Scientific and Statistical Computing Core

National Institute Of Mental Health

Investigators

Linked publications, trials & patents

Abstract

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)? - 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 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 (f)MRI data analysis. In this second year of the coronavirus, consultations and presentations were carried out with Zoom. Notable developments during FY 2021 include: - New demos of AFNI programs and pipelines for various user applications, available for download on the AFNI website. - Expanded the automatic Quality Control output of the main program for creating fMRI pipelines (afni_proc.py) to help researchers check their processing efficiently. - Add new pipelines, and improved older ones, for processing (f)MRI data from macaques, including nonlinear image warping to templates. Assisted with methods in the development of 4 new macaque templates. - Developed alignment tools for brain images from patients with very large lesions (e.g., hemispherectomies and lobectomies) (with Carnegie Mellon U) - Started a project using machine learning methods for brain image skull stripping and tissue classification. - New capabilities for viewing brain surface layers, to improve visualization of the new layer-fMRI imaging techniques developed in SFIM (Bandettini) and with extramural collaborators. - Developed new methods for test-retest reliability at the individual trial-level, rather than at the combined run-level, to allow for random fluctuations in individual trial responses that have not been taken into account in previous analyses (program 3dLMEr-TRR). - Developed methods for applying nonlinear multilevel splines to fMRI longitudinal group studies (program 3dMSS). - Made changes to the software and compilation process to be able to build the AFNI package on Apples new M1 processor computers (significantly faster than Intel-based systems). Public Health Impact: From Oct 2019 to Aug 2020, the principal AFNI publication has been cited in 577 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 several 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).