Qingbao Yu1, Yuhui Du2, Jiayu Chen3, Hao He4, Jing Sui5, Godfrey Pearlson6, Vince D Calhoun7. 1. The Mind Research Network, Albuquerque, NM, 87106, USA. Electronic address: qyu@mrn.org. 2. The Mind Research Network, Albuquerque, NM, 87106, USA; School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China. 3. The Mind Research Network, Albuquerque, NM, 87106, USA. 4. The Mind Research Network, Albuquerque, NM, 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87106, USA. 5. The Mind Research Network, Albuquerque, NM, 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences in Beijing, 100049, China. 6. Olin Neuropsychiatry Research Center, Hartford, CT, 06106, USA; Department of Psychiatry, Yale University, New Haven, CT, 06520, USA; Department of Neuroscience, Yale University, New Haven, CT, 06520, USA. 7. The Mind Research Network, Albuquerque, NM, 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87106, USA; Department of Psychiatry, Yale University, New Haven, CT, 06520, USA. Electronic address: vcalhoun@unm.edu.
Abstract
BACKGROUND: A key challenge in building a brain graph using fMRI data is how to define the nodes. Spatial brain components estimated by independent components analysis (ICA) and regions of interest (ROIs) determined by brain atlas are two popular methods to define nodes in brain graphs. It is difficult to evaluate which method is better in real fMRI data. NEW METHOD: Here we perform a simulation study and evaluate the accuracies of a few graph metrics in graphs with nodes of ICA components, ROIs, or modified ROIs in four simulation scenarios. RESULTS: Graph measures with ICA nodes are more accurate than graphs with ROI nodes in all cases. Graph measures with modified ROI nodes are modulated by artifacts. The correlations of graph metrics across subjects between graphs with ICA nodes and ground truth are higher than the correlations between graphs with ROI nodes and ground truth in scenarios with large overlapped spatial sources. Moreover, moving the location of ROIs would largely decrease the correlations in all scenarios. COMPARISON WITH EXISTING METHOD (S): Evaluating graphs with different nodes is promising in simulated data rather than real data because different scenarios can be simulated and measures of different graphs can be compared with a known ground truth. CONCLUSION: Since ROIs defined using brain atlas may not correspond well to real functional boundaries, overall findings of this work suggest that it is more appropriate to define nodes using data-driven ICA than ROI approaches in real fMRI data.
BACKGROUND: A key challenge in building a brain graph using fMRI data is how to define the nodes. Spatial brain components estimated by independent components analysis (ICA) and regions of interest (ROIs) determined by brain atlas are two popular methods to define nodes in brain graphs. It is difficult to evaluate which method is better in real fMRI data. NEW METHOD: Here we perform a simulation study and evaluate the accuracies of a few graph metrics in graphs with nodes of ICA components, ROIs, or modified ROIs in four simulation scenarios. RESULTS: Graph measures with ICA nodes are more accurate than graphs with ROI nodes in all cases. Graph measures with modified ROI nodes are modulated by artifacts. The correlations of graph metrics across subjects between graphs with ICA nodes and ground truth are higher than the correlations between graphs with ROI nodes and ground truth in scenarios with large overlapped spatial sources. Moreover, moving the location of ROIs would largely decrease the correlations in all scenarios. COMPARISON WITH EXISTING METHOD (S): Evaluating graphs with different nodes is promising in simulated data rather than real data because different scenarios can be simulated and measures of different graphs can be compared with a known ground truth. CONCLUSION: Since ROIs defined using brain atlas may not correspond well to real functional boundaries, overall findings of this work suggest that it is more appropriate to define nodes using data-driven ICA than ROI approaches in real fMRI data.
Authors: Matthew L Stanley; Malaak N Moussa; Brielle M Paolini; Robert G Lyday; Jonathan H Burdette; Paul J Laurienti Journal: Front Comput Neurosci Date: 2013-11-22 Impact factor: 2.380
Authors: Hao He; Qingbao Yu; Yuhui Du; Victor Vergara; Teresa A Victor; Wayne C Drevets; Jonathan B Savitz; Tianzi Jiang; Jing Sui; Vince D Calhoun Journal: J Affect Disord Date: 2015-10-31 Impact factor: 4.839
Authors: Janine Bijsterbosch; Samuel J Harrison; Saad Jbabdi; Mark Woolrich; Christian Beckmann; Stephen Smith; Eugene P Duff Journal: Nat Neurosci Date: 2020-10-26 Impact factor: 24.884
Authors: Qingbao Yu; Jiayu Chen; Yuhui Du; Jing Sui; Eswar Damaraju; Jessica A Turner; Theo G M van Erp; Fabio Macciardi; Aysenil Belger; Judith M Ford; Sarah McEwen; Daniel H Mathalon; Bryon A Mueller; Adrian Preda; Jatin Vaidya; Godfrey D Pearlson; Vince D Calhoun Journal: J Neurosci Methods Date: 2019-03-19 Impact factor: 2.390
Authors: Armin Iraji; Zening Fu; Eswar Damaraju; Thomas P DeRamus; Noah Lewis; Juan R Bustillo; Rhoshel K Lenroot; Aysneil Belger; Judith M Ford; Sarah McEwen; Daniel H Mathalon; Bryon A Mueller; Godfrey D Pearlson; Steven G Potkin; Adrian Preda; Jessica A Turner; Jatin G Vaidya; Theo G M van Erp; Vince D Calhoun Journal: Hum Brain Mapp Date: 2018-12-26 Impact factor: 5.038