Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
668-03-010 ISMRM Abstract

Finding variance line artifacts in FMRI data using AFNI

Primary: Analysis Methods - Data Processing
Secondary: Analysis Methods - Software Tools
668-03-010 · Quantitative MRI Software: Tools and Applications · Thursday, 14 May, 1:40 PM–2:35 PM · Digital Posters Row I
Keywords: Artifacts fMRI Analysis Visualization Pipeline Quality control
Accepted
Paul A Taylor 1, Daniel Glen2, Justin K Rajendra3, Richard C Reynolds3
1National Institute of Mental Health, Bethesda, United States of America
2Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), Bethesda, United States of America
3National Institute of Mental Health (NIMH), Bethesda, United States of America
Presenting Author: Paul A Taylor

Synopsis

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References

1. [1] Taylor PA, Glen DR, Reynolds RC, Basavaraj A, Moraczewski D, Etzel JA (2023). Editorial: Demonstrating quality control (QC) procedures in fMRI. Front. Neurosci. 17:1205928. doi: 10.3389/fnins.2023.1205928 [doi]
2. [2] Reynolds RC, Taylor PA, Glen DR (2023). Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI. Front. Neurosci. 16:1073800. doi: 10.3389/fnins.2022.1073800 [doi]
3. [3] Cox RW (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162-173.
4. [4] Song S, Bokkers RPH, Edwardson MA , Brown T, Shah S, Cox RW, Saad ZS, Reynolds RC, Glen DR, Cohen LG, Latour LL (2017). Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke. PLoS ONE 12, Article number e0185552. doi: 10.1371/journal.pone.0185552 [doi]
5. [5] Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, et al (2010). Toward discovery science of human brain function. Proc Natl Acad Sci USA 107(10):4734-9. doi: 10.1073/pnas.0911855107. [doi]
6. [6] Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, et al (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. (6):659-67. doi: 10.1038/mp.2013.78. [doi]
7. [7] Markiewicz CJ, Gorgolewski KJ, Feingold F, Blair R, Halchenko YO, Miller E, Hardcastle N, Wexler J, Esteban O, Goncalves M, Jwa A, Poldrack R (2021). The OpenNeuro resource for sharing of neuroscience data. Elife 10:e71774. doi: 10.7554/eLife.71774. [doi]
8. [8] Allen EA, Erhardt EB, Calhoun VD (2012). Data Visualization in the Neurosciences: overcoming the Curse of Dimensionality. Neuron 74:603-608.
9. [9] Taylor PA, Reynolds RC, Calhoun V, Gonzalez-Castillo J, Handwerker DA, Bandettini PA, Mejia AF, Chen G (2023). Highlight Results, Don’t Hide Them: Enhance interpretation, reduce biases and improve reproducibility. Neuroimage 274:120138.
10. [10] Reynolds RC, Glen DR, Chen G, Saad ZS, Cox RW, Taylor PA (2024). Processing, evaluating and understanding FMRI data with afni_proc.py. Imaging Neuroscience 2:1-52. https://doi.org/10.1162/imag_a_00347 [doi]
11. [11] Taylor PA, Glen DR, Chen G, Cox RW, Hanayik T, Rorden C, Nielson DM, Rajendra JK, Reynolds RC (2024). A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more. Imaging Neuroscience 2: 1–39. doi: 10.1162/imag_a_00246 [doi]

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http://echo.ismrm.org/p/ISMRM2026/668-03-010