Robert E Kelly1, Matthew J Hoptman2, Soojin Lee3, George S Alexopoulos4, Faith M Gunning5, Martin J McKeown6. 1. Department of Psychiatry, Weill Cornell Medical College, 21 Bloomingdale Road, White Plains, NY 10605, USA. Electronic address: rek2005@med.cornell.edu. 2. Clinical Research Division, Nathan S. Kline Institute for Psychiatric Research,140 Old Orangeburg Road, Orangeburg, NY 10962, USA; Department of Psychiatry, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA. Electronic address: matthew.hoptman@nki.rfmh.org. 3. Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK. Electronic address: soojin.lee@ndcn.ox.ac.uk. 4. Department of Psychiatry, Weill Cornell Medical College, 21 Bloomingdale Road, White Plains, NY 10605, USA. Electronic address: gsalexop@med.cornell.edu. 5. Department of Psychiatry, Weill Cornell Medical College, 21 Bloomingdale Road, White Plains, NY 10605, USA. Electronic address: fgd2002@med.cornell.edu. 6. Neurology, Pacific Parkinson's Research Center, University of British Columbia, 2221 Wesbrook Mall, Vancouver, British Columbia V6T 2B5 Canada. Electronic address: martin.mckeown@ubc.ca.
Abstract
BACKGROUND: Functional connectivity (FC) maps from brain fMRI data are often derived with seed-based methods that estimate temporal correlations between the time course in a predefined region (seed) and other brain regions (SCA, seed-based correlation analysis). Standard dual regression, which uses a set of spatial regressor maps, can detect FC with entire brain "networks," such as the default mode network, but may not be feasible when detecting FC associated with a single small brain region alone (for example, the amygdala). NEW METHOD: We explored seed-based dual regression (SDR) from theoretical and practical points of view. SDR is a modified implementation of dual regression where the set of spatial regressors is replaced by a single binary spatial map of the seed region. RESULTS: SDR allowed detection of FC with small brain regions. COMPARISON WITH EXISTING METHOD: For both synthetic and natural fMRI data, detection of FC with SDR was identical to that obtained with SCA after removal of global signal from fMRI data with global signal regression (GSR). In the absence of GSR, detection of FC was significantly improved when using SDR compared with SCA. CONCLUSION: The improved FC detection achieved with SDR was related to a partial filtering of the global signal that occurred during spatial regression, an integral part of dual regression. This filtering can sometimes lead to spurious negative correlations that result in a widespread negative bias in FC derived with any application of dual regression. We provide guidelines for how to identify and correct this potential problem.
BACKGROUND: Functional connectivity (FC) maps from brain fMRI data are often derived with seed-based methods that estimate temporal correlations between the time course in a predefined region (seed) and other brain regions (SCA, seed-based correlation analysis). Standard dual regression, which uses a set of spatial regressor maps, can detect FC with entire brain "networks," such as the default mode network, but may not be feasible when detecting FC associated with a single small brain region alone (for example, the amygdala). NEW METHOD: We explored seed-based dual regression (SDR) from theoretical and practical points of view. SDR is a modified implementation of dual regression where the set of spatial regressors is replaced by a single binary spatial map of the seed region. RESULTS: SDR allowed detection of FC with small brain regions. COMPARISON WITH EXISTING METHOD: For both synthetic and natural fMRI data, detection of FC with SDR was identical to that obtained with SCA after removal of global signal from fMRI data with global signal regression (GSR). In the absence of GSR, detection of FC was significantly improved when using SDR compared with SCA. CONCLUSION: The improved FC detection achieved with SDR was related to a partial filtering of the global signal that occurred during spatial regression, an integral part of dual regression. This filtering can sometimes lead to spurious negative correlations that result in a widespread negative bias in FC derived with any application of dual regression. We provide guidelines for how to identify and correct this potential problem.
Authors: Kathleen Y Haaland; Catherine L Elsinger; Andrew R Mayer; Sally Durgerian; Stephen M Rao Journal: J Cogn Neurosci Date: 2004-05 Impact factor: 3.225
Authors: Robert E Kelly; Matthew J Hoptman; George S Alexopoulos; Faith M Gunning; Martin J McKeown Journal: Hum Brain Mapp Date: 2019-06-12 Impact factor: 5.038
Authors: Robert E Kelly; George S Alexopoulos; Zhishun Wang; Faith M Gunning; Christopher F Murphy; Sarah Shizuko Morimoto; Dora Kanellopoulos; Zhiru Jia; Kelvin O Lim; Matthew J Hoptman Journal: J Neurosci Methods Date: 2010-04-08 Impact factor: 2.390
Authors: Chao-Gan Yan; Brian Cheung; Clare Kelly; Stan Colcombe; R Cameron Craddock; Adriana Di Martino; Qingyang Li; Xi-Nian Zuo; F Xavier Castellanos; Michael P Milham Journal: Neuroimage Date: 2013-03-15 Impact factor: 6.556
Authors: Matthew J Hoptman; Debra D'Angelo; Dean Catalano; Cristina J Mauro; Zarrar E Shehzad; A M Clare Kelly; Francisco X Castellanos; Daniel C Javitt; Michael P Milham Journal: Schizophr Bull Date: 2009-03-30 Impact factor: 9.306
Authors: Jie Song; Alok S Desphande; Timothy B Meier; Dana L Tudorascu; Svyatoslav Vergun; Veena A Nair; Bharat B Biswal; Mary E Meyerand; Rasmus M Birn; Pierre Bellec; Vivek Prabhakaran Journal: PLoS One Date: 2012-12-05 Impact factor: 3.240