Abstract
Our objective is to examine the layer and spectrotemporal architecture and laminar distribution of high-frequency oscillations (HFOs) in a neonatal freeze lesion model of focal cortical dysplasia (FCD) associated with a high prevalence of spontaneous spike-wave discharges (SWDs). Electrophysiological recording of local field potentials (LFPs) in control and freeze lesion animals were obtained with linear micro-electrode arrays to detect presence of HFOs as compared to changes in spectral power, signal coherence, and single-unit distributions during “hyper-excitable” epochs of anesthesia-induced burst-suppression (B-S). Result were compared to HFOs observed during spontaneous SWDs in animals during sleep. Micro-electrode array recordings from the malformed cortex indicated significant increases in the presence of HFOs above 100 Hz and associated increases in spectral power and altered LFP coherence of recorded signals across cortical lamina of freeze-lesioned animals with spontaneous bursts of high-frequency activity, confined predominately to granular and supragranular layers. Spike sorting of well-isolated single-units recorded from freeze-lesioned cortex indicated an increase in putative excitatory cell activity in the outer cortical layers that showed only a weak association with HFOs while deeper inhibitory units were strongly phase-locked to high-frequency ripple (HFR) oscillations (300–800 Hz). Both SWDs and B-S show increases in HFR activity that were phase-locked to the high-frequency spike pattern occurring at the trough of low frequency oscillations. The spontaneous cyclic spiking of cortical inhibitory cells appears to be the driving substrate behind the HFO patterns associated with SWDs and a hyperexcitable supragranular layer near the malformed cortex may play a key role in epileptogenesis in our model. These data, derived from a mouse model with a distinct focal cortical malformation, support recent clinical data that HFOs, particularly fast ripples, is a biomarker to help define the cortical seizure zone, and provide limited insights toward understanding cellular level changes underlying the HFOs.
Highlights
Focal epilepsy is associated with the derangement of local neural network activity leading to hypersynchronous neuronal discharges in the affected brain tissues (van Diessen et al, 2013; Perucca et al, 2014; Ferrari-Marinho et al, 2015)
We evaluate the response of the malformed cortex to hyper-excitable activation due to anesthesia-induced B-S
Previous studies have indicated that the incidence and spectral power of B-S events are significantly increased in freeze lesion animals, confined to the upper cortical layers, and often contain spike-and-wave components similar to the spike-wave discharges (SWDs) observed in unanesthetized animals (Williams et al, 2016)
Summary
Focal epilepsy is associated with the derangement of local neural network activity leading to hypersynchronous neuronal discharges (ictogenesis) in the affected brain tissues (van Diessen et al, 2013; Perucca et al, 2014; Ferrari-Marinho et al, 2015). High frequency oscillations (HFOs) have recently emerged as a sensitive biomarker of hyper-excitable tissues responsible for focal epilepsy (Brázdil et al, 2010; Ferrari-Marinho et al, 2015; van Klink et al, 2016; Cuello-Oderiz et al, 2018) and resection of brain regions with increased HFO rates has been associated with a high incidence of seizure-free outcome in clinical patients (Fujiwara et al, 2012; Fedele et al, 2016; Cuello-Oderiz et al, 2018) Both physiological and pathological HFOs are common within cortical brain regions of epileptic patients (Matsumoto et al, 2013) though fast ripple activity (>250 Hz), Currently the spectral characteristics that distinguish physiological vs pathological HFOs have not been clearly defined (Kerber et al, 2013; Frauscher et al, 2015). The spectrotemporal dissection of pathological waveforms obtained with high-density microelectrode arrays including the analysis of cross-laminar and cross-frequency coupling may serve as useful high-resolution tools for studying the underlying pathologies and neural networks associated with different forms of epileptogenesis (Buzsáki and Silva, 2012; Ibrahim et al, 2014; Williams et al, 2016)
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