Scientific and Statistical Computing Core

科学与统计计算核心

• 批准号: 10264686
• 负责人: Robert Cox
• 金额: $ 196.28万
• 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/10264686
• 关键词: 3-Dimensional; Academy; Age; Alcoholism; Anatomy; Animals; Anxiety Disorders; Atlases; Basic Science; Brain; Brain Mapping; Brain imaging; Brain region; Breathing; Brodmann' s area; COVID-19; COVID-19 pandemic; Collaborations; Collection; Communities; Complex; Computer Systems; Computer software; Consult; Consultations; Core Facility; Data; Data Analyses; Data Reporting; Data Set; Deep Brain Stimulation; Development; Development Plans; Diffusion Magnetic Resonance Imaging; Disease; Drug Addiction; Drug abuse; Educational process of instructing; Ensure; Experimental Designs; Extramural Activities; Feedback; Fetal Alcohol Exposure; Fetal alcohol effects; Functional Magnetic Resonance Imaging; Future; Goals; Head; Hour; Human; Human Activities; Image; Individual; Infant; Institution; Instruction; Judgment; Left; Libraries; Linux; Literature; MRI Scans; Magnetic Resonance Imaging; Mental Depression; Methodology; Methods; Mission; Mood Disorders; Morphologic artifacts; Motion; National Institute of Biomedical Imaging and Bioengineering; National Institute of Drug Abuse; National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; Online Systems; Operative Surgical Procedures; Output; Panic; Paper; Participant; Patients; Persons; Pharmaceutical Preparations; Process; Production; Psyche structure; Psychotic Disorders; Public Health; Publications; Quality Control; Reporting; Research; Research Personnel; Research Project Grants; Rest; Sampling; Scanning; Site; Slide; Smoking; Software Tools; Source Code; Space Perception; Statistical Computing; Statistical Data Interpretation; Stream; Structure; Students; Techniques; Technology; Time; Time Series Analysis; Toddler; United States National Institutes of Health; Update; Vision; Work; algorithm development; animal data; autism spectrum disorder; base; brain tissue; computational neuroscience; computerized data processing; data quality; data standards; design; experimental study; flexibility; genetic information; gigabyte; hackathon; human data; image processing; image warping; imaging modality; imaging software; interest; learning materials; magnetic field; meetings; member; movie; nervous system disorder; neuroimaging; open source; outreach; programs; research study; response; scientific computing; software development; stroke patient; tool; virtual; 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 artifacts (e.g., from breathing)? - Why don't AFNI results agree with SPM/FSL/other software? - 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 registration 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 developed (and updated) a 40-hour hands-on course on how to design and analyze fMRI data that was taught once at the NIH during FY 2020 to about 200 students. 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 the Spring 2020 NIH course; instead, we accelerated our production of AFNI Academy videos. By invitation, and prior to Covid-19. we also taught versions of this course at 4 non-NIH sites (expenses for these trips were sponsored by the hosts). 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 (f)MRI data analysis. Notable developments during FY 2020 include: - A set of new detailed instructional videos for using AFNI was created: the AFNI Academy. This collection will continue to grow into 2021 (at least). - A technique for detecting left-right flipping of human brain images was developed when Core staff noticed that a few percent of downloadable open datasets were marked with the wrong spatial orientation. This tool is now included in the AFNI standard data processing stream. - The Bayesian region of interest (ROI) analysis tools mentioned in last year's reports have been significantly extended to analyze new types of fMRI datasets, including connectivity (brain networks) and inter-participant correlations (e.g., during movie watching). - A standard processing pipeline for diffusion weighted MRI datasets was created, in collaboration with the Pierpaoli group in NIBIB. - A 5-day hackathon was held at the NIH campus in November 2019, attended by 20 neuroscience computational experts, and a number of projects were started as part of the Cores outreach efforts to the Open Source community. - Core staff presented at the (virtual) Organization for Human Brain Mapping Annual Meeting in 2020. - New quality control (QC) tools were added to the AFNI standard computing pipelines, making it easy for users to view summaries of the image processing steps and results, to help with data and analysis quality judgments (e.g., how many data points were corrupted by head motion). - A 3D Brodmann area brain atlas (human), and two 3D animal brain atlases were incorporated into AFNI. Public Health Impact: From Oct 2019 to Aug 2020, the principal AFNI publication has been cited in 477 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 consult frequently with NIMH researchers (e.g., Drs. Pine, Ernst, Grillon, Leibenluft) working in mood and anxiety disorders. - We consult with Dr. Elliot Stein (NIDA) in his research applying fMRI methods to drug abuse and addiction, and with Dr. Reza Momenan (NIAAA) in his studies of alcoholism. - We collaborate with Dr Ernesta Meintjes (U Cape Town) on data analysis of the effects of prenatal alcohol exposure on the brains of infants and toddlers. - Our instant 3D correlation tool is being used for mapping intact brain tissue in stroke patients, and for mapping brain connectivity to aid in deep-brain stimulation surgical planning. - Our precise registration tools (for aligning fMRI scans to anatomical reference scans) are important for individual participant applications of brain mapping, such as pre-surgical fMRI planning. - Our real-time fMRI software (first in the world) is being used for studies on brain mapping feedback in neurological disorders, is used daily for quality control at the NIH fMRI scanners, and is used at a few extramural sites. - Components of AFNI are being used in analyses of drug effects in human brain data, including studies of depression, drug abuse, psychosis, and smoking (based on citations in FY 2020).

核心的主要任务是帮助美国国立卫生研究院的研究人员分析他们的fMRI（大脑激活图）和结构MRI（大脑解剖）数据。在此过程中，我们也帮助非nih的研究人员，许多在美国，也有一些在国外。从短期直接援助到长期发展和规划，提供了几个层次的帮助。