Multimodal brain maturation indices modulating psychopathology and neurocognition

 描述（由申请人提供）：目前的研究通常检查单一的神经影像学方式，以建立正常值，发展相关的差异，和神经精神疾病的异常。关于大脑结构和功能的这些互补参数如何相互关联，以及这些参数中反映的组合过程如何导致成熟，健康的大脑，我们知之甚少。表现在心理健康和神经认知表现上的行为功能表现出显著的发展效应。虽然这些措施已涉及到特定的神经成像方式，有有限的知识多模态脑参数的发展影响有关的精神病理学和神经认知。从生物过程到行为的途径是通过基因组学，它可以阐明神经生物学过程的机制，从而为早期识别、预防和干预异常发育提供希望。最后，要了解大脑变化与行为变化之间的关系，必须有纵向数据。我们建议利用我们的努力，建立费城神经发育队列（PNC），旨在获得神经精神病学特征，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，其中包括典型的精神病发展和临床高风险（PNC）患者。因此，我们将能够建立维度和纵向的临床，神经认知，神经影像学和基因组参数的组合最好地预测进展为精神病。PNC数据分析将根据与精神病理学和神经认知领域维度相关的主要脑结构和系统的区域多模态表征中与发育相关的差异来识别“生物型”。我们将应用先进的解剖parcellation和voxelwise connectome-wide关联研究来描述多模式发展对结构和功能连接的影响，并确定与精神病理学和神经认知缺陷相关的畸变。网络将使用超图和多尺度社区检测方法定义的隔离和模块化等参数进行检查。这些努力将建立基因组分析的候选参数，并将用于检查GWAS-PGC和相关多基因评分的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导出的发育生物型预测大脑健康的能力 在PNC数据收集后的24个月和36个月的时间间隔内，在500名参与者的子样本中进行随访数据和临床状态。由于随访是在200个典型的发展，200精神病倾向和100个其他疾病的人，我们将重点放在精神病风险的亚组，同时探索与其他临床因素评分的关联。重复测量的数据将确定这些参数的变化如何告知发展轨迹。

 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.