Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Machine learning for genetic prediction of schizophrenia

The complexity of schizophrenia raises a formidable challenge. Its diverse genetic architecture, influence from environmental factors from the prenatal period through to adolescence, and the absence of a laboratory-based diagnostic test complicate efforts to "carve nature a its joints". Twinned with attempts to disentangle schizophrenia’s origins are those aiming to predict it. Prediction is essential to precision psychiatry and attempts to improve patient outcomes. Genetic prediction only became feasible relatively recently, following the discovery of robust risk loci in association studies. Polygenic risk scoring (PRS) is a popular method which relies on univariable tests of association and typically assumes additivity within and between loci, but explains only a small fraction of liability to schizophrenia. Machine learning (ML) methods have evolved out of the artificial intelligence and statistics communities which learn predictive patterns from labelled data. They are an enticing option in genetics, as they allow for multivariable predictive modelling, complex predictor relationships including interactions and can learn from datasets where the number of predictors exceeds observations. However,their predictive performance in schizophrenia is largely unknown. The ability of penalised logistic regression, support vector machines, random forests (RFs), gradient boosting machines (GBMs) and neural networks to predict schizophrenia from genetic data was investigated. A review systematically assessed predictive performance and methodology in machine learning on psychiatric disorders, finding poor reporting, widespread inadequate modelling approaches and high risk of bias. Simulations assessed performance in the presence of additive or interaction effects. Flexible ML approaches including RFs and GBMs performed best under interactions, but worse than PRS and sparse linear models for additive effects. Evaluation in real data assessed modelling procedures including calibration and deconfounding. Prediction was maximised when combining genetic and non-genetic factors; no evidence was found to support choosing machine learning approaches over logistic regression or PRS

Examinations of pathomechanisms in schizophrenic and bipolar disorders – results from two functional magnetic resonance imaging studies

Psychiatric disorders, in particular schizophrenia and bipolar disorder, affect the patients’ lives deeply on many levels and place a heavy burden on the healthcare system. The treatment of these diseases is often complicated and marked by many setbacks. Symptoms that have the strongest consequences for coping with everyday life are the impairments of cognitive performance, for example memory or attention deficits. Therefore, it is of great interest to better understand the underlying pathomechanisms to eventually improve treatment options for those patients. In this thesis two different fMRI studies were used to investigate the functional correlates of patients suffering from schizophrenia or bipolar disorder while performing a combined oddball-incongruence task and a reward associated task. Study A conducted a categorical comparison between bipolar and schizophrenia patients of the brain activation during an oddball and incongruence task. The results showed pathophysiological differences in the activation intensities between bipolar and schizophrenia patients as well as between the patient groups and healthy individuals. Overall it seems as if the brain activation severely depended on the task difficulty leading to compensatory hyperactivations in frontal brain areas of bipolar patients during the oddball task. Schizophrenia patients demonstrated low threshold hyperactivations in the intraparietal cortex compared to healthy controls. In the cognitively more demanding incongruence condition these compensatory mechanisms seemed to fail leading to hypoactivations in various brain regions such as the middle frontal gyrus or ventral pathway. Pilot study B searched retrospectively for functional markers which enable support vector machine algorithms predicting specific treatment response to typical and atypical antipsychotics as well as aripiprazole in a transnosological sample consisting of bipolar and schizophrenia patients. Consequently, (de-)activation differences between responders and non-responders in their respective treatment arm resulting from the desire-reason-dilemma paradigm were applied to support vector machine algorithms. The implementation of parameter estimates from deactivations of aripiprazole non-responders in brain regions partially associated with the default mode network, led to a successful treatment response prediction of patients receiving aripiprazole. Even though in future studies the sample sizes should be increased and monotherapeutical treatment ensured, this thesis already provides important insights on the pathomechanisms of bipolar disorder and schizophrenia patients or more specifically within the spectrum of both diseases. Prospectively, further studies can help to specify potential functional biomarkers which also might be able to predict treatment response and consequently approach personalized precision treatment in psychiatric disorders

The Making of Modern Psychiatry

The field of psychiatry changed dramatically in the latter half of the nineteenth century, largely by embracing science. The transformation was most evident in Germany, where many psychiatrists began to work concurrently in the clinic and the laboratory. Some researchers sought to discover brain correlates of mental illness, while others looked to experimental psychology for insights into mental dynamics. Featured here, are the lives and works of Emil Kraepelin - often considered the founder of modern scientific psychiatry, his teacher Bernhard Gudden, and his anatomist colleague Franz Nissl. The book describes scientific findings together with the methods used; it explains why diagnoses were then (and are still now) so difficult to make; it also explores mind-brain controversies. The Making of Modern Psychiatry will inform and delight mental health professionals as well as all persons curious about the origins of modern psychiatry

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare