Basabdatta Sen Bhattacharya

2011 Special Issue: A thalamo-cortico-thalamic neural mass model to study alpha rhythms in Alzheimer's disease

We present a lumped computational model of the thalamo-cortico-thalamic circuitry. The model essentially consists of two modules: a thalamic module and a cortical module. The thalamic module circuitry is a modified version of a classic neural mass computational model of the thalamic circuitry to simulate cortical alpha rhythms and which we have used in previous research to study EEG abnormality associated with Alzheimers Disease (AD). Here, we introduce a modified synaptic structure representing a neuronal population in the thalamic model. Furthermore, the synaptic organisation and connectivity parameter values in the model are based on experimental data reported from the dorsal Lateral Geniculate Nucleus of different species. The cortical module circuitry is based on a recent work studying cortical brain rhythms. We vary the synaptic connectivity parameters in the thalamic module of the model to simulate the effects of AD on brain synaptic circuitry and study power within the alpha frequency bands. The power and dominant frequencies of the model output are studied in three sub-bands within the alpha band: lower alpha (7-9 Hz), middle alpha (9-11 Hz) and upper alpha (11-13 Hz). Such an analytical method conforms to recent comparative EEG studies on young adults, healthy aged adults and MCI or early stage AD patients. The results show a remarkable role of the synaptic connectivities in the inhibitory thalamic cell populations on the alpha band power and frequency. Furthermore, the total number of active synapses in the thalamic cell populations produces the slowing of alpha rhythms and a simultaneous decrease of alpha band power in the brain as a result of AD.

Using a virtual cortical module implementing a neural field model to modulate brain rhythms in Parkinson's disease.

We propose a new method for selective modulation of cortical rhythms based on neural field theory, in which the activity of a cortical area is extensively monitored using a two-dimensional microelectrode array. The example of Parkinsons disease illustrates the proposed method, in which a neural field model is assumed to accurately describe experimentally recorded activity. In addition, we propose a new closed-loop stimulation signal that is both space- and time- dependent. This method is especially designed to specifically modulate a targeted brain rhythm, without interfering with other rhythms. A new class of neuroprosthetic devices is also proposed, in which the multielectrode array is seen as an artificial neural network interacting with biological tissue. Such a bio-inspired approach may provide a solution to optimize interactions between the stimulation device and the cortex aiming to attenuate or augment specific cortical rhythms. The next step will be to validate this new approach experimentally in patients with Parkinsons disease.

Model-based bifurcation and power spectral analyses of thalamocortical alpha rhythm slowing in Alzheimer's Disease

The focus of this paper is to correlate the bifurcation behaviour of a thalamocortical neural mass model with the power spectral alpha (8–13 Hz) oscillatory activity in Electroencephalography (EEG). The aim is to understand the neural correlates of alpha rhythm slowing (decrease in mean frequency of oscillation), a hallmark in the EEG of Alzheimers Disease (AD) patients. The neural mass model used, referred to herein as the modARm, is a modified version of Lopes da Silvas alpha rhythm model (ARm). Previously, the power spectral behaviour of the modARm was analysed in context to AD. In this work, we revisit the modARm to make a combined study of the dynamical behaviour of the model and its power spectral behaviour within the alpha band while simulating the hallmark neuropathological condition of ‘synaptic depletion’ in AD. The results show that the modARm exhibits two ‘operating modes’ in the time-domain i.e. a point attractor and a limit cycle mode; the alpha rhythmic content in the model output is maximal at the vicinity of the point of bifurcation. Furthermore, the inhibitory synaptic connectivity from the cells of the Thalamic Reticular Nucleus to the Thalamo-Cortical Relay cells significantly influence bifurcation behaviour—while a decrease in the inhibition can induce limit-cycle behaviour corresponding to abnormal brain states such as seizures, an increase in inhibition in awake state corresponding to a point attractor mode may result in the slowing of the alpha rhythms as observed in AD. These observations help emphasise the importance of bifurcation analysis of model behaviour in inferring the biological relevance of results obtained from power-spectral analysis of the neural models in the context of understanding neurodegeneration.

Thalamocortical circuitry and alpha rhythm slowing: An empirical study based on a classic computational model

This paper describes a study of the effects of variations in the thalamocortical synaptic activity on alpha rhythms (8 – 13 Hz) using a computational model. The study aims to investigate alpha rhythm slowing associated with Alzheimers Disease. It is observed that for a certain range of values of the input, an increase in inhibitory activity results in an increase of the lower alpha band (8 – 10 Hz) power and a corresponding decrease in the upper alpha band (11 – 13 Hz) power, thus indicating a slowing of the alpha rhythms. On the other hand, an increase in the excitatory synaptic activity results in an overall shift of the peak power in the output signal from the lower alpha band to the upper alpha band. However, for values of input outside this range, the output signal shows a bifurcation in behaviour and enters a limit cycle mode. In this state, the output power lies dominantly in the lower alpha band. Variation in the inhibitory or excitatory synaptic parameters has little or no effect on the frequency band of the output power.

Nonlinear Origin of SSVEP Spectra—A Combined Experimental and Modeling Study

Steady state visual evoked potentials (SSVEPs) are steady state oscillatory potentials elicited in the electroencephalogram (EEG) by flicker stimulation. The frequency of these responses maches the frequency of the stimulation and of its harmonics and subharmonics. In this study, we investigated the origin of the harmonic and subharmonic components of SSVEPs, which are not well understood. We applied both sine and square wave visual stimulation at 5 and 15 Hz to human subjects and analyzed the properties of the fundamental responses and harmonically related components. In order to interpret the results, we used the well-established neural mass model that consists of interacting populations of excitatory and inhibitory cortical neurons. In our study, this model provided a simple explanation for the origin of SSVEP spectra, and showed that their harmonic and subharmonic components are a natural consequence of the nonlinear properties of neuronal populations and the resonant properties of the modeled network. The model also predicted multiples of subharmonic responses, which were subsequently confirmed using experimental data.

Implementing the cellular mechanisms of synaptic transmission in a neural mass model of the thalamo-cortical circuitry

A novel direction to existing neural mass modeling technique is proposed where the commonly used “alpha function” for representing synaptic transmission is replaced by a kinetic framework of neurotransmitter and receptor dynamics. The aim is to underpin neuro-transmission dynamics associated with abnormal brain rhythms commonly observed in neurological and psychiatric disorders. An existing thalamocortical neural mass model is modified by using the kinetic framework for modeling synaptic transmission mediated by glutamatergic and GABA (gamma-aminobutyric-acid)-ergic receptors. The model output is compared qualitatively with existing literature on in vitro experimental studies of ferret thalamic slices, as well as on single-neuron-level model based studies of neuro-receptor and transmitter dynamics in the thalamocortical tissue. The results are consistent with these studies: the activation of ligand-gated GABA receptors is essential for generation of spindle waves in the model, while blocking this pathway leads to low-frequency synchronized oscillations such as observed in slow-wave sleep; the frequency of spindle oscillations increase with increased levels of post-synaptic membrane conductance for AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic-acid) receptors, and blocking this pathway effects a quiescent model output. In terms of computational efficiency, the simulation time is improved by a factor of 10 compared to a similar neural mass model based on alpha functions. This implies a dramatic improvement in computational resources for large-scale network simulation using this model. Thus, the model provides a platform for correlating high-level brain oscillatory activity with low-level synaptic attributes, and makes a significant contribution toward advancements in current neural mass modeling paradigm as a potential computational tool to better the understanding of brain oscillations in sickness and in health.

Biologically Inspired Means for Rank-Order Encoding Images: A Quantitative Analysis

In this paper, we present biologically inspired means to enhance perceptually important information retrieval from rank-order encoded images. Validating a retinal model proposed by VanRullen and Thorpe, we observe that on average only up to 70% of the available information can be retrieved from rank-order encoded images. We propose a biologically inspired treatment to reduce losses due to a high correlation of adjacent basis vectors and introduce a filter-overlap correction algorithm (FoCal) based on the lateral inhibition technique used by sensory neurons to deal with data redundancy. We observe a more than 10% increase in perceptually important information recovery. Subsequently, we present a model of the primate retinal ganglion cell layout corresponding to the foveal-pit. We observe that information recovery using the foveal-pit model is possible only if FoCal is used in tandem. Furthermore, information recovery is similar for both the foveal-pit model and VanRullen and Thorpes retinal model when used with FoCal. This is in spite of the fact that the foveal-pit model has four ganglion cell layers as in biology while VanRullen and Thorpes retinal model has a 16-layer structure.

Kinetic modelling of synaptic functions in the alpha rhythm neural mass model

In this work, we introduce the kinetic framework for modelling synaptic transmission in an existing neural mass model of the thalamocortical circuitry to study Electroencephalogram (EEG) slowing within the alpha frequency band (8---13 Hz), a hallmark of Alzheimers disease (AD). Ligand-gated excitatory and inhibitory synapses mediated by AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) and GABAA (gamma-amino-butyric acid) receptors respectively are modelled. Our results show that the concentration of the GABA neurotransmitter acts as a bifurcation parameter, causing the model to switch from a limit cycle mode to a steady state. Further, the retino-geniculate pathway connectivity plays a significant role in modulating the power within the alpha band, thus conforming to research proposing ocular biomarkers in AD. Overall, kinetic modelling of synaptic transmission in neural mass models has enabled a more detailed investigation into the neural correlates underlying abnormal EEG in AD.

Engineering a thalamo-cortico-thalamic circuit on SpiNNaker: a preliminary study toward modeling sleep and wakefulness

We present a preliminary study of a thalamo-cortico-thalamic (TCT) implementation on SpiNNaker (Spiking Neural Network architecture), a brain inspired hardware platform designed to incorporate the inherent biological properties of parallelism, fault tolerance and energy efficiency. These attributes make SpiNNaker an ideal platform for simulating biologically plausible computational models. Our focus in this work is to design a TCT framework that can be simulated on SpiNNaker to mimic dynamical behavior similar to Electroencephalogram (EEG) time and power-spectra signatures in sleep-wake transition. The scale of the model is minimized for simplicity in this proof-of-concept study; thus the total number of spiking neurons is ≈1000 and represents a “mini-column” of the thalamocortical tissue. All data on model structure, synaptic layout and parameters is inspired from previous studies and abstracted at a level that is appropriate to the aims of the current study as well as computationally suitable for model simulation on a small 4-chip SpiNNaker system. The initial results from selective deletion of synaptic connectivity parameters in the model show similarity with EEG power spectra characteristics of sleep and wakefulness. These observations provide a positive perspective and a basis for future implementation of a very large scale biologically plausible model of thalamo-cortico-thalamic interactivity—the essential brain circuit that regulates the biological sleep-wake cycle and associated EEG rhythms.

Assessing retino-geniculo-cortical connectivities in Alzheimer's Disease with a neural mass model

Longitudinal studies have shown that increase of mean frequency within the theta band may be considered as an early symptom of progression into Alzheimers Disease (AD). Also, slowing of mean frequency within the alpha band has long since been known to be a def nitive marker in AD. This work is aimed at developing a better understanding of alterations in neuronal connectivity underlying Electroencephalogram (EEG) changes in AD. Specif cally, connectivity changes in the dorso-lateral geniculo-cortical pathway are studied using a neural mass computational model. Connectivity parameters in the model are informed by the most recent experimental data on mammalian Lateral Geniculate Nucleus (dorsal). A slowing of the mean power spectra of the model output is observed with increase in both excitatory and inhibitory parameters in the intra-thalamic and thalamocortical pathways and a decrease of sensory pathway synaptic connectivity. The biological plausibility of the results suggest potential of further model extension in AD research.