15.1. Citations and references

If you make use of AFNI and its tools in your work, we ask that you please cite the main paper and any accompanying items as appropriate.

15.1.1. AFNI software package

If you use AFNI in your work, please cite:

15.1.2. Methods: General functionality

If you use the realtime functionality from AFNI, please refer to:

If you use (fast) ANATICOR to de-noise FMRI datasets, such as in afni_proc.py, please refer to:

  • Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage. 2010;52(2):571-582. doi:10.1016/j.neuroimage.2010.04.246
    https://pubmed.ncbi.nlm.nih.gov/20420926/
  • Jo HJ, Reynolds RC, Gotts SJ, Handwerker DA, Balzekas I, Martin A, Cox RW, Bandettini PA (2020). Fast detection and reduction of local transient artifacts in resting-state fMRI. Comput Biol Med 120:103742.
    https://pubmed.ncbi.nlm.nih.gov/32421647/

If you use InstaCorr to investigate your data (it is definitely fun and even highly probably informative), please refer to:

  • 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
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5626465/

If you use 3ddelay, please refer to:

If you use 3dSeg for segmentation, please check out:

... and if you want to read more about automatic segmentation using other classification methods, with application to a variety of healthy brains, as well as those with disease, severe atrophy and lesions, please see:

  • Selvaganesana K, Whitehead E, DeAlwis PM, Schindler MK, Inati S Saad ZS, Ohayona JE, Cortese ICM, Smith B, Jacobson S, Nath A, Reich DS, Inati S, Nair G (2019). Robust, atlas-free, automatic segmentation of brain MRI in health and disease. Heliyon 5(2): e01226. doi: 10.1016/j.heliyon.2019.e01226
    https://www.sciencedirect.com/science/article/pii/S2405844018354860

If you use 3dReHo, 3dNetCorr, 3dRSFC, 3dLombScargle (yes, really a program), 3dAmpToRSFC, 3dSpaceTimeCorr, and/or 3dSliceNDice, please refer to:

If you use dcm2niix_afni in your processing, which is a copy of the program dcm2niix kindly contributed by Chris Rorden, please cite:

  • Li X, Morgan PS, Ashburner J, Smith J, Rorden C (2016). The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods. 264:47-56. doi: 10.1016/j.jneumeth.2016.03.001. PMID: 26945974
    https://github.com/rordenlab/dcm2niix

If you use 3dPFM for “paradigm free mapping” to identify brief BOLD events (order of sec) in fMRI time series without prior knowledge of their timing, please cite:

  • Caballero-Gaudes C, Petridou N, Dryden IL, Bai L, Francis ST, Gowland PA (2011). Detection and characterization of single-trial fMRI bold responses: Paradigm free mapping. Hum Brain Mapp, 32(9):1400-18.
    http://dx.doi.org/10.1002/hbm.21116

  • Caballero-Gaudes C, Petridou N, Francis ST, Dryden IL, Gowland PA (2013). Paradigm Free Mapping with Sparse Regression Automatically detects Single-Trial Functional Magnetic Resonance Imaging Blood Oxygenation Level Dependent Responses. Hum Brain Mapp 34(3):501-18.
    http://dx.doi.org/10.1002/hbm.21452

  • Comment: this is specifically for applying PFM to resting state data:
    Petridou N, Caballero-Gaudes C, Dryden IL, Francis ST Gowland PA (2013). Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity. Hum Brain Mapp 34(6):1319-29.
    http://dx.doi.org/10.1002/hbm.21513

If you use 3dMEPFM for multi-echo “paradigm free mapping”, please cite:

  • Caballero-Gaudes C, Moia S, Panwar P, Bandettini PA, Gonzalez-Castillo J (2019). A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping. NeuroImage 202:116081.
    https://pubmed.ncbi.nlm.nih.gov/31419613/

If you are interested in using population receptive fields (pRFs) in your analysis (e.g., via 3dNLfim), please check out tools for their analysis in AFNI here:

  • Silson EH, Reynolds RC, Kravitz DJ, Baker CI (2018). Differential Sampling of Visual Space in Ventral and Dorsal Early Visual Cortex. J Neurosci 38:2294–2303.
    https://www.jneurosci.org/content/38/9/2294

  • Silson EH, Chan AW, Reynolds RC, Kravitz DJ, Baker CI (2015). A retinotopic basis for the division of high-level scene processing between lateral and ventral human occipitotemporal cortex. J Neurosci 35:11921–11935.
    https://europepmc.org/article/pmc/pmc4549403

  • Silson EH, Groen II, Kravitz DJ, Baker CI (2016). Evaluating the correspondence between face-, scene-, and object-selectivity and retinotopic organization within lateral occipitotemporal cortex. J Vis 16(6):14, 1–21.
    https://pubmed.ncbi.nlm.nih.gov/27105060/

If you are interested in multiecho fMRI (see also the afni_proc.py help page for ways to process this kind of data conveniently), please see:

  • Kundu P, Brenowitz ND, Voon V, Worbe Y, Vertes PE, Inati SJ, Saad ZS, Bandettini PA, Bullmore ET (2013). Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proc Natl Acad Sci USA. 110:16187–92.
    https://pubmed.ncbi.nlm.nih.gov/24038744/

If you use the program PTA for profile-tracking analysis (PTA) to estimate nonlinear trajectories, trends or profiles through smoothing splines; or, if you use 3dMSS for multilevel smoothing splines at the population-level, please refer to:

  • Chen G, Nash TA, Reding KM, Kohn PD, Wei S-M, Gregory MD, Eisenberg DP, Cox RW, Berman KF, Kippenhan JS (2020). Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies. NeuroImage 233:117891.
    https://pubmed.ncbi.nlm.nih.gov/33667672/

If you are using AFNI’s defacing/refacing tool @afni_refacer_run (which has a tutorial page here), please check out its presentation at OHBM-2020:

  • Cox RW, Taylor PA (2020). Why de-face when you can re-face? Presented at the 26th Annual Meeting of the Organization for Human Brain Mapping.
    AFNI_reface_OHBM2020.pdf

  • Comment: you might also be interested in this independent evaluation that found @afni_refacer_run to be the overall best among the tested/currently available defacing/refacing tools:
    Theyers AE, Zamyadi M, O’Reilly M, Bartha R, Symons S, MacQueen GM, Hassel S, Lerch JP, Anagnostou E, Lam RW, Frey BN, Milev R, Müller DJ, Kennedy SH, Scott CJM, Strother SC, and Arnott SR (2021). Multisite Comparison of MRI Defacing Software Across Multiple Cohorts. Front. Psychiatry 12:617997. doi: 10.3389/fpsyt.2021.617997
    https://www.frontiersin.org/articles/10.3389/fpsyt.2021.617997/full

For information on different ways to estimate thickness measures (e.g., cortical thickness), please check out this presentation from OHBM-2018:

  • Glen D, Taylor PA, Seidlitz J, Glen M, Liu C, Molfese P, Reynolds R, (2018). Through Thick and Thin: Measuring Thickness in MRI with AFNI. Presented at the 24th Annual Meeting of the Organization for Human Brain Mapping.
    OHBM_2018_Thickness.pdf

If you are interested in calculating degree centrality (DC) and local functional density (lFCD), consider checking out 3dDegreeCentrality and 3dLFCD, respectively, and please see:

If you are interested in edge detection and visualization in volumetric data, consider checking out 3dedgedog and please see:

  • Rorden C, Newman-Norlund R, Drake C, Glen DR, Fridriksson J, Hanayik T, Taylor PA (2024). Improving 3D Edge Detection for Visual Inspection of MRI Coregistration and Alignment. J Neurosci Methods. 406:110112. doi: 10.1016/j.jneumeth.2024.110112.
    https://pubmed.ncbi.nlm.nih.gov/38508496/

If you are interested in modeling a detailed, voxelwise hemodynamic response function (HRF) without assuming a constant+canonical shape and with useful regularization, then see:

  • Chen G, Taylor PA, Reynolds RC, Leibenluft E, Pine DS, Brotmas MA, Pagliaccio D, Haller SP (2023). BOLD response is more than just magnitude: improving detection sensitivity through capturing hemodynamic profiles. Neuroimage 277:120224.
    https://pubmed.ncbi.nlm.nih.gov/37327955/

If you want to include physio data like cardiac and respiratory measures, consider checking out physio_calc.py, which includes QC images and even and interactive mode for fixing peak/trough estimation:

  • Lauren PD, Glen DR, Reynolds RC, Taylor PA (2023). physio_calc.py: New program to model cardiac & respiratory contributions to BOLD signal in AFNI. Presented at the 29th Annual Meeting of the Organization for Human Brain Mapping.
    ohbm_2023_PeterLauren.pdf

  • Lauren PD, Glen DR, Reynolds RC, Dean JN, Handwerker DA, Taylor PA (2024). New in AFNI’s physio_calc.py (for FMRI physio regressors): QC images, reports and interactive mode. Presented at the 30th Annual Meeting of the Organization for Human Brain Mapping.
    ohbm_2024_taylor_etal_physio_calc_updates.pdf

15.1.3. Methods: SUMA

If you use SUMA in your work, such as for surface calculations and/or visualizations, please refer to:

  • Saad ZS, Reynolds RC, Argall B, Japee S, Cox RW (2004). SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI, in: 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821). Presented at the 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821), pp. 1510-1513 Vol. 2. doi.org/10.1109/ISBI.2004.1398837
    https://ieeexplore.ieee.org/document/1398837

If you use standard meshes within your surface/SUMA analysis, please check out:

If you want to learn about AFNI+SUMA results on the FIAC dataset, please see:

If you use SUMA’s clipping plane and/or the SurfLayers functionality, please refer to:

  • Torrisi S, Lauren P, Taylor PA, Park S, Feinberg D, Glen DR (2021). Creating Layered Surfaces to Visualize with AFNI + SUMA, with applications to laminar fMRI. Presented at the 27th Annual Meeting of the Organization for Human Brain Mapping.
    OHBM2021_SurfLayers_v2.pdf

  • Lauren P, Glen DR, Reynolds RC, Torrisi S, Taylor PA (2022). Using Clipping PLanes to Analyze Brain Data in SUMA. Presented at the 28th Annual Meeting of the Organization for Human Brain Mapping.
    OHBM2022_lauren_clippingPlanes.pdf

15.1.4. Methods: FMRI processing and pipelines

NB: there are also several examples of various processing pipeslines for full projects and papers here:
The Codex: AFNI Code Examples.

Do you like processing FMRI data? Like, for either single-or multi-echo FMRI, on a surface or a volume, for ROI-based or voxelwise analysis, using physio regressors or B0 inhomogeneity distortion, and all with an interactive quality control HTML report? If so, please check out this description of using afni_proc.py, which also contains various tips, suggestions, scripts, option guidelines, and more:

  • 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

  • Comment: check out the associated demo that runs the code described in the above paper. Details, including how to download the unprocessed data with scripts, is described on this GitHub README page. A copy of all afni_proc.py results directories from the paper and demo are available on this OSF page: https://osf.io/gn7b5/.

For an earlier discussion of several choices of FMRI processing with afni_proc.py, please check out the following:

  • Taylor PA, Chen G, Glen DR, Rajendra JK, Reynolds RC, Cox RW (2018). FMRI processing with AFNI: Some comments and corrections on ‘Exploring the Impact of Analysis Software on Task fMRI Results’. bioRxiv 308643; doi:10.1101/308643
    https://www.biorxiv.org/content/10.1101/308643v1.abstract

For an example of using afni_proc.py to process non-human data, please see:

  • Jung B, Taylor PA, Seidlitz PA, Sponheim C, Perkins P, Ungerleider LG, Glen DR, Messinger A (2021). A Comprehensive Macaque FMRI Pipeline and Hierarchical Atlas. NeuroImage 235:117997.
    https://pubmed.ncbi.nlm.nih.gov/33789138/

For an example of using afni_proc.py to process multiecho FMRI data (with an accompanying demo with scripts and data that can be downloaded by running @Install_APMULTI_Demo1_rest), please see:

  • Taylor PA, Gotts SJ, Gilmore AW, Teves J, Reynolds RC (2022). A multi-echo FMRI processing demo including TEDANA in afni_proc.py pipelines. Proc. OHBM-2022.
    OHBM2022_tayloretal_apmulti.pdf

15.1.5. Methods: Quality control (QC)

To see a description of several QC tools in AFNI—including afni_proc.py's APQC HTML, gen_ss_review_table.py and gtkyd_check-– please check out:

  • 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
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11382598/
  • Comment: it is also worth checking out this fun, online demo of the APQC HTML and some of its interactive functionality, described in the above paper:
    https://afni.github.io/qc-demo-repo/

For detailed examples and descriptions of investigating the quality of your FMRI data, including using the afni_proc.py quality control (APQC) HTML report and gen_ss_review_table.py, please check out:

The above article was created as part of a Research Topic on demonstrating quality control in FMRI. The Editorial for that Project—with a description of its inception, a summary of its contributions and some recommendations for moving forward—is here:

If you are interested in detailed QC discussions in FMRI, please see the following Frontiers Research Topic project page and related public data for download:

  • Taylor PA, Etzel JA, Glen D, Reynolds RC (2022). Demonstrating Quality Control (QC) Procedures in fMRI.
    Research Topic homepage

  • Taylor PA, Etzel JA, Glen D, Reynolds RC, Moraczewski D, Basavaraj A (2022). FMRI Open QC Project. DOI 10.17605/OSF.IO/QAESM
    https://osf.io/qaesm/

If you use the left-right flip checking for consistency in your MRI data (and you should!), please see:

  • Glen DR, Taylor PA, Buchsbaum BR, Cox RW, Reynolds RC (2020). Beware (Surprisingly Common) Left-Right Flips in Your MRI Data: An Efficient and Robust Method to Check MRI Dataset Consistency Using AFNI. Front. Neuroinformatics 14. doi.org/10.3389/fninf.2020.00018
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7263312/

15.1.6. Methods: Group analysis, stats and clustering

If you use either of the linear mixed effects (LME) modeling programs 3dLME or 3dLMEr in your work, please refer to:

If you use multivariate modeling (MVM) program 3dMVM in your work (or if you want to learn more about within-group centering, which is also discussed more here), please cite/check out:

  • Chen G, Adleman NE, Saad ZS, Leibenluft E, Cox RW (2014). Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model. NeuroImage 99:571-588.
    https://pubmed.ncbi.nlm.nih.gov/24954281/

If you use the mixed effects meta analysis (MEMA) program 3dMEMA in your work, please refer to:

If you use the Bayesian multilevel (BML) modeling approach for matrix-based analysis with the MBA program, please refer to:

If you use the Bayesian Multilevel (BML) modeling approach for region-based analysis with the RBA program, please refer to:

  • Chen G, Xiao Y, Taylor PA, Rajendra JK, Riggins T, Geng F, Redcay E, Cox RW (2019). Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling. Neuroinformatics. 17(4):515-545. doi:10.1007/s12021-018-9409-6
    https://pubmed.ncbi.nlm.nih.gov/30649677/

If you adopt the trial-level modeling approach at the subject level followed by multilevel modeling (Bayesian, of course) at the population level, please refer to:

If you perform test-rest reliability analysis with the TRR program (or 3dLMEr -TRR ..), please refer to:

If you use IntraClass Correlation (ICC) methods within AFNI via 3dICC, please refer to:

  • Chen G, Taylor PA, Haller SP, Kircanski K, Stoddard J, Pine DS, Leibenluft E, Brotman MA, Cox RW (2018). Intraclass correlation: Improved modeling approaches and applications for neuroimaging. Hum Brain Mapp. 2018;39(3):1187-1206.
    https://pubmed.ncbi.nlm.nih.gov/29218829/

If you use 3dISC for inter-subject correlation, please refer to:

  • Chen G, Taylor PA, Shin YW, Reynolds RC, Cox RW (2017). Untangling the Relatedness among Correlations, Part II: Inter-Subject Correlation Group Analysis through Linear Mixed-Effects Modeling. Neuroimage 147:825-840.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5303634/

For an ROI-based approach through Bayesian multilevel (BML) modeling to ISC (inter-subject correlation) and naturalistic FMRI, please check out:

  • Chen G, PA Taylor, Qu X, Molfese PJ, Bandettini PA, Cox RW, Finn ES (2020). Untangling the Relatedness among Correlations, Part III: Inter-Subject Correlation Analysis through Bayesian Multilevel Modeling for Naturalistic Scanning. NeuroImage 216:116474. doi:10.1016/j.neuroimage.2019.116474
    https://pubmed.ncbi.nlm.nih.gov/31884057/

For a nonparametric (voxelwise) approach to ISC (inter-subject correlation) and naturalistic FMRI, you might want to check out:

  • Chen GC, Shin Y-W, Taylor PA, Glen DR, Reynolds RC, Israel RB, Cox RW (2016). Untangling the Relatedness among Correlations, Part I: Nonparametric Approaches to Inter-Subject Correlation Analysis at the Group Level. Neuroimage 142:248-259. doi:10.1016/j.neuroimage.2016.05.023
    https://pubmed.ncbi.nlm.nih.gov/27195792/

If you want to learn more about building your data and modeling it in a systematic way—in particular, how to know what kind of covariates to collect and/or include—please check out this introduction to causal inference (it contains both concepts and helpful guidelines):

If you use 1dSVAR (Structured Vector AutoRegression), please refer to:

  • Chen G, Glen DR, Saad ZS, Hamilton JP, Thomason ME, Gotlib IH, Cox RW (2011). Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis. Comput Biol Med 41(12):1142-55. doi: 10.1016/j.compbiomed.2011.09.004.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3223325/

If you use clustering approaches such as 3dClustSim, 3dttest++ -Clustsim, and/or the mixed autocorrelation function (ACF) smoothness estimation in your work, please refer to:

  • Cox RW, Chen G, Glen DR, Reynolds RC, Taylor PA (2017). fMRI clustering and false-positive rates. Proc Natl Acad Sci USA. 114(17):E3370-E3371. doi:10.1073/pnas.1614961114
    https://pubmed.ncbi.nlm.nih.gov/28420798/

  • Cox RW, Chen G, Glen DR, Reynolds RC, Taylor PA (2017). FMRI Clustering in AFNI: False-Positive Rates Redux. Brain Connect 7(3):152-171. doi: 10.1089/brain.2016.0475.
    https://pubmed.ncbi.nlm.nih.gov/28398812/

If you use the equitable thresholding and clustering (ETAC) method in your work, please refer to:

  • Cox RW (2017). Equitable Thresholding and Clustering: A Novel Method for Functional Magnetic Resonance Imaging Clustering in AFNI. 9(7):529-538. doi: 10.1089/brain.2019.0666.
    https://pubmed.ncbi.nlm.nih.gov/31115252/

If you use the FAT-MVM approach to group analysis (combining FATCAT and multivariate modeling with 3dMVM), please refer to (as well as the main FATCAT paper, above):

  • Taylor PA, Jacobson SW, van der Kouwe A, Molteno CD, Chen G, Wintermark P, Alhamud A, Jacobson JL, Meintjes EM (2015). A DTI-based tractography study of effects on brain structure associated with prenatal alcohol exposure in newborns. Hum Brain Mapp. 36(1):170-186. doi:10.1002/hbm.22620
    https://pubmed.ncbi.nlm.nih.gov/25182535/

  • Chen G, Adleman NE, Saad ZS, Leibenluft E, Cox RW (2014). Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model. NeuroImage 99:571-588.
    https://pubmed.ncbi.nlm.nih.gov/24954281/

  • Taylor PA, Chen G, Cox RW, Saad ZS (2016). Open Environment for Multimodal Interactive Connectivity Visualization and Analysis. Brain Connect. 6, 109–121. doi.org/10.1089/brain.2015.0363
    https://pubmed.ncbi.nlm.nih.gov/26447394/

15.1.7. Methods: Alignment

If you use either the local Pearson correlation (lpc) or local Pearson absolute (lpa) cost function in your alignment (e.g., with 3dAllineate, align_epi_anat.py, afni_proc.py, 3dQwarp, @SSwarper, sswarper2, @animal_warper, etc.), please refer to:

  • Saad ZS, Glen DR, Chen G, Beauchamp MS, Desai R, Cox RW (2009). A new method for improving functional-to-structural MRI alignment using local Pearson correlation. Neuroimage 44 839–848. doi: 10.1016/j.neuroimage.2008.09.037
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649831/

If you use nonlinear warping in AFNI, in particular 3dQwarp, please refer to:

If you use sswarper2 (which should provide similar or slightly improved results to its predecessor @SSwarper, with which it shares mostly similar usage and output format), please refer to:

  • Taylor PA, Reynolds RC, Glen DR (2024). Improving skullstripping and nonlinear warping in AFNI: sswarper2. Presented at the 30th Annual Meeting of the Organization for Human Brain Mapping.
    hbm_2024_taylor_etal_ssw2.pdf

If you use @animal_warper (esp. for alignment in animal studies), please refer to:

  • Jung B, Taylor PA, Seidlitz PA, Sponheim C, Perkins P, Ungerleider LG, Glen DR, Messinger A (2021). A Comprehensive Macaque FMRI Pipeline and Hierarchical Atlas. NeuroImage 235:117997.
    https://pubmed.ncbi.nlm.nih.gov/33789138/

  • Saad ZS, Glen DR, Chen G, Beauchamp MS, Desai R, Cox RW (2009). A new method for improving functional-to-structural MRI alignment using local Pearson correlation. Neuroimage 44 839–848. doi: 10.1016/j.neuroimage.2008.09.037
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649831/

15.1.8. Methods: Diffusion, DWI, DTI and HARDI

If you use the diffusion/DWI/DTI tools in AFNI, please cite the main FATCAT paper:

If you use mini-probabilistic tracking and/or SUMA tract visualization, please refer to (as well as the main FATCAT and SUMA papers, above):

  • Taylor PA, Chen G, Cox RW, Saad ZS (2016). Open Environment for Multimodal Interactive Connectivity Visualization and Analysis. Brain Connect. 6, 109–121. doi.org/10.1089/brain.2015.0363
    https://pubmed.ncbi.nlm.nih.gov/26447394/

If you use probabilistic or deterministic tractography in your work with 3dTrackID, please refer to (as well as the main FATCAT paper, above):

If you want to learn more about ways to reduce motion effects in DWI/DTI data (including using volumetric navigators during acquisition, and looking at how different software behave at different levels of motion), please check out:

  • Taylor PA, Alhamud A, van der Kouwe A, Saleh MG, Laughton B, Meintjes E (2016). Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction. Hum Brain Mapp 37(12):4405-4424.
    https://pubmed.ncbi.nlm.nih.gov/27436169/

Several of the AFNI/FATCAT demos (for example, this one) also include using the TORTOISE package for accompanying diffusion-based processing, including running DIFFPREP, DR_BUDDI, etc. If using TORTOISE is of interest, then please check out:

  • The TORTOISE homepage, which contains links to code, the package’s message board, and further reading.

15.1.9. Methods: Additional applications

If you use DBSproc (for Deep Brain Stimulation processing), please cite:

  • Lauro PM, Vanegas-Arroyave N, Huang L, Taylor PA, Zaghloul KA, Lungu C, Saad ZS, Horovitz SG (2016). DBSproc: An open source process for DBS electrode localization and tractographic analysis. Hum Brain Mapp. 37(1):422-433. doi:10.1002/hbm.23039
    https://pubmed.ncbi.nlm.nih.gov/26523416/

If you use ALICE (Automatic Localization of Intra-Cranial Electrodes; an interface for the alignment of datasets, clustering and ordering of electrodes for ECOG and SEEG and reprojection to the brain surface using CT and MRI imaging), please cite:

  • Branco MP, Gaglianese A, Glen DR, Hermes D, Saad ZS, Petridou N, Ramsey NF (2018). ALICE: a tool for automatic localization of intra-cranial electrodes for clinical and high-density grids. J. Neurosci. Methods 301, 43–51. doi: 10.1016/j.jneumeth.2017.10.022
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952625/

A method using AFNI to model dynamic contrast enhanced (DCE) MRI for analysis of brain tumors:

  • Sarin H, Kanevsky AS, Fung SH, Butman JA, Cox RW, Glen D, Reynolds R, Auh S (2009). Metabolically stable bradykinin B2 receptor agonists enhance transvascular drug delivery into malignant brain tumors by increasing drug half-life. J Transl Med 7:33. doi:10.1186/1479-5876-7-33
    https://pubmed.ncbi.nlm.nih.gov/19439100/

A numerical method for measuring symmetry in brain FMRI data:

  • Jo HJ, Saad ZS, Gotts SJ, Martin A, Cox RW (2012). Quantifying agreement between anatomical and functional interhemispheric correspondences in the resting brain. PLoS One 7:e48847. doi: 10.1371/journal.pone.0048847
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3493608/

... and if you are still curious about symmetry in the brain, check out this paper for methodology:

  • Gotts SJ, Jo HJ, Wallace GL, Saad ZS, Cox RW, Martin A (2013). Two distinct forms of functional lateralization in the human brain. Proc Natl Acad Sci USA. 110(36):E3435-E3444. doi:10.1073/pnas.1302581110
    https://pubmed.ncbi.nlm.nih.gov/23959883/

If you are curious about using multiecho/MEICA FMRI, please see:

  • Kundu P, Brenowitz ND, Voon V, Worbe Y, Vertes PE, Inati SJ, Saad ZS, Bandettini PA, Bullmore ET (2013). Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proc Natl Acad Sci USA. 110(40):16187-16192. doi:10.1073/pnas.1301725110
    https://pubmed.ncbi.nlm.nih.gov/24038744/

If you are curious about estimating slice-based motion correction in FMRI, please see recent updates to SLOMOCO (Beall and Lowe, 2014), which uses a large amount of AFNI functionality under the hood, here:

15.1.10. Meta-methodology, commentary and validations

If you want to note the good performance of AFNI’s time series autocorrelation modeling (3dREMLfit) compared with other software, you might consider reading:

If you want to note the good performance of AFNI’s defacing/refacing tool @afni_refacer_run, you can check out this independent study that found it to be the overall best among currently available refacing/defacing tools:

  • Theyers AE, Zamyadi M, O’Reilly M, Bartha R, Symons S, MacQueen GM, Hassel S, Lerch JP, Anagnostou E, Lam RW, Frey BN, Milev R, Müller DJ, Kennedy SH, Scott CJM, Strother SC, and Arnott SR (2021). Multisite Comparison of MRI Defacing Software Across Multiple Cohorts. Front. Psychiatry 12:617997. doi: 10.3389/fpsyt.2021.617997
    https://www.frontiersin.org/articles/10.3389/fpsyt.2021.617997/full

If you want to note the good performance of AFNI’s volume registration for motion correction with 3dvolreg, you might consider viewing:

  • Oakes TR, Johnstone T, Ores Walsh KS, Greischar LL, Alexander AL, Fox AS, Davidson RJ (2005). Comparison of fMRI motion correction software tools. Neuroimage. 28(3):529-543. doi:10.1016/j.neuroimage.2005.05.058
    https://pubmed.ncbi.nlm.nih.gov/16099178/

If you want to know about spatial smoothness estimation and resampling stability in AFNI, have a gander at:

  • Cox RW, Taylor PA (2017). Stability of spatial smoothness and cluster-size threshold estimates in FMRI using AFNI. arXiv:1709.07471 [stat.AP]
    https://arxiv.org/abs/1709.07471

If you use proper statistical testing in your work (two-sided testing in most cases, or one-sided testing where clearly applicable), you might consider checking out:

  • Chen G, Cox RW, Glen DR, Rajendra JK, Reynolds RC, Taylor PA (2019). A tail of two sides: Artificially doubled false positive rates in neuroimaging due to the sidedness choice with t-tests. Human Brain Mapping 40:1037-1043.
    https://pubmed.ncbi.nlm.nih.gov/30265768/

If you display effect estimates (rather than just stats), and/or if you scale your data in a voxelwise manner, you might consider referring to:

If you’d like to display more full results with transparent thresholding (rather than hiding away much information with all-or-nothing thresholding), then check out:

  • 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. doi: 10.1016/j.neuroimage.2023.120138
    https://pubmed.ncbi.nlm.nih.gov/37116766/

If you are curious about how to deal with multiplicity issues in your statistical analysis of MRI, consider this discussion of neighborhood leverage (new!) vs global calibration (old!) with a Bayesian multilevel (BML) approach:

  • Chen G, Taylor PA, Cox RW, Pessoa L. Fighting or embracing multiplicity in neuroimaging? neighborhood leverage versus global calibration. Neuroimage. 2020;206:116320. doi:10.1016/j.neuroimage.2019.116320
    https://pubmed.ncbi.nlm.nih.gov/31698079/

If you would like an overview of many methods for denoising BOLD FMRI data (including phase-based and multi-echo FMRI approaches), as well as practical recommendations for preprocessing pipelines, consider reading:

We illustrate that the trial sample size in experimental design is almost as important as subject sample size, in terms of statistical efficiency. Here we investigate the crucial role of trial number in neuroimaging from the perspectives of both statistical efficiency and condition-level generalizability:

  • Chen G, Pine DS, Brotman MA, Smith AR, Cox RW, Taylor PA, Haller SP (2022). Hyperbolic trade-off: the importance of balancing trial and subject sample sizes in neuroimaging. NeuroImage 247:118786.
    https://pubmed.ncbi.nlm.nih.gov/34906711/

In this commentary, we suggest: 1) adopting a modeling approach through accurately mapping the data hierarchy; 2) incorporating the spatial information across the brain; and 3) avoiding information over-reduction in result reporting:

  • Chen G, Taylor PA, Stoddard J, Cox RW, Bandettini PA, Pessoa L (2022). Sources of information waste in neuroimaging: mishandling structures, thinking dichotomously, and over-reducing data. Aperture Neuro. 2: DOI: 10.52294/2e179dbf-5e37-4338-a639-9ceb92b055ea
    https://doi.org/10.52294/ApertureNeuro.2022.2.ZRJI8542

For work checking out different methods of diffusion/DWI acquisition and correction, such as prospective motion correction and the TORTOISE toolbox, particularly in the case where subjects move (kids these days…), then please check out:

  • Taylor PA, Alhamud A, van der Kouwe A, Saleh MG, Laughton B, Meintjes E (2016). Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction. Hum. Brain Mapp. 37, 4405–4424. doi: 10.1002/hbm.23318
    https://pubmed.ncbi.nlm.nih.gov/27436169/

If you want to learn about AFNI+SUMA results on the FIAC dataset, please see:

If you want to find out more about modeling the hemodynamic response in FMRI (particularly with using multivariate and linear mixed-effects modeling), then please see:

If you want to read about getting better tissue contrast in your EPI images (particularly with flip angle selection, among other factors), please see:

  • Gonzalez-Castillo J, Duthie KN, Saad ZS, Chu C, Bandettini PA, Luh W-M (2013). Effects of image contrast on functional MRI image registration. Neuroimage 67:163-74. doi: 10.1016/j.neuroimage.2012.10.07
    https://pubmed.ncbi.nlm.nih.gov/23128074/

Re. Global signal regression (noooo…)

For papers discussing global signal regression (GSR), and several reasons why not to do it (note: there are many other papers by other groups that show this as well…), as well as the proposal to use GCOR as an alternative, please check out/reference:

  • Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ, Martin A, Cox RW (2012). Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression. Brain Connectivity 2(1):25-32. doi: 10.1089/brain.2012.0080
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3484684/

  • Comment: this is the “GCOR” (global correlation) parameter paper:
    Saad ZS, Reynolds RC, Jo HJ, Gotts SJ, Chen G, Martin A, Cox RW (2013). Correcting Brain-Wide Correlation Differences in Resting-State FMRI. Brain Connectivity 3(4):339-352. doi: 10.1089/brain.2013.0156
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3749702/

  • Jo HJ, Gotts SJ, Reynolds RC, Bandettini PA, Martin A, Cox RW, Saad ZS (2013). Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. Journal of Applied Mathematics: art.no. 935154.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886863/

  • Gotts SJ, Saad ZS, Jo HJ, Wallace GL, Cox RW, Martin A (2013). The perils of global signal regression for group comparisons: A case study of Autism Spectrum Disorders. Front. Hum. Neurosci. 7:356. doi: 10.3389/fnhum.2013.00356
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709423/

  • Gotts SJ, Simmons WK, Milbury LA, Wallace GL, Cox RW, Martin A (2012). Fractionation of Social Brain Circuits in Autism Spectrum Disorders. Brain, 135: 2711-2725.
    https://pubmed.ncbi.nlm.nih.gov/22791801/

  • Caballero-Gaudes C, Reynolds RC (2017). Methods for cleaning the BOLD fMRI signal. Neuroimage 154:128-149. doi: 10.1016/j.neuroimage.2016.12.018
    https://pubmed.ncbi.nlm.nih.gov/27956209/

Code history

If you want to know more about AFNI and its development and underpinnings, please see:

If you want to know more about SUMA and its development and underpinnings, please see the pithily titled:

15.1.11. Data projects: human templates and atlases

India Brain Template (IBT). We present a series of five age-specific brain templates and accompanying atlases (IBTAs), spanning an age range of 6-60 years. These templates and atlases were created from a large number of subjects (total n=466), spanning a large number of different Indian states and and acquired at multiple 3T MRI sites, using a new AFNI tool called make_template_dask.py:

  • Holla B, Taylor PA, Glen DR, Lee JA, Vaidya N, Mehta UM, Venkatasubramanian G, Pal P, Saini J, Rao NP, Ahuja C, Kuriyan R, Krishna M, Basu D, Kalyanram K, Chakrabarti A, Orfanos DP, Barker GJ, Cox RW, Schumann G, Bharath RD, Benegal V (2020). A series of five population-specific Indian brain templates and atlases spanning ages 6 to 60 years. Hum Brain Mapp 41(18):5164-5175.
    https://pubmed.ncbi.nlm.nih.gov/32845057/

Haskins pediatric atlas. The Haskins pediatric templates and atlases were generated with nonlinear methods using structural MRI from 72 children (age range 7-14 years, median 10 years), allowing for a detailed template with corresponding parcellations of labeled atlas regions. The accuracy of these templates and atlases was assessed using multiple metrics of deformation distance and overlap:

  • Molfese PJ, Glen D, Mesite L, Cox RW, Hoeft F, Frost SJ, Mencl WE, Pugh KR, Bandettini PA (2020). The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas. Pediatric Radiology 51(4):628-639. DOI: 10.1007/s00247-020-04875-y.
    https://pubmed.ncbi.nlm.nih.gov/33211184/

Schaefer-Yeo Atlases. The original set of Schaefer-Yeo atlases (2018) have seen wide usage partly because the multi-scale resolution of this atlas has made it flexible for a variety of studies and for its ready usage for network analysis. This project improves some features of both the volumetric and surface atlases in terms of the spatial contiguity of the regions, removal of the jagged edges, placement on the higher resolution grid and better correspondence to the improved template space of the MNI 2009c volume. Furthermore, the standard mesh versions of the surface atlases allow for propagation into subject- specific native space via each subject’s FreeSurfer registration and SUMA by enforcing spatial correspondence across subjects:

15.1.12. Data projects: animal templates and atlases

Multimodal Marmoset resource. This project provides a new resource for marmoset brain mapping, which integrates the largest awake resting-state fMRI dataset to date (39 marmosets, 709 runs, and 12053 mins), cellular- level neuronal-tracing dataset (52 marmosets and 143 injections), and multi-resolution diffusion MRI dataset:

  • Tian X, Chen Y, Majka P, Szczupak D, Perl YS, Yen CC, Tong C, Song K, Jiang H, Glen D, Deco G, Rosa MGP, Silva AC, Liang Z, Liu C (2022). Integrated resource for functional and structural connectivity of the marmoset brain. Nat Commun 13(1):7416. doi: 10.1038/s41467-022-35197-2.
    https://pubmed.ncbi.nlm.nih.gov/36456558/

Marmoset atlas v3. This project provides new population-based in-vivo standard templates and tools derived from multi-modal data of 27 marmosets, including multiple types of T1w and T2w contrast images, DTI contrasts, large field-of-view MRI and CT images, atlases and surfaces:

Marmoset atlas v2. This project provides some of the highest resolution nonhuman primate MRI templates and atlas for gray and white matter with multi-modal MRI imaging at 0.150 mm, 0.060 mm, 0.080 mm and 0.050 mm spatial resolution:

  • Liu C, Ye FQ, Newman JD, Szczupak D, Tian X, Yen CC, Majka P, Glen D, Rosa MGP, Leopold DA, Silva AC (2020). A resource for the detailed 3D mapping of white matter pathways in the marmoset brain. Nat Neurosci 23(2):271-280. doi: 10.1038/s41593-019-0575-0.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007400/

Marmoset atlas v1: NIH Marmoset. This atlas introduces a high-resolution template and atlas for cortical gray matter at 0.150 mm (see also the marmoset atlas v2, above):

D99 atlas. Based on the Saleem macaque atlas, this project introduces a high resolution digital MRI template together with new meticulous delineations of macaque cortical regions:

  • Reveley C, Gruslys A, Ye FQ, Glen D, Samaha J, E Russ B, Saad Z, K Seth A, Leopold DA, Saleem KS (2017). Three-Dimensional Digital Template Atlas of the Macaque Brain. Cereb Cortex 27(9):4463-4477. doi: 10.1093/cercor/bhw248.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6075609/

NMT v1: Macaque brain group template. Using the data from 31 macaques, this template provides a high resolution group template for macaques at 0.250 mm (this is NMT v1; see below for NMT v2):

  • Seidlitz J, Sponheim C, Glen DR, Ye FQ, Saleem KS, Leopold DA, Ungerleider L, Messinger A (2018). A Population MRI Brain Template and Analysis Tools for the Macaque. NeuroImage 170: 121–31. doi: 10.1016/j.neuroimage.2017.04.063.
    https://pubmed.ncbi.nlm.nih.gov/28461058/

NMT v2 and CHARM: Macaque brain group template and hierarchical cortical atlas. This project introduces version the macaque template NMT v2 using a stererotaxic (ear-bar-zero) reference frame and a hierarchical atlas (CHARM) for structural region labels (and see these pages for more information about the related templates and atlases and task and rest FMRI Demos):

  • Jung B, Taylor PA, Seidlitz PA, Sponheim C, Perkins P, Ungerleider LG Glen DR, Messinger A (2021). A Comprehensive Macaque FMRI Pipeline and Hierarchical Atlas. NeuroImage 235:117997.
    https://pubmed.ncbi.nlm.nih.gov/33789138/

SARM: Hierarchical subcortical atlas. Subcortical Atlas of the Rhesus Macaque (SARM) for structural region labels (and see these pages for more information about the related atlas and related template:

  • Hartig R, Glen D, Jung B, Logothetis NK, Paxinos G, Garza-Villareal EA, Messinger A, Evrard HC (2021). Subcortical Atlas of the Rhesus Macaque (SARM) for neuroimaging. NeuroImage 235:117996.
    https://pubmed.ncbi.nlm.nih.gov/33794360/

PRIME-RE: the PRIMatE Resource Exchange. A collaborative online platform for nonhuman primate (NHP) neuroimaging, including AFNI tools (such as @animal_warper and afni_proc.py applied to macaque datasets; see al Jung et al., 2021, above, and these pages for more information about the related templates and atlases and task and rest FMRI Demos):

  • Messinger A, Sirmpilatze N, Heuer K, Loh K, Mars R, Sein J, Xu T, Glen D, Jung B, Seidlitz J, Taylor P, Toro R, Garza-Villareal E, Sponheim C, Wang X, Benn A, Cagna B, Dadarwal R, Evrard H, Garcia-Saldivar P, Giavasis S, Hartig R, Lepage C, Liu C, Majka P, Merchant H, Milham M, Rosa M, Tasserie J, Uhrig L, Margulies D, Klink PC (2021). A collaborative resource platform for non-human primate neuroimaging. Neuroimage, 226:117519.
    https://pubmed.ncbi.nlm.nih.gov/33227425/

SC21 (subcortical template) and updated D99: high resolution macaque atlasing. Anatomical delineation using histology and high-resolution MAP-MRI (and data are available in NIFTI and GIFTI formats):

  • Saleem KS, Avram AV, Glen D, Yen CC-C, Ye FQ, Komlosh M, Basser PJ (2021). High-resolution mapping and digital atlas of subcortical regions in the macaque monkey based on matched MAP-MRI and histology. Neuroimage 245:118759.
    https://doi.org/10.1016/j.neuroimage.2021.118759

White matter atlas of the domestic canine brain. A WM atlas of the canine brain, derived from DTI tracking and manual segmentation (affectionately and colloquially known as the “CornDog” atlas, given its roots at Cornell University):

15.1.13. Data projects: NIFTI format

For technical reference for the NIFTI data format (which AFNI continues to use, as well as to maintain), you can refer to:

  • Cox RW, Ashburner J, Breman H, Fissell K, Haselgrove C, Holmes CJ, Lancaster JL, Rex DE, Smith SM, Woodward JB, Strother SC (2004). A (sort of) new image data format standard: NiFTI-1. Presented at the 10th Annual Meeting of the Organization for Human Brain Mapping.
    The poster: hbm_nifti_2004.pdf
    NIFTI web docs, such as they are: https://nifti.nimh.nih.gov/
    NIFTI-1 page: https://nifti.nimh.nih.gov/nifti-1