Moving beyond the ‘CAP’ of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping
Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networ...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922001422 |
| _version_ | 1818288754526257152 |
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| author | A. Iraji A. Faghiri Z. Fu P. Kochunov B.M. Adhikari A. Belger J.M. Ford S. McEwen D.H. Mathalon G.D. Pearlson S.G. Potkin A. Preda J.A. Turner T.G.M. Van Erp C. Chang V.D. Calhoun |
| author_facet | A. Iraji A. Faghiri Z. Fu P. Kochunov B.M. Adhikari A. Belger J.M. Ford S. McEwen D.H. Mathalon G.D. Pearlson S.G. Potkin A. Preda J.A. Turner T.G.M. Van Erp C. Chang V.D. Calhoun |
| author_sort | A. Iraji |
| collection | DOAJ |
| description | Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns.We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches. |
| first_indexed | 2024-12-13T02:01:24Z |
| format | Article |
| id | doaj.art-9289d0920d554b66a4018c4b76efc3a4 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-13T02:01:24Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-9289d0920d554b66a4018c4b76efc3a42022-12-22T00:03:15ZengElsevierNeuroImage1095-95722022-05-01251119013Moving beyond the ‘CAP’ of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlappingA. Iraji0A. Faghiri1Z. Fu2P. Kochunov3B.M. Adhikari4A. Belger5J.M. Ford6S. McEwen7D.H. Mathalon8G.D. Pearlson9S.G. Potkin10A. Preda11J.A. Turner12T.G.M. Van Erp13C. Chang14V.D. Calhoun15Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America; Corresponding authorsTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of AmericaTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of AmericaMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of AmericaMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of AmericaDepartment of Psychiatry, University of North Carolina, Chapel Hill, NC, United States of AmericaDepartment of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of AmericaDepartment of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States of AmericaDepartment of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of AmericaDepartments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, United States of AmericaDepartment of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of AmericaDepartment of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of AmericaDepartment of Psychology, Georgia State University, Atlanta, GA, United States of AmericaClinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of AmericaDepartment of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States of AmericaTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America; Corresponding authorsResting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns.We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.http://www.sciencedirect.com/science/article/pii/S1053811922001422Functional connectivity (FC)Event present time points (EPTs)Event absent time points (EATs)Activation spatial map (ASM)Independent component analysis (ICA)Intrinsic connectivity networks (ICNs) |
| spellingShingle | A. Iraji A. Faghiri Z. Fu P. Kochunov B.M. Adhikari A. Belger J.M. Ford S. McEwen D.H. Mathalon G.D. Pearlson S.G. Potkin A. Preda J.A. Turner T.G.M. Van Erp C. Chang V.D. Calhoun Moving beyond the ‘CAP’ of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping NeuroImage Functional connectivity (FC) Event present time points (EPTs) Event absent time points (EATs) Activation spatial map (ASM) Independent component analysis (ICA) Intrinsic connectivity networks (ICNs) |
| title | Moving beyond the ‘CAP’ of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping |
| title_full | Moving beyond the ‘CAP’ of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping |
| title_fullStr | Moving beyond the ‘CAP’ of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping |
| title_full_unstemmed | Moving beyond the ‘CAP’ of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping |
| title_short | Moving beyond the ‘CAP’ of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping |
| title_sort | moving beyond the cap of the iceberg intrinsic connectivity networks in fmri are continuously engaging and overlapping |
| topic | Functional connectivity (FC) Event present time points (EPTs) Event absent time points (EATs) Activation spatial map (ASM) Independent component analysis (ICA) Intrinsic connectivity networks (ICNs) |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922001422 |
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