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研究人员对其fMRI（大脑激活映射）和结构MRI（脑解剖结构）数据进行分析。在此过程中，我们还帮助非NIH调查人员，许多在美国，还有一些国外的调查员。从短期立即援助到长期发展和计划，提供了几个级别的帮助。 咨询： 最短的帮助包括与调查人员有关其研究中出现的问题的面对面咨询。涉及的问题非常多样化，因为进行fMRI和MRI数据分析有许多步骤，并且实验有许多不同的类型。常见问题包括： - 如何设置实验设计，以便可以有效地分析数据？ - MRI成像伪像的解释和校正（例如：扫描过程中的参与者头运动；由于磁场异常引起的图像翘曲）。 - 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像（例如，呼吸）？ - 如何分析数据以揭示特定心理任务期间或静止期间大脑区域之间的联系？ - 如何识别质量差数据？ - 如何进行可靠的患者跨统计分析，尤其是当需要纳入非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以及不同参与者的大脑图像之间获得良好的对齐方式？ - 哪种程序序列是“最好的”，用于分析特定类型的数据？ - AFNI软件中的真实或想象中的错误的报告以及功能请求（小，大，奢侈）。 - 与扩散加权MRI数据有关的分析问题，这些问题被获取以揭示大脑的解剖联系。 在许多咨询中都有熟悉的主题，但是每次会议和每个实验都提出了独特的问题，并且需要挖掘研究项目的目标和细节，以确保没有忽视任何关键的问题。用户问的第一个问题通常根本不是正确的问题。复杂的统计或数据处理问题通常会提出。通常，需要开发或修改软件，以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对审阅者的回答，通常是我们职责的一部分。 教育工作： 核心已经开发了（并更新）40小时的动手课程，介绍了如何设计和分析fMRI数据。我们的网站（https://afni.nimh.nih.gov）免费获得此不断发展的课程的所有材料（软件，示例数据，脚本，PDF幻灯片，字幕视频）。该课程材料包括用于说明整个过程的样本数据集，从MRI扫描仪输出的图像开始，然后继续进行参与者组的集体统计分析。 19009年的大流行取消了面对面的培训课程；取而代之的是，我们加快了AFNI学院视频的制作。核心成员进行了1000多个AFNI论坛发布，主要是回答用户的查询。 算法和软件开发： 最长的时间支持包括开发（或适应）新方法和MRI数据分析的软件，包括解决当前问题和预期新需求。我们所有的软件都包含在AFNI软件包中，该软件包是UNIX/Linux/Macintosh开源，可供源代码（GITHUB）或二进制格式（Core Server）中的任何人下载。创建了新程序，并根据特定的用户请求以及核心对将来需要的内容的愿景进行了修改，并修改了旧程序。该核心还协助NIH实验室设置用于与AFNI一起使用的计算机系统，并维护一个有效的网站，其中包含有关（和答案）的论坛（和答案），以分析（F）MRI数据，结构fMRI和基于扩散的MRI。在冠状病毒的第三年中，与Zoom进行了咨询和演讲。 2022财年期间的显着发展包括： - 开发了一种层次建模方法，以捕获双极疾病和对照之间的大脑反应的细微差异（pine and brotman，nimh）。 - 扩展的软件安装和构建到多个新系统：新的Mac M1，Windows，New Linux OSS和云计算系统。 我们更新了分布式Docker构建。 这促进了跨更广泛的平台和系统范围的开源fMRI分析。 - 使用afni_proc.py创建了一些新的开放式管道示例和fMRI处理的演示，并与其他工具集成到某些步骤（例如，用于多回声fMRI的Tedana）。 - 领导FMRI质量控制（QC）的项目，与圣路易斯Wash-U的J. Etzel一起，促进了整个QC实践的更广泛的共享和集合。它将创造一个开放的教育资源，并普遍改善QC在fMRI处理中为整个神经成像社区的重要（通常被低估且报告不足的）步骤。 - 为新的标准模板和地图集提供了多个不同的非人成像研究的合作者，包括猕猴，棉花糖，犬和小鼠。 这些资源可以改善内部和跨物种的理解，包括在人脑中。 - 进一步的演示来处理多回声fMRI（ME-FMRI），该演示具有许多有益的特性，可增加SNR和过滤混淆（与Alex Martin及其同事，NIMH）。 - 证明了试验样本量在fMRI实验设计中的重要性，这通常被忽略了。这项工作的建议（与Pine和Brotman博士，NIMH）通常应改善研究的普遍性和可重复性。 - 改进对大胆的fMRI成像的非神经元贡献（例如呼吸和验证液）的方法和工具，以评估局部大脑活动，从而有意义地提高信号质量。 - 添加了新功能和演示，用于在扫描仪上实时处理ME-FMRI数据。 这些有助于获取和QC。 - 提高了心理测量数据试验级数据的遗传力的估计准确性（与NIMH的Thomas博士）。 公共卫生影响： 从2021年10月到2022年8月，在502篇论文（CF Scopus）中引用了主要的AFNI出版物。我们的大多数工作都支持大脑功能的基础研究，但是我们的某些工作更与特定疾病紧密相关或适用： - 我们与Alex Martin博士（NIMH）合作，将我们的静止状态分析方法应用于自闭症谱系障碍。 - 我们使用PET-CT（与Barber，Niaid博士）一起与研究人员合作就猕猴大脑中的共证效应进行了合作；这有助于我们理解人类的这种疾病。 - 我们开发了半球切除术和肺切除术患者脑对齐与标准模板和地图集的方法，以及重大切除后脑重组的分析（与Behrmann博士，CMU）。 - 我们创建了婴儿和儿童发展模板和地图集，并从事癫痫持续状态的表征（与Dr. You and Gaillard，CNMC）。 - 我们与研究人员和临床医生（与Ninds的Bhagavatheeshwaran和Horovitz博士）合作开发了低场MRI获取，用于便携式和经济结构MRI获取，以提供有关大脑健康的快速可靠信息，ESP。在很难进入高场扫描仪的地方。 - 我们与Drs合作。 Brotman，Leibenluft和Pine（NIMH），他们使用AFNI研究情绪，情绪变异性和相关压力。 - AFNI对齐，管道，统计和其他软件工具用于了解许多患者人群。