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. 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 FY2011, we logged 200 in-person consultations with NIH researchers (not all meetings get logged), plus about 4200 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 comes from 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 status) 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. 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 missed. Complex statistical issues are often raised. Often, software needs to be developed or tweaked to help researchers answer their specific questions. 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 2011 (Feb 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: University of Texas Houston (Oct), Princeton University (Jan), University of Tulsa (Apr), and Dartmouth College (Aug), . Algorithm and Software Development: The longest term support consists of developing new methods and software for fMRI data analysis, both to solve immediate 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. Notable developments during FY 2011 include: - Development of a new method for measuring inter-hemispheric symmetry and asymmetry in resting-state FMRI time series. A manuscript is in preparation about this method and its results. - Development of a new method for brain tissue segmentation -- a method that is much simpler in concept than methods now commonly used, and potentially much more efficient. A paper was published on this method, and an oral presentation given at the AAnnual ISMRM meeting. - A new method for analyzing inter-regional connectivity in brain imaging data was developed, and submitted for publication. That paper is currently undergoing revision after the first round of reviews. - Nonlinear 3D image registration was implemented using ninth order Legendre polynomial spatial basis functions. Based on the results from this experiment, we are now extending the method to a higher order method based on repeated compositions of nonlinear spatial warps built on radial basis functions. - A significant effort for the last 2 years has been the development of the software infrastructure in AFNI to allow for the easy incorporation of brain atlases, to aid in navigation and in data analysis. It is now simple to add brain atlas datasets to AFNI. Current efforts include the generalization of this software to allow for remote (Web-based) queries of atlases that the creators do not want to release in toto. - 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. A graphical interface for setting up single subject analysis was introduced this summer. A text script for setting up group analysis was also put out this year. Further efforts along these lines will continue into the foreseeable future. - We are continuing to extend our resting-state FMRI analysis efforts, including collaborations with Alex Martin's group on the use of resting-state data for segmentation of cortical visual areas -- the early results on this are showing promise, indicating that resting-state FMRI can be used to find the horizontal meridian in visual cortex, for example. Extramural Collaborations: - We incorporated into AFNI yet more brain atlas databases developed by Dr Karl Zilles (Julich); - We worked with Dr Michael Beauchamp (UT Houston) to improve our InstaCorr software for groups of subjects. - We worked with Dr Stephen Laconte (formerly Baylor, now Virginia Tech) on methods for realtime FMRI analysis of human brain states. Public Health Impact: Thus far in FY 2011 (Oct-Aug), the principal AFNI publication has been cited in 286 papers (cf Scopus): 30 from the NIH (NIMH, NIDA, NINDS), 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 working in mood and anxiety disorders. - 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. - We have discussed with a non-NIH group the possibility of using the interactive resting-state correlation analysis tools in AFNI for surgical planning;however, a severe and lingering illness of this collaborator caused this effort to stall. - 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. Publications: The first 4 publications listed include Core authors. The remainder are NIMH-authored publications that cited the primary AFNI paper.

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National Institute of Mental Health (NIMH)
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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
Riva-Posse, P; Choi, K S; Holtzheimer, P E et al. (2018) A connectomic approach for subcallosal cingulate deep brain stimulation surgery: prospective targeting in treatment-resistant depression. Mol Psychiatry 23:843-849
Finn, Emily S; Corlett, Philip R; Chen, Gang et al. (2018) Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative. Nat Commun 9:2043
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

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