Yuksel Cakir

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.

On ear biometrics

Today the most successful biometric based identification technologies such as fingerprint, iris, retina, palm and face recognition are used worldwide in both criminal investigations and high security facilities. These technologies are well-studied, but research shows they have many drawbacks which decrease the success of the methods applied. Ear images are not affected by emotional expression, illumination, aging, poses and alike. In this study principal component analysis (PCA), fisher linear discriminant analysis (FLDA), discriminative common vector analysis (DCVA), and locality preserving projections (LPP) were applied to ear images for personal identification. The error and hit rates of four algorithms were calculated by random subsampling and k-fold cross validation.

Effects of the facial and racial features on gender classification

This paper presents an experimental study on examining the effects of facial and racial features on gender classification. In order to show which facial feature is the most influential for gender classification, parts of several face images, such as, forehead, eyebrows, eyes, nose, lip and chin were masked. For dimension reduction, Principal Component Analysis (PCA) and for determination of gender, Fisher Linear Discriminant (FLD) algorithms were applied to masked face images. Moreover, the effects of racial features on gender classification were studied. Experimental results indicated that the nose is the most influential part for gender classification. Furthermore the gender of the Asian people is more easily distinguished than that of the people of African origin.

Modeling of synchronization behavior of bursting neurons at nonlinearly coupled dynamical networks

ABSTRACT Synchronization behaviors of bursting neurons coupled through electrical and dynamic chemical synapses are investigated. The Izhikevich model is used with random and small world network of bursting neurons. Various currents which consist of diffusive electrical and time-delayed dynamic chemical synapses are used in the simulations to investigate the influences of synaptic currents and couplings on synchronization behavior of bursting neurons. The effects of parameters, such as time delay, inhibitory synaptic strengths, and decay time on synchronization behavior are investigated. It is observed that in random networks with no delay, bursting synchrony is established with the electrical synapse alone, single spiking synchrony is observed with hybrid coupling. In small world network with no delay, periodic bursting behavior with multiple spikes is observed when only chemical and only electrical synapse exist. Single-spike and multiple-spike bursting are established with hybrid couplings. A decrease in the synchronization measure is observed with zero time delay, as the decay time is increased in random network. For synaptic delays which are above active phase period, synchronization measure increases with an increase in synaptic strength and time delay in small world network. However, in random network, it increases with only an increase in synaptic strength.

Modeling influences of dopamine on synchronization behavior of striatum

ABSTRACT A network model of striatum that comprises medium spiny neurons (MSNs) and fast spiking interneurons (FSIs) is constructed following the work of Humphries et al. (2009). The dynamic behavior of striatum microcircuit is investigated using a dopamine-modulated modified Izhikevich neuron model. The influences of dopamine on the synchronization behavior of the striatal microcircuit and the dependence on receptor type are investigated with and without time delay. To investigate the role of two types of dopamine receptors, D1 and D2, on the overall activity of the striatum microcircuit, the activities of two groups are considered as disconnected and connected. When the connection exists between D1 and D2 sub-networks with zero dopamine and time delay, neuronal activity decreases because of an inhibitory effect of the connected neurons of the other sub-network. In the presence of dopamine, an increase in the activity of D1 type MSNs and quiescent behavior of D2 type MSNs are observed when the time delay is zero. However, the diversity in synchronization of D1 and D2 type MSNs is observed for different synaptic time delays and synaptic strengths in the case that dopamine is present.

Spectral and non-linear analysis of thalamocortical neural mass model oscillatory dynamics

The chapter is organised in two parts: In the first part, the focus is on a combined power spectral and non-linear behavioural analysis of a neural mass model of the thalamocortical circuitry. The objective is to study the effectiveness of such “multi-modal” analytical techniques in model-based studies investigating the neural correlates of abnormal brain oscillations in Alzheimer’s disease (AD). The power spectral analysis presented here is a study of the “slowing” (decreasing dominant frequency of oscillation) within the alpha frequency band (8–13 Hz), a hallmark of electroencephalogram (EEG) dynamics in AD. Analysis of the non-linear dynamical behaviour focuses on the bifurcating property of the model. The results show that the alpha rhythmic content is maximal at close proximity to the bifurcation point—an observation made possible by the “multi-modal” approach adopted herein. Furthermore, a slowing in alpha rhythm is observed for increasing inhibitory connectivity—a consistent feature of our research into neuropathological oscillations associated with AD. In the second part, we have presented power spectral analysis on a model that implements multiple feed-forward and feed-back connectivities in the thalamo-cortico-thalamic circuitry, and is thus more advanced in terms of biological plausibility. This study looks at the effects of synaptic connectivity variation on the power spectra within the delta (1–3 Hz), theta (4–7 Hz), alpha (8–13 Hz) and beta (14–30 Hz) bands. An overall slowing of EEG with decreasing synaptic connectivity is observed, indicated by a decrease of power within alpha and beta bands and increase in power within the theta and delta bands. Thus, the model behaviour conforms to longitudinal studies in AD indicating an overall slowing of EEG.