Scientific and Statistical Computing Core

科学与统计计算核心

• 批准号: 10706209
• 负责人: Paul Taylor
• 金额: $ 255.45万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 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/10706209
• 关键词: Academy; Age; Anatomy; Atlases; Barbering; Basic Science; Bipolar Disorder; Brain; Brain imaging; Brain region; Breathing; COVID-19 pandemic; Callithrix; Canis familiaris; Childhood; Cloud Computing; Communities; Complex; Computer Systems; Computer software; Consult; Consultations; Core Facility; Coronavirus; Data; Data Analyses; Data Reporting; Data Set; Development; Development Plans; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Docking; Economics; Educational process of instructing; Emotions; Ensure; Excision; Experimental Designs; Functional Magnetic Resonance Imaging; Future; Goals; Head; Hemispherectomy; Heritability; Hour; Human; Human Activities; Image; Infant; Institution; Lead; Libraries; Linux; Literature; Lobectomy; Macaca; Magnetic Resonance Imaging; Methodology; Methods; Mission; Modeling; Moods; Morphologic artifacts; Motion; Mus; National Institute of Allergy and Infectious Disease; National Institute of Mental Health; National Institute of Neurological Disorders and Stroke; Online Systems; Output; Panic; Paper; Participant; Patients; Persons; Positron-Emission Tomography; Process; Production; Property; Psyche structure; Psychometrics; Public Health; Publications; Quality Control; Recommendation; Reporting; Reproducibility; Research; Research Personnel; Research Project Grants; Resources; Rest; Sample Size; Sampling; Scanning; Signal Transduction; Slide; Software Tools; Source Code; Statistical Computing; Statistical Data Interpretation; Status Epilepticus; Stress; System; Techniques; Technology; Time Series Analysis; Training; United States National Institutes of Health; Update; Vision; Work; algorithm development; animal data; autism spectrum disorder; base; brain health; brain magnetic resonance imaging; computerized data processing; coronavirus disease; data handling; data quality; design; disorder control; education resources; experimental study; flexibility; genetic information; gigabyte; human data; image warping; imaging study; improved; interest; learning materials; magnetic field; meetings; member; neuroimaging; open source; patient population; portability; programs; research study; response; scientific computing; software development; tool; web site

The principal mission of the Core is to help NIH researchers with analyses of their fMRI (brain activation mapping) and structural MRI (brain anatomy) data. Along the way, we also help non-NIH investigators, many in the USA but also some abroad. Several levels of help are provided, from short-term immediate aid to long-term development and planning. Consultations: The shortest-term help comprises in-person consultations with investigators about issues that arise in their research. The issues involved are quite varied, since there are many steps in carrying out fMRI and MRI data analyses and there are many different types of experiments. Common problems include: - How to set up experimental design so that data can be analyzed effectively? - Interpretation and correction of MRI imaging artifacts (for example: participant head motion during scanning; image warping due to magnetic field anomalies). - How to set up time series analysis to extract brain activation effects of interest, and to suppress non-activation imaging artifacts (e.g., from breathing)? - How to analyze data to reveal connections between brain regions during specific mental tasks, or at rest? - How to recognize poor quality data? - How to carry out reliable inter-patient (group) statistical analysis, especially when non-MRI data (e.g., genetic information, age, disease rating) needs to be incorporated? - How to get good alignment between the functional results and the anatomical reference images, and between the brain images from different participants? - What sequence of programs is "best" for analyzing a particular kind of data? - Reports of real or imagined bugs in the AFNI software, as well as feature requests (small, large, extravagant). - Analysis problems related to diffusion weighted MRI data, which are acquired to reveal anatomical connections in the brain. There are familiar themes in many of these consultations, but each meeting and each experiment raises unique questions, and requires digging into the goals and details of the research project in order to ensure that nothing critical is being overlooked. The first question asked by a user is often not the right question at all. Complex statistical or data processing issues are often raised. Often, software needs to be developed or modified to help researchers answer their specific questions. Helping with the Methods sections of papers, or with responses to reviewers, is often part of our duties. Educational Efforts: The Core has developed (and updated) a 40-hour hands-on course on how to design and analyze fMRI data. All material for this continually evolving course (software, sample data, scripts, PDF slides, captioned videos) are freely available on our Web site (https://afni.nimh.nih.gov). The course material includes sample datasets, used to illustrate the entire process, starting with images output by MRI scanners and continuing through to the collective statistical analysis of groups of participants. The Covid-19 pandemic canceled in-person training courses; instead, we accelerated our production of AFNI Academy videos. More than 1000 AFNI forum postings were made by Core members, mostly in answer to queries from users. Algorithm and Software Development: The longest-term support consists of developing (or adapting) new methods and software for MRI data analysis, both to solve current problems and in anticipation of new needs. All of our software is incorporated into the AFNI package, which is Unix/Linux/Macintosh-based open-source and is available for download by anyone in source code (GitHub) or binary formats (Core server). New programs are created, and old programs modified, in response to specific user requests and in response to the Core's vision of what will be needed in the future. The Core also assists NIH labs in setting up computer systems for use with AFNI and maintains an active Web site with a forum for questions (and answers) about analysis of (f)MRI data, structural FMRI and diffusion-based MRI. In this third year of the coronavirus, consultations and presentations were carried out with Zoom. Notable developments during FY 2022 include: - Developed a hierarchical modeling approach to capture subtle differences in brain responses between bipolar disorders and controls (with Drs. Pine and Brotman, NIMH). - Expanded software installation and building to several new systems: new Mac M1, Windows, new Linux OSs, and cloud computing systems. We updated our distributed Docker build. This promotes open source FMRI analysis across a wider range of platforms and systems. - Created several new open and reproducible pipeline examples and demos for FMRI processing, using afni_proc.py and integrating with other tools for certain steps (e.g., tedana for multi-echo FMRI). - Leading a project on FMRI Quality Control (QC), with J. Etzel of Wash-U, St. Louis, to promote a broader sharing and pooling of QC practices across the field. It will create an open, educational resource, and generally improve the important (and often under-appreciated and under-reported) step of QC in FMRI processing for the entire neuroimaging community. - Contributing to new standard templates and atlases with several different collaborators for nonhuman imaging studies, including for macaques, marmosets, canines and mice. These resources improve both within- and cross-species understanding, including in the human brain. - Further demos for processing multi-echo FMRI (ME-FMRI), which has many beneficial properties for increasing SNR and filtering confounds (with Dr. Alex Martin and colleagues, NIMH). - Demonstrated the importance of trial sample size in FMRI experimental designs, which is often overlooked. The recommendations of this work (with Drs. Pine and Brotman, NIMH) should generally lead to improvements of generalizability and reproducibility of studies. - Improving methods and tools for removing non-neuronal contributions (e.g., breathing and heartrate) to the BOLD FMRI imaging to assess localized brain activity, meaningfully improving signal quality. - Added new functionality and demos for processing ME-FMRI data in realtime, at the scanner. These facilitate acquisition and QC. - Improved the estimation accuracy of heritability for trial-level data for psychometric data (with Dr. Thomas, NIMH). Public Health Impact: From Oct 2021 to Aug 2022, the principal AFNI publication has been cited in 502 papers (cf Scopus). Most of our work supports basic research into brain function, but some of our work is more closely tied to or applicable to specific diseases: - We collaborate with Dr. Alex Martin (NIMH) to apply our resting state analysis methods to autism spectrum disorder. - We collaborate with researchers on covid effects in the macaque brain, using PET-CT (with Dr. Barber, NIAID); this helps our understanding of this disease in humans. - We developed methods for hemispherectomy and lobectomy patient brain alignment to standard templates and atlases, and analyses of brain reorganization following major resections (with Dr. Behrmann, CMU). - We created infant and childhood development templates and atlases, and worked on pediatric status epilepticus characterization (with Drs. You and Gaillard, CNMC). - We collaborated with researchers and clinicians (with Drs. Bhagavatheeshwaran and Horovitz, NINDS) developing low-field MRI acquisition for portable and economic structural MRI acquisitions, for providing quick and reliable information on brain health, esp. in places where access to high-field scanners is difficult. - We collaborate with Drs. Brotman, Leibenluft and Pine (NIMH), who use AFNI in studying emotions, mood variability and COVID-related stress. - AFNI alignment, pipeline, statistical and other software tools were applied to understanding a number of patient populations.

核心的主要任务是帮助 NIH 研究人员分析他们的 fMRI（大脑激活图谱）和结构 MRI（大脑解剖学）数据。在此过程中，我们还为非 NIH 研究人员提供帮助，其中许多在美国，也有一些在国外。提供多个级别的帮助，从短期即时援助到长期发展和规划。 咨询： 最短期的帮助包括与研究人员就研究中出现的问题进行面对面咨询。涉及的问题多种多样，因为进行 fMRI 和 MRI 数据分析有很多步骤，并且有许多不同类型的实验。常见问题包括： - 如何设置实验设计以便有效分析数据？ - MRI 成像伪影的解释和校正（例如：扫描期间参与者头部运动；由于磁场异常导致的图像扭曲）。 - 如何设置时间序列分析来提取感兴趣的大脑激活效应，并抑制非激活成像伪影（例如，来自呼吸的伪影）？ - 如何分析数据以揭示特定心理任务或休息时大脑区域之间的联系？ - 如何识别质量差的数据？ - 如何进行可靠的患者间（群体）统计分析，特别是当需要纳入非 MRI 数据（例如遗传信息、年龄、疾病评级）时？ - 如何在功能结果和解剖参考图像之间以及不同参与者的大脑图像之间获得良好的一致性？ - 哪种程序序列最适合分析特定类型的数据？ - AFNI 软件中真实或想象的错误报告，以及功能请求（小、大、夸张）。 - 分析与扩散加权 MRI 数据相关的问题，获取这些数据是为了揭示大脑中的解剖连接。 许多此类磋商都有熟悉的主题，但每次会议和每次实验都会提出独特的问题，需要深入研究研究项目的目标和细节，以确保不会忽视任何关键的内容。用户提出的第一个问题通常根本不是正确的问题。经常会出现复杂的统计或数据处理问题。通常，需要开发或修改软件来帮助研究人员回答他们的具体问题。帮助论文的方法部分，或回复审稿人，通常是我们职责的一部分。 教育工作： Core 开发（并更新）了一个 40 小时的实践课程，介绍如何设计和分析 fMRI 数据。这个不断发展的课程的所有材料（软件、示例数据、脚本、PDF 幻灯片、带字幕的视频）均可在我们的网站 (https://afni.nimh.nih.gov) 上免费获取。课程材料包括示例数据集，用于说明整个过程，从 MRI 扫描仪输出的图像开始，一直到对参与者组进行集体统计分析。 Covid-19 大流行取消了现场培训课程；相反，我们加快了 AFNI 学院视频的制作速度。核心成员在 AFNI 论坛上发了 1000 多条帖子，大部分是为了回答用户的询问。 算法和软件开发： 最长的支持包括开发（或采用）MRI 数据分析的新方法和软件，以解决当前问题并预测新需求。我们的所有软件都包含在 AFNI 软件包中，该软件包是基于 Unix/Linux/Macintosh 的开源软件，任何人都可以以源代码 (GitHub) 或二进制格式（核心服务器）下载。创建新程序并修改旧程序，以响应特定的用户请求并响应核心对未来需求的愿景。该核心还协助 NIH 实验室建立与 AFNI 一起使用的计算机系统，并维护一个活跃的网站，其中包含有关 (f)MRI 数据分析、结构 FMRI 和基于扩散的 MRI 的问题（和答案）的论坛。在冠状病毒爆发的第三个年头，我们通过 Zoom 进行了咨询和演示。 2022 财年的显着进展包括： - 开发了一种分层建模方法来捕捉双相情感障碍和对照之间大脑反应的细微差异（与 NIMH 的 Pine 和 Brotman 博士合作）。 - 将软件安装和构建扩展到多个新系统：新的 Mac M1、Windows、新的 Linux 操作系统和云计算系统。 我们更新了分布式 Docker 版本。 这促进了在更广泛的平台和系统上进行开源 FMRI 分析。 - 使用 afni_proc.py 并针对某些步骤与其他工具集成（例如，用于多回波 FMRI 的 tedana），为 FMRI 处理创建了几个新的开放且可重复的管道示例和演示。 - 与圣路易斯华盛顿大学的 J. Etzel 一起领导 FMRI 质量控制 (QC) 项目，以促进整个领域更广泛的 QC 实践共享和汇集。它将创建一个开放的教育资源，并普遍改善整个神经影像界 FMRI 处理中重要的（且经常被低估和报道不足的）QC 步骤。 - 与多个不同的合作者共同为非人类成像研究（包括猕猴、狨猴、犬科动物和小鼠）制定新的标准模板和图集。 这些资源改善了物种内和跨物种的理解，包括人脑的理解。 - 处理多回波 FMRI (ME-FMRI) 的进一步演示，它具有许多有益于提高 SNR 和过滤混杂因素的特性（与 NIMH 的 Alex Martin 博士及其同事合作）。 - 证明了 FMRI 实验设计中试验样本量的重要性，而这一点经常被忽视。这项工作（与 NIMH 的 Pine 博士和 Brotman 博士一起）的建议通常应该会提高研究的普遍性和可重复性。 - 改进方法和工具，消除 BOLD FMRI 成像的非神经元贡献（例如呼吸和心率），以评估局部大脑活动，从而有意义地提高信号质量。 - 添加了用于在扫描仪上实时处理 ME-FMRI 数据的新功能和演示。 这些有助于采集和质量控制。 - 提高了心理测量数据试验级数据遗传力的估计准确性（与 NIMH 的 Thomas 博士合作）。 公共卫生影响： 从 2021 年 10 月到 2022 年 8 月，AFNI 主要出版物已被 502 篇论文引用（参见 Scopus）。我们的大部分工作支持大脑功能的基础研究，但我们的一些工作与特定疾病更密切相关或适用于： - 我们与 Alex Martin 博士 (NIMH) 合作，将我们的静息态分析方法应用于自闭症谱系障碍。 - 我们与研究人员合作，利用 PET-CT（与 NIAID 的 Barber 博士合作）研究新冠病毒对猕猴大脑的影响；这有助于我们了解人类这种疾病。 - 我们开发了半球切除术和肺叶切除术患者大脑与标准模板和图集对齐的方法，并对大切除术后的大脑重组进行了分析（与卡耐基梅隆大学的 Behrmann 博士合作）。 - 我们创建了婴儿和儿童发育模板和图集，并致力于儿科癫痫持续状态特征描述（与 CNMC 的 You 博士和 Gaillard 博士一起）。 - 我们与研究人员和临床医生（NINDS 的 Bhagavatheeshwaran 博士和 Horovitz 博士）合作，开发用于便携式和经济结构 MRI 采集的低场 MRI 采集，以提供有关大脑健康的快速可靠的信息，尤其是大脑健康。在难以使用高场扫描仪的地方。 - 我们与博士合作。 Brotman、Leibenluft 和 Pine (NIMH)，他们使用 AFNI 研究情绪、情绪变化和与新冠病毒相关的压力。 - 应用 AFNI 对齐、流程、统计和其他软件工具来了解大量患者群体。