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Principal Investigator: Paul Taylor
Organization: NATIONAL INSTITUTE OF MENTAL HEALTH
Fiscal Year: 2024
Award: $2,347,803
Funding agency: National Institute of Mental Health
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/pub/dist/doc/htmldoc/). 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. 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:
- Published a guide to quality controlling (QCing) FMRI data with AFNI tools. This is an often overlooked area of processing in the field, and we have tried to make the tools easier to use, as well as providing helpful guides for people to understand data better. We think these can greatly help the field produce better results.
- With Drs. Chris Rorden (Univ of South Carolina) and Taylor Hanayik (U of Oxford) we developed and implemented an improved method for visualizing edges in data sets. This is helpful for ascertaining quality of alignment, in particular, which is a key step in much of neuroimaging processing.
- We showed that standard heritability estimation in twin studies have biases, and proposed an improved statistical framework for modeling that key quantity.
- We taught several educational FMRI training “Bootcamps”, including at NIH campus (USA), Dartmouth College (USA), UNAM-Juriquilla (Mexico) and for the Brain Engineering Society of Korea (S. Korea).
- We contributed to the development of a canine white matter atlas and template, working with Dr. Philippa Johnson (Cornell U). This will help facilitate canine and multi-species imaging studies.
- We helped improve methods for mapping regions from MRI-based atlases onto photographic images of post-mortem sections, collaborating with Drs. Eugenio Iglesias (MGH) and Stefano Marenco (NIMH).
- We majorly updated and improved a piece of software that helps incorporate physiological measures of breathing and heartrate in FMRI studies. Since FMRI measures blood oxygenation to investigate neuronal activity, being able to separate out physiological measures can be quite helpful for reducing noise features. We improved the algorithm, modularizing it and making it easier to maintain, improved its usage interface and added a number of quality control plots and text outputs to help the user.
Public Health Impact:
From Oct 2023 to Aug 2024, the principal AFNI publication has been cited in 719 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 helped on multiple projects that combined task-based FMRI and repetitive transcranial magnetic stimulation (rTMS) with Dr. Sarah Lisanby’s (NIMH) group. One involved setting up a realtime neurofeedback analysis framework using AFNI, focusing on information within the participant’s amgydala (also collaborating with Dr. Vinai Roopchansingh, NIMH).
- We collaborated with Drs. Daniel Pine (NIMH), Ellen Leibenluft (NIMH) and Melissa Brotman (NIMH) to investigate the association of neurodevelopmental pathways functioning with social reticence and anxiety symptoms.
- We provided analysis and modeling support for an inter-subject correlation study of emergence reading traits using FMRI with Drs. Kenneth Pugh (Yale U) and Peter Molfese (NIMH). This study reported on patterns of functional synchrony within and between high- and low-ability reading groups.
- We worked with Prof. Ernesta Meintjes (Univ. of Cape Town) to analyze FMRI scans of children with fetal alcohol spectrum disorders resulting from prenatal alcohol exposure. This work measured neurocorrelates of alcohol exposure with arithmetic performance.
- We contributed to an ongoing study of deep brain stimulation (DBS) for treatment-resistent depression, led by Dr. Helen Mayberg (School of Medicine at Mount Sinai). This study looked at functional network effects of the DBS over time, characterizing network pattern changes for responders and non-responders to the treatment.
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