Investigation into the mechanisms of depressive illness

Functional and structural brain abnormalities have been reported in many imaging studies of depressive illness. However, the mechanisms by which these abnormalities give rise to symptoms remain unknown. The work described in this thesis focuses on such mechanisms, particularly with regard to neural predictive error signals. Recently, these signals have been reported to be present in many studies on animals and healthy humans. The central hypothesis explored in this thesis is that depressive illness comprises a disorder of associative learning. Chapter 2 reviews the brain regions frequently reported as abnormal in imaging studies of depressive illness, and the normal function of these particular brain regions. It is concluded that such regions comprise the neural substrate for associative learning and emotion. However, confidence in this conclusion is limited by considerable variability in the human imaging literature. Therefore, chapter 3 describes a meta-analysis, which tests the hypothesis that, consistent with the non-imaging literature, the ventromedial prefrontal cortex is most active during emotional experience. The results of the meta-analysis were clearly consistent with this hypothesis. Chapter 4 provides an introduction to neural predictive error signals from the general perspective of homeostatic physiological regulation. Both experimental evidence supporting the error signals, and various formal mathematical theories describing the error signals, are summarised. This provides the background to chapter 5, which describes an original fMRI study which tested the hypothesis that patients with depressive illness would exhibit abnormal predictive error signals in response to unexpected motivationally significant stimuli. Evidence of such abnormality was found. Chapter 6 describes a further original study using transcranial ultrasound and diffusion tensor imaging of the brainstem, which investigated reports of a subtle structural abnormality in depressed patients. If present, it might give rise to abnormal error signals. However, no structural abnormality was found. Finally, chapter 7 discusses the significance of these findings in the context of clinical features of depressive illness and a wide range of treatments, ranging from psychotherapy through antidepressants to physical treatments. A number of potential future studies are identified, which could clarify understanding of depressive illness

Dementia in Parkinson’s Disease

An estimated 50% to 80% of individuals with Parkinson’s disease experience Parkinson’s disease dementia (PDD). Based on the prevalence and clinical complexity of PDD, this book provides an in-depth update on topics including epidemiology, diagnosis, and treatment. Chapters discuss non-medical therapies and examine views on end-of-life issues as well. This book is a must-read for anyone interested in PDD whether they are a patient, caregiver, or doctor

The Stylometric Processing of Sensory Open Source Data

This research project’s end goal is on the Lone Wolf Terrorist. The project uses an exploratory approach to the self-radicalisation problem by creating a stylistic fingerprint of a person's personality, or self, from subtle characteristics hidden in a person's writing style. It separates the identity of one person from another based on their writing style. It also separates the writings of suicide attackers from ‘normal' bloggers by critical slowing down; a dynamical property used to develop early warning signs of tipping points. It identifies changes in a person's moods, or shifts from one state to another, that might indicate a tipping point for self-radicalisation. Research into authorship identity using personality is a relatively new area in the field of neurolinguistics. There are very few methods that model how an individual's cognitive functions present themselves in writing. Here, we develop a novel algorithm, RPAS, which draws on cognitive functions such as aging, sensory processing, abstract or concrete thinking through referential activity emotional experiences, and a person's internal gender for identity. We use well-known techniques such as Principal Component Analysis, Linear Discriminant Analysis, and the Vector Space Method to cluster multiple anonymous-authored works. Here we use a new approach, using seriation with noise to separate subtle features in individuals. We conduct time series analysis using modified variants of 1-lag autocorrelation and the coefficient of skewness, two statistical metrics that change near a tipping point, to track serious life events in an individual through cognitive linguistic markers. In our journey of discovery, we uncover secrets about the Elizabethan playwrights hidden for over 400 years. We uncover markers for depression and anxiety in modern-day writers and identify linguistic cues for Alzheimer's disease much earlier than other studies using sensory processing. In using these techniques on the Lone Wolf, we can separate their writing style used before their attacks that differs from other writing