Biomedical Engineering - Theses
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ItemDynamics of functional brain connectivity in schizophrenia: machine learning models for diagnosis and prognosis( 2019)To date, most studies of resting-state functional connectivity implicitly assume that connections remain unchanged over time. However, recent studies suggest that functional connectivity across a range of species exhibits time-varying behaviour. Understanding the time-varying properties of functional connectivity appears particularly beneficial in studying disorders such as schizophrenia. This thesis scrutinises the potential applications of dynamic functional connectivity (dFC) in aiding both the diagnosis and prognosis of schizophrenia. While previous dFC studies focussed on assessing variability of connection strengths over time, we propose a model that can simultaneously capture dynamics in both time and space. Temporal sliding windows were used to map dynamics over time. Spatial variability was accounted by a modified seed-based connectivity approach that allowed different network regions to vary their spatial layout, by expanding or contracting over time according to their connectivity profile. Connectivity measures based on the proposed method were then compared to those derived from traditional static and temporally dynamic connectivity, in predicting the diagnostic status in schizophrenia using support vector machine-based classifier models. Prediction accuracies exceeding 91% were obtained with our method, while previous methods yielded significantly lower accuracies. This suggests that the proposed method provides a better characterisation of connectivity dynamics and extracts novel disease-specific information that can potentially yield new insights into the pathophysiology of schizophrenia. Compared to healthy individuals, schizophrenia patients exhibited both temporally and spatially diminished, but more variable functional connectivity across different resting-state networks. Further, dynamic interactions among different resting-state networks were characterised using a hidden Markov model (HMM). Fluctuations in fMRI activity within 14 canonical networks, derived from both healthy individuals and schizophrenia patients, were concatenated and then quantized into 12 states using the HMM. We observed that patients spent significantly greater amounts of time in states characterised by low default-mode network (DMN) activation and heightened activity within different sensory networks. It was also found that patients lacked the ability to effectively up/downregulate the activity within the DMN. Furthermore, measures of dynamics derived from the model associated significantly with positive symptoms of schizophrenia and provided high predictive diagnostic accuracy (~85%). Finally, we examined the prognostic predictive power of dFC measures. Specifically, we tested if measures derived from dynamic connectivity among the DMN regions aid in classifying patients into worsening or improving in symptom severity after a year. Classifiers trained on DMN connectivity dynamics yielded 75-80% accuracies in predicting prognostic status in all the three types of scores considered (positive, negative and overall symptom severity). Importantly, dynamic connectivity measures were found to be better predictors than other, previously proposed variables such as cortical thickness, grey matter volume, clinical and behavioural measures and static connectivity. Together, the analyses presented in this thesis validate the utility of dynamic functional connectivity in characterising schizophrenia pathology and in aiding the adoption of more evidence-based treatment options.
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ItemAdjusting the parameters of electrical stimulation of retinal ganglion cells to reduce neural adaptation and improve efficacy of retinal prostheses( 2018)Retinal prostheses aim to provide visual percepts through electrical stimulation of the retina to blind people affected by diseases caused by photoreceptor degeneration. Two challenges presented by current devices are a lack of selectivity in the activation of retinal ganglion cells (RGCs) and neural adaptation in the retina, which is believed to be the cause of fading—an effect where artificially produced percepts disappear over a short period of time, despite continuous stimulation of the retina. We aim to (1) understand the neural adaptation generated in RGCs during electrical stimulation, (2) obtain the preferred stimulation parameters (waveform) of each morphological class of RGCs and (3) use the preferred waveform of each morphological class to selectively activate different neurons. RGCs have been classified by morphology into 4 main groups: A, B, C and D. We performed an spike-triggered covariance (STC) analysis on the responses of 44 RGCs to extracellular electrical white noise and 43 RGCs to intracellular white noise. We then recovered their temporal electrical receptive fields (tERF), or waveform. A number of RGCs were stimulated with all the previously recovered waveforms to test the efficacy of each waveform on each other. The waveform recovered from the responses to intracellular stimulation have shown that RGCs can be classified into their respective morphological types by using a K-means clustering algorithm. Extracellular stimulation did not result in waveforms with a clear correlation between clusters and morphological classes. Cells from B and D morphological types had lower thresholds when stimulated with the waveform recovered from cells in the same morphological class. A-RGCs on the contrary, did not seem to share the same temporal features in their waveform with other A-type neurons. Further studies involving a larger data set might determine whether the waveform could preferentially stimulate cells from a specific morphological class. Current visual prostheses use electrical pulses with fixed frequencies and amplitudes modulated over hundreds of milliseconds to stimulate the retina. However, in nature, neuronal spiking occurs with stochastic timing, hence the information received naturally from other neurons by RGCs is irregularly timed. We used a single epiretinal electrode to stimulate and compare rat RGC responses to stimulus trains of biphasic pulses delivered at regular and random inter-pulse intervals (IPI), the latter taken from an exponential distribution. Our observations suggest that stimulation with random IPIs result in lower adaptation rates than stimulation with constant IPIs at frequencies of 50 Hz and 200 Hz. We also found a high proportion of lower amplitude action potentials, or spikelets. The spikelets were more prominent at high stimulation frequencies (50 Hz and 200 Hz) and were less susceptible to adaptation, but it was not clear if they propagated along the axon. Using random IPI stimulation in retinal prostheses reduces the decay of RGCs and this could potentially reduce fading of electrically induced visual perception.