14.1. 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.

14.1.1. AFNI software package

If you use AFNI in your work, please cite:

14.1.2. Methods: General functionality

If you use the realtime functionality from AFNI, please cite:

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

  • 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

For several choices of FMRI processing with afni_proc.py, please check out (and cite, as relevant) 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

If you use ANATICOR to de-noise FMRI datasets (e.g., in afni_proc.py), please cite:

  • 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

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

  • 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

If you use 3ddelay, please cite:

If you use 3dSeg for segmentation, please cite:

... 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

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

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

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.
  • 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.
  • 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.

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.

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.
  • 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.
  • 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.

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.

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 cite:

14.1.3. Methods: SUMA

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

  • 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

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

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

14.1.4. 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 cite:

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.

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

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

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

  • 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

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

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

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

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

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

  • 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

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

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

  • 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.

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

  • 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
  • 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.

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

  • 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.

If you use the FAT-MVM approach to group analysis (combining FATCAT and multivariate modeling with 3dMVM), please cite (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
  • 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.
  • 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

14.1.5. 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, @animal_warper, etc.), please cite:

  • 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

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

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

14.1.6. Methods: Diffusion, DWI, DTI and HARDI

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

... and if you use the TORTOISE package for accompanying diffusion-based processing (such as DIFFPREP, DR_BUDDI, etc.), then please:

If you use mini-probabilistic tracking and/or SUMA tract visualization, please cite (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

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

14.1.7. 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

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

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

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

... 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

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

14.1.8. Meta-methodology, commentary and validations

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

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 tools (also presented at OHBM 2020):

  • 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

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

  • 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

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]

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 citing:

  • 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.

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

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

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:

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:

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:

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

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

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
  • 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
  • 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.
  • 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
  • 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.
  • 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

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:

14.1.9. 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:

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 (in press). DOI: 10.1007/s00247-020-04875-y.

14.1.10. 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. (submitted) bioRxiv 2021.11.12.468389.

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.

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.

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.

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):

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:

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.

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):

14.1.11. Data projects: NIFTI format

For technical reference for the NIFTI data format, you can cite: