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.

精神分裂症是一种异质性疾病，在症状和神经病理学中具有一些共同点。精神分裂症中这些不同的综合征的特征是不同症状特征的优势。该提案的总体主题是使用功能性脑成像，遗传标记和神经认知测试的综合方法将生物亚型与临床综合征谱之间区分开。 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，抗精神病药可以减轻阳性症状，而无需特别有助于治疗阴性症状，而非典型抗精神病药对负面症状具有很小但可测量的影响。神经影像学和遗传分析已经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将某些药物反应者与无反应者区分开。遗传标记也可以预测某些药理学干预措施的副作用。这些治疗反应亚型可以代表更均匀的精神分裂症，而精神分裂症可能会具有不同的大脑结构/功能特征。 该提议的目的是研究这些综合征概况是一个假设 在单独的大脑区域或电路中功能障碍的结果，所有这些都与背侧前额叶皮层（DPFC）通信。 DPFC包括背侧前额叶皮层（DLPFC； BA 46/腹侧9）和前额回（背部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输出本身或最终与DPFC相互作用的各种电路和电路组合引起的。 我们对fMRI的使用将集中在我们假设的局部和扩展电路上，这些电路涉及产生精神分裂症的不同方面。在背景部分（下图）中，我们讨论了与精神分裂症有关的特定大脑领域，以及我们关于这些领域在产生功能障碍方面如何联系的假设。为了检验这些假设，需要改进和更准确的鉴定和分割这些大脑区域。我们建议通过添加临床措施，神经认知评估和遗传数据来扩展电路分析，以确定可以用作末端表型的常见模式。这种组合的动力是，精神分裂症是可遗传的，并且与特定的神经认知缺陷有关。结合此类数据是一个发展中的领域，并且由于相对于受试者数量的数量大量变量而带来了许多计算和统计挑战。作为驾驶生物学项目（DPB），这项努力需要在该赠款资助的项目中进行互动协作中的计算优势和策略实施软件，以应对合并成像和遗传数据分析的新兴领域的挑战。