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, mostly 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 usually 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 to answer. 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 2017 to over 250 students. All material for this continually evolving course (software, sample data, scripts, and PDF slides) are freely available on our Web site (https://afni.nimh.nih.gov). The course material includes several sample datasets that are 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): U Shenzhen, South China Normal University, MIT, and U Washington. More than 1200 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 fMRI 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 2017 include: - A great deal of infrastructure software maintenance and development was undertaken; for example, the widespread Python programming language underwent major revisions, which required the Core to modify a number of scripts written in Python; AFNI is beginning to support the new BIDS structure for storing all the data from an multi-subject experiment; various tools for visualizing MRI data quality were developed; a simpler AFNI installation script for Mac OS X was created; the online documentation for AFNI software was greatly expanded. - A new set of diffusion MRI processing tools was implemented, including: automatic quality control, a full demo with data and processing scripts, a series of webpages containing step-by-step instructions, and a script for data quality control that is fast enough to run while the subject is still in the scanner. - A push to incorporate multi-echo fMRI processing into our principal analysis script was started. At this report, the ME-fMRI script does work, but it needs improvement and will benefit from coordination with related extramural efforts. - Tools were developed for: measuring cortical thickness; making brain templates from a collection of subject 3D images (this was applied to create a new Pan-Indian brain template, helping researchers in Bangalore to create a toddler brain template, and to create a marmoset brain template)); for checking brain image data to see if it is left-right flipped (which happens!). - A method for carrying out simultaneous 3D brain image alignment and removal of non-brain tissue was developed and implemented, and is our recommended technique for these processing steps. - A novel method, using Bayesian ideas, was created and implemented for carrying out group analyses in fMRI datasets. This method pools noise estimates across brain regions, reducing uncertainty in the final output brain maps. - Core personnel helped create the Rene Fleming brain map video using the Core's surface mapping software (SUMA): www.youtube.com/watch?v=1d-PlEAQMBY Public Health Impact: From Oct 2017 to Aug 2018, the principal AFNI publication has been cited in 427 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 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. - Our statistical methods are being applied to epilepsy patients undergoing surgical planning with electro-corticography.
| Haller, Simone P; Kircanski, Katharina; Stoddard, Joel et al. (2018) Reliability of neural activation and connectivity during implicit face emotion processing in youth. Dev Cogn Neurosci 31:67-73 |
| Silson, Edward H; Reynolds, Richard C; Kravitz, Dwight J et al. (2018) Differential Sampling of Visual Space in Ventral and Dorsal Early Visual Cortex. J Neurosci 38:2294-2303 |
| Branco, Mariana P; Gaglianese, Anna; Glen, Daniel R et al. (2018) ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids. J Neurosci Methods 301:43-51 |
| Torrisi, Salvatore; Chen, Gang; Glen, Daniel et al. (2018) Statistical power comparisons at 3T and 7T with a GO / NOGO task. Neuroimage 175:100-110 |
| Choi, Ki Sueng; Noecker, Angela M; Riva-Posse, Patricio et al. (2018) Impact of brain shift on subcallosal cingulate deep brain stimulation. Brain Stimul 11:445-453 |
| Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh et al. (2018) Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline. Pac Symp Biocomput 23:307-318 |
| Liu, Cirong; Ye, Frank Q; Yen, Cecil Chern-Chyi et al. (2018) A digital 3D atlas of the marmoset brain based on multi-modal MRI. Neuroimage 169:106-116 |
| Moraczewski, Dustin; Chen, Gang; Redcay, Elizabeth (2018) Inter-subject synchrony as an index of functional specialization in early childhood. Sci Rep 8:2252 |
| Seidlitz, Jakob; Sponheim, Caleb; Glen, Daniel et al. (2018) A population MRI brain template and analysis tools for the macaque. Neuroimage 170:121-131 |
| Warton, Fleur L; Taylor, Paul A; Warton, Christopher M R et al. (2018) Prenatal methamphetamine exposure is associated with corticostriatal white matter changes in neonates. Metab Brain Dis 33:507-522 |
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