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Cattarinussi G, Grimaldi DA, Aarabi MH, Sambataro F. Static and Dynamic Dysconnectivity in Early Psychosis: Relationship With Symptom Dimensions. Schizophr Bull 2024:sbae142. [PMID: 39212653 DOI: 10.1093/schbul/sbae142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
BACKGROUND AND HYPOTHESIS Altered functional connectivity (FC) has been frequently reported in psychosis. Studying FC and its time-varying patterns in early-stage psychosis allows the investigation of the neural mechanisms of this disorder without the confounding effects of drug treatment or illness-related factors. STUDY DESIGN We employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore FC in individuals with early psychosis (EP), who also underwent clinical and neuropsychological assessments. 96 EP and 56 demographically matched healthy controls (HC) from the Human Connectome Project for Early Psychosis database were included. Multivariate analyses using spatial group independent component analysis were used to compute static FC and dynamic functional network connectivity (dFNC). Partial correlations between FC measures and clinical and cognitive variables were performed to test brain-behavior associations. STUDY RESULTS Compared to HC, EP showed higher static FC in the striatum and temporal, frontal, and parietal cortex, as well as lower FC in the frontal, parietal, and occipital gyrus. We found a negative correlation in EP between cognitive function and FC in the right striatum FC (pFWE = 0.009). All dFNC parameters, including dynamism and fluidity measures, were altered in EP, and positive symptoms were negatively correlated with the meta-state changes and the total distance (pFWE = 0.040 and pFWE = 0.049). CONCLUSIONS Our findings support the view that psychosis is characterized from the early stages by complex alterations in intrinsic static and dynamic FC, that may ultimately result in positive symptoms and cognitive deficits.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
| | | | - Mohammad Hadi Aarabi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
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Iraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari BM, Mathalon DH, Pearlson GD, Macciardi F, Preda A, van Erp TGM, Bustillo JR, Díaz-Caneja CM, Andrés-Camazón P, Dhamala M, Adali T, Calhoun VD. Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk. Biol Psychiatry 2024; 96:188-197. [PMID: 38070846 PMCID: PMC11156799 DOI: 10.1016/j.biopsych.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/15/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification. METHODS We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data. RESULTS Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks. CONCLUSIONS Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia.
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Noah Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California; San Francisco Veteran Affairs Medical Center, San Francisco, California
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Pablo Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Mukesh Dhamala
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia.
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Iraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari BM, Mathalon D, Pearlson G, Macciardi F, Preda A, van Erp T, Bustillo JR, Díaz-Caneja CM, Andrés-Camazón P, Dhamala M, Adali T, Calhoun V. Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.18.548880. [PMID: 37503085 PMCID: PMC10370141 DOI: 10.1101/2023.07.18.548880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. Methods We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. Results Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. Conclusions Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.
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Affiliation(s)
- A. Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - J. Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - N. Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of CSE, Georgia Institute of Technology, Atlanta, Georgia
| | - A. Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Z. Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - O. Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - P. Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - B. M. Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - D.H. Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - G.D. Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - F. Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - A. Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - T.G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - J. R. Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - C. M. Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - P. Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - M. Dhamala
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA
| | - T. Adali
- Department of CSEE, University of Maryland, Baltimore County, Baltimore, Maryland
| | - V.D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of CSE, Georgia Institute of Technology, Atlanta, Georgia
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