Neuroimaging and Machine Learning to Redefine Anxiety and Depression

神经影像和机器学习重新定义焦虑和抑郁

• 批准号: 9120715
• 负责人: Andrea Goldstein-Piekarski
• 金额: $ 5.05万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9120715
• 关键词: Address; Algorithms; Americas; Amygdaloid structure; Anxiety; Anxiety Disorders; Base of the Brain; Behavior; Behavioral; Brain; Caring; Categories; Classification; Clinic; Complex; Computing Methodologies; Data; Data Set; Diagnosis; Diagnostic; Disease; Elements; Emotional; Emotions; Face; Functional disorder; Heterogeneity; Image; Individual; Link; Machine Learning; Magnetic Resonance Imaging; Major Depressive Disorder; Maps; Measurement; Measures; Mental Depression; Modality; Modeling; Neurobiology; Neuronal Dysfunction; Outcome; Participant; Patient Self-Report; Pattern; Persons; Pharmacological Treatment; Phenotype; Physiological; Physiology; Population; Principal Component Analysis; Procedures; Process; Productivity; Recruitment Activity; Regulation; Risk Factors; Sampling; Severities; Societies; Stress; Structure; Symptoms; System; Taxonomy; Testing; Translating; Validation; Variant; acute stress; anxiety symptoms; base; biological adaptation to stress; comparison group; cost; depression model; depressive symptoms; design; disability; emotion dysregulation; indexing; individualized medicine; neural circuit; neuroimaging; new therapeutic target; novel; patient subsets; phenomenological models; public health relevance; relating to nervous system; research study; response; social stigma; trait

 DESCRIPTION (provided by applicant): I aim to identify a data-driven taxonomy of depression and anxiety from multiple neurobiological measures of brain function, physiology and behavior that is not constrained by existing diagnostic boundaries. Anxiety Disorders and Major Depressive Disorder are highly prevalent and together cost over $100 billion per year in care and lost productivity. While the symptoms used in the diagnosis of these disorders convey useful information and reflect real phenomenology, the way in which symptoms are grouped makes for "fuzzy" diagnostic boundaries, with substantial symptom overlap across disorders, yet vast symptom heterogeneity within. Moreover, experiments aiming to identify the neural contribution to dysfunction have been intrinsically tied to these traditional diagnostic categories As a consequence, we do not have a clear understanding of how the neural circuitry underlying depression and anxiety relates to the expressed symptoms at the level of physiology and behavior, independent from these traditional diagnoses. These blurry diagnostic lines hamper our progress toward understanding the mechanisms of dysfunction and developing novel, targeted therapeutics. Therefore, it would be beneficial to establish a complementary characterization of anxiety and depression that reflects cohesive clusters of distinct neural causes. Addressing these issues I propose to use a data driven approach to develop an alternate classification for depression and anxiety. Under Aim 1 I will use computational methods on a rich existing dataset of over 600 participants, to derive dimensional constructs of emotion processing from neuroimaging probes of emotion reactivity and regulation and determine how these constructs are associated with other levels of function spanning behavior, physiology and self-report. Under Aim 2 I will use sparse clustering algorithms to classify individual subjects according to the neuroimaging constructs and then determine how each classification is expressed across behavioral, physiological and self-report symptom measures, independent of traditional diagnosis. To address Aim 3 I will use experimental stress probes to parse state versus trait-like components of the relationships between neuroimaging and each other unit of measurement. The outcome will be a novel classification that will advance our progress toward both understanding the mechanisms of neural dysfunction in depression and anxiety as well as developing novel therapeutics for targeting such dysfunction. Critically, the proposed multi-modal approach utilizes unsupervised machine learning algorithms to identify the underlying patterns within this complex system in a manner that is free from the assumptions of the current diagnostic paradigms. The resulting characterization from this approach will provide a dimensional space to understand the natural variation in neural circuit function and how this variation relates to each person's functional phenotype. Such a characterization will be a significant step forward in transforming the way that depression and anxiety are understood, removing stigma, and allowing novel treatments to be developed from mechanistic models that can be more effectively translated to the clinic.