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Kazemivash B, van Erp TGM, Kochunov P, Calhoun VD. A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia. FRONTIERS IN NEUROIMAGING 2023; 2:1097523. [PMID: 37554628 PMCID: PMC10406273 DOI: 10.3389/fnimg.2023.1097523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/12/2023] [Indexed: 08/10/2023]
Abstract
Schizophrenia is a severe brain disorder with serious symptoms including delusions, disorganized speech, and hallucinations that can have a long-term detrimental impact on different aspects of a patient's life. It is still unclear what the main cause of schizophrenia is, but a combination of altered brain connectivity and structure may play a role. Neuroimaging data has been useful in characterizing schizophrenia, but there has been very little work focused on voxel-wise changes in multiple brain networks over time, despite evidence that functional networks exhibit complex spatiotemporal changes over time within individual subjects. Recent studies have primarily focused on static (average) features of functional data or on temporal variations between fixed networks; however, such approaches are not able to capture multiple overlapping networks which change at the voxel level. In this work, we employ a deep residual convolutional neural network (CNN) model to extract 53 different spatiotemporal networks each of which captures dynamism within various domains including subcortical, cerebellar, visual, sensori-motor, auditory, cognitive control, and default mode. We apply this approach to study spatiotemporal brain dynamism at the voxel level within multiple functional networks extracted from a large functional magnetic resonance imaging (fMRI) dataset of individuals with schizophrenia (N = 708) and controls (N = 510). Our analysis reveals widespread group level differences across multiple networks and spatiotemporal features including voxel-wise variability, magnitude, and temporal functional network connectivity in widespread regions expected to be impacted by the disorder. We compare with typical average spatial amplitude and show highly structured and neuroanatomically relevant results are missed if one does not consider the voxel-wise spatial dynamics. Importantly, our approach can summarize static, temporal dynamic, spatial dynamic, and spatiotemporal dynamics features, thus proving a powerful approach to unify and compare these various perspectives. In sum, we show the proposed approach highlights the importance of accounting for both temporal and spatial dynamism in whole brain neuroimaging data generally, shows a high-level of sensitivity to schizophrenia highlighting global but spatially unique dynamics showing group differences, and may be especially important in studies focused on the development of brain-based biomarkers.
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Affiliation(s)
- Behnam Kazemivash
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Theo G. M. van Erp
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Vince D. Calhoun
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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Kazemivash B, Calhoun VD. A 5D approach to study spatio-temporal dynamism of resting-state brain networks in schizophrenia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3737-3740. [PMID: 36085717 DOI: 10.1109/embc48229.2022.9871362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Schizophrenia is a serious brain disorder that can affect all aspects of patient's life such as thinking, behaving and even feeling. The principal cause of schizophrenia is still unknown, but there is some evidence that differences in brain networks interactions along with functional dysconnectivity may play a significant role. Prior work has mostly focused on static summaries of functional data, or more recently changes in temporal coupling between fixed networks. Here, we study differences in spatio-temporal brain dynamics using resting state fMRI images in a dataset including 510 control and 708 schizophrenia patients. To do this, we utilized a deep residual network to extract 5 different spatiotemporal networks each of which captures spatial and temporal dynamics within sensory-motor, auditory, and default mode domains. Our analysis shows significant group differences in various aspects of spatio-temporal dynamics including magnitude, voxel-wise variability, and temporal functional network connectivity. Clinical relevance- Our study explores effects of spatio-temporal brain dynamism in schizophrenia, which is rarely taken into account, but could provide unique and more sensitive information about the disorder. Here we incorporate a novel 5D brain parcellation model, that enables us to encode spatio-temporal dynamics, to extract and characterize multiple resting fMRI brain networks.
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Iraji A, Faghiri A, Fu Z, Rachakonda S, Kochunov P, Belger A, Ford JM, McEwen S, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Turner JA, van Erp TGM, Calhoun VD. Multi-spatial-scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia. Netw Neurosci 2022; 6:357-381. [PMID: 35733435 PMCID: PMC9208002 DOI: 10.1162/netn_a_00196] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 04/12/2021] [Indexed: 11/04/2022] Open
Abstract
We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. Multiscale ICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and sex-common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (a) within the subcortical domain, (b) between the somatomotor and cerebellum domains, and (c) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial-scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial-scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- * Corresponding Authors: ;
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Srinivas Rachakonda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Judy M. Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Sarah McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Daniel H. Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Bryon A. Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Godfrey D. Pearlson
- Departments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, USA
| | - Steven G. Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Jessica A. Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Theodorus G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- * Corresponding Authors: ;
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Iraji A, Faghiri A, Fu Z, Kochunov P, Adhikari BM, Belger A, Ford JM, McEwen S, Mathalon DH, Pearlson GD, Potkin SG, Preda A, Turner JA, Van Erp TGM, Chang C, Calhoun VD. Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping. Neuroimage 2022; 251:119013. [PMID: 35189361 PMCID: PMC9107614 DOI: 10.1016/j.neuroimage.2022.119013] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/11/2022] [Accepted: 02/17/2022] [Indexed: 11/05/2022] Open
Abstract
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.
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Affiliation(s)
- A Iraji
- Tri-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.
| | - A Faghiri
- Tri-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
| | - Z Fu
- Tri-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
| | - P Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - B M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - A Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States of America
| | - J M Ford
- Department 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 America
| | - S McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States of America
| | - D H Mathalon
- Department 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 America
| | - G D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, United States of America
| | - S G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - A Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - J A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States of America
| | - T G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - C Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - V D Calhoun
- Tri-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.
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Tang D, Zhang X, Xu Y, Dai L, Sun J, Hu H, Jiang H, Jin P, Chen L, Fang J. The Central Response of Electroacupuncture on Trigeminal Neuralgia Based on Resting-State Functional Magnetic Resonance Imaging: A Protocol for a Pre-Experimental, Single-Centre, Randomized, Controlled Trial. J Pain Res 2021; 14:3321-3331. [PMID: 34707400 PMCID: PMC8543029 DOI: 10.2147/jpr.s334078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 09/30/2021] [Indexed: 11/23/2022] Open
Abstract
Objective To verify the efficacy of electroacupuncture (EA) on classical trigeminal neuralgia (CTN), and to observe the brain functional status of patients with CTN and the intervention effects of EA on brain function by resting-state functional magnetic resonance imaging (rs-fMRI). Methods and Analysis Thirty CTN patients will be randomly divided into EA combined with carbamazepine group and carbamazepine group in 2:1 ratio by using a random number table. Patients in EA combined with carbamazepine will receive EA treatment and carbamazepine for four weeks. The carbamazepine group will only receive carbamazepine treatment. VAS (visual analogue scale), HAMA (Hamilton Anxiety Scale), HAMD (Hamilton Depression Scale) and SF-36 (short form 36 health survey) will be performed before, after four-week treatments and at three-month follow-up in CTN patients. Six CTN patients will be randomly selected from EA combined with carbamazepine group and carbamazepine group, respectively, before treatment, and twelve paired healthy participants will be recruited at the same time. The twelve CTN patients will be scanned by rs-fMRI before and after treatment, and the healthy participants will be scanned by rs-fMRI only at baseline. Regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) analysis will be carried out to compare the dysfunctional brain regions between CTN patients and healthy participants, as well as the differences between two groups of patients with CTN after treatment. Trial Registration ChiCTR-1900027873.
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Affiliation(s)
- Ding Tang
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Xufen Zhang
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Yani Xu
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Linglin Dai
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Jianlan Sun
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Hantong Hu
- Department of Acupuncture, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Huangwei Jiang
- Department of Radiological, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Ping Jin
- Department of Radiological, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Lifang Chen
- Department of Acupuncture, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Jianqiao Fang
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Hangzhou, Zhejiang, People's Republic of China
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Iraji A, Miller R, Adali T, Calhoun VD. Space: A Missing Piece of the Dynamic Puzzle. Trends Cogn Sci 2020; 24:135-149. [PMID: 31983607 DOI: 10.1016/j.tics.2019.12.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 11/15/2019] [Accepted: 12/03/2019] [Indexed: 01/24/2023]
Abstract
There has been growing interest in studying the temporal reconfiguration of brain functional connectivity to understand the role of dynamic interaction (e.g., integration and segregation) among neuronal populations in cognitive functions. However, it is crucial to differentiate between various dynamic properties because nearly all existing dynamic connectivity studies are presented as spatiotemporally dynamic, even though they fall into different categories. As a result, variation in the spatial patterns of functional structures are not well characterized. Here, we present the concepts of spatially, temporally, and spatiotemporally dynamics and use this terminology to categorize existing approaches. We review current spatially dynamic connectivity work, emphasizing that explicit incorporation of space into dynamic analyses can expand our understanding of brain function and disorder.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Robyn Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Tulay Adali
- Department of CSEE, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.
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