From Manic Symptoms to Bipolar Disorder: Neural-behavioral Markers Using Two Analytic Models

从躁狂症状到双相情感障碍：使用两种分析模型的神经行为标记

• 批准号: 10116487
• 负责人: Michele A Bertocci
• 金额: $ 69.6万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10116487
• 关键词: Adolescence; Adolescent; Affect; Affective; Age; Age of Onset; Amygdaloid structure; Anterior; Behavior; Behavior assessment; Behavioral; Biological; Biological Markers; Biosensor; Bipolar Disorder; Brain; Childhood; Clinical; Clinical assessments; Cognition; Cognitive; Corpus striatum structure; Development; Diagnosis; Dimensions; Disease; Disease Marker; Disease Progression; Early Diagnosis; Emotions; Evaluation; Future; Gender; Hospitals; Impairment; Individual; Inpatients; Intervention; Length; Light; Linear Models; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Manic; Matched Group; Measures; Modeling; Mood Disorders; Moods; Morbidity - disease rate; Multimodal Imaging; Neurocognitive; Neurophysiology - biologic function; Onset of illness; Outcome; Outpatients; Parietal; Patient Self-Report; Pattern Recognition; Physical activity; Polysomnography; Population; Prefrontal Cortex; Proxy; Resources; Rest; Rewards; Risk; Sampling; Severities; Sleep; Specific qualifier value; Stimulus; Structure; Suicide; Symptoms; Syndrome; Testing; Thick; Time; Training; Uncertainty; Validation; Variant; Ventral Striatum; Work; Youth; actigraphy; aged; behavior measurement; brain behavior; childhood bipolar disorder; cingulate cortex; clinical predictors; cognitive testing; comorbidity; computerized; cost; digital; experience; follow-up; functional disability; gray matter; high risk; high risk population; high-risk adolescents; hypomania; imaging modality; improved; learning classifier; mobile application; mood symptom; multimodality; neural network; neuroimaging; neuropsychiatry; novel; outcome prediction; personalized intervention; predictive marker; predictive modeling; predictive test; recruit; relating to nervous system; response; reward processing; treatment planning; two-dimensional; white matter; young adult

PROJECT SUMMARY Bipolar disorder (BD) is a devastating neuropsychiatric illness that affects 2-5% of youth and causes morbidity, functional impairment, and suicide. Prodromal manic symptoms without manic episodes usually emerge before BD types I and II (BD-I/II) develop, but less than 60% of youth with manic symptoms will develop BD-I/II. The uncertainty of diagnosis and illness progression results in potentially detrimental interventions and 7-10 years delay appropriate treatments. It is thus imperative that objective biomarkers of risk for conversion to BD-I/II are identified and tested in youth before the peak onset of illness. Given that neural measures of structure and function associated with emotion and reward processing, in combination with clinical and behavior measures, can improve prediction of psychiatric outcomes in youth, this project will investigate brain-behavior relations in the most severely ill youth during inpatient stays and aims to build a predictive model of BD. We aim to use two distinct analytic models to test our hypotheses. First a general linear model (GLM) with a machine learning (ML) model of regularized regression with cross validation and second a whole brain ML pattern recognition model. We will first identify neural and behavioral markers of BD-I/II in circuitry associated with emotion and reward processing. We hypothesize that decreased activity and connectivity in prefrontal, amygdala, and striatal regions and behavioral measures showing less sleep, lower activity, and poorer mood and cognition will distinguish BD- I/II from clinically matched youth without mania and healthy. Next, we will identify using ML a whole brain neural classifier of BD-I/II relative to clinically matched inpatients without mania. Aim 2 is to, after two years, identify and quantify the neural and behavioral measures that predict conversion to BD-I/II, and to test individual conversion in an independent group of high symptomatic risk adolescents. Aim 3 is to identify brain-behavior associations for app development. Training samples include mid-/post- pubertal adolescents aged 13-17 years recruited from the nation’s only specialized inpatient unit for adolescents with BD and the general adolescent unit at our hospital; 70 well-characterized adolescents with BD-I/II, a clinically matched group of 70 inpatient youth without mania. Testing sample is an independent group of 180 adolescents with manic symptoms without BD-I/II. 60 healthy controls will be recruited. The project includes emotion and reward processing neural function and structure, clinical and behavioral measures including sleep and activity with actigraphy, computerized cognitive measures, and self-reports during inpatient evaluation and for two weeks post discharge. At two-year follow up, clinical assessments will confirm diagnoses. This is the first study to employ a multimodal assessment of behavior and mood symptoms combined with multimodal imaging methods to comprehensively assess disease-specific abnormalities and prediction of BD-I/II. Findings from this study may identify biological and behavioral markers of conversion to BD-I/II in adolescents and may contribute developing disease-specific risk calculators, low-cost biosensors for mobile applications, and novel targets of intervention.

项目摘要 双相情感障碍（BD）是一种毁灭性的神经精神疾病，影响2-5%的青年并导致发病， 功能障碍和自杀没有躁狂发作的前驱躁狂症状通常出现在 BD I型和II型（BD-I/II）的发展，但不到60%的青年躁狂症状将发展BD-I/II。的 诊断和疾病进展的不确定性导致潜在的有害干预， 推迟适当的治疗。因此，当务之急是，转换为BD-I/II的风险的客观生物标志物， 在发病高峰期之前在青年中进行鉴定和测试。考虑到神经结构和 与情绪和奖励处理相关的功能，结合临床和行为测量， 可以提高对青少年精神病结果的预测，该项目将研究大脑行为的关系， 在住院期间病情最严重的年轻人，旨在建立BD的预测模型。我们的目标是使用两个 不同的分析模型来测试我们的假设。首先，使用机器学习（ML）的一般线性模型（GLM） 第一个是具有交叉验证的正则化回归模型，第二个是全脑ML模式识别模型。 我们将首先确定在与情绪和奖励相关的电路中BD-I/II的神经和行为标记 处理.我们假设，前额叶、杏仁核和纹状体区域的活动和连接性降低， 行为测量显示睡眠减少，活动减少，情绪和认知较差，将区分BD- I/II来自临床匹配的无躁狂和健康的青年。接下来，我们将使用ML识别整个大脑神经元 BD-I/II相对于临床匹配的无躁狂症住院患者的分类。目标2：两年后， 量化预测转化为BD-I/II的神经和行为指标，并测试个体 转换在一个独立的组的高症状风险的青少年。目标3是识别大脑行为 APP开发协会。培训样本包括13-17岁的青春期中期/后青少年 从全国唯一的专门住院单位招募的青少年与BD和一般青少年 单位在我院; 70个特征良好的青少年BD-I/II，临床匹配组70住院 青春无躁测试样本是一个独立的180名青少年躁狂症状， BD-I/II.将招募60名健康对照。该项目包括情绪和奖励处理神经功能 和结构，临床和行为测量，包括睡眠和活动， 在住院评估期间和出院后两周内进行认知测量和自我报告。是两年制 后续，临床评估将确认诊断。这是第一个采用多模式评估的研究 行为和情绪症状结合多模态成像方法， 疾病特异性异常和BD-I/II的预测。这项研究的结果可以确定生物学和 在青少年中转换为BD-I/II的行为标志物，可能有助于发展疾病特异性风险 计算器、用于移动的应用的低成本生物传感器以及新的干预目标。