Computational Study of Hippocampal-Septal Theta Rhythm Changes Due to Beta-Amyloid-Altered Ionic Channels

Electroencephagraphy (EEG) of many dementia patients has been characterized by an increase in low frequency field potential oscillations. One of the characteristics of early stage Alzheimer’s disease (AD) is an increase in theta band power (4–7 Hz). However, the mechanism(s) underlying the changes in theta oscillations are still unclear. To address this issue, we investigate the theta band power changes associated with β-Amyloid (Aβ) peptide (one of the main markers of AD) using a computational model, and by mediating the toxicity of hippocampal pyramidal neurons. We use an established biophysical hippocampal CA1-medial septum network model to evaluate four ionic channels in pyramidal neurons, which were demonstrated to be affected by Aβ. They are the L-type Ca2+ channel, delayed rectifying K+ channel, A-type fast-inactivating K+ channel and large-conductance Ca2+-activated K+ channel. Our simulation results demonstrate that only the Aβ inhibited A-type fast-inactivating K+ channel can induce an increase in hippocampo-septal theta band power, while the other channels do not affect theta rhythm. We further deduce that this increased theta band power is due to enhanced synchrony of the pyramidal neurons. Our research may elucidate potential biomarkers and therapeutics for AD. Further investigation will be helpful for better understanding of AD-induced theta rhythm abnormalities and associated cognitive deficits

Classification-based prediction of effective connectivity between timeseries with a realistic cortical network model

Effective connectivity measures the pattern of causal interactions between brain regions. Traditionally, these patterns of causality are inferred from brain recordings using either non-parametric, i.e., model-free, or parametric, i.e., model-based, approaches. The latter approaches, when based on biophysically plausible models, have the advantage that they may facilitate the interpretation of causality in terms of underlying neural mechanisms. Recent biophysically plausible neural network models of recurrent microcircuits have shown the ability to reproduce well the characteristics of real neural activity and can be applied to model interacting cortical circuits. Unfortunately, however, it is challenging to invert these models in order to estimate effective connectivity from observed data. Here, we propose to use a classification-based method to approximate the result of such complex model inversion. The classifier predicts the pattern of causal interactions given a multivariate timeseries as input. The classifier is trained on a large number of pairs of multivariate timeseries and the respective pattern of causal interactions, which are generated by simulation from the neural network model. In simulated experiments, we show that the proposed method is much more accurate in detecting the causal structure of timeseries than current best practice methods. Additionally, we present further results to characterize the validity of the neural network model and the ability of the classifier to adapt to the generative model of the data

A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced threecompartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential that contributes to the local field potential of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the dendritic field potential around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. [Mazzoni, A., S. Panzeri, N. K. Logothetis, and N. Brunel (2008). Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Computational Biology 4 (12), e1000239], and conclude that our biophysically motivated approach yields substantial improvement.Comment: 31 pages, 4 figure

Propriétés de l'activité de décharge neuronale de masse chez les humains mesurée par EEG non invasive

Abstract : Electroencephalography (EEG) is a non-invasive neuroimaging modality that was first introduced over 80 years ago. Surface EEG does not directly measure neuronal activity, and it is often assumed that it cannot provide indications on the underlying neuronal firing. However, recent studies based on invasive measurements in monkeys have shown that the coupling between two EEG frequency bands, namely the Gamma (25-45 Hz) and Delta (2-4 Hz) bands, is a good predictor of underlying mass-spiking activity. Specifically, when the Delta signal is in its trough and Gamma power is high, the probability of mass- firing of neurons is large. Here, we investigate this property in healthy human EEG acquired during resting-state. Using the interaction between Delta phase and Gamma power, we derived a modeled spike signal (MSS) from the recorded EEG. We found the power spectrum density (PSD) pattern of the MSS to be similar to that observed in animal studies. Specifically, between 1-10 Hz that the PSD deviates from a 1/[florin] trend and exhibits a small peak at about 2-3Hz. In addition, an inter-hemispheric correlation was found between the MSS of the different pairs of electrode in opposite hemispheres. Our results open the possibility of studying underlying neuronal output with non-invasive EEG. // Résumé : L'électroencéphalographie (EEG) est une modalité de neuro-imagerie non invasive qui a été introduite il y a plus de 80 ans. L’EEG de surface ne mesure pas directement l’activité neuronale et il est généralement supposé qu’elle ne donne pas d’indications sur la décharge neuronale sous-jacente. Cependant des études récentes ont montré à l’aide de mesures invasives que le couplage entre deux bandes de fréquences EEG, soit les bandes Gamma (25-45 Hz) et Delta (2-4 Hz), est un bon indicateur de l’activité neuronale de masse sous-jacente chez les singes. Plus précisément, lorsque le signal Delta est dans un creux (phase de π) et que la puissance dans le signal Gamma est élevée, la probabilité de décharge de masse des neurones est grande. Cette propriété est ici étudiée dans les signaux EEG d’humains sains en état de repos. En se basant sur l'interaction entre la phase du signal Delta et la puissance du signal Gamma, nous avons dérivé un modèle de l’activité neuronale de masse sous-jacente (modeled spike signal-MSS) obtenu à partir du signal l'EEG enregistrée. On trouve que la densité spectrale de puissance (power spectal density-PSD) du MSS est similaire à celle observée dans les études animales. Plus spécifiquement, entre 1-10 Hz la PSD s’écarte d’une tendance en 1 / [florin] et présente un pic de faible amplitude à environ 2-3Hz. En outre, une corrélation inter-hémisphérique a été observée entre les MSS de différentes paires d'électrodes positionnées sur les hémisphères opposés. Nos résultats ouvrent la possibilité d'étudier l’activité neuronale sous-jacente par EEG non-invasive

Method for Spatial Overlap Estimation of Electroencephalography and Functional Magnetic Resonance Imaging Responses

Background Simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) measurements may represent activity from partially divergent neural sources, but this factor is seldom modeled in fMRI-EEG data integration. New method This paper proposes an approach to estimate the spatial overlap between sources of activity measured simultaneously with fMRI and EEG. Following the extraction of task-related activity, the key steps include, 1) distributed source reconstruction of the task-related ERP activity (ERP source model), 2) transformation of fMRI activity to the ERP spatial scale by forward modelling of the scalp potential field distribution and backward source reconstruction (fMRI source simulation), and 3) optimization of fMRI and ERP thresholds to maximize spatial overlap without a priori constraints of coupling (overlap calculation). Results FMRI and ERP responses were recorded simultaneously in 15 subjects performing an auditory oddball task. A high degree of spatial overlap between sources of fMRI and ERP responses (in 9 or more of 15 subjects) was found specifically within temporoparietal areas associated with the task. Areas of non-overlap in fMRI and ERP sources were relatively small and inconsistent across subjects. Comparison with existing method The ERP and fMRI sources estimated with solely jICA overlapped in just 4 of 15 subjects, and strictly in the parietal cortex. Conclusion The study demonstrates that the new fMRI-ERP spatial overlap estimation method provides greater spatiotemporal detail of the cortical dynamics than solely jICA. As such, we propose that it is a superior method for the integration of fMRI and EEG to study brain function

Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model

Despite the widespread use of EEGs to measure the large-scale dynamics of the human brain, little is known on how the dynamics of EEGs relates to that of the underlying spike rates of cortical neurons. However, progress was made by recent neurophysiological experiments reporting that EEG delta-band phase and gamma-band amplitude reliably predict some complementary aspects of the time course of spikes of visual cortical neurons. To elucidate the mechanisms behind these findings, here we hypothesize that the EEG delta phase reflects shifts of local cortical excitability arising from slow fluctuations in the network input due to entrainment to sensory stimuli or to fluctuations in ongoing activity, and that the resulting local excitability fluctuations modulate both the spike rate and the engagement of excitatory–inhibitory loops producing gamma-band oscillations. We quantitatively tested these hypotheses by simulating a recurrent network of excitatory and inhibitory neurons stimulated with dynamic inputs prese nting temporal regularities similar to that of thalamic responses during naturalistic visual stimulation and during spontaneous activity. The network model reproduced in detail the experimental relationships between spike rate and EEGs, and suggested that the complementariness of the prediction of spike rates obtained from EEG delta phase or gamma amplitude arises from nonlinearities in the engagement of excitatory–inhibitory loops and from temporal modulations in the amplitude of the network input, which respectively limit the predictability of spike rates from gamma amplitude or delta phase alone. The model suggested also ways to improve and extend current algorithms for online prediction of spike rates from EEGs

Sensory Stream Adaptation in Chaotic Networks

Implicit expectations induced by predictable stimuli sequences affect neuronal response to upcoming stimuli at both single cell and neural population levels. Temporally regular sensory streams also phase entrain ongoing low frequency brain oscillations but how and why this happens is unknown. Here we investigate how random recurrent neural networks without plasticity respond to stimuli streams containing oddballs. We found the neuronal correlates of sensory stream adaptation emerge if networks generate chaotic oscillations which can be phase entrained by stimulus streams. The resultant activity patterns are close to critical and support history dependent response on long timescales. Because critical network entrainment is a slow process stimulus response adapts gradually over multiple repetitions. Repeated stimuli generate suppressed responses but oddball responses are large and distinct. Oscillatory mismatch responses persist in population activity for long periods after stimulus offset while individual cell mismatch responses are strongly phasic. These effects are weakened in temporally irregular sensory streams. Thus we show that network phase entrainment provides a biologically plausible mechanism for neural oddball detection. Our results do not depend on specific network characteristics, are consistent with experimental studies and may be relevant for multiple pathologies demonstrating altered mismatch processing such as schizophrenia and depression

Perzeptuelles Lernen am Beispiel des Bewegungssehens: objektive und subjektive Leistung und der Einfluss von transkranieller Wechselstromstimulation

Unsere visuellen Leistungen sind das Ergebnis kontinuierlichen Lernens - ein Lernen, das früh in der Kindheit beginnt und für Patienten eine besondere Rolle wiedererlangt, die durch Hirnschädigung, wie beispielsweise Schlaganfälle, Teile ihrer Sehleistungen einbüßen. Für die Rehabilitation von Sehstörungen nach neurologischen Erkrankungen sind deshalb Strategien wertvoll, die das perzeptuelle Lernen fördern. Am Beispiel des Bewegungssehens wurde perzeptuelles Lernen als Funktion zweier Faktoren untersucht, die geeignet scheinen perzeptuelles Lernen günstig zu beeinflussen. Einerseits wurde geprüft, ob Entscheidungssicherheit, d.h. die subjektive Gewißheit über den eigenen visuellen Diskriminationserfolg, für Lernvorgänge eine Rolle spielen könnte. Andererseits wurde geprüft, ob durch transkranielle, alternierende Stromstimulation (tACS) die Wahrnehmungs-schwelle für das Bewegungssehen beeinflusst und / oder perzeptuelle Lernvorgänge gefördert werden können. Um diese Fragen zu beantworten, wurden 30 gesunde, naive Human-probanenden einer visuellen Bewegungsdiskriminationsaufgabe unterzogen. Aufgabe der Probanden war es, die globale Bewegung eines Punkte-Kinematogramms inmitten von Störsignalen zu erkennen und die eigene Sicherheit dieser Diskrimination durch einen weiteren Tastendruck anzuzeigen. Die objektive Diskriminationsleistung und die subjektive Entscheidungs-sicherheit wurden im Zeitverlauf und in drei getrennten Blöcken untersucht, in welchen verschiedene Arten transkranieller Stromstimulation appliziert wurden (3 Hz Stimulation in zwei verschiedenen Phasenbezügen zur visuellen Reizung sowie Sham-Stimulation). Im Ergebnis zeigte sich, dass tACS in der genannten Spezifizierung keinen Einfluss auf das subjektive oder objektive Diskriminationsverhalten der Probanden hatte. Andererseits wurde im Zeitverlauf eine signifikante Verbesserung der visuellen Bewegungsdiskrimination beobachtet, d.h. die Probanden lernten die Bewegungssignale besser zu unterscheiden. Auch verbesserte sich die Entscheidungssicherheit im Verlauf des Experimentes parallel mit den Diskriminationsschwellen. Das Ausmaß des Lernens, d.h. die Verbesserung der visuellen Diskrimination im Zeitverlauf, hing allein von der Qualität der Diskrimination zu Anfang des Experimentes ab: Probanden mit hohen Ausgangsschwellen verbesserten sich im Verlauf des Experimentes am deutlichsten. Das Ausmaß der Schwellenverbesserung war nicht zusätzlich durch Entscheidungssicherheit beeinflusst. In der Summe wird durch die vorliegende Arbeit somit nicht das Konzept gestützt, dass durch externe alternierende Stromstimulation mit einer Frequenz von 3 Hz kortikale Funktionen des Sehens signifikant beeinflusst werden können. Entscheidungssicherheit nimmt während des Lernens zu, ohne selbst die perzeptuelle Diskriminationsverbesserung zu steuern

Characterization of Neuroimage Coupling Between EEG and FMRI Using Within-Subject Joint Independent Component Analysis

The purpose of this dissertation was to apply joint independent component analysis (jICA) to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to characterize the neuroimage coupling between the two modalities. EEG and fMRI are complimentary imaging techniques which have been used in conjunction to investigate neural activity. Understanding how these two imaging modalities relate to each other not only enables better multimodal analysis, but also has clinical implications as well. In particular, Alzheimer’s, Parkinson’s, hypertension, and ischemic stroke are all known to impact the cerebral blood flow, and by extension alter the relationship between EEG and fMRI. By characterizing the relationship between EEG and fMRI within healthy subjects, it allows for comparison with a diseased population, and may offer ways to detect some of these conditions earlier. The correspondence between fMRI and EEG was first examined, and a methodological approach which was capable of informing to what degree the fMRI and EEG sources corresponded to each other was developed. Once it was certain that the EEG activity observed corresponded to the fMRI activity collected a methodological approach was developed to characterize the coupling between fMRI and EEG. Finally, this dissertation addresses the question of whether the use of jICA to perform this analysis increases the sensitivity to subcortical sources to determine to what degree subcortical sources should be taken into consideration for future studies. This dissertation was the first to propose a way to characterize the relationship between fMRI and EEG signals using blind source separation. Additionally, it was the first to show that jICA significantly improves the detection of subcortical activity, particularly in the case when both physiological noise and a cortical source are present. This new knowledge can be used to design studies to investigate subcortical signals, as well as to begin characterizing the relationship between fMRI and EEG across various task conditions

STRUCTURAL AND FUNCTIONAL ALTERATIONS IN NEOCORTICAL CIRCUITS AFTER MILD TRAUMATIC BRAIN INJURY

National concern over traumatic brain injury (TBI) is growing rapidly. Recent focus is on mild TBI (mTBI), which is the most prevalent injury level in both civilian and military demographics. A preeminent sequelae of mTBI is cognitive network disruption. Advanced neuroimaging of mTBI victims supports this premise, revealing alterations in activation and structure-function of excitatory and inhibitory neuronal systems, which are essential for network processing. However, clinical neuroimaging cannot resolve the cellular and molecular substrates underlying such changes. Therefore, to understand the full scope of mTBI-induced alterations it is necessary to study cortical networks on the microscopic level, where neurons form local networks that are the fundamental computational modules supporting cognition. Recently, in a well-controlled animal model of mTBI, we demonstrated in the excitatory pyramidal neuron system, isolated diffuse axonal injury (DAI), in concert with electrophysiological abnormalities in nearby intact (non-DAI) neurons. These findings were consistent with altered axon initial segment (AIS) intrinsic activity functionally associated with structural plasticity, and/or disturbances in extrinsic systems related to parvalbumin (PV)-expressing interneurons that form GABAergic synapses along the pyramidal neuron perisomatic/AIS domains. The AIS and perisomatic GABAergic synapses are domains critical for regulating neuronal activity and E-I balance. In this dissertation, we focus on the neocortical excitatory pyramidal neuron/inhibitory PV+ interneuron local network following mTBI. Our central hypothesis is that mTBI disrupts neuronal network structure and function causing imbalance of excitatory and inhibitory systems. To address this hypothesis we exploited transgenic and cre/lox mouse models of mTBI, employing approaches that couple state-of-the-art bioimaging with electrophysiology to determine the structural- functional alterations of excitatory and inhibitory systems in the neocortex