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（由申请人提供）：目前的研究通常检查单一的神经影像模式来建立神经精神疾病的规范值、发育相关的差异和异常，但人们对大脑结构和功能的这些互补参数如何相互关联以及这些过程如何反映的组合过程知之甚少。行为功能（表现为心理健康和神经认知表现）显示出显着的发育影响，虽然这些措施与特定的神经影像学模式有关，但对发育的了解有限。与精神病理学和神经认知相关的多模式大脑参数的影响从生物过程到行为的途径是通过基因组学，它可以阐明机械神经生物学过程，从而为异常发育的早期识别、预防和干预提供希望。对于行为改变，拥有纵向数据至关重要。我们建议利用我们建立费城神经发育队列（PNC）的努力，该队列旨在获取有关神经精神特征、神经认知表现、除了分析我们在 dbGaP 中分享的 PNC 样本的初步评估数据外，我们还跟踪了 PNC 参与者的子样本，其中包括典型发育中的参与者和临床高风险 (CHR) 的参与者。因此，我们将能够从维度和纵向确定哪种临床、神经认知、神经影像和基因组参数组合最能预测精神病的进展，并根据发育情况识别“生物型”。我们将应用先进的解剖分区和体素连接组关联研究来描述多模式发育对结构和功能连接的影响，并识别畸变。与精神病理学和神经认知缺陷相关的网络将使用超图和参数进行检查，例如由多尺度社区检测方法定义的隔离和模块化。并将用于检查 PGC 和相关多基因评分的 GWAS 结果及其对发育模式和新兴生物型的影响，我们将测试从当前数据集得出的发育生物型预测大脑健康的能力。 收集 PNC 数据后，每隔 24 个月和 36 个月进行一次随访数据，并在 500 名参与者的子样本中进行临床状态分析。由于随访通常针对 200 人，其中 200 人患有精神病，100 人患有其他疾病，因此我们将重点关注有精神病风险的亚组，同时探索与其他临床因素评分的关联。重复测量数据将确定这些参数的变化如何影响发育轨迹。

项目成果

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

青少年的反应抑制受到大脑连接和社交网络结构的调节。

• 发表时间: 2020
• 作者: Tompson, Steven H;Falk, Emily B;O'Donnell, Matthew Brook;Cascio, Christopher N;Bayer, Joseph B;Vettel, Jean M;Bassett, Danielle S
• 通讯作者: Bassett, Danielle S

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration.

超越模块化：大脑网络重新配置的精细机制和规则。

• 发表时间: 2018
• 影响因子: 5.7
• 作者: Khambhati, Ankit N;Mattar, Marcelo G;Wymbs, Nicholas F;Grafton, Scott T;Bassett, Danielle S
• 通讯作者: Bassett, Danielle S