Development of Low-Frequency Repetitive Transcranial Magnetic Stimulation as a Tool to Modulate Visual Disorders: Insights from Neuroimaging

Repetitive transcranial magnetic stimulation (rTMS) has become a popular neuromodulation technique, increasingly employed to manage several neurological and psychological conditions. Despite its popular use, the underlying mechanisms of rTMS remain largely unknown, particularly at the visual cortex. Moreover, the application of rTMS to modulate visual-related disorders is under-investigated. The goal of the present research was to address these issues. I employ a multitude of neuroimaging techniques to gain further insight into neural mechanisms underlying low-frequency (1 Hz) rTMS to the visual cortex. In addition, I begin to develop and refine clinical low-frequency rTMS protocols applicable to visual disorders as an alternative therapy where other treatment options are unsuccessful or where there are simply no existing therapies. One such visual disorder that can benefit from rTMS treatment is the perception of visual hallucinations that can occur following visual pathway damage in otherwise cognitively healthy individuals. In Chapters 23, I investigate the potential of multiday low-frequency rTMS to the visual cortex to alleviate continuous and disruptive visual hallucinations consequent to occipital injury. Combining rTMS with magnetic resonance imaging techniques reveals functional and structural cortical changes that lead to the perception of visual hallucinations; and rTMS successfully attenuates these anomalous visual perceptions. In Chapters 45, I compare the effects of alternative doses of low-frequency rTMS to the visual cortex on neurotransmitter levels and intrinsic functional connectivity to gain insight into rTMS mechanisms and establish the most effective protocol. Differential dose-dependent effects are observed on neurotransmitter levels and functional connectivity that suggest the choice of protocol critically depends on the neurophysiological target. Collectively, this work provides a basic framework for the use of low-frequency rTMS and neuroimaging in clinical application for visual disorders

A pervasive body sensor network for monitoring post-operative recovery

Over the past decade, miniaturisation and cost reduction brought about by the semiconductor industry has led to computers smaller in size than a pin head, powerful enough to carry out the processing required, and affordable enough to be disposable. Similar technological advances in wireless communication, sensor design, and energy storage have resulted in the development of wireless “Body Sensor Network (BSN) platforms comprising of tiny integrated micro sensors with onboard processing and wireless data transfer capability, offering the prospect of pervasive and continuous home health monitoring. In surgery, the reduced trauma of minimally invasive interventions combined with initiatives to reduce length of hospital stay and a socioeconomic drive to reduce hospitalisation costs, have all resulted in a trend towards earlier discharge from hospital. There is now a real need for objective, pervasive, and continuous post-operative home recovery monitoring systems. Surgical recovery is a multi-faceted and dynamic process involving biological, physiological, functional, and psychological components. Functional recovery (physical independence, activities of daily living, and mobility) is recognised as a good global indicator of a patient’s post-operative course, but has traditionally been difficult to objectively quantify. This thesis outlines the development of a pervasive wireless BSN system to objectively monitor the functional recovery of post-operative patients at home. Biomechanical markers were identified as surrogate measures for activities of daily living and mobility impairment, and an ear-worn activity recognition (e-AR) sensor containing a three-axis accelerometer and a pulse oximeter was used to collect this data. A simulated home environment was created to test a Bayesian classifier framework with multivariate Gaussians to model activity classes. A real-time activity index was used to provide information on the intensity of activity being performed. Mobility impairment was simulated with bracing systems and a multiresolution wavelet analysis and margin-based feature selection framework was used to detect impaired mobility. The e-AR sensor was tested in a home environment before its clinical use in monitoring post-operative home recovery of real patients who have undergone surgery. Such a system may eventually form part of an objective pervasive home recovery monitoring system tailored to the needs of today’s post-operative patient.Open acces

Holistic System Design for Distributed National eHealth Services

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