Functional genomics of the human connectome in psychiatric illness

精神疾病中人类连接组的功能基因组学

基本信息

批准号：
10417214
负责人：
AVRAM J HOLMES
金额：
$ 63.12万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10417214
关键词：
Address Affective Atlases Automobile Driving Behavior Behavioral Behavioral Genetics Biological Bipolar Depression Bipolar Disorder Brain Categories Classification Classification Scheme Clinical Cognition Cognitive Complex Data Development Diagnosis Diagnostic Dimensions Disease Dissection Etiology Executive Dysfunction Fingerprint Future Gene Expression Profile General Population Generations Genes Genetic Genetic Risk Genetic Transcription Genetic study Genomic approach Genomics Health Heritability Heterogeneity Human Impairment Individual Individual Differences Lateral Link Maintenance Maps Measures Mental Health Mental disorders Methods Modeling Molecular Monitor National Institute of Mental Health Nature Negative Valence Neurobiology Parietal Participant Pathogenesis Pathology Patients Pattern Performance Phenotype Population Psychiatric Diagnosis Psychopathology Psychoses Psychotic Disorders Research Research Personnel Resources Risk Schizophrenia Severities Severity of illness Social Functioning Strategic Planning Symptoms Syndrome System Temporal Lobe Translating Unipolar Depression Variant Vulnerable Populations Work behavioral genomics clinical biomarkers clinically relevant cognitive control comorbidity connectome disease classification functional genomics imaging genetics in vivo individual variation insight network models neural circuit neural network neurogenetics neuroimaging novel patient population predictive modeling profiles in patients psychologic relating to nervous system social deficits trait

项目摘要

PROJECT SUMMARY/ABSTRACT Converging evidence indicates that the boundaries separating nominally distinct psychiatric diagnoses are not sharp or discontinuous with normal behavioral traits and brain function. In healthy populations, individual differences in behavior are reflected in variability across the collective set of functional brain connections (functional connectome). These data suggest that the spectra of transdiagnostic symptom profiles observed in patient populations may arise through detectable patterns of network function. The intrinsic connectivity of the functional connectome is under strong genetic influence. Spatial patterns of gene transcription recapitulate the topography of large-scale brain networks, potentially driving comorbidity between symptomatically related disorders. However, most of what we currently know about the human connectome comes from the study of healthy populations, impeding the development of fully dimensional models of brain function and obscuring the interactions through which genetic and neurobiological variation might coalesce to support suites of behaviors and illness risk within an individual. To address the disconnect between mechanism and nosology, the NIMH strategic plan calls for a bottom-up reappraisal of psychopathology across multiple levels of analysis; facilitating the study of relationships from genes to neural circuits and networks through behavior, cutting across disorders as traditionally defined. Directly addressing these objectives, our proposal will link individual variation in functional connectomes with symptom profiles across unipolar depression, bipolar depression, and schizophrenia through the combined application of neuroimaging, behavioral, and genomic methods. We will establish key biological and clinical features of the functional connectome in three stages. First, we recently established that disruptions within the frontoparietal network (spanning aspects of dorsolateral and dorsomedial prefrontal, lateral parietal, and posterior temporal cortices) reflect a shared feature of schizophrenia and psychotic bipolar disorder. Building upon this work, we will quantify the extent to which frontoparietal connectivity may reflect a disorder-general marker of symptom severity across both affective and psychotic illnesses, validating the key psychological features (Aim 1). Second, we will map transdiagnostic functional connectome variability to the diversity of clinical presentations, extending our analyses across cortex to develop predictive models of multidimensional symptom profiles (Aim 2). Third, we will identify novel patterns of gene expression that follow the spatial organization of large-scale brain networks, establish the impact of contributing loci on in vivo connectome functioning, and assess co-heritability with illness risk (Aim 3). Completion of these aims will yield insights into the neural, behavioral, and genetic basis of affective and psychotic illnesses, providing a crucial step towards the establishment of a new framework for psychiatric classification grounded in etiology and pathogenesis.

项目概要/摘要汇聚的证据表明，名义上不同的精神病学诊断之间的界限并不存在。与正常行为特征和大脑功能尖锐或不连续。在健康人群中，个体行为的差异反映在大脑功能连接集合的变异性上（功能连接体）。这些数据表明，在患者群体可能通过可检测的网络功能模式而出现。的内在连通性功能连接组受到强烈的遗传影响。基因转录的空间模式概括了大规模大脑网络的拓扑结构，可能会导致症状相关的合并症失调。然而，我们目前对人类连接组的了解大部分来自对健康人群，阻碍了大脑功能全维度模型的发展并模糊了遗传和神经生物学变异可能通过相互作用来支持一系列行为以及个人的疾病风险。为了解决机制和疾病分类学之间的脱节问题，NIMH 战略计划要求跨多个分析层次对精神病理学进行自下而上的重新评估；通过行为促进从基因到神经回路和网络的关系的研究，跨越传统定义的疾病。直接解决这些目标，我们的提案将把个人联系起来功能性连接组与单相抑郁症、双相抑郁症和抑郁症症状特征的差异通过神经影像学、行为学和基因组方法的结合应用来治疗精神分裂症。我们将分三个阶段建立功能性连接组的关键生物学和临床特征。首先，我们最近确定额顶网络内的破坏（跨越背外侧和背内侧前额叶、外侧顶叶和后颞叶皮质）反映了共同的特征精神分裂症和精神病性双相情感障碍。在此工作的基础上，我们将量化额顶叶连接可能反映情感和症状严重程度的障碍一般标志精神病，验证关键心理特征（目标 1）。其次，我们将绘制跨诊断图功能连接组变异性与临床表现的多样性，将我们的分析扩展到整个皮质开发多维症状特征的预测模型（目标 2）。三、我们要识别小说遵循大规模大脑网络空间组织的基因表达模式，建立贡献位点对体内连接组功能的影响，并评估疾病风险的共同遗传性（目标 3）。完成这些目标将深入了解情感和行为的神经、行为和遗传基础。精神疾病，为建立精神疾病新框架迈出了关键一步基于病因学和发病机制的分类。