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Epilepsy is a chronic neurological disease which affects over 50 million people worldwide [1], caused by the disruption of the finely tuned inhibitory and excitatory balance in brain networks, manifesting clinically as seizures. Electroencephalographic (EEG) monitoring in rodent disease models of epilepsy is critical in the understanding of disease mechanisms and the development of anti-seizure drugs. However, the visual annotation of EEG traces is time-consuming, and is complicated by different models and seizure types. Automated annotation systems can help to solve these problems by reducing expert annotation time and increasing the throughput and reliability of seizure quantification. As machine learning is becoming increasingly popular for modelling sequential signals such as EEG, several researchers have tried machine learning to detect seizures in EEG traces from mouse models of epilepsy. Most existing work [2], [3] can only detect seizures in single mouse models of epilepsy and research on multiple mouse models has been limited to-date.

Epilepsy diagnosis is complex, requires a team of specialists and relies on in-depth patient and family history, MRI-imaging and EEG monitoring. There is therefore an unmet clinical need for a non-invasive, molecular-based, biomarker to either predict the development of epilepsy or diagnose a patient with epilepsy who may not have had a witnessed seizure. Recent studies have demonstrated a role for microRNAs in the pathogenesis of epilepsy. MicroRNAs are short non-coding RNA molecules which negatively regulate gene expression, exerting profound influence on target pathways and cellular processes. The presence of microRNAs in biofluids, ease of detection, resistance to degradation and functional role in epilepsy render them excellent candidate biomarkers. Here we performed the first multi-model, genome-wide profiling of plasma microRNAs during epileptogenesis and in chronic temporal lobe epilepsy animals. From video-EEG monitored rats and mice we serially sampled blood samples and identified a set of dysregulated microRNAs comprising increased miR-93-5p, miR-142-5p, miR-182-5p, miR-199a-3p and decreased miR-574-3p during one or both phases. Validation studies found miR-93-5p, miR-199a-3p and miR-574-3p were also dysregulated in plasma from patients with intractable temporal lobe epilepsy. Treatment of mice with common anti-epileptic drugs did not alter the expression levels of any of the five miRNAs identified, however administration of an anti-epileptogenic microRNA treatment prevented dysregulation of several of these miRNAs. The miRNAs were detected within the Argonuate2-RISC complex from both neurons and microglia indicating these miRNA biomarker candidates can likely be traced back to specific brain cell types. The current studies identify additional circulating microRNA biomarkers of experimental and human epilepsy which may support diagnosis of temporal lobe epilepsy via a quick, cost-effective rapid molecular-based test.

Objective. Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals. Approach. In this study, a machine learning-based seizure detection approach, ‘Epi-AI’, which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach. Main results. Epi-AI demonstrated a superior sensitivity, 91.4%–98.8%, and specificity, 93.1%–98.8%, in Models I–III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I–III ranged from 66.9%–91.3% sensitivity and 60.8%–97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I–III at 73.2%–80.1% and 75.8%–98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity. Significance. Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.

The hippocampal place cells encode spatial representation but it remains unclear how they store concurrent positive or negative experiences. Here, we report on place field reconfiguration in response to an innately aversive odor trimethylthiazoline (TMT). The advantage of TMT is the absence of learning curve required for associative fear conditioning. Our study investigated if CA1 place cells, recorded from behaving rats, remap randomly or if their reconfiguration depends on the location of the aversive stimulus perception. Exposure to TMT increased the amplitude of hippocampal beta oscillations in two arms of a maze (TMT arms). We found that a population of place cells with fields located outside the TMT arms increased their activity (extra-field spiking) in the TMT arms during the aversive episodes. These cells exhibited significant shift of the center of mass towards the TMT arms in the subsequent post-TMT recording. The induction of extra-field plasticity was mediated by the basolateral amygdala complex (BLA). Photostimulation of the BLA triggered aversive behavior, synchronized response of hippocampal local field oscillations, augmented theta rhythm amplitude and increased the spiking of place cells for the first 100ms after the light delivery. This occurred only for the extra-field-but not for intra-field spikes. Optogenetic BLA-triggered an increase in extra-field spiking activity correlated to the degree of place field plasticity in the post-ChR2 recording session. Our findings demonstrate that the increased extra-field activity during aversive episodes mediates the degree of subsequent field plasticity.

Highlights • 1 mg/kg LPS i.p. injection robustly suppresses theta frequency in hippocampus. • LPS administration augments delta frequency in hippocampus but not mPFC. • LPS injection triggers hippocampal spike-wave discharges., The early electrophysiological phenomena linked to systemic inflammation are largely underexplored. We developed here local field analyses to detect prodromal oscillatory abnormalities. We identified early band-specific patterns in local field potential recorded from freely-moving rats injected intraperitoneally with lipopolysaccharide (LPS, 1 mg/kg). Theta frequency was significantly reduced and this effect was not related to the decreased locomotion of the animal. Furthermore, LPS-induced alterations show a region-specific response when compared between the hippocampal region and medial prefrontal cortex. Delta mean frequency increased in the hippocampal region but not in the prefrontal cortex. We explored also the hypothesis that systemic inflammation increases the propensity of abnormally synchronized brain activity. Our data indicate that the LPS-evoked alteration of delta and theta frequency parameters reflects the formation of abnormal synchronization in similar frequency ranges. The onset of abnormal brain activity was indicated by spike-wave discharges in the range of 1–10 Hz with three main frequency domains. Importantly, the occurrence of spike-wave discharges was observed in the hippocampus but not in the cortex. In summary, the hippocampal theta rhythm is an accurate indicator of the oscillatory changes evoked by LPS application. The findings offer clear patterns of altered brain function that will facilitate mechanistic investigations of brain dysfunction and delirium occurring during sepsis.

The hippocampus is the main locus of episodic memory formation and the neurons there encode the spatial map of the environment. Hippocampal place cells represent location, but their role in the learning of preferential location remains unclear. The hippocampus may encode locations independently from the stimuli and events that are associated with these locations. We have discovered a unique population code for the experience-dependent value of the context. The degree of reward-driven navigation preference highly correlates with the spatial distribution of the place fields recorded in the CA1 region of the hippocampus. We show place field clustering towards rewarded locations. Optogenetic manipulation of the ventral tegmental area demonstrates that the experience-dependent place field assembly distribution is directed by tegmental dopaminergic activity. The ability of the place cells to remap parallels the acquisition of reward context. Our findings present key evidence that the hippocampal neurons are not merely mapping the static environment but also store the concurrent context reward value, enabling episodic memory for past experience to support future adaptive behavior., Author summary Episodic memories relate positive or negative experiences to environmental context. The neurophysiological mechanisms of this connection, however, remain unknown. Hippocampal place cells represent location, but it is unclear if they encode only the spatial representation of the environment or if they are also processing information about the reward valence for different locations. Here, we use population analysis to test the hypothesis that the place cells process the dual encoding of spatial representation and experience-dependent reward expectation. We show a unique population code for the experience-dependent value of the context. We present evidence that the accumulation of the place fields mediates the learning of the reward context of the environment. Our data reveal that the causal link between place field distribution and behavioral place preference is mediated by the tegmental dopaminergic activity. Optogenetic control of the ventral tegmental area demonstrates that dopaminergic signaling integrates the encoding of location and reward from hippocampal neurons. These findings shed a new light on the ability of hippocampal neurons to store the experience-dependent context reward value, enabling episodic memory for past experience to support future adaptive behavior.

Hippocampal place cells are known to have a key role in encoding spatial information. Aversive stimuli, such as predator odor, evoke place field remapping and a change in preferred firing locations. However, it remains unclear how place cells use positive or negative experiences to remap. We investigated whether CA1 place cells, recorded from behaving rats, remap randomly or whether their reconfiguration depends on the perceived location of the aversive stimulus. Exposure to trimethylthiazoline (TMT; an innately aversive odor), increased the amplitude of hippocampal β oscillations in the two arms of the maze in which TMT exposure occurred. We found that a population of place cells with fields located outside the TMT arms increased their activity (extrafield spiking) in the TMT arms during the aversive episodes. Moreover, in the subsequent post-TMT recording, these cells exhibited a significant shift in their center of mass (COM) towards the TMT arms. The induction of extrafield plasticity was mediated by the basolateral amygdala complex (BLA). Photostimulation of the BLA triggered aversive behavior, synchronized hippocampal local field oscillations, and increased the extrafield spiking of the hippocampal place cells for the first 100 ms after light delivery. Optogenetic BLA activation triggered an increase in extrafield spiking activity that was correlated with the degree of place field plasticity. Furthermore, BLA-mediated increase of the extrafield activity predicts the degree of subsequent field plasticity. Our findings demonstrate that that the remapping of hippocampal place cells during aversive episodes is not random but it depends on the location of the aversive stimulus.

The hippocampal circuitry undergoes attentional modulation by the cholinergic medial septum. However, it is unclear how septal activation regulates the spatial properties of hippocampal neurons. We investigated here what is the functional effect of selective-cholinergic and non-selective septal stimulation on septo-hippocampal system. We show for the first time selective activation of cholinergic cells and their differential network effect in medial septum of freely-behaving transgenic rats. Our data show that depolarization of cholinergic septal neurons evokes frequency-dependent response from the non-cholinergic septal neurons and hippocampal interneurons. Our findings provide vital evidence that cholinergic effect on septo-hippocampal axis is behavior-dependent. During the active behavioral state the activation of septal cholinergic projections is insufficient to evoke significant change in the spiking of the hippocampal neurons. The efficiency of septo-hippocampal processing during active exploration relates to the firing patterns of the non-cholinergic theta-bursting cells. Non-selective septal theta-burst stimulation resets the spiking of hippocampal theta cells, increases theta synchronization, entrains the spiking of hippocampal place cells, and tunes the spatial properties in a timing-dependent manner. The spatial properties are augmented only when the stimulation is applied in the periphery of the place field or 400 – 650ms before the animals approached the center of the field. In summary, our data show that selective cholinergic activation triggers a robust network effect in the septo-hippocampal system during inactive behavioral state, whereas the non-cholinergic septal activation regulates hippocampal functional properties during explorative behavior. Together, our findings uncover fast septal modulation on hippocampal network and reveal how septal inputs up-regulate and down-regulate the encoding of spatial representation.