Modelling and analysis of cortico-hippocampal interactions and dynamics during sleep and anaesthesia

The standard memory consolidation model assumes that new memories are temporarily stored in the hippocampus and later transferred to the neocortex, during deep sleep, for long-term storage, signifying the importance of studying functional and structural cortico-hippocampal interactions. Our work offers a thorough analysis on such interactions between neocortex and hippocampus, along with a detailed study of their intrinsic dynamics, from two complementary perspectives: statistical data analysis and computational modelling. The first part of this study reviews mathematical tools for assessing directional interactions in multivariate time series. We focus on the notion of Granger Causality and the related measure of generalised Partial Directed Coherence (gPDC) which we then apply, through a custom built numerical package, to electrophysiological data from the medial prefrontal cortex (mPFC) and hippocampus of anaesthetized rats. Our gPDC analysis reveals a clear lateral-to-medial hippocampus connectivity and suggests a reciprocal information flow between mPFC and hippocampus, altered during cortical activity. The second part deals with modelling sleep-related intrinsic rhythmic dynamics of the two areas, and examining their coupling. We first reproduce a computational model of the cortical slow oscillation, a periodic alteration between activated (UP) states and neuronal silence. We then develop a new spiking network model of hippocampal areas CA3 and CA1, reproducing many of their intrinsic dynamics and exhibiting sharp wave-ripple complexes, suggesting a novel mechanism for their generation based on CA1 interneuronal activity and recurrent inhibition. We finally couple the two models to study interactions between the slow oscillation and hippocampal activity. Our simulations propose a dependence of the correlation between UP states and hippocampal spiking on the excitation-to-inhibition ratio induced by the mossy fibre input to CA3 and by a combination of the Schaffer collateral and temporoammonic input to CA1. These inputs are shown to affect reported correlations between UP states and ripples

Modelling and analysis of cortico-hippocampal interactions and dynamics during sleep and anaesthesia

The standard memory consolidation model assumes that new memories are temporarily stored in the hippocampus and later transferred to the neocortex, during deep sleep, for long-term storage, signifying the importance of studying functional and structural cortico-hippocampal interactions. Our work offers a thorough analysis on such interactions between neocortex and hippocampus, along with a detailed study of their intrinsic dynamics, from two complementary perspectives: statistical data analysis and computational modelling. The first part of this study reviews mathematical tools for assessing directional interactions in multivariate time series. We focus on the notion of Granger Causality and the related measure of generalised Partial Directed Coherence (gPDC) which we then apply, through a custom built numerical package, to electrophysiological data from the medial prefrontal cortex (mPFC) and hippocampus of anaesthetized rats. Our gPDC analysis reveals a clear lateral-to-medial hippocampus connectivity and suggests a reciprocal information flow between mPFC and hippocampus, altered during cortical activity. The second part deals with modelling sleep-related intrinsic rhythmic dynamics of the two areas, and examining their coupling. We first reproduce a computational model of the cortical slow oscillation, a periodic alteration between activated (UP) states and neuronal silence. We then develop a new spiking network model of hippocampal areas CA3 and CA1, reproducing many of their intrinsic dynamics and exhibiting sharp wave-ripple complexes, suggesting a novel mechanism for their generation based on CA1 interneuronal activity and recurrent inhibition. We finally couple the two models to study interactions between the slow oscillation and hippocampal activity. Our simulations propose a dependence of the correlation between UP states and hippocampal spiking on the excitation-to-inhibition ratio induced by the mossy fibre input to CA3 and by a combination of the Schaffer collateral and temporoammonic input to CA1. These inputs are shown to affect reported correlations between UP states and ripples

Influence of slow oscillation on hippocampal activity and ripples through cortico-hippocampal synaptic interactions, analyzed by a cortical-CA3-CA1 network model

Hippocampal sharp wave-ripple complexes (SWRs) involve the synchronous discharge of thousands of cells throughout the CA3-CA1-subiculum-entorhinal cortex axis. Their strong transient output affects cortical targets, rendering SWRs a possible means for memory transfer from the hippocampus to the neocortex for long-term storage. Neurophysiological observations of hippocampal activity modulation by the cortical slow oscillation (SO) during deep sleep and anesthesia, and correlations between ripples and UP states, support the role of SWRs in memory consolidation through a cortico-hippocampal feedback loop. We couple a cortical network exhibiting SO with a hippocampal CA3-CA1 computational network model exhibiting SWRs, in order to model such cortico-hippocampal correlations and uncover important parameters and coupling mechanisms controlling them. The cortical oscillatory output entrains the CA3 network via connections representing the mossy fiber input, and the CA1 network via the temporoammonic pathway (TA). The spiking activity in CA3 and CA1 is shown to depend on the excitation-to-inhibition ratio, induced by combining the two hippocampal inputs, with mossy fiber input controlling the UP-state correlation of CA3 population bursts and corresponding SWRs, whereas the temporoammonic input affects the overall CA1 spiking activity. Ripple characteristics and pyramidal spiking participation to SWRs are shaped by the strength of the Schaffer collateral drive. A set of in vivo recordings from the rat hippocampus confirms a model-predicted segregation of pyramidal cells into subgroups according to the SO state where they preferentially fire and their response to SWRs. These groups can potentially play distinct functional roles in the replay of spike sequences

Brain–Computer Interface (BCI) Applications in Mapping of Epileptic Brain Networks Based on Intracranial-EEG: An Update

The main applications of the Brain–Computer Interface (BCI) have been in the domain of rehabilitation, control of prosthetics, and in neuro-feedback. Only a few clinical applications presently exist for the management of drug-resistant epilepsy. Epilepsy surgery can be a life-changing procedure in the subset of millions of patients who are medically intractable. Recording of seizures and localization of the Seizure Onset Zone (SOZ) in the subgroup of “surgical” patients, who require intracranial-EEG (icEEG) evaluations, remain to date the best available surrogate marker of the epileptogenic tissue. icEEG presents certain risks and challenges making it a frontier that will benefit from optimization. Despite the presentation of several novel biomarkers for the localization of epileptic brain regions (HFOs-spikes vs. Spikes for instance), integration of most in practices is not at the prime time as it requires a degree of knowledge about signal and computation. The clinical care remains inspired by the original practices of recording the seizures and expert visual analysis of rhythms at onset. It is becoming increasingly evident, however, that there is more to infer from the large amount of EEG data sampled at rates in the order of less than 1 ms and collected over several days of invasive EEG recordings than commonly done in practice. This opens the door for interesting areas at the intersection of neuroscience, computation, engineering and clinical care. Brain–Computer interface (BCI) has the potential of enabling the processing of a large amount of data in a short period of time and providing insights that are not possible otherwise by human expert readers. Our practices suggest that implementation of BCI and Real-Time processing of EEG data is possible and suitable for most standard clinical applications, in fact, often the performance is comparable to a highly qualified human readers with the advantage of producing the results in real-time reliably and tirelessly. This is of utmost importance in specific environments such as in the operating room (OR) among other applications. In this review, we will present the readers with potential targets for BCI in caring for patients with surgical epilepsy

Detection, Prediction and Control of Epileptic Seizures

abstract: From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical and psychological level. Although not lethal by themselves, they can bring about total disruption in consciousness which can, in hazardous conditions, lead to fatality. Roughly 1\% of the world population suffer from epilepsy and another 30 to 50 new cases per 100,000 increase the number of affected annually. Controlling seizures in epileptic patients has therefore become a great medical and, in recent years, engineering challenge. In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures. Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be. The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

A model of delta frequency neuronal network activity and theta-gamma interactions in rat sensorimotor cortex in vitro

In recent decades, advances in electrophysiological techniques have enabled understanding of neuronal network activity, with in vitro brain slices providing insights into the mechanisms underlying oscillations at various frequency ranges. Understanding the electrical and neuro-pharmacological properties of brain networks using selective receptor modulators in native tissue allows to compare such properties with those in disease models (e.g. epilepsy and Parkinson’s). In vivo and in vitro studies have implicated M1 in execution of voluntary movements and, from both local network in vitro and whole brain in vivo perspectives. M1 has been shown to generate oscillatory activity at various frequencies, including beta frequency and nested theta and gamma oscillations similar to those of rat hippocampus. In vivo studies also confirmed slow wave oscillations in somatosensory cortex including delta and theta band activity. However, despite these findings, non-thalamic mechanisms underlying cortical delta oscillations remain almost unexplored. Therefore, we determined to explore these oscillations in vitro in M1 and S1. Using a modified sagittal plane slice preparation with aCSF containing neuroprotectants, we have greatly improved brain slice viability, enabling the generation and study of dual rhythms (theta and gamma oscillations) in deep layers (LV) of the in vitro sensorimotor slice (M1 and S1) in the presence of KA and CCh. We found that theta-gamma activity in M1 is led by S1 and that the amplitude of gamma oscillations was (phase-amplitude) coupled to theta phase in both regions. Oscillations were dependent on GABAAR, AMPAR and NMDAR and were augmented by DAR activation. Experiments using cut/reduced slices showed both M1 and S1 could be intrinsic generators of oscillatory activity. Delta oscillations were induced in M1 and S1 by maintaining a neuromodulatory state mimicking deep sleep, characterised by low dopaminergic and low cholinergic tone, achieved using DAR blockade and low CCh. Delta activity depends on GABAAR, GABABR and AMPAR but not NMDAR, and once induced was not reversible. Unlike theta-gamma activity, delta was led by M1, and activity took >20mins to develop in S1 after establishement of peak power in M1. Unlike M1, S1 alone was unable to support delta activity. Dopamine modulates network activity in M1 and it is known that fast-spiking interneurons are the pacemakers of network rhythmogenesis. Recent studies reported that dopamine (DA) controled Itonic in medium spiny, ventrobasal thalamus and nucleus accumbens neurons by modulation of GABARs or cation channels. In the current study, voltage-clamp whole cell recordings were performed in fast spiking interneurons (FS cells) in Layer V of M1. These recordings revealed tonic and phasic GABAAR inhibition and when DA was bath applied, a slow inward current (IDA) was induced. IDA was mediated by non-specific cationic TRPC channels following D2R-like receptor activation. Overall, my studies show the strong interdependence of theta-gamma rhythmogenesis between M1 and S1, dominanace of M1 at delta frequency and the crucial role of dopamine in controlling FS cell activity. Further exploration of these rhythms in models of pathological conditions such as Parkinson`s disease and Epilepsy may provide insights into network changes underlying these disease conditions

Activation of the pro-resolving receptor Fpr2 attenuates inflammatory microglial activation

Poster number: P-T099 Theme: Neurodegenerative disorders & ageing Activation of the pro-resolving receptor Fpr2 reverses inflammatory microglial activation Authors: Edward S Wickstead - Life Science & Technology University of Westminster/Queen Mary University of London Inflammation is a major contributor to many neurodegenerative disease (Heneka et al. 2015). Microglia, as the resident immune cells of the brain and spinal cord, provide the first line of immunological defence, but can become deleterious when chronically activated, triggering extensive neuronal damage (Cunningham, 2013). Dampening or even reversing this activation may provide neuronal protection against chronic inflammatory damage. The aim of this study was to determine whether lipopolysaccharide (LPS)-induced inflammation could be abrogated through activation of the receptor Fpr2, known to play an important role in peripheral inflammatory resolution. Immortalised murine microglia (BV2 cell line) were stimulated with LPS (50ng/ml) for 1 hour prior to the treatment with one of two Fpr2 ligands, either Cpd43 or Quin-C1 (both 100nM), and production of nitric oxide (NO), tumour necrosis factor alpha (TNFα) and interleukin-10 (IL-10) were monitored after 24h and 48h. Treatment with either Fpr2 ligand significantly suppressed LPS-induced production of NO or TNFα after both 24h and 48h exposure, moreover Fpr2 ligand treatment significantly enhanced production of IL-10 48h post-LPS treatment. As we have previously shown Fpr2 to be coupled to a number of intracellular signaling pathways (Cooray et al. 2013), we investigated potential signaling responses. Western blot analysis revealed no activation of ERK1/2, but identified a rapid and potent activation of p38 MAP kinase in BV2 microglia following stimulation with Fpr2 ligands. Together, these data indicate the possibility of exploiting immunomodulatory strategies for the treatment of neurological diseases, and highlight in particular the important potential of resolution mechanisms as novel therapeutic targets in neuroinflammation. References Cooray SN et al. (2013). Proc Natl Acad Sci U S A 110: 18232-7. Cunningham C (2013). Glia 61: 71-90. Heneka MT et al. (2015). Lancet Neurol 14: 388-40

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

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