The Search For Biomarkers In Psychiatry

This volume addresses one of the Holy Grails in Psychiatry, namely the evidence for and potential to adopt ‘Biomarkers’ for prevention, diagnosis, and treatment responses in mental health conditions. It meshes together state of the art research from international renowned pre-clinical and clinical scientists to illustrate how the fields of anxiety disorders, depression, psychotic disorders, and autism spectrum disorder have advanced in recent years.

Personalized Psychiatry presents the first book to explore this novel field of biological psychiatry that covers both basic science research and its translational applications. The book conceptualizes personalized psychiatry and provides state-of-the-art knowledge on biological and neuroscience methodologies, all while integrating clinical phenomenology relevant to personalized psychiatry and discussing important principles and potential models. It is essential reading for advanced students and neuroscience and psychiatry researchers who are investigating the prevention and treatment of mental disorders. Combines neurobiology with basic science methodologies in genomics, epigenomics and transcriptomics Demonstrates how the statistical modeling of interacting biological and clinical information could transform the future of psychiatry Addresses fundamental questions and requirements for personalized psychiatry from a basic research and translational perspective

"Diagnosis in psychiatry relies on the subjective assessment of symptoms, both reported by patients and observed by clinicians. This purely symptom-based classification has its limitations. Patients with the same diagnosis express different symptoms, and there is considerable overlap in symptoms across different mental disorders, leading to the possibility of over- or misdiagnosis. Moreover, these diagnoses tell us little about the individual patient, as treatment guidelines tend to follow a one-size-fits-all approach that may not be effective for every patient, resulting in delayed or ineffective treatment for some. To address these issues, there has been a shift towards more objective approaches to diagnosis and personalized treatment in psychiatry . An important means to this end is the search for objective biomarkers , which are measurable characteristics that indicate normal (neuro)biological processes or responses to therapeutic intervention. These biomarkers could help clinicians make more evidence-based decisions that are tailored to individual patients, potentially leading to more efficient and effective psychiatric care. The aim of this thesis was to investigate whether machine learning techniques could be used to find generalizable biomarkers for various psychiatric disorders. We used neuroimaging data to develop diagnostic biomarkers for OCD, (trans)diagnostic biomarkers for anxiety disorders in juveniles, and biomarkers predictive for ECT treatment outcome in patients with depression. This thesis showed that machine learning techniques can be applied to predict diagnosis and treatment outcome with variable success. Future studies should carefully consider sources of heterogeneity to facilitate the development of more accurate and clinically relevant biomarkers, ultimately improving psychiatric care."--

Biomarkers hold immense promise for the early detection of disease. Unlike other disorders like diabetes and heart disease where a limited number of biological markers are at hand that allow the physician to come up with a reliable diagnosis, there are currently no such markers available for affective disorders. As in any other disease area a major goal is therefore the identification of early markers that can categorize subsets of subjects in a consistent manner. This will allow a more precise definition and categorization of affective disorders and in turn facilitate investigations of the pathogenesis of the diseases and enhance our ability for treatment. This edited volume will not only address the area of affective disorders but also other brain disorders that are neurological in nature, including Multiple Sclerosis and Alzheimer Disease.

This book reviews key recent advances and new frontiers within psychiatric research and clinical practice. These advances either represent or are enabling paradigm shifts in the discipline and are influencing how we observe, derive and test hypotheses, and intervene. Progress in information technology is allowing the collection of scattered, fragmented data and the discovery of hidden meanings from stored data, and the impacts on psychiatry are fully explored. Detailed attention is also paid to the applications of artificial intelligence, machine learning, and data science technology in psychiatry and to their role in the development of new hypotheses, which in turn promise to lead to new discoveries and treatments. Emerging research methods for precision medicine are discussed, as are a variety of novel theoretical frameworks for research, such as theoretical psychiatry, the developmental approach to the definition of psychopathology, and the theory of constructed emotion. The concluding section considers novel interventions and treatment avenues, including psychobiotics, the use of neuromodulation to augment cognitive control of emotion, and the role of the telomere-telomerase system in psychopharmacological interventions.

This book presents a thorough and critical review of current knowledge on the role of immunology in major psychiatric disorders and its potential applications. The opening chapters offer general information on the immune influence of the brain to provide readers with a better understanding of the end of immune privilege. The book then examines possible underlying mechanisms leading to psychiatric disorders, from early infections to pro-inflammatory markers, stress, and immune genetic background, linking etiology and psychiatry. The third section describes each disorder (ie autism, schizophrenia, bipolar disorder, depression...) with an overview of underlying immune dysfunctions. Lastly, the authors discuss the innovative immune-therapies that may result from the discovery of immune system biomarkers and their associated mechanisms. A better understanding of the role of the immune system in psychiatric disorders makes it possible to identify stratification biomarkers, to explain underlying mechanisms, and to develop innovative, efficient, targeted treatment strategies and management. As such, the book is of value to clinicians, mental health professionals, mental health researchers, immunologists, industry practitioners, and various stakeholders in the mental health field.

With recent studies using genetic, epigenetic, and other molecular and neurochemical approaches, a new era has begun in understanding pathophysiology of suicide. Emerging evidence suggests that neurobiological factors are not only critical in providing potential risk factors but also provide a promising approach to develop more effective treatment and prevention strategies. The Neurobiological Basis of Suicide discusses the most recent findings in suicide neurobiology. Psychological, psychosocial, and cultural factors are important in determining the risk factors for suicide; however, they offer weak prediction and can be of little clinical use. Interestingly, cognitive characteristics are different among depressed suicidal and depressed nonsuicidal subjects, and could be involved in the development of suicidal behavior. The characterization of the neurobiological basis of suicide is in delineating the risk factors associated with suicide. The Neurobiological Basis of Suicide focuses on how and why these neurobiological factors are crucial in the pathogenic mechanisms of suicidal behavior and how these findings can be transformed into potential therapeutic applications.

Neuroscientists are mining nucleic acids, fluids, and brain images for biomarkers of risk of brain disorders. This book brings clarity to several debates on the neuroethics of biomarkers by arguing for the abandonment of a categorical concept of disorder (sick vs. well) and the adoption of an explicitly probabilistic one.