Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy.

In temporal lobe epilepsy, the ability of the dentate gyrus to limit excitatory cortical input to the hippocampus breaks down, leading to seizures. The dentate gyrus is also thought to help discriminate between similar memories by performing pattern separation, but whether epilepsy leads to a breakdown in this neural computation, and thus to mnemonic discrimination impairments, remains unknown. Here we show that temporal lobe epilepsy is characterized by behavioral deficits in mnemonic discrimination tasks, in both humans (females and males) and mice (C57Bl6 males, systemic low-dose kainate model). Using a recently developed assay in brain slices of the same epileptic mice, we reveal a decreased ability of the dentate gyrus to perform certain forms of pattern separation. This is because of a subset of granule cells with abnormal bursting that can develop independently of early EEG abnormalities. Overall, our results linking physiology, computation, and cognition in the same mice advance our understanding of episodic memory mechanisms and their dysfunction in epilepsy.SIGNIFICANCE STATEMENT People with temporal lobe epilepsy (TLE) often have learning and memory impairments, sometimes occurring earlier than the first seizure, but those symptoms and their biological underpinnings are poorly understood. We focused on the dentate gyrus, a brain region that is critical to avoid confusion between similar memories and is anatomically disorganized in TLE. We show that both humans and mice with TLE experience confusion between similar situations. This impairment coincides with a failure of the dentate gyrus to disambiguate similar input signals because of pathologic bursting in a subset of neurons. Our work bridges seizure-oriented and memory-oriented views of the dentate gyrus function, suggests a mechanism for cognitive symptoms in TLE, and supports a long-standing hypothesis of episodic memory theories.

Sleep-wake characteristics in a mouse model of severe traumatic brain injury: Relation to posttraumatic epilepsy.

Study objectives: Traumatic brain injury (TBI) results in sequelae that include posttraumatic epilepsy (PTE) and sleep-wake disturbances. Here, we sought to determine whether sleep characteristics could predict development of PTE in a model of severe TBI. Methods: Following controlled cortical impact (CCI) or sham injury (craniotomy only), CD-1 mice were implanted with epidural electroencephalography (EEG) and nuchal electromyography (EMG) electrodes. Acute (1st week) and chronic (months 1, 2, or 3) 1-week-long video-EEG recordings were performed after the injury to examine epileptiform activity. High-amplitude interictal events were extracted from EEG using an automated method. After scoring sleep-wake patterns, sleep spindles and EEG delta power were derived from nonrapid eye movement (NREM) sleep epochs. Brain CTs (computerized tomography) were performed in sham and CCI cohorts to quantify the brain lesions. We then employed a no craniotomy (NC) control to perform 1-week-long EEG recordings at week 1 and month 1 after surgery. Results: Posttraumatic seizures were seen in the CCI group only, whereas interictal epileptiform activity was seen in CCI or sham. Sleep-wake disruptions consisted of shorter wake or NREM bout lengths and shorter duration or lower power for spindles in CCI and sham. NREM EEG delta power increased in CCI and sham groups compared with NC though the CCI group with posttraumatic seizures had lower power at a chronic time point compared with those without. Follow-up brain CTs showed a small lesion in the sham injury group suggesting a milder form of TBI that may account for their interictal activity and sleep changes. Significance: In our TBI model, tracking changes in NREM delta power distinguishes between CCI acutely and animals that will eventually develop PTE, but further work is necessary to identify sleep biomarkers of PTE. Employing NC controls together with sham controls should be considered in future TBI studies.

Histone deacetylase inhibitors restore normal hippocampal synaptic plasticity and seizure threshold in a mouse model of Tuberous Sclerosis Complex.

Abnormal synaptic plasticity has been implicated in several neurological disorders including epilepsy, dementia and Autism Spectrum Disorder (ASD). Tuberous Sclerosis Complex (TSC) is an autosomal dominant genetic disorder that manifests with seizures, autism, and cognitive deficits. The abnormal intracellular signaling underlying TSC has been the focus of many studies. However, nothing is known about the role of histone modifications in contributing to the neurological manifestations in TSC. Dynamic regulation of chromatin structure via post translational modification of histone tails has been implicated in learning, memory and synaptic plasticity. Histone acetylation and associated gene activation plays a key role in plasticity and so we asked whether histone acetylation might be dysregulated in TSC. In this study, we report a general reduction in hippocampal histone H3 acetylation levels in a mouse model of TSC2. Pharmacological inhibition of Histone Deacetylase (HDAC) activity restores histone H3 acetylation levels and ameliorates the aberrant plasticity in TSC2+/- mice. We describe a novel seizure phenotype in TSC2+/- mice that is also normalized with HDAC inhibitors (HDACis). The results from this study suggest an unanticipated role for chromatin modification in TSC and may inform novel therapeutic strategies for TSC patients.

A predictive epilepsy index based on probabilistic classification of interictal spike waveforms.

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate- versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.