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. These are quite varied, since there are many steps in carrying out fMRI and MRI data analyses and 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 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 that require digging into the goals and details of the research project to ensure that nothing critical is overlooked. The first question asked by a user is often not the right question at all. When complex statistical or data-processing issues are raised, software often 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 also a 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 data from groups of participants. When the Covid-19 pandemic canceled in-person training courses, 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 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 2023 include: - Led a community-wide research topic on Quality Control (QC) in FMRI, about improving quality in FMRI acquisition and analysis. This involved the creation of a public resource for performing QC with many popular software tools in the field, as well as a day-long education course at the OHBM-2023 Conference (with Jo Etzel, Washington University) - Developed new macaque D99 subcortical atlas based on high resolution MAP-MRI and histology (with Kadharbatcha Saleem and Peter Basser, NICHD) - Released structural and functional brain network atlases for marmoset brain (with Cirong Liu, Institute of Neuroscience, Shanghai) - Expanded several infrastructure and installation items, including: software installation across new mac OS systems, as well as new flavors of Linux (such as Rocky 8); - Migrated to a new, modern Message Board interface to improve global user support and interaction. - Showed how improving results reporting with "transparent thresholding" can (greatly) improve clarity, understanding and interpretation of work, as well as improve cross-study comparisons; standard reporting of thresholded values is artificial, biases results and inherently harms reproducibility. - Showed that the number of trials in a study are often overlooked as an important feature of study design; many studies focus on just increasing number of subjects, but increasing the number of samples/trials can often improve generalizability more efficiently (as most current studies are designed). - Developed a framework to estimate hemodynamic responses (with noise reduction/regularization) across the brain, - Showed how using this framework improves results and their interpretation, since hemodynamic responses are quite varied, even across gray matter. Public Health Impact: From Oct 2022 to Aug 2023, the principal AFNI publication has been cited in 460 papers (cf Scopus) or in 739 works (cf Google Scholar). 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 developed methods for hemispherectomy and lobectomy subjects to be analyzed with standard pipelines like FreeSurfer, with Dr. Marlene Behrmann (CMU, U.Pitt.) - We collaborated with Dr. Shaozheng Qin (Beijing Normal University) to investigate the neurobiological mechanisms that negative family environments can cause and maintain psychopathological symptoms in children through atypical childparent neural synchrony. This was done using AFNI tools for inter-subject correlation of FMRI paradigms during naturalistic movie-watching. - We provided modeling support to Dr. Shruti Japee (NIMH) for research on emotion perception. Even though individuals with Moebius Syndrome can perform challenging perceptual control tasks, they are less efficient than controls at extracting emotion from facial expressions, , a deficit likely linked to reduced engagement of the amygdala. - We collaborated with Dr. Lauren Atlas (NCCIH) applying AFNI to multi-echo FMRI data analysis to evaluate how predictions dynamically affect subjective pain in humans. - We contributed to a study of functional connectivity patterns and changes in cases of major depression with various treatments (with Dr. Helen Mayberg, Mount Sinai School of Medicine). - We applied AFNI alignment, pipeline, statistical and other software tools to increase researchers understanding of different patient populations.