The main mission of the Core is to help NIH researchers with analyses of their functional MRI (brain activation mapping) data. Along the way, we also help non-NIH investigators, mostly in the US 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. For FY2012, we logged 200 in-person consultations with NIH researchers (not all meetings get logged), plus about 4000 messages on our Web-forum, about half of which are from us and about half from users (NIH and extramural). The issues are quite varied, since there are many steps in carrying out fMRI data analyses. Common problems include: - How to set up experimental design so that data can be analyzed effectively? - Interpretation and correction of MRI imaging artifacts (a common one: 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 certain mental tasks, or at rest? - How to carry out 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, of course, reports of real or imagined bugs in the AFNI software, as well as feature requests (large and small). 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 crucial is being overlooked. Complex statistical 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 is often part of our duties, as well. Educational Efforts: The Core has developed a 40 hour course on how to design and analyze fMRI data. This course is taught in a one week hands-on "bootcamp", and was taught twice at the NIH during FY 2012 (Mar and Sep). All material for this continually evolving course (software, sample data, scripts, and PowerPoint/PDF slides) are freely available on our Web site . 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. We also taught versions of this course at 4 non-NIH sites this year: NCKU in Taiwan and SNUH in Korea (Oct), Princeton University (Jan), and BNU in Beijing (Jun) -- the expenses for these trip were covered by the host universities. Algorithm and Software Development: The longest term support consists of developing 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. 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. AFNI is "pushed" to NIH computers whenever updates are made;users on non-NIH systems must download and install the software themselves. The Core also assists NIH labs in setting up computer systems for use with AFNI, and maintains an active Web site. Notable developments during FY 2012 include: - Further implementation of our software for incorporating brain atlases into AFNI analyses, including tools for reseearchers to create their own atlases, and tools to access atlases stored on Web sites. This latter feature draws upon the Elsevier online brain atlas project (site-licensed to the NIH), but is generalized enough to be able to use with other remotely-accessible datasets. Multiple species are supported, and inter-species transformations are also allowed. Besides the "Where Am I" feature (click on a brain location and see the atlas labels for it), the user can also get a report of which regions are being shown as "active" in a brain map. - The SUMA package within AFNI was updated to allow for interactive analysis and spatial clustering. - Many small-to-medium changes were made to the software in response to specific NIH researcher requests and needs. Many small bug fixes were made -- we pride ourselves on fixing bugs in AFNI rapidly. In addition, small changes/fixes to the NIfTI and GIFTI software suites (standards for data interchange) are made as needed by the FMRI research community. - Major work was done to make AFNI data analyses easier to carry out. Last year's graphical interface for running complex analyses was extended in several directions, including new resting state fMRI analysis options, and the ability to analyze data directly on the subject's cortical surface representation. - Sample data + script packages have been developed, which allow researchers to see a particular kind of data analysis in "real life", walking them through the steps using real datasets. Extramural Collaborations: - We incorporated into AFNI yet more brain atlas databases developed by Dr Karl Zilles (Julich); - We worked with Dr Stephen Laconte (Virginia Tech) on methods for realtime FMRI analysis of human brain states. - We worked with Dr Paul Taylor (New Jersey School of Medicine) to develop several new tools for AFNI, including diffusion-tensor (white matter) tract tracing and some resting state fMRI summary statistics programs. Public Health Impact: Thus far in FY 2012 (Oct-Aug), the principal AFNI publication has been cited in 359 papers (cf Scopus): 54 from the NIH (NIMH, NIDA, NINDS, etc.), and the rest from over 100 extramural institutions in the USA and abroad. 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 very 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. - Our Gd-DTPA nonlinear analysis method is used in the NIH Clinical Center to analyze data from brain cancer patients. - Our precise registration tools (for aligning functional MRI scans to anatomical reference scans) are important for individual subject applications of brain mapping, such as pre-surgical fMRI planning. - Our realtime fMRI software is being used for studies on brain mapping feedback in neurological disorders, and is also used daily for quality control at the NIH fMRI scanners. and also at a few extramural sites. Publications: #1-7 originated in the Core #8-18 are from other groups, who included Core authors as significant contributors. #19-46 are NIMH-authored publications that cited the primary AFNI paper, as an indication of their use of the Core facility. Papers from NIH-but-not-NIMH authors are not included here, nor are papers from extramural researchers.

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National Institute of Mental Health (NIMH)
Scientific Cores Intramural Research (ZIC)
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Wiggins, Jillian Lee; Brotman, Melissa A; Adleman, Nancy E et al. (2016) Neural Correlates of Irritability in Disruptive Mood Dysregulation and Bipolar Disorders. Am J Psychiatry 173:722-30
Gonzalez-Castillo, Javier; Panwar, Puja; Buchanan, Laura C et al. (2016) Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI. Neuroimage 141:452-68
Lauro, Peter M; Vanegas-Arroyave, Nora; Huang, Ling et al. (2016) DBSproc: An open source process for DBS electrode localization and tractographic analysis. Hum Brain Mapp 37:422-33
White, Stuart F; Tyler, Patrick M; Erway, Anna K et al. (2016) Dysfunctional representation of expected value is associated with reinforcement-based decision-making deficits in adolescents with conduct problems. J Child Psychol Psychiatry 57:938-46
Pauli, Ruth; Bowring, Alexander; Reynolds, Richard et al. (2016) Exploring fMRI Results Space: 31 Variants of an fMRI Analysis in AFNI, FSL, and SPM. Front Neuroinform 10:24
Mellem, Monika S; Jasmin, Kyle M; Peng, Cynthia et al. (2016) Sentence processing in anterior superior temporal cortex shows a social-emotional bias. Neuropsychologia 89:217-24
Meffert, Harma; Hwang, Soonjo; Nolan, Zachary T et al. (2016) BOLD data representing activation and connectivity for rare no-go versus frequent go cues. Data Brief 7:66-70
Chen, Gang; Shin, Yong-Wook; Taylor, Paul A et al. (2016) Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level. Neuroimage 142:248-259
Zhang, Hui; Japee, Shruti; Nolan, Rachel et al. (2016) Face-selective regions differ in their ability to classify facial expressions. Neuroimage 130:77-90
Alexander-Bloch, Aaron; Clasen, Liv; Stockman, Michael et al. (2016) Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI. Hum Brain Mapp 37:2385-97

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