging-based streamline length measures. We determined whether the spatial relationship between the estimated spike sources and resection was associated with postoperative seizure outcomes. We generated videos presenting the spatiotemporal change of spike-related fiber activation sites by estimating the propagation velocity using the streamline length and spike latency measures. Results:The spike propagation velocity from the source was 1.03 mm/ms on average (95% confidence interval = .91-1.15) across 133 tracts noted in the 19 patients. The estimated spike sources in mTLE patients with International League Against Epilepsy Class 1 outcome were more likely to be in the resected area (83.9% vs. 72.3%, φ = .137, p < .001) and in the medial temporal lobe region (80.5% vs. 72.5%, φ = .090, p = .002) than those associated with the Class ≥2 outcomes. The resulting video successfully animated spike propagations, which were confined within the temporal lobe in mTLE but involved extratemporal lobe areas in lesional TLE. Significance: We have, for the first time, provided dynamic tractography visualizing the spatiotemporal profiles of rapid propagations of interictal spikes | 2373 MITSUHASHI et al.

Epileptic discharges initiate from brain areas with elevated accumulation of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors

“… 30 IEDs have often been observed in the lateral temporal cortex beyond the mesial temporal structures and sometimes beyond the surgical resection areas, and these propagations were correlated with unfavourable seizure outcomes after surgery. 31 Furthermore, the cumulative evidence indicated that the selective treatment to the HIP or to the mesial temporal structures were inferior to the anterior temporal lobectomy in terms of the chance of seizure freedom. 32 , 33 The finding of the elevated uptake of [ 11 C]K-2 in the lateral temporal cortex might be useful for the prediction of treatment outcomes.…”

Temporally and functionally distinct large-scale brain network dynamics supporting task switching

“…We acquired iEEG and MRI data using methods, as reported previously ( Nakai et al, 2017 ; Mitsuhashi et al, 2021 ). We placed platinum disk electrodes (10 mm center-to-center distance) on the pial surface of the brain ( Fig.…”

“…The FreeSurfer script ( http://surfer.nmr.mgh.harvard.edu ) spatially normalized given electrode sites to standard Montreal Neurological Institute (MNI) space ( Fig. 2 ; Ghosh et al, 2010 ; Mitsuhashi et al, 2021 ).…”

“…We measured event-related high-gamma activity, as reported in our previous studies ( Mitsuhashi et al, 2021 ; Sonoda et al, 2022 ). Using the FieldTrip toolbox ( http://www.fieldtriptoolbox.org/ ), we performed the Morlet wavelet time-frequency analysis on iEEG signals referenced to a common average ( Crone et al, 2001 ; Mitsuhashi et al, 2021 ).…”

“…We measured event-related high-gamma activity, as reported in our previous studies ( Mitsuhashi et al, 2021 ; Sonoda et al, 2022 ). Using the FieldTrip toolbox ( http://www.fieldtriptoolbox.org/ ), we performed the Morlet wavelet time-frequency analysis on iEEG signals referenced to a common average ( Crone et al, 2001 ; Mitsuhashi et al, 2021 ). We transformed iEEG voltage signals during a 2400 ms period centered on each task cue onset into time-frequency bins, at a 30–110 Hz broadband range (5 Hz frequency bins; a given frequency band divided by seven cycles; sliding in 10 ms steps; Mitsuhashi et al, 2021 ).…”