Application of point-process system identification techniques to complex physiological systems

This thesis is concerned with the application of system identification techniques to the analysis of complex physiological systems. The techniques are applied to neuronal spike-train data obtained from elements of the neuromuscular system. A brief description of the neuromuscular system is given in chapter 1, along with a more detailed discussion of the muscle spindle, which is the component of the neuromuscular system which this study deals with. In addition, some possibilities for system identification studies of the muscle spindle are discussed. The identification procedure is based on statistical methods for the treatment of point-process data. The point-process representation of a spike-train is introduced in chapter 2 with definitions of time and frequency domain point-process parameters. Estimates for these parameters are given, along with expressions for their asymptotic distributions. The linear point-process system identification model is introduced and estimates are described for the model parameters in terms of the previously defined point-process parameters. These point-process and linear parameter estimates are applied to muscle spindle spike-train data. In the analysis of a single spike-train certain important features only show up in the frequency domain, and for input and output spike-trains a linear transfer function type description is constructed in the frequency domain. The mathematical model of this transfer function is used as the basis for an analogue computer simulation of a subsystem of the muscle spindle. This consists of a linear first order filter followed by an encoder which generates output spikes. Data logged from the simulation is processed in the same manner as experimental data, and the effect of varying the simulation parameters on the linear model estimates is looked at. It is shown that in general the linear model description reflects the properties of the linear filter in the simulation, and varying the simulation parameters can be used to accurately match results from simulated data with those obtained from real data. Chapter 3 compares the point-process approach with a more conventional filtering and sampled data approach to estimate power spectra. The filtering of spike-trains with broad band spectra is investigated, and this shows up a pitfall in the choice of filter cut-off frequency. It is concluded that the point-process approach is preferable due to shorter computational times, and the well documented statistical propeties of the point-process estimates. The application of the point-process techniques described in chapter 2 to the analysis of more general spike-train data is considered in chapter 4. Three techniques for measuring the degree of coupling between two spike-trains are compared, and the point-process frequency domain measure is found to be the most sensitive. This measure is also applied to a data set containing a strong single periodicity, and the ability to detect coupling at a single harmonic is demonstrated. The analysis of coupling between spike-trains in the frequency domain is extended to deal with multiple spike-trains, and the ability to distinguish genuine coupling from the effect of a common input is shown to be a powerful tool which can be used to investigate communications pathways in neural systems. Finally, one special feature of the muscle spindle response to a spike-train input is analysed using the simulation. It is demonstrated that the point-process approach can produce results about a particular phenomenon from a single experiment much more rapidly than using a repetitive trial and error approach. Chapter 5 considers the extension of the linear point-process identification model introduced in chapter 2. Higher order time and frequency domain point-process parameters are defined and estimates given. In the time domain, a new technique for rapidly generating higher order time domain parameters is developed. The quadratic point-process model is introduced and solutions for its parameters given. These estimates are applied to muscl

Single-trial multiwavelet coherence in application to neurophysiological time series

A method of single-trial coherence analysis is presented, through the application of continuous muldwavelets. Multiwavelets allow the construction of spectra and bivariate statistics such as coherence within single trials. Spectral estimates are made consistent through optimal time-frequency localization and smoothing. The use of multiwavelets is considered along with an alternative single-trial method prevalent in the literature, with the focus being on statistical, interpretive and computational aspects. The multiwavelet approach is shown to possess many desirable properties, including optimal conditioning, statistical descriptions and computational efficiency. The methods. are then applied to bivariate surrogate and neurophysiological data for calibration and comparative study. Neurophysiological data were recorded intracellularly from two spinal motoneurones innervating the posterior,biceps muscle during fictive locomotion in the decerebrated cat

Apolipoprotein-E forms dimers in human frontal cortex and hippocampus

<p>Abstract</p> <p>Background</p> <p>Apolipoprotein-E (apoE) plays important roles in neurobiology and the apoE4 isoform increases risk for Alzheimer's disease (AD). ApoE3 and apoE2 are known to form disulphide-linked dimers in plasma and cerebrospinal fluid whereas apoE4 cannot form these dimers as it lacks a cysteine residue. Previous in vitro research indicates dimerisation of apoE3 has a significant impact on its functions related to cholesterol homeostasis and amyloid-beta peptide degradation. The possible occurrence of apoE dimers in cortical tissues has not been examined and was therefore assessed. Human frontal cortex and hippocampus from control and AD post-mortem samples were homogenised and analysed for apoE by western blotting under both reducing and non-reducing conditions.</p> <p>Results</p> <p>In apoE3 homozygous samples, ~12% of apoE was present as a homodimer and ~2% was detected as a 43 kDa heterodimer. The level of dimerisation was not significantly different when control and AD samples were compared. As expected, these dimerised forms of apoE were not detected in apoE4 homozygous samples but were detected in apoE3/4 heterozygotes at a level approximately 60% lower than seen in the apoE3 homozygous samples. Similar apoE3 dimers were also detected in lysates of SK-N-SH neuroblastoma cells and in freshly prepared rabbit brain homogenates. The addition of the thiol trapping agent, iodoacetamide, to block reactive thiols during both human and rabbit brain sample homogenisation and processing did not reduce the amount of apoE homodimer recovered. These data indicate that the apoE dimers we detected in the human brain are not likely to be post-mortem artefacts.</p> <p>Conclusion</p> <p>The identification of disulphide-linked apoE dimers in human cortical and hippocampal tissues represents a distinct structural difference between the apoE3 and apoE4 isoforms that may have functional consequences.</p

A Unified Framework of Third Order Time and Frequency Domain Analysis for Neural Spike Trains

Third order time and frequency analysis has exhibited great potential for correlation analysis of multi-sensor datasets, but is usually presented as separate time domain and frequency domain approaches. A combined framework of both frequency domain and time domain has rarely been used. This paper proposes a non-parametric third order time and frequency domain framework which used two dimensional Fourier transforms to bridge the gap between time domain and frequency domain. A unified framework offers flexibility and efficiency to apply to data. In this paper we study neural spike train data treated as stochastic point processes. In time domain direct analysis, third order cumulant densities of spike trains are applied, which need all first-, secondand third order product densities to be calculated before constructing the third order cumulant density, which brings additional challenges. The novelty in this study is that a new framework is proposed which can offer an alternative approach without calculating lower order quantities and can reveal nonlinear relationship between neural recordings. The results show that the present framework provides a novel non-parametric method to estimate both time and frequency domain measurements which is applicable to neural spike trains

Wavelet-based method for coherence analysis with suppression of low frequency envelope modulation in non-stationary signals

Techniques for non-stationary signal analysis are important in understanding dynamical behaviour of complex systems. Time-frequency coherence is widely used to analyse time-varying characteristics in non-stationary signals. This paper presents wavelet-based methods, using Airy wavelet, to estimate coherence. We incorporate a novel technique for removal of low frequency components due to envelope modulation in non-stationary signals. The technique is demonstrated on synthetic and real neurophysiological data. Results not only provide a clear description of desired features in non-stationary signals, but also suppress low frequency components due to envelope modulation. Our novel technique shows an effectiveness in extracting features hidden within the signals. It may lead to improved results in coherence analysis of medical, biological, physical and geophysical data containing low frequency envelope modulation besides non-stationarities

Self-repairing mobile robotic car using astrocyte-neuron networks

A self-repairing robot utilising a spiking astrocyte-neuron network is presented in this paper. It uses the output spike frequency of neurons to control the motor speed and robot activation. A software model of the astrocyte-neuron network previously demonstrated self-detection of faults and its self-repairing capability. In this paper the application demonstrator of mobile robotics is employed to evaluate the fault-tolerant capabilities of the astrocyte-neuron network when implemented in a hardware-based robotic car system. Results demonstrated that when 20% or less synapses associated with a neuron are faulty, the robot car can maintain system performance and complete the task of forward motion correctly. If 80% synapses are faulty, the system performance shows a marginal degradation, however this degradation is much smaller than that of conventional fault-tolerant techniques under the same levels of faults. This is the first time that astrocyte cells merged within spiking neurons demonstrates a self-repairing capabilities in the hardware system for a real application

Sex differences in auditory fear discrimination are associated with altered medial prefrontal cortex function

The increased prevalence of post-traumatic stress disorder (PTSD) that is observed in women may involve sex differences in learned fear inhibition and medial prefrontal cortex (mPFC) function. PTSD is characterized by fear overgeneralization involving impaired fear regulation by safety signals. We recently found that males show fear discrimination and females show fear generalization involving reduced safety signalling after extended fear discrimination training. Here we determined if these sex differences involve altered mPFC function. Male and female rats underwent three days of auditory fear discrimination training, where one tone (CS+) was paired with footshock and another tone (CS-) was presented alone. Local field potentials were recorded from prelimbic (PL) and infralimbic (IL) mPFC during retrieval. We found that males discriminated and females generalized based on cue-induced freezing at retrieval. This was accompanied by sex differences in basal theta and gamma oscillations in PL and IL. Importantly, males also showed PL/IL theta activation during safety signalling by the CS- and IL gamma activation in response to the threat-related CS+, both of which were absent in females. These results add to growing evidence indicating that sex differences in learned fear inhibition are associated with altered mPFC function

Prevalence of vitamin D insufficiency and risk factors for type 2 diabetes and cardiovascular disease among African migrant and refugee adults in Melbourne

Migration to industrialised countries poses a &ldquo;double whammy&rdquo; for type 2 diabetes among sub-Saharan African migrant and refugee adults. This population group has been found to be at an increased risk of obesity and type 2 diabetes, which may be further aggravated by inadequate vitamin D status. Thus, this study aimed to describe the demographics of vitamin D insufficiency, obesity, and risk factors for type 2 diabetes among sub-Saharan African migrants and refugees aged 20 years or older living in Melbourne, Australia (n=49). Data were obtained by a questionnaire, medical assessment, and fasting blood samples. The mean serum 25-hydroxyvitamin D level was 27.3 nmol/L (95% CI: 22.2, 32.4 nmol/L); with 25-hydroxyvitamin D levels &lt;50 nmol/L occurring in 88% of participants. Participants displayed a cluster of risk factors for type 2 diabetes and cardiovascular disease: 62% were overweight or obese, 47% had insulin resistance (HOMA-IR &ge;2), 25% had low density lipoprotein cholesterol levels &ge;3.5 mmol/L, 24.5% had high density lipoprotein cholesterol levels &le;1.03 mmol/L, 34.6% had borderline or high levels of total cholesterol (&ge;5.2 mmol/L), 18.2% had borderline or high levels of triglyceride (&ge;1.7 mmol/L), and 16% had hypertension (systolic blood pressure &ge;140 mmHg or diastolic blood pressure &ge;90 mmHg). These findings suggest that sub-Saharan African migrants and refugees may be at risk of type 2 diabetes and atherosclerosis-related diseases such as ischemic heart disease, stroke, and peripheral vascular disease. Well-designed vitamin D interventions that incorporate lifestyle changes are urgently needed in this sub-population.<br /