Multimodal brain maturation indices modulating psychopathology and neurocognition

调节精神病理学和神经认知的多模式大脑成熟指数

• 批准号: 9275046
• 负责人: Ruben C. Gur
• 金额: $ 51.16万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9275046
• 关键词: Acceleration; Address; Age; Anatomy; Anisotropy; Architecture; Back; Behavior; Behavioral; Biological Markers; Biological Process; Brain; Brain imaging; Cerebrum; Chronology; Clinical; Cognition; Cognitive; Communities; Complex; Data; Data Analyses; Data Set; Detection; Development; Diagnostic; Dimensions; Disease; Early identification; Emotions; Four-dimensional; Functional Magnetic Resonance Imaging; Genetic; Genomics; Genotype; Goals; Image; Individual; Knowledge; Lead; Link; Maps; Measures; Mediating; Memory; Mental Health; Mental disorders; Methods; Modality; Modeling; Molecular; Neurobiology; Neurocognition; Neurocognitive; Neurocognitive Deficit; Parietal; Participant; Pattern; Performance; Perfusion; Phenotype; Philadelphia; Physiological; Physiological Processes; Preventive Intervention; Procedures; Process; Psychopathology; Psychotic Disorders; Research; Rest; Risk; Sampling; Sex Characteristics; Staging; Structure; Subgroup; System; Testing; Variant; Youth; age group; age related; base; brain abnormalities; brain health; cohort; connectome; database of Genotypes and Phenotypes; density; design; follow-up; genetic association; genome wide association study; genomic data; gray matter; high dimensionality; high risk; improved; indexing; multimodality; neurodevelopment; neuroimaging; neuropsychiatric disorder; neuropsychiatry; novel; prospective; public health relevance; risk variant; segregation; social cognition; white matter

 DESCRIPTION (provided by applicant): Current research typically examines single neuroimaging modalities to establish normative values, development related differences, and abnormalities in neuropsychiatric disorders. Little is known about how these complementary parameters of brain structure and function interrelate and how combined processes reflected in these parameters lead to a mature, healthy brain. Behavioral functioning, manifested in mental health and neurocognitive performance, shows marked developmental effects. While such measures have been related to specific neuroimaging modalities, there is limited knowledge on developmental effects of multimodal brain parameters related to psychopathology and neurocognition. The path from biological processes to behavior is through genomics, which can elucidate mechanistic neurobiological processes thereby offering hope for early identification, prevention and intervention in aberrant development. Finally, to understand how brain changes relate to behavioral changes it is essential to have longitudinal data. We propose to capitalize on our efforts to establish the Philadelphia Neurodevelopmental Cohort (PNC), which was designed to obtain data on neuropsychiatric features, neurocognitive performance, multimodal neuroimaging and genomics. In addition to analyzing the data on the initial assessment of the PNC sample that we share in dbGaP, we have been following a subsample of PNC participants that includes both typically developing and those at clinical high-risk (CHR) for psychosis. Therefore, we will be able to establish dimensionally and longitudinally which combination of clinical, neurocognitive, neuroimaging and genomic parameters best predicts progression to psychosis. PNC data analysis will identify "biotypes" based on development related differences in regional multimodal characterization of major brain structures and systems related to dimensions of psychopathology and neurocognitive domains. We will apply advanced anatomic parcellation and voxelwise connectome-wide association studies to delineate multi-modal development effects on structural and functional connectivity, and identify aberrations associated with psychopathology and neurocognitive deficits. Networks will be examined using hypergraphs and parameters such as segregation and modularity defined by multi- scale community detection methods. These efforts will establish candidate parameters for genomic analysis and will be used to examine the GWAS- findings from the PGC and associated polygene scores and their effects on patterns of development and emerging biotypes. We will test the ability of developmental biotypes derived from the current dataset to predict brain health and clinical status in a subsample of 500 participants with follow-up data at 24 and 36 months intervals after the PNC data were collected. Since the follow-up is on 200 typically developing, 200 psychosis prone and 100 individuals with other disorders, we will focus on the subgroup with psychosis risk while exploring associations with other clinical factor scores. The repeated- measures data will establish how changes in these parameters inform about developmental trajectories.

 描述（由适用提供）：当前的研究通常检查单个神经影像学方式，以建立正常值，与发展相关的差异和神经精神疾病的异常。关于这些参数中反映的大脑结构和功能的这些互补参数如何导致成熟，健康的大脑。表现为心理健康和神经认知表现的行为功能显示出明显的发育效果。尽管此类措施与特定的神经影像模式有关，但对与心理病理学和神经认知有关的多模式脑参数的发育效果的了解有限。从生物过程到行为的路径是通过基因组学，它可以阐明机械性神经生物学过程，从而为早期鉴定，预防和干预异常发展提供希望。最后，要了解大脑的变化如何与行为变化有关，必须拥有纵向数据。我们建议利用我们建立费城神经发育队列（PNC）的努力，该研究旨在获取有关神经精神病特征，神经认知性能，多模式神经影像学和基因组学的数据。除了分析我们在DBGAP中共享的PNC样本的初始评估的数据外，我们还遵循PNC参与者的子样本，包括通常发展的PNC参与者和临床高风险（CHR）的PNC参与者。因此，我们将能够在维度和纵向上建立哪种临床，神经认知，神经影像和基因组参数的组合最能预测到精神病的发展。 PNC数据分析将基于开发与精神病理学和神经认知领域有关的主要大脑结构和系统的区域多模式表征的相关差异鉴定“生物型”。我们将应用先进的解剖分析和voxelwise范围的连接关联研究来描述对结构和功能连通性的多模式发展影响，并确定与精神病理学和神经认知缺陷有关的畸变。将使用超图和参数（例如隔离和由多规模社区检测方法定义的隔离和模块化）来检查网络。这些努力将建立用于基因组分析的候选参数，并将用于检查来自PGC和相关的聚必需分数的GWAS发现及其对发展和新兴生物型的影响。我们将测试从当前数据集中得出的发育生物型预测大脑健康的能力 在收集PNC数据后24和36个月间隔的500名参与者的子组中，有500名参与者的临床状况和临床状况。由于该随访是对200个典型发展的200个，有200名精神病和100名患有其他疾病的人，因此我们将重点关注具有精神病风险的亚组，同时探索与其他临床因素得分的关联。重复测量数据将确定这些参数的变化如何告知发育轨迹。

项目成果

Response inhibition in adolescents is moderated by brain connectivity and social network structure.

• DOI: 10.1093/scan/nsaa109
• 发表时间: 2020-10-08
• 期刊: Social cognitive and affective neuroscience
• 影响因子: 4.2
• 作者: Tompson SH;Falk EB;O'Donnell MB;Cascio CN;Bayer JB;Vettel JM;Bassett DS
• 通讯作者: Bassett DS