Dissecting the brain-cognition relationship in schizophrenia and adult attention deficit hyperactivity disorder

过去几年的研究表明，精神疾病的精神病理学症状、认知障碍、脑形态学变化、疾病过程以及遗传和环境风险因素存在重叠。这些见解强烈表明，有必要研究精神疾病的翻译方式，以解开共同点，以及仍然存在的差异，在这些实体。目前的建议的目的是研究精神分裂症和成人注意力缺陷多动障碍的这些差异和共性。为此，将在各种水平上对患者和健康对照组进行深入的表型分析：建议研究认知，个性以及风险和弹性因素。本研究的主要重点是对所有受试者进行深入的脑形态测量MRI研究。在这一重点，完善的标志物，如纤维束的完整性和灰质形态将进行调查。对于这些标志物，可以使用大型队列进行复制和扩增。这项建议的一个独特的新元素是额外使用了迄今为止尚未广泛用于精神病学研究的新MRI方法，即定量T1映射和定量易感性映射。这些脑形态学标志物将与额外评估的表型相关联，以研究患者和对照组内和之间的脑行为关系。除了标准的单变量和多变量分析外，还将采用无假设的机器学习技术来产生对这些疾病中大脑行为关系的差异和共性的新见解。数据将在波恩进行分析，但将提供给国家和国际财团，如ENIGMA。

Studies of the last years have suggested an overlap of psychopathological symptoms, cognitive impairments, brain morphometric changes, course of disorder as well as genetic and environmental risk factors in psychiatric disorders. These insights strongly suggest the necessity to study psychiatric disorders in a translational manner to disentangle commonalities as well as still existing differences in these entities. The aim of the current proposal is to study these differences and commonalities in schizophrenia and adult attention-deficit-hyperactivity disorder. To this end, a cohort of patients and healthy controls will be deeply phenotyped on a variety of levels: It is proposed to study cognition, personality as well as factors of risk and resilience. The main focus of the study will be an in-depths brain morphometric MRI investigation of all subjects. Within this focus, well-established markers such as fiber-tract integrity and grey matter morphometry will be investigated. For these markers, large cohorts for replication and augmentation are available. A uniquely new element of this proposal is the additional use of new MRI methods not widely used in psychiatric research so far, Quantitative T1-Mapping and Quantitative Susceptibility Mapping. These brain morphometric markers will be associated with the additionally assessed phenotypes to investigate brain-behavior-relationships within and between patients and controls. In addition to standard uni- and multivariate analyses, hypothesis-free machine learning techniques will be employed to generate new insights into differences and commonalities of brain-behavior relationships in these disorders. Data will be analyzed in Bonn but will be available to national and international consortia such as ENIGMA.