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. Most of our computer-based logs for collaborations were lost in a computer backup glitch this year. For the four months during FY2010 for which we have incomplete logs, we show about 140 hours of consultations (70% NIMH, 20% NIDA, 10% other NIH). We logged 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 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? There are familiar themes in many of these consultations, but each meeting and each experiment raises unique questions and usually requires delving 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 2010 (Feb and Sep). All material for this continually evolving course (sample data, scripts, and PowerPoint/PDF slides) are freely available on our Web site http://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. We also taught this course at 3 non-NIH sites this year: Tainan University (Taiwan;Nov), Princeton University (Jan), and the Chinese National Academy (Beijing;Apr). 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 2010 include: - Development of a new method for correcting resting-state time series data for scanner-induced artifacts that can grossly corrupt the results. This work resulted in a publication #1 and a well-received presentation at the Human Brain Mapping meeting in June. - Extensive improvement in our """"""""super-script"""""""" for FMRI data analysis. We now recommend that all users start with this script, which will simplify their lives (and ours). - An interactive tool for multiple subject resting-state analysis was developed. On a desktop computer, it is feasible to """"""""click-and-see"""""""" a collective correlation map from 900 subjects in 3 seconds. On a laptop, the data from 100 subjects can be analyzed interactively. To our knowledge, this is the first such tool capable of interactively analyzing 10+ Gigabytes of resting-state data at once. This software can display results in 3D volumes (AFNI) or on unfolded cortical surface maps (SUMA). - Early in 2010, a consortium released the """"""""Fcon 1000"""""""" collection of about 1000 resting-state FMRI datasets. These datasets were almost completely un-processed. We have downloaded all of them and pre-processed them to make further collective analyses simpler: time series registration, alignment between the structural and functional datasets, and aligmnment to standard (MNI) space. We are making the collection of about 930 3 Tesla datasets available to the NIH community, and are about to give these datasets back to the original NITRC Web site for wider dissemination. We are also now processing the structural datasets to produce cortical surface models. About 600 of these surface extraction efforts were successful, and will be made available soon. - The development of a new method for 3D brain image segmentation is underway, and a paper is about to be submitted. This method is much faster and more robust than existing methods. - Four posters were presented at the HBM meeting in June, in addition to the previously-mentioned talk. - 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. Our development plans include the incorporation of more brain atlases into the AFNI software. The 3D human atlases already present have been very helpful to users, and there have been many requests for rodent and primate atlases to be added. 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. 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. Public Health Impact: Thus far in FY 2010 (Oct-Aug), the principal AFNI publication has been cited in 222 papers (cf Scopus): 32 from the NIH (NIMH, NIDA, NINDS, NIAAA, and NIA), and the rest from extramural institutions. 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 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 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 this purpose, as well - Our realtime fMRI software is being used for studies on brain mapping feedback in neurological disorders, and is also used for quality control at the NIH fMRI scanner. Publications: The first 5 publications listed include Core authors. The remainder are NIMH-authored publications that cited AFNI.
| 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 |
| 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 |
Showing the most recent 10 out of 223 publications
