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 FY2013, we logged about 210 in-person consultations with NIH researchers (not all meetings make it into the log), plus about 3300 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 between the brain images from different subjects? - And, of course, reports of real or imagined bugs in the AFNI software, as well as feature requests (small, large, and extravagant). 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 (and updates) 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 once at the NIH during FY 2013 (Feb) -- for the first time, this course was taught in a large auditorium, and non-NIH researchers were allowed to attend -- over 120 students were there. 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. In the past, we have also taught versions of this course at non-NIH sites (the expenses for these trips were covered by the host universities), but no such extramural courses were taught in FY 2013. 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 in source code or binary formats. 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 at non-NIH sites 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 2013 include: - A novel method for fully nonlinear registration of 3D brain images was created and written into software. Evaluations show that it is as least as accurate as the competing software tools in other packages, and better in many cases. Work continues to integrate this new tool into the AFNI standard FMRI processing pipeline, and to apply it to novel problems, such as the creation of brain atlases. - A lot of effort was put into evaluating different methods for analysis of resting state FMRI (rsFMRI) datasets. In several papers, we showed that a widely used analysis component (Global Signal Regression) is a bad idea, illustrating our conclusions with statistical reasoning and with several disparate datasets. However, this area remains controversial, as Global Signal Regression continues to be widely recommended (e.g., by the Human Connectome Project) -- incorrectly, in our opinion. We have implemented our recommended analysis tools in an AFNI pipeline, to make them easy to use. (See publications 3, 5, 6, 7.) - Two new methods for inter-subject (group) analyses of FMRI datasets were developed in the last year (publications 2, 4). These methods were developed in response to various complex protocols of NIMH IRP investigators, and are being used by them (and others) in their research. Extramural Collaborations: - We continued our work with Dr Stephen Laconte (Virginia Tech) on methods for realtime FMRI analysis of human brain states. - We worked with Dr Paul Taylor (African Institute for Mathematical Sciences) to develop several new tools for AFNI, including diffusion-tensor (white matter) tract tracing. - We initiated a collaboration with Dr Helen Mayberg (Emory University) to enhance AFNI to aid her research into pre-surgical planning for deep brain stimulation -- which will use enhanced versions of Paul Taylor's new tools. Public Health Impact: Thus far in FY 2013 (Oct-Aug), the principal AFNI publication has been cited in 318 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 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, and to Drs Hommer/Momenan (NIAAA) in their studies of alcholism. - 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. The new nonlinear registration tool has particular promise in this area, to aid in aligning brain images from the same patient before and after surgery;a new collaboration with NINDS has begun to explore this avenue. This nonlinear registration tool has also been used to develop a pediatric brain atlas. - 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. #8-25 are from other groups, who included Core authors as significant contributors. #26-57 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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