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: 10541252
• 负责人: ANDREW J DWORK
• 金额: $ 45.25万
• 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 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10541252
• 关键词: 3-Dimensional; Academy; Age; Anisotropy; Antibodies; Architecture; Area; Arts; Autopsy; Axon; Biochemical; Biochemistry; Brain; Cerebrum; Cessation of life; Characteristics; Clinical; Collaborations; Collection; Confocal Microscopy; Consensus; DNA Methylation; Data; Data Set; Deformity; Dependence; Diagnosis; Diagnostic; Diameter; Diffusion Magnetic Resonance Imaging; Dimensions; Electron Microscope; Electron Microscopy; Electrons; Evaluation; Face; Fiber; Forensic Medicine; Genetic Transcription; Heavy Metals; Histologic; Histology; Image; Image Analysis; Immunofluorescence Immunologic; Immunohistochemistry; Individual; Interviewer; Knowledge; Label; Left; Macedonia; Machine Learning; Magnetic Resonance Imaging; Major Depressive Disorder; Measurement; Measures; Mental disorders; Messenger RNA; Methodology; Methods; Microscopy; 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; Phosphorylation; Probability; 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; Structural defect; Structure; Techniques; Testing; Thick; Time; Tissues; Toxicology; Training; Transcript; Universities; Variant; Visualization; cognitive function; cohort; data archive; data fusion; deep neural network; design; diffusion anisotropy; glial activation; high resolution imaging; histological studies; imaging study; innovation; instrument; interest; microscopic imaging; multidimensional data; network models; neuropathology; 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.

项目摘要 白色物质的缺失在精神分裂症中是重要的。大量研究发现 尸检大脑中髓鞘相关蛋白的转录水平降低。有些人发现， 蛋白质本身，有些没有。数百项扩散张量成像（DTI）研究发现， 分数各向异性（FA）在许多人的大脑精神分裂症（SCH）。前额白色物质 通常涉及的领域。FA降低被解释为正常结构的破坏。然而，在这方面， 尸检未能发现特征性异常，这表明导致 FA的减少是微妙的，尸检没有使用正确的工具来发现它们。我们有 因此，作为FIC/NIMH与马其顿科学院合作的一部分， 艺术，两种新的方法来表征白色物质在高分辨率。第一个是机器学习协议， 在电子显微镜（EM）图像中测量前额叶白色的轴突直径和髓鞘厚度 问题，识别和避免尸检组织EM中的伪影。这将使我们能够测量成千上万的 纤维EM图像，从个人SCH，重度抑郁症（MDD），或没有精神疾病（NPI）。 第二种方法，由DTI结果建议，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3维（3D）重建，通过 磷酸化神经丝蛋白的Bielschowsky银染色或免疫组化。为了获得高- Bielschowsky染色的分辨率图像，我们将利用Mark Sonders博士最近的观察， 该项目的共同研究者，这些和其他重金属染色剂在双光子红外线下发光 激发这项技术产生了清晰的图像，可以跟踪和测量的个别轴突，在3 尺寸.我们将对构成完整组织的现有石蜡块切片执行这些程序 来自36个三联体的左前额叶冠状面，其中SCH、MDD或NPI各1例，性别匹配， 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、髓磷脂mRNA、 相关蛋白、DNA甲基化、小胶质细胞活化和半定量髓鞘组织学。在第三种情况下， 探索性的目的，我们将采用图形模型在三个多组学数据融合方法，联合收割机 不同类型的高维数据，包括目标1和2产生的数据，具有已知的结构 轴突和髓鞘在白色物质中的特性，以建立模型或检测什么是新的依赖性， 患有精神分裂症我们期望新的数据融合技术将揭示基于 多维相关性无法通过单独建模单域数据集来检测。