Application of Advanced Quantitative Methods to Schizophrenia Research

先进定量方法在精神分裂症研究中的应用

• 批准号: 10329955
• 负责人: ANDREW J DWORK
• 金额: $ 57.15万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10329955
• 关键词: 3-Dimensional; Academy; Age; Anisotropy; Antibodies; Architecture; Area; Arts; Autopsy; Axon; Biochemical; Biochemistry; Brain; Caliber; Cerebrum; Characteristics; Clinical; Collaborations; Collection; Confocal Microscopy; Consensus; DNA Methylation; Data; Data Set; Deformity; Dependence; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Electron Microscope; Electron Microscopy; Electrons; Evaluation; Face; Fiber; Forensic Medicine; Genetic Transcription; Heavy Metals; Histologic; Histology; Image; Image Analysis; Immunofluorescence Immunologic; Immunohistochemistry; Individual; Institutes; Interviewer; Knowledge; Label; Left; Macedonia; Machine Learning; Magnetic Resonance Imaging; Major Depressive Disorder; Measurement; Measures; Mental disorders; Messenger RNA; Methodology; Methods; Modeling; Molecular; Morphologic artifacts; Multiomic Data; Myelin; Myelin Sheath; National Institute of Mental Health; Neurofibrillary Tangles; Neurofilament Proteins; New York; Oligodendroglia; Online Systems; Optic Nerve; Paraffin; Pathologist; Persons; Pharmaceutical Preparations; Procedures; Process; Property; Proteins; Proteomics; Protocols documentation; Psychiatrist; Psychologist; Recording of previous events; Reporting; Research; Research Personnel; Resolution; Schizophrenia; Science; Shotguns; Silver Staining; Space Perception; Stains; Techniques; Testing; Thick; Time; Tissues; Toxicology; Training; Transcript; Triad Acrylic Resin; Universities; Variant; Visualization; base; cognitive function; cohort; data archive; data fusion; deep neural network; density; design; diffusion anisotropy; high resolution imaging; histological studies; imaging study; innovation; instrument; interest; microscopic imaging; multidimensional data; multiple omics; network models; novel; precursor cell; psychologic; reconstruction; sex; symposium; tool; two photon microscopy; two-photon; water diffusion; white matter

PROJECT SUMMARY Abnormalities of white matter are important in schizophrenia. A preponderance of studies have found decreased levels of transcripts for myelin-related proteins in autopsy brains. Some have found a decrease in the proteins themselves, and some have not. Hundreds of diffusion tensor imaging (DTI) studies have found reduced fractional anisotropy (FA) in the brains of many people with schizophrenia (SCH). Prefrontal white matter is among the areas usually involved. Decreased FA is interpreted as disruption of normal architecture. However, postmortem examination has failed to identify characteristic abnormalities, suggesting that abnormalities causing diminished FA are subtle, and that postmortem examinations have not used the right tools to find them. We have therefore been developing, as part of a FIC/NIMH collaboration with the Macedonian Academy of Sciences and Arts, two new methods to characterize white matter at high resolution. The first is a machine learning protocol to measure axonal diameters and myelin sheath thickness in electron microscope (EM) images of prefrontal white matter, recognizing and avoiding artifacts in EM of autopsy tissue. This will enable us to measure thousands of fibers in EM images, from individuals with SCH, major depressive disorder (MDD), or no psychiatric illness (NPI). The second method, suggested by the DTI findings, is to analyze the spatial orientation of the axons themselves. We will use 3-dimensional (3D) reconstructions of high-resolution images of the axons themselves, identified by Bielschowsky silver stain or immunohistochemistry for phosphorylated neurofilament protein. To obtain high- resolution images of Bielschowsky stains, we will take advantage of the recent observation by Dr. Mark Sonders, co-investigator on this project, that these and other heavy metal stains luminesce under 2-photon infrared excitation. This technique yields clear images of individual axons that can be traced and measured in 3 dimensions. We will perform these procedures on sections from existing paraffin blocks that comprise a complete left prefrontal coronal section from 36 triads containing 1 case each of SCH, MDD, or NPI, matched for sex and age. These brains were included in earlier studies that yielded data on protein composition, mRNA for myelin- related proteins, DNA methylation, microglial activation, and semiquantitative myelin histology. In a third, exploratory aim, we will employ graphical models in three multi-omics data fusion approaches to combine different types of high-dimensional data, including those produced by Aims 1 and 2, with known structural properties of axons and myelin in white matter, in order to build a model or detect novel dependencies of what is disturbed in schizophrenia. We expect that novel techniques for data fusion will reveal associations based on multidimensional correlations that could not be detected by modeling the single-domain datasets separately.

项目总结 脑白质的异常在精神分裂症中很重要。大多数研究发现， 尸检大脑中髓鞘相关蛋白的转录水平降低。一些人发现， 蛋白质本身，有些则没有。数百项扩散张量成像(DTI)研究发现 精神分裂症(SCH)患者大脑中的分数各向异性(FA)。前额叶白质是 在通常涉及的领域中。FA减少被解释为对正常架构的破坏。然而， 尸检未能发现特征异常，表明异常导致 FA的减少是微妙的，尸检没有使用正确的工具来找到它们。我们有 因此，作为FIC/NIMH与马其顿科学院合作的一部分，正在开发 ARTS，两种高分辨率表征白质的新方法。第一个是机器学习协议，以 前额叶白质电子显微镜图像中轴突直径和髓鞘厚度的测量 物质，识别和避免尸检组织EM中的伪影。这将使我们能够测量数千个 EM图像中的纤维，来自患有SCH、严重抑郁障碍(MDD)或没有精神疾病(NPI)的人。 DTI发现建议的第二种方法是分析轴突本身的空间方向。 我们将使用轴突本身的高分辨率图像的三维(3D)重建，由 Bielschowsky银染或免疫组织化学检测磷酸化神经丝蛋白。为了获得高- 关于Bielschowsky污渍的分辨率图像，我们将利用Mark Sonders博士最近的观察， 这个项目的合作研究员，这些和其他重金属污渍在双光子红外下发光 激动人心。这种技术可以产生清晰的单个轴突图像，可以在3分钟内追踪和测量 尺寸。我们将在现有石蜡切片上执行这些程序，这些切片构成完整的 36个三联体的左前额冠状切面，其中包括1例SCH、MDD或NPI，与性别和 年龄。这些大脑被包括在早期的研究中，这些研究产生了关于蛋白质组成、髓鞘mRNA- 相关蛋白质、DNA甲基化、小胶质细胞激活和半定量髓鞘组织学。在三分之一的时候， 探索性目的，我们将利用图形模型中的三种多组学数据融合方法相结合 不同类型的高维数据，包括目标1和目标2产生的数据，具有已知的结构 白质中轴突和髓鞘的特性，以建立模型或检测什么的新相关性 精神分裂症患者精神错乱。我们预计，数据融合的新技术将揭示基于 通过单独对单域数据集进行建模无法检测到的多维相关性。