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)? - 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.