Oktay Agcaoglu1, Tony W Wilson2, Yu-Ping Wang3,4, Julia M Stephen5, Vince D Calhoun1,5. 1. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA. 2. Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA. 3. Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana, USA. 4. Department of Global Biostatistics and Data Science, Tulane University, New Orleans, Louisiana, USA. 5. The Mind Research Network, Albuquerque, New Mexico, USA.
Abstract
Introduction: Previous studies have shown significant conditional differences between eyes open, fixated at an image (EO) and eyes closed (EC) in the acquired resting-state functional magnetic resonance imaging (rs-fMRI) data. Aim: We recently showed significant functional network connectivity (FNC) differences between EO and EC across a variety of networks. In this study, we aim at further evaluating differences in dynamic FNC (dFNC) between EO and EC. Materials and Methods: Rs-fMRI were collected from adolescents aged 9-15 years old during both EO and EC conditions, and dFNC was calculated by using the independent component analysis framework. Results: We found that out of five states (clusters), state 1 was observed to be more dominant in the EO condition, whereas state 2 was observed to be more dominant in the EC condition. States 1 and 2 showed significant differences in the mean dwell time based on false discovery rate, and states 1, 2, 3, and 4 differed in the frequency of occurrences. These results are consistent with our previous study of static connectivity in suggesting that EO and EC differences not only are relatively strong but also importantly reveal that these differences vary over time, especially in one particularly transient connectivity pattern. Conclusion: Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. Overall, our findings indicate that both static and dynamic rs-fMRI connectivity patterns are strongly impacted by basic conditional differences such as EO and EC. Impact statement Our findings not only suggest that eyes open, fixated at an image (EO) and eyes closed (EC) condition-related resting state functional magnetic resonance imaging differences are relatively strong, but they also reveal an important attribute of these conditions that these differences vary over time, especially in one particularly transient connectivity pattern. Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. We believe there is benefit in having the EO/EC as a contrast of interest in future studies, if time allows.
Introduction: Previous studies have shown significant conditional differences between eyes open, fixated at an image (EO) and eyes closed (EC) in the acquired resting-state functional magnetic resonance imaging (rs-fMRI) data. Aim: We recently showed significant functional network connectivity (FNC) differences between EO and EC across a variety of networks. In this study, we aim at further evaluating differences in dynamic FNC (dFNC) between EO and EC. Materials and Methods: Rs-fMRI were collected from adolescents aged 9-15 years old during both EO and EC conditions, and dFNC was calculated by using the independent component analysis framework. Results: We found that out of five states (clusters), state 1 was observed to be more dominant in the EO condition, whereas state 2 was observed to be more dominant in the EC condition. States 1 and 2 showed significant differences in the mean dwell time based on false discovery rate, and states 1, 2, 3, and 4 differed in the frequency of occurrences. These results are consistent with our previous study of static connectivity in suggesting that EO and EC differences not only are relatively strong but also importantly reveal that these differences vary over time, especially in one particularly transient connectivity pattern. Conclusion: Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. Overall, our findings indicate that both static and dynamic rs-fMRI connectivity patterns are strongly impacted by basic conditional differences such as EO and EC. Impact statement Our findings not only suggest that eyes open, fixated at an image (EO) and eyes closed (EC) condition-related resting state functional magnetic resonance imaging differences are relatively strong, but they also reveal an important attribute of these conditions that these differences vary over time, especially in one particularly transient connectivity pattern. Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. We believe there is benefit in having the EO/EC as a contrast of interest in future studies, if time allows.
Authors: D Cordes; V M Haughton; K Arfanakis; J D Carew; P A Turski; C H Moritz; M A Quigley; M E Meyerand Journal: AJNR Am J Neuroradiol Date: 2001-08 Impact factor: 3.825
Authors: Rémi Patriat; Erin K Molloy; Timothy B Meier; Gregory R Kirk; Veena A Nair; Mary E Meyerand; Vivek Prabhakaran; Rasmus M Birn Journal: Neuroimage Date: 2013-04-15 Impact factor: 6.556
Authors: Elena A Allen; Eswar Damaraju; Sergey M Plis; Erik B Erhardt; Tom Eichele; Vince D Calhoun Journal: Cereb Cortex Date: 2012-11-11 Impact factor: 5.357
Authors: Barnaly Rashid; Laura M E Blanken; Ryan L Muetzel; Robyn Miller; Eswar Damaraju; Mohammad R Arbabshirani; Erik B Erhardt; Frank C Verhulst; Aad van der Lugt; Vincent W V Jaddoe; Henning Tiemeier; Tonya White; Vince Calhoun Journal: Hum Brain Mapp Date: 2018-03-30 Impact factor: 5.038
Authors: E Damaraju; E A Allen; A Belger; J M Ford; S McEwen; D H Mathalon; B A Mueller; G D Pearlson; S G Potkin; A Preda; J A Turner; J G Vaidya; T G van Erp; V D Calhoun Journal: Neuroimage Clin Date: 2014-07-24 Impact factor: 4.881
Authors: Daniel J Lurie; Daniel Kessler; Danielle S Bassett; Richard F Betzel; Michael Breakspear; Shella Kheilholz; Aaron Kucyi; Raphaël Liégeois; Martin A Lindquist; Anthony Randal McIntosh; Russell A Poldrack; James M Shine; William Hedley Thompson; Natalia Z Bielczyk; Linda Douw; Dominik Kraft; Robyn L Miller; Muthuraman Muthuraman; Lorenzo Pasquini; Adeel Razi; Diego Vidaurre; Hua Xie; Vince D Calhoun Journal: Netw Neurosci Date: 2020-02-01
Authors: Oktay Agcaoglu; Tony W Wilson; Yu-Ping Wang; Julia M Stephen; Zening Fu; Vince D Calhoun Journal: J Neurosci Methods Date: 2022-02-23 Impact factor: 2.390
Authors: Nathan M Petro; Lauren R Ott; Samantha H Penhale; Maggie P Rempe; Christine M Embury; Giorgia Picci; Yu-Ping Wang; Julia M Stephen; Vince D Calhoun; Tony W Wilson Journal: Neuroimage Date: 2022-05-27 Impact factor: 7.400
Authors: Brittany K Taylor; Michaela R Frenzel; Jacob A Eastman; Christine M Embury; Oktay Agcaoglu; Yu-Ping Wang; Julia M Stephen; Vince D Calhoun; Tony W Wilson Journal: Neuroimage Date: 2021-12-23 Impact factor: 6.556