**************************************************** ***** This is a list of papers about AFNI, SUMA, ***** ****** and various algorithms implemented therein ****** ---------------------------------------------------------------------------- RW Cox. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29: 162-173, 1996. * The very first AFNI paper, and the one I prefer you cite if you want to refer to the AFNI package as a whole. * https://sscc.nimh.nih.gov/sscc/rwcox/papers/CBM_1996.pdf ---------------------------------------------------------------------------- RW Cox, A Jesmanowicz, JS Hyde. Real-time functional magnetic resonance imaging. Magnetic Resonance in Medicine, 33: 230-236, 1995. * The first paper on realtime FMRI; describes the algorithm used in in the realtime plugin for time series regression analysis. * https://sscc.nimh.nih.gov/sscc/rwcox/papers/Realtime_FMRI.pdf ---------------------------------------------------------------------------- RW Cox, JS Hyde. Software tools for analysis and visualization of FMRI Data. NMR in Biomedicine, 10: 171-178, 1997. * A second paper about AFNI and design issues for FMRI software tools. ---------------------------------------------------------------------------- RW Cox, A Jesmanowicz. Real-time 3D image registration for functional MRI. Magnetic Resonance in Medicine, 42: 1014-1018, 1999. * Describes the algorithm used for image registration in 3dvolreg and in the realtime plugin. * The first paper to demonstrate realtime MRI volume image registration running on a standard workstation (not a supercomputer). * https://sscc.nimh.nih.gov/sscc/rwcox/papers/RealtimeRegistration.pdf ---------------------------------------------------------------------------- ZS Saad, KM Ropella, RW Cox, EA DeYoe. Analysis and use of FMRI response delays. Human Brain Mapping, 13: 74-93, 2001. * Describes the algorithm used in 3ddelay (cf. '3ddelay -help'). * https://sscc.nimh.nih.gov/sscc/rwcox/papers/Delays2001.pdf ---------------------------------------------------------------------------- ZS Saad, RC Reynolds, BD Argall, S Japee, RW Cox. SUMA: An interface for surface-based intra- and inter-subject analysis within AFNI. 2004 IEEE International Symposium on Biomedical Imaging: from Nano to Macro. IEEE, Arlington VA, pp. 1510-1513. * A brief description of SUMA. * https://dx.doi.org/10.1109/ISBI.2004.1398837 * https://sscc.nimh.nih.gov/sscc/rwcox/papers/SUMA2004paper.pdf ---------------------------------------------------------------------------- ZS Saad, G Chen, RC Reynolds, PP Christidis, KR Hammett, PSF Bellgowan, RW Cox. FIAC Analysis According to AFNI and SUMA. Human Brain Mapping, 27: 417-424, 2006. * Describes how we used AFNI to analyze the FIAC contest data. * https://dx.doi.org/10.1002/hbm.20247 * https://sscc.nimh.nih.gov/sscc/rwcox/papers/FIAC_AFNI_2006.pdf ---------------------------------------------------------------------------- BD Argall, ZS Saad, MS Beauchamp. Simplified intersubject averaging on the cortical surface using SUMA. Human Brain Mapping 27: 14-27, 2006. * Describes the 'standard mesh' surface approach used in SUMA. * https://dx.doi.org/10.1002/hbm.20158 * https://sscc.nimh.nih.gov/sscc/rwcox/papers/SUMA2006paper.pdf ---------------------------------------------------------------------------- ZS Saad, DR Glen, G Chen, MS Beauchamp, R Desai, RW Cox. A new method for improving functional-to-structural MRI alignment using local Pearson correlation. NeuroImage 44: 839-848, 2009. * Describes the algorithm used in 3dAllineate (and thence in align_epi_anat.py) for EPI-to-structural volume image registration. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649831/ * https://dx.doi.org/10.1016/j.neuroimage.2008.09.037 * https://sscc.nimh.nih.gov/sscc/rwcox/papers/LocalPearson2009.pdf ---------------------------------------------------------------------------- H Sarin, AS Kanevsky, SH Fung, JA Butman, RW Cox, D Glen, R Reynolds, S Auh. Metabolically stable bradykinin B2 receptor agonists enhance transvascular drug delivery into malignant brain tumors by increasing drug half-life. Journal of Translational Medicine, 7: #33, 2009. * Describes the method used in AFNI for modeling dynamic contrast enhanced (DCE) MRI for analysis of brain tumors. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2689161/ * https://dx.doi.org/10.1186/1479-5876-7-33 ---------------------------------------------------------------------------- HJ Jo, ZS Saad, WK Simmons, LA Milbury, RW Cox. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. NeuroImage, 52: 571-582, 2010. * Describes the ANATICOR method for de-noising FMRI datasets. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2897154/ * https://dx.doi.org/10.1016/j.neuroimage.2010.04.246 ---------------------------------------------------------------------------- A Vovk, RW Cox, J Stare, D Suput, ZS Saad. Segmentation Priors From Local Image Properties: Without Using Bias Field Correction, Location-based Templates, or Registration. Neuroimage, 55: 142-152, 2011. * Describes the earliest basis for 3dSeg. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3031751/ * https://dx.doi.org/10.1016/j.neuroimage.2010.11.082 ---------------------------------------------------------------------------- G Chen, ZS Saad, DR Glen, JP Hamilton, ME Thomason, IH Gotlib, RW Cox. Vector Autoregression, Structural Equation Modeling, and Their Synthesis in Neuroimaging Data Analysis. Computers in Biology and Medicine, 41: 1142-1155, 2011. * Describes the method implemented in 1dSVAR (Structured Vector AutoRegression). * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3223325/ * https://dx.doi.org/10.1016/j.compbiomed.2011.09.004 ---------------------------------------------------------------------------- RW Cox. AFNI: what a long strange trip it's been. NeuroImage, 62: 747-765, 2012. * A Brief History of AFNI, from its inception to speculation about the future. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3246532/ * https://dx.doi.org/10.1016/j.neuroimage.2011.08.056 ---------------------------------------------------------------------------- ZS Saad, RC Reynolds. SUMA. Neuroimage. 62: 768-773, 2012. * The biography of SUMA. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260385/ * https://dx.doi.org/10.1016/j.neuroimage.2011.09.016 ---------------------------------------------------------------------------- G Chen, ZS Saad, AR Nath, MS Beauchamp, RW Cox. FMRI Group Analysis Combining Effect Estimates and Their Variances. Neuroimage, 60: 747-765, 2012. * The math behind 3dMEMA (Mixed Effects Meta-Analysis) -- AKA super-3dttest. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3404516/ * https://dx.doi.org/10.1016/j.neuroimage.2011.12.060 ---------------------------------------------------------------------------- ZS Saad, SJ Gotts, K Murphy, G Chen, HJ Jo, A Martin, RW Cox. Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression. Brain Connectivity, 2: 25-32, 2012. * Our first paper on why Global Signal Regression in resting state FMRI is a bad idea when doing any form of group analysis. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3484684/ * https://dx.doi.org/10.1089/brain.2012.0080 ---------------------------------------------------------------------------- SJ Gotts, WK Simmons, LA Milbury, GL Wallace, RW Cox, A Martin. Fractionation of Social Brain Circuits in Autism Spectrum Disorders. Brain, 135: 2711-2725, 2012. * In our humble opinion, this shows how to use resting state FMRI correctly when making inter-group comparisons (hint: no global signal regression is used). * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3437021/ * https://dx.doi.org/10.1093/brain/aws160 ---------------------------------------------------------------------------- HJ Jo, ZS Saad, SJ Gotts, A Martin, RW Cox. Quantifying Agreement between Anatomical and Functional Interhemispheric Correspondences in the Resting Brain. PLoS ONE, 7: art.no. e48847, 2012. * A numerical method for measuring symmetry in brain functional imaging data. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3493608/ * https://dx.doi.org/10.1371/journal.pone.0048847 ---------------------------------------------------------------------------- ZS Saad, SJ Gotts, K Murphy, G Chen, HJ Jo, A Martin, RW Cox. Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression. Brain Connectivity, 2012: 25-32. * Another paper in the battle against Global Signal Regression. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3484684/ * https://dx.doi.org/10.1089/brain.2012.0080 ---------------------------------------------------------------------------- G Chen, ZS Saad, JC Britton, DS Pine, RW Cox Linear mixed-effects modeling approach to FMRI group analysis. NeuroImage, 73: 176-190, 2013. * The math behind 3dLME. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3404516/ * https://dx.doi.org/10.1016/j.neuroimage.2011.12.060 ---------------------------------------------------------------------------- SJ Gotts, ZS Saad, HJ Jo, GL Wallace, RW Cox, A Martin. The perils of global signal regression for group comparisons: A case study of Autism Spectrum Disorders. Frontiers in Human Neuroscience: art.no. 356, 2013. * The long twilight struggle against Global Signal Regression continues. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709423/ * https://dx.doi.org/10.3389/fnhum.2013.00356 ---------------------------------------------------------------------------- HJ Jo, SJ Gotts, RC Reynolds, PA Bandettini, A Martin, RW Cox, ZS Saad. Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. Journal of Applied Mathematics: art.no. 935154, 2013. * A reply to the Power 2012 paper on pre-processing resting state FMRI data, showing how they got it wrong. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886863/ * https://dx.doi.org/10.1155/2013/935154 ---------------------------------------------------------------------------- SJ Gotts, HJ Jo, GL Wallace, ZS Saad, RW Cox, A Martin. Two distinct forms of functional lateralization in the human brain. PNAS, 110: E3435-E3444, 2013. * More about methodology and results for symmetry in brain function. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767540/ * https://dx.doi.org/10.1073/pnas.1302581110 ---------------------------------------------------------------------------- ZS Saad, RC Reynolds, HJ Jo, SJ Gotts, G Chen, A Martin, RW Cox. Correcting Brain-Wide Correlation Differences in Resting-State FMRI. Brain Connectivity, 2013: 339-352. * Just when you thought it was safe to go back into the waters of resting state FMRI, another paper explaining why global signal regression is a bad idea and a tentative step towards a different solution. * https://www.ncbi.nlm.nih.gov/pubmed/23705677 * https://dx.doi.org/10.1089/brain.2013.0156 ---------------------------------------------------------------------------- P Kundu, ND Brenowitz, V Voon, Y Worbe, PE Vertes, SJ Inati, ZS Saad, PA Bandettini, ET Bullmore. Integrated strategy for improving functional connectivity mapping using multiecho fMRI. PNAS 110: 16187-16192, 2013. * A data acquisition and processing strategy for improving resting state FMRI. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3791700/ * https://dx.doi.org/10.1073/pnas.1301725110 ---------------------------------------------------------------------------- PA Taylor, ZS Saad. FATCAT: (An Efficient) Functional And Tractographic Connectivity Analysis Toolbox. Brain Connectivity 3:523-535, 2013. * Introducing diffusion-based tractography tools in AFNI, with particular emphases on complementing FMRI analysis and in performing interactive visualization with SUMA. * https://www.ncbi.nlm.nih.gov/pubmed/23980912 * https://dx.doi.org/10.1089/brain.2013.0154 ---------------------------------------------------------------------------- G Chen, NE Adleman, ZS Saad, E Leibenluft, RW Cox. Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model. NeuroImage 99:571-588, 2014. * The fun stuff behind 3dMVM == more complex linear modeling for groups. * https://dx.doi.org/10.1016/j.neuroimage.2014.06.027 * https://sscc.nimh.nih.gov/pub/dist/doc/papers/3dMVM_2014.pdf ---------------------------------------------------------------------------- Taylor PA, Chen G, Cox RW, Saad ZS. Open Environment for Multimodal Interactive Connectivity Visualization and Analysis. Brain Connectivity 6(2):109-21, 2016. * Visualization and MVM stats tools using tracking (or even functional connectivity). * https://dx.doi.org/10.1089/brain.2015.0363 * https://sscc.nimh.nih.gov/pub/dist/papers/ASF_2015_draft_BCinpress.pdf ---------------------------------------------------------------------------- G Chen, Y-W Shin, PA Taylor, DR GLen, RC Reynolds, RB Israel, RW Cox. Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level. NeuroImage 142:248-259, 2016. Proper statistical analysis (FPR control) when correlating FMRI time series data amongst multiple subjects, using nonparametric methods. * https://doi.org/10.1016/j.neuroimage.2016.05.023 ---------------------------------------------------------------------------- G Chen, PA Taylor, Y-W Shin, RC Reynolds, RW Cox. Untangling the relatedness among correlations, Part II: Inter-subject correlation group analysis through linear mixed-effects modeling. NeuroImage 147:825-840 2017. * Just when you thought it was safe to go back into the brain data: this time, using parametric methods. * https://doi.org/10.1016/j.neuroimage.2016.08.029 ---------------------------------------------------------------------------- G Chen, PA Taylor, X Qu, PJ Molfese, PA Bandettini, RW Cox, ES Finn. Untangling the relatedness among correlations, part III: Inter-subject correlation analysis through Bayesian multilevel modeling for naturalistic scanning. NeuroImage, 2019. * https://doi.org/10.1016/j.neuroimage.2019.116474 * https://www.ncbi.nlm.nih.gov/pubmed/31884057 * https://www.biorxiv.org/content/10.1101/655738v1.full ---------------------------------------------------------------------------- RW Cox, G Chen, DR Glen, RC Reynolds, PA Taylor. fMRI clustering and false-positive rates. PNAS 114:E3370-E3371, 2017. * Response to Eklund's (et al.) paper about clustering in PNAS 2016. * https://arxiv.org/abs/1702.04846 * https://doi.org/10.1073/pnas.1614961114 ---------------------------------------------------------------------------- RW Cox, G Chen, DR Glen, RC Reynolds, PA Taylor. FMRI Clustering in AFNI: False Positive Rates Redux. Brain Connectivity 7:152-171, 2017. * A discussion of the cluster-size thresholding updates made to AFNI in early 2017. * https://arxiv.org/abs/1702.04845 * https://doi.org/10.1089/brain.2016.0475 ---------------------------------------------------------------------------- S Song, RPH Bokkers, MA Edwardson, T Brown, S Shah, RW Cox, ZS Saad, RC Reynolds, DR Glen, LG Cohen LG, LL Latour. Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke. PLoS ONE 12, Article number e0185552, 2017. * Applying AFNI's InstaCorr module to stroke perfusion mapping. * https://doi.org/10.1371/journal.pone.0185552 * https://www.ncbi.nlm.nih.gov/pubmed/28973000 ---------------------------------------------------------------------------- G Chen, PA Taylor, SP Haller, K Kircanski, J Stoddard, DS Pine, E Leibenluft, Brotman MA, RW Cox. Intraclass correlation: Improved modeling approaches and applications for neuroimaging. Human Brain Mapping, 39:1187-1206 2018. * Discussion of ICC methods, and distinctions among them. * https://doi.org/10.1002/hbm.23909 * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5807222/ ---------------------------------------------------------------------------- PA Taylor, G Chen, DR Glen, JK Rajendra, RC Reynolds, RW Cox. FMRI processing with AFNI: Some comments and corrections on 'Exploring the Impact of Analysis Software on Task fMRI Results'. * https://www.biorxiv.org/content/10.1101/308643v1.abstract * https://doi.org/10.1101/308643 ---------------------------------------------------------------------------- RW Cox. Equitable Thresholding and Clustering: A Novel Method for Functional Magnetic Resonance Imaging Clustering in AFNI. Brain Connectivity 9:529-538, 2019. * https://doi.org/10.1089/brain.2019.0666 ---------------------------------------------------------------------------- G Chen, RW Cox, DR Glen, JK Rajendra, RC Reynolds, PA Taylor. 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, 2019. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328330/ * https://dx.doi.org/10.1002/hbm.24399 ---------------------------------------------------------------------------- G Chen, Y Xiao, PA Taylor, JK Rajendra, T Riggins, F Geng, E Redcay, RW Cox. Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling. Neuroinformatics 17:515-545, 2019. * https://link.springer.com/article/10.1007/s12021-018-9409-6 * https://www.biorxiv.org/content/10.1101/238998v1.abstract ---------------------------------------------------------------------------- DR Glen, PA Taylor, BR Buchsbaum, RW Cox, and RC Reynolds. Beware (Surprisingly Common) Left-Right Flips in Your MRI Data: An Efficient and Robust Method to Check MRI Dataset Consistency Using AFNI. Frontiers in Neuroinformatics, 25 May 2020. * https://doi.org/10.3389/fninf.2020.00018 * https://www.medrxiv.org/content/10.1101/19009787v4 ---------------------------------------------------------------------------- V Roopchansingh, JJ French Jr, DM Nielson, RC Reynolds, DR Glen, P D’Souza, PA Taylor, RW Cox, AE Thurm. EPI Distortion Correction is Easy and Useful, and You Should Use It: A case study with toddler data. * https://www.biorxiv.org/content/10.1101/2020.09.28.306787v1 ---------------------------------------------------------------------------- G Chen, TA Nash, KM Cole, PD Kohn, S-M Wei, MD Gregory, DP Eisenberg, RW Cox, KF Berman, JS Kippenham. Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies. * https://doi.org/10.1016/j.neuroimage.2021.117891 * https://pubmed.ncbi.nlm.nih.gov/33667672/ ----------------------------------------------------------------------------