Functional imaging and genetics in Schizophrenia; search for endophenotypes

精神分裂症的功能成像和遗传学；

• 批准号: 7479782
• 负责人: JAMES L KENNEDY
• 金额: $ 17.66万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7479782
• 关键词: Address; Adverse effects; Anterior; Antipsychotic Agents; Area; Automobile Driving; Biological; Brain; Brain imaging; Characteristics; Clinical; Collaborations; Computer software; Data; Data Analyses; Disease; Dorsal; Functional Imaging; Functional Magnetic Resonance Imaging; Functional disorder; Funding; Genetic; Genetic Markers; Grant; Image; Intervention; Link; Measurable; Measures; Methods; Neurocognitive; Neurocognitive Deficit; Numbers; Output; Pattern; Pharmaceutical Preparations; Prefrontal Cortex; Relative (related person); Schizophrenia; Structure; Structure of superior frontal gyrus; Symptoms; Syndrome; Testing; atypical antipsychotic; endophenotype; genetic analysis; improved; neurocognitive test; neuroimaging; neuropathology; response

Schizophrenia is a heterogeneous group of disorders, which shares some commonality in symptoms and neuropathology. These different syndromes within schizophrenia are characterized by the predominance of distinct symptom profiles. The overarching theme of this proposal is to distinguish biological subtypes from among the clinical syndrome profiles, using the combined approach of functional brain imaging, genetic markers, and neurocognitive testing. Clinically defined syndromes respond differently to antipsychotic treatments; conventional antipsychotics can mitigate positive symptoms, for example, without particularly aiding in treating negative symptoms, while atypical antipsychotics have a small but measurable effect on negative symptoms. Neuroimaging and genetic analyses have begun to elucidate the relationships among the clinical profiles and treatment response. We have found that there are genetic markers that can differentiate certain drug responders from nonresponders. Genetic markers as well can predict the occurrence of side effects to certain pharmacological interventions. These treatment response subtypes could represent more homogeneous forms of schizophrenia that may, in turn, have distinct brain structure/function characteristics. The aim of this proposal is to investigate the hypothesis that these syndrome profiles are a consequence of dysfunction in separate brain areas or circuits, all of which communicate with the dorsal prefrontal cortex (DPFC). The DPFC includes the dorsolateral prefrontal cortex (DLPFC; BA 46/ventral 9) and anterior superior frontal gyrus (dorsal BA 9). We hypothesize that the DPFC dysfunction commonly found in schizophrenia can arise from abnormalities in the DPFC output, itself, or from various circuits and combinations of circuits that ultimately interact with the DPFC. Our use of fMRI will focus on both local and extended circuitry that we hypothesize is involved in producing different aspects of schizophrenia. In the Background section (below) we discuss the specific brain areas that are implicated in schizophrenia and our hypotheses as to how these areas are functionally linked in producing dysfunction. To test these hypotheses, improved and more accurate methods of identification and segmentation of these brain areas are required. We propose to extend our circuitry analyses by adding clinical measures, neurocognitive assessments, and genetic data to determine common patterns that could serve as endophenotypes. The impetus for this combination is the fact that schizophrenia is heritable and associated with specific neurocognitive deficits. Combining such data is a developing field and brings with it many computational and statistical challenges due to the large numbers of variables relative to the number of subjects. As a Driving Biological Project (DPB), this endeavor requires computational strengths and strategies implementation software in an interactive collaboration among the projects funded by this grant to address the challenges of the emerging field of combined imaging and genetic data analysis.

精神分裂症是一组异质性疾病，在症状和神经病理学方面具有一些共同点。精神分裂症中的这些不同综合征的特点是具有不同的症状特征。该提案的首要主题是结合使用功能性脑成像、遗传标记和神经认知测试的方法，从临床综合征谱中区分生物亚型。 临床定义的综合征对抗精神病药物的反应不同；传统的 例如，抗精神病药物可以减轻阳性症状，但对治疗阴性症状没有特别帮助，而非典型抗精神病药物对阴性症状的影响虽小，但可测量。神经影像学和遗传分析已开始阐明临床特征和治疗反应之间的关系。我们发现，有一些遗传标记可以区分某些药物反应者和无反应者。遗传标记也可以预测某些药物干预措施副作用的发生。这些治疗反应亚型可能代表更同质的精神分裂症形式，进而可能具有不同的大脑结构/功能特征。 该提案的目的是调查以下假设：这些综合症特征是 这是各个大脑区域或回路功能障碍的结果，所有这些区域或回路都与背侧前额皮质（DPFC）进行通信。 DPFC 包括背外侧前额皮质（DLPFC；BA 46/腹侧 9）和前额上回（背侧 BA 9）。我们假设精神分裂症中常见的 DPFC 功能障碍可能是由 DPFC 输出本身的异常引起的，也可能是由最终与 DPFC 相互作用的各种电路和电路组合引起的。 我们对功能磁共振成像的使用将集中于局部和扩展电路，我们假设这些电路与精神分裂症的不同方面有关。在背景部分（下文）中，我们讨论与精神分裂症有关的特定大脑区域，以及我们关于这些区域如何在功能障碍中产生功能联系的假设。为了检验这些假设，需要改进和更准确的方法来识别和分割这些大脑区域。我们建议通过添加临床测量、神经认知评估和遗传数据来扩展我们的电路分析，以确定可以作为内表型的常见模式。这种组合的推动力是精神分裂症具有遗传性并与特定的神经认知缺陷相关。结合此类数据是一个正在发展的领域，由于相对于受试者数量而言存在大量变量，因此带来了许多计算和统计挑战。作为一个驱动生物学项目（DPB），这项工作需要计算能力和策略实施软件，以便在这笔赠款资助的项目之间进行交互式协作，以应对组合成像和遗传数据分析这一新兴领域的挑战。