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成像模式，以确定神经精神障碍的标准值、与发育相关的差异和异常。关于大脑结构和功能的这些互补参数是如何相互联系的，以及这些参数所反映的组合过程如何导致成熟、健康的大脑，人们知之甚少。行为功能表现为心理健康和神经认知能力，表现出明显的发育效应。虽然这些测量与特定的神经成像方式有关，但对与精神病理学和神经认知相关的多模式大脑参数的发育影响的了解有限。从生物过程到行为的途径是通过基因组学，它可以阐明机械的神经生物学过程，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的关系，就必须有纵向数据。我们建议利用我们的努力建立费城神经发育队列(PNC)，该队列旨在获得有关神经精神特征、神经认知表现、多模式神经成像和基因组学的数据。除了分析我们在数据库中共享的PNC样本的初步评估数据外，我们一直在跟踪PNC参与者的子样本，其中包括典型的精神病发展中的和处于临床高危(CHR)的人。因此，我们将能够从维度和纵向上确定临床、神经认知、神经成像和基因组参数的哪一种组合最能预测精神病的进展。PNC数据分析将根据与精神病理学和神经认知领域相关的主要大脑结构和系统的区域多模式表征的与发育相关的差异来确定“生物类型”。我们将应用先进的解剖分离和体素连接全组关联研究来描绘多模式发展对结构和功能连接的影响，并识别与精神病理学和神经认知缺陷相关的异常。将使用超图和由多尺度社区检测方法定义的隔离和模块化等参数来检查网络。这些努力将建立基因组分析的候选参数，并将用于检查GWAS-来自PGC和相关多基因评分的发现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 以及在收集PNC数据后每隔24个月和36个月随访数据的500名参与者的子样本的临床状态。由于对200名典型发展中的人、200名易患精神病的人和100名有其他障碍的人进行了随访，我们将重点放在有精神疾病风险的亚组上，同时探索与其他临床因素得分的联系。重复测量的数据将确定这些参数的变化如何影响发育轨迹。

 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.