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

• 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
• 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

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.

项目摘要/摘要 融合证据表明，分开名义上不同的精神诊断的边界不是 正常的行为特征和大脑功能，尖锐或不连续。在健康的人群中 行为的差异反映在整个功能性脑连接集的变异性中 （功能连接）。这些数据表明，在 患者人群可能通过可检测到的网络功能模式而产生。固有的连通性 功能连接组受到强大的遗传影响。基因转录的空间模式概括了 大规模脑网络的地形，有可能在症状相关的情况下驱动合并症 疾病。但是，我们目前对人类连接组的了解大多数来自于研究 健康的人群，阻碍了大脑功能的完全维度模型的发展，并掩盖了 遗传和神经生物学变异可能会合并以支持行为套件的相互作用 和一个人内的疾病风险。为了解决机制和诺斯科之间的断开连接，NIMH 战略计划要求在多个分析层面上重新评估心理病理学的自下而上； 通过行为促进从基因到神经回路和网络的关系的研究，切割 传统上定义的跨疾病。直接解决这些目标，我们的建议将链接个人 与单极抑郁症，躁郁症抑郁症和 精神分裂症通过神经影像，行为和基因组方法的联合应用。我们将 在三个阶段中建立功能连接组的关键生物学和临床特征。首先，我们最近 确定了额叶网络中的破坏（跨越背外侧的各个方面 背额前额叶，外侧顶叶和后颞皮层）反映出的共同特征 精神分裂症和精神病性躁郁症。在这项工作的基础上，我们将量化 额叶的连通性可能反映了情感和 精神病疾病，验证关键的心理特征（AIM 1）。其次，我们将映射转诊 临床表现多样性的功能连接变异性，扩展了我们跨皮质的分析 开发多维症状特征的预测模型（AIM 2）。第三，我们将确定小说 遵循大型大脑网络空间组织的基因表达模式，建立 贡献基因座对体内连接组功能的影响，并评估与疾病风险的共同性（目标 3）。这些目标的完成将产生对情感和情感和遗传基础的见解 精神病疾病，为建立新的精神病框架提供了至关重要的一步 基于病因和发病机理的分类。