Scientific and Statistical Computing Core

科学与统计计算核心

• 批准号: 10043766
• 负责人: Robert Cox
• 金额: $ 213.47万
• 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/10043766
• 关键词: 2 year old; 6 year old; Age; Alcoholism; Algorithmic Software; Anatomy; Anxiety Disorders; Atlases; Basic Science; Brain; Brain Mapping; Brain imaging; Brain region; Brain scan; Callithrix; Child; Code; Complex; Computer Systems; Computer software; Consult; Consultations; Core Facility; Data; Data Analyses; Data Collection; Data Quality; 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; Image Analysis; India; Individual; Infant; Institution; Libraries; Linux; Literature; MRI Scans; Macaca mulatta; Magnetic Resonance Imaging; Maps; Methodology; Methods; Mission; Mood Disorders; Morphologic artifacts; Motion; 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; Process; Psyche structure; Public Health; Publications; Quality Control; Research; Research Personnel; Research Project Grants; Resistance; Rest; Sampling; Scanning; Site; Slide; Software Tools; Source Code; Statistical Computing; Statistical Data Interpretation; Structure; Students; Techniques; Technology; Testing; Time; Time Series Analysis; Toddler; United States National Institutes of Health; Update; Vision; Work; Zebrafish; animal data; autism spectrum disorder; base; brain tissue; computerized data processing; design; experimental study; flexibility; genetic information; gigabyte; hackathon; human data; imaging modality; imaging software; interest; learning materials; meetings; member; nervous system disorder; neuroimaging; open source; programs; research study; response; scientific computing; software development; stroke patient; symposium; tool; web page; 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: subject head motion during scanning). - How to set up time series analysis to extract brain activation effects of interest, and to suppress non-activation artifacts? - Why don't AFNI results agree with SPM/FSL/something else? - 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 subjects? - 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, and extravagant). - Analysis problems related to diffusion weighted MRI data, which are acquired to reveal the 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 central 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 twice at the NIH during FY 2019 to over 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 subjects. By invitation, 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 fMRI data analysis. Notable developments during FY 2019 include: - A new method for spatial cluster thresholding of fMRI brain maps was developed; this method does not have the statistical errors of the methods widely used in the field, and also is more resistant to p-hacking, since it removes some arbitrary choices from the analysis. - The Cores central data processing script was enhanced in many ways. Most notably, it now creates a Web page for each participant's analysis, with images and tables to view the quality of the data and the processing. For example, it is now easy to check how well the image alignments worked, and how badly the data was damaged by head motion during the brain scan. - New Bayesian region of interest (ROI) analysis programs were developed for combining multiple participants' datasets into group brain maps. The advantages of this approach is that the entire brain is analyzed together, rather than one region at a time, which gives tighter bounds on the results. - New brain atlases and templates were created, for mapping datasets into common reference spaces: marmoset brains, rhesus macaques, age-stratified brain maps from India, 2 year old toddler brain maps, and 4-6 year old child brain maps. Even a zebrafish brain atlas was created. - A new framework for testing the AFNI software was created for regression testing, to make sure that changes to the code base do not significantly alter the results of data analyses. Our next task is to expand the coverage of the tests to include all the major components of AFNI. - Core staff presented at the Organization for Human Brain Mapping in 2019, and at the Conference on Brain Connectivity in late 2018. - Core staff organized and participated in a week-long hackathon at the NIH, to jumpstart our efforts to make AFNI more compatible with the evolving BIDS standard for storing brain imaging data collections. - Software was developed for unwarping distorted brain images, using two different methods. Public Health Impact: From Oct 2018 to Aug 2019, the principal AFNI publication has been cited in 448 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 to 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 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 subject applications of brain mapping, such as pre-surgical fMRI planning. - Our real-time fMRI software 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 also used at a few extramural sites.