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Fazio G, Olivo D, Wolf ND, Hirjak D, Schmitgen MM, Werler F, Witteman M, Kubera KM, Calhoun VD, Reith W, Wolf RC, Sambataro F. The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study. Addict Biol 2024; 29:e13395. [PMID: 38709211 PMCID: PMC11072977 DOI: 10.1111/adb.13395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 02/05/2024] [Accepted: 03/26/2024] [Indexed: 05/07/2024]
Abstract
The brain mechanisms underlying the risk of cannabis use disorder (CUD) are poorly understood. Several studies have reported changes in functional connectivity (FC) in CUD, although none have focused on the study of time-varying patterns of FC. To fill this important gap of knowledge, 39 individuals at risk for CUD and 55 controls, stratified by their score on a self-screening questionnaire for cannabis-related problems (CUDIT-R), underwent resting-state functional magnetic resonance imaging. Dynamic functional connectivity (dFNC) was estimated using independent component analysis, sliding-time window correlations, cluster states and meta-state indices of global dynamics and were compared among groups. At-risk individuals stayed longer in a cluster state with higher within and reduced between network dFNC for the subcortical, sensory-motor, visual, cognitive-control and default-mode networks, relative to controls. More globally, at-risk individuals had a greater number of meta-states and transitions between them and a longer state span and total distance between meta-states in the state space. Our findings suggest that the risk of CUD is associated with an increased dynamic fluidity and dynamic range of FC. This may result in altered stability and engagement of the brain networks, which can ultimately translate into altered cortical and subcortical function conveying CUD risk. Identifying these changes in brain function can pave the way for early pharmacological and neurostimulation treatment of CUD, as much as they could facilitate the stratification of high-risk individuals.
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Affiliation(s)
- Giovanni Fazio
- Department of Neuroscience, Padua Neuroscience CenterUniversity of PaduaPaduaItaly
| | - Daniele Olivo
- Department of Neuroscience, Padua Neuroscience CenterUniversity of PaduaPaduaItaly
| | - Nadine D. Wolf
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Mike M. Schmitgen
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Florian Werler
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Miriam Witteman
- Department of Psychiatry and PsychotherapySaarland UniversitySaarbrückenGermany
| | - Katharina M. Kubera
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | - Wolfgang Reith
- Department of NeuroradiologySaarland UniversitySaarbrückenGermany
| | - Robert Christian Wolf
- Department of General Psychiatry at the Center for Psychosocial MedicineHeidelberg UniversityHeidelbergGermany
| | - Fabio Sambataro
- Department of Neuroscience, Padua Neuroscience CenterUniversity of PaduaPaduaItaly
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Soleimani N, Iraji A, Belger A, Calhoun VD. A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583731. [PMID: 38559041 PMCID: PMC10979844 DOI: 10.1101/2024.03.06.583731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HC). Functional dysconnectivity between different brain regions has been reported in schizophrenia, yet the neural mechanisms behind it remain elusive. Using resting state fMRI and ICA on a dataset consisting of 151 schizophrenia patients and 160 age and gender-matched healthy controls, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD) and visual (VIS) networks in patients, as well as hypoconnectivity in sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/ default mode network (DMN), SC/ AUD/ SM/ cerebellar (CB), and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/ CB state based on the first gradient, while HCs favor the DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB/ VIS domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/ SC networks and transmodal CC/ DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM and CB centroids in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in schizophrenia patients. By employing dFNG, we highlight a new perspective to capture large scale fluctuations across the brain while maintaining the convenience of brain networks and low dimensional summary measures.
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Affiliation(s)
- Najme Soleimani
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - 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, Georgia, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, 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, Georgia, USA
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Masias Bruns M, Ramirez-Mahaluf JP, Valli I, Ortuño M, Ilzarbe D, de la Serna E, Navarro OP, Crossley NA, González Ballester MÁ, Baeza I, Piella G, Castro-Fornieles J, Sugranyes G. Altered Temporal Dynamics of Resting-State Functional Magnetic Resonance Imaging in Adolescent-Onset First-Episode Psychosis. Schizophr Bull 2024; 50:418-426. [PMID: 37607335 PMCID: PMC10919773 DOI: 10.1093/schbul/sbad107] [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] [Indexed: 08/24/2023]
Abstract
BACKGROUND Dynamic functional connectivity (dFC) alterations have been reported in patients with adult-onset and chronic psychosis. We sought to examine whether such abnormalities were also observed in patients with first episode, adolescent-onset psychosis (AOP), in order to rule out potential effects of chronicity and protracted antipsychotic treatment exposure. AOP has been suggested to have less diagnostic specificity compared to psychosis with onset in adulthood and occurs during a period of neurodevelopmental changes in brain functional connections. STUDY DESIGN Seventy-nine patients with first episode, AOP (36 patients with schizophrenia-spectrum disorder, SSD; and 43 with affective psychotic disorder, AF) and 54 healthy controls (HC), aged 10 to 17 years were included. Participants underwent clinical and cognitive assessments and resting-state functional magnetic resonance imaging. Graph-based measures were used to analyze temporal trajectories of dFC, which were compared between patients with SSD, AF, and HC. Within patients, we also tested associations between dFC parameters and clinical variables. STUDY RESULTS Patients with SSD temporally visited the different connectivity states in a less efficient way (reduced global efficiency), visiting fewer nodes (larger temporal modularity, and increased immobility), with a reduction in the metabolic expenditure (cost and leap size), relative to AF and HC (effect sizes: Cohen's D, ranging 0.54 to.91). In youth with AF, these parameters did not differ compared to HC. Connectivity measures were not associated with clinical severity, intelligence, cannabis use, or dose of antipsychotic medication. CONCLUSIONS dFC measures hold potential towards the development of brain-based biomarkers characterizing adolescent-onset SSD.
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Affiliation(s)
- Mireia Masias Bruns
- BCN-MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan Pablo Ramirez-Mahaluf
- BCN-MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Isabel Valli
- Clinical and Experimental Neuroscience, Fundació de Recerca Clínic Barcelona, Institut d’Investigacions Biomèdiques Agustí Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - María Ortuño
- Clinical and Experimental Neuroscience, Fundació de Recerca Clínic Barcelona, Institut d’Investigacions Biomèdiques Agustí Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Daniel Ilzarbe
- Clinical and Experimental Neuroscience, Fundació de Recerca Clínic Barcelona, Institut d’Investigacions Biomèdiques Agustí Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Group G04, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- Department of Medicine, Universitat de Barcelona, Barcelona, Spain
| | - Elena de la Serna
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Group G04, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
| | - Olga Puig Navarro
- Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Group G04, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
| | - Nicolas A Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Miguel Ángel González Ballester
- BCN-MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Clinical and Experimental Neuroscience, Fundació de Recerca Clínic Barcelona, Institut d’Investigacions Biomèdiques Agustí Pi i Sunyer (IDIBAPS), Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Inmaculada Baeza
- Clinical and Experimental Neuroscience, Fundació de Recerca Clínic Barcelona, Institut d’Investigacions Biomèdiques Agustí Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Group G04, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- Department of Medicine, Universitat de Barcelona, Barcelona, Spain
| | - Gemma Piella
- BCN-MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Josefina Castro-Fornieles
- Clinical and Experimental Neuroscience, Fundació de Recerca Clínic Barcelona, Institut d’Investigacions Biomèdiques Agustí Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Group G04, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- Department of Medicine, Universitat de Barcelona, Barcelona, Spain
| | - Gisela Sugranyes
- Clinical and Experimental Neuroscience, Fundació de Recerca Clínic Barcelona, Institut d’Investigacions Biomèdiques Agustí Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Child and Adolescent Psychiatry, 2017SGR881, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Group G04, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- Department of Medicine, Universitat de Barcelona, Barcelona, Spain
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Chen Z, Cai Y, Xiao L, Wei XE, Liu Y, Lin C, Liu D, Liu H, Rong L. Increased functional connectivity between default mode network and visual network potentially correlates with duration of residual dizziness in patients with benign paroxysmal positional vertigo. Front Neurol 2024; 15:1363869. [PMID: 38500812 PMCID: PMC10944895 DOI: 10.3389/fneur.2024.1363869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
Objective To assess changes in static and dynamic functional network connectivity (sFNC and dFNC) and explore their correlations with clinical features in benign paroxysmal positional vertigo (BPPV) patients with residual dizziness (RD) after successful canalith repositioning maneuvers (CRM) using resting-state fMRI. Methods We studied resting-state fMRI data from 39 BPPV patients with RD compared to 38 BPPV patients without RD after successful CRM. Independent component analysis and methods of sliding window and k-means clustering were adopted to investigate the changes in dFNC and sFNC between the two groups. Additionally, temporal features and meta-states were compared between the two groups. Furthermore, the associations between fMRI results and clinical characteristics were analyzed using Pearson's partial correlation analysis. Results Compared with BPPV patients without RD, patients with RD had longer duration of BPPV and higher scores of dizziness handicap inventory (DHI) before successful CRM. BPPV patients with RD displayed no obvious abnormal sFNC compared to patients without RD. In the dFNC analysis, patients with RD showed increased FNC between default mode network (DMN) and visual network (VN) in state 4, the FNC between DMN and VN was positively correlated with the duration of RD. Furthermore, we found increased mean dwell time (MDT) and fractional windows (FW) in state 1 but decreased MDT and FW in state 3 in BPPV patients with RD. The FW of state 1 was positively correlated with DHI score before CRM, the MDT and FW of state 3 were negatively correlated with the duration of BPPV before CRM in patients with RD. Additionally, compared with patients without RD, patients with RD showed decreased number of states and state span. Conclusion The occurrence of RD might be associated with increased FNC between DMN and VN, and the increased FNC between DMN and VN might potentially correlate with the duration of RD symptoms. In addition, we found BPPV patients with RD showed altered global meta-states and temporal features. These findings are helpful for us to better understand the underlying neural mechanisms of RD and potentially contribute to intervention development for BPPV patients with RD.
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Affiliation(s)
- Zhengwei Chen
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yaxian Cai
- Department of Neurology, General Hospital of the Yangtze River Shipping, Wuhan, Hubei, China
| | - Lijie Xiao
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiu-E Wei
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yueji Liu
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Cunxin Lin
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Dan Liu
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Haiyan Liu
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Liangqun Rong
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
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Kuang LD, Li HQ, Zhang J, Gui Y, Zhang J. Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework. J Neural Eng 2024; 21:016032. [PMID: 38335544 DOI: 10.1088/1741-2552/ad27ee] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/09/2024] [Indexed: 02/12/2024]
Abstract
Objective.Dynamic functional network connectivity (dFNC), based on data-driven group independent component (IC) analysis, is an important avenue for investigating underlying patterns of certain brain diseases such as schizophrenia. Canonical polyadic decomposition (CPD) of a higher-way dynamic functional connectivity tensor, can offer an innovative spatiotemporal framework to accurately characterize potential dynamic spatial and temporal fluctuations. Since multi-subject dFNC data from sliding-window analysis are also naturally a higher-order tensor, we propose an innovative sparse and low-rank CPD (SLRCPD) for the three-way dFNC tensor to excavate significant dynamic spatiotemporal aberrant changes in schizophrenia.Approach.The proposed SLRCPD approach imposes two constraints. First, the L1regularization on spatial modules is applied to extract sparse but significant dynamic connectivity and avoid overfitting the model. Second, low-rank constraint is added on time-varying weights to enhance the temporal state clustering quality. Shared dynamic spatial modules, group-specific dynamic spatial modules and time-varying weights can be extracted by SLRCPD. The strength of connections within- and between-IC networks and connection contribution are proposed to inspect the spatial modules. K-means clustering and classification are further conducted to explore temporal group difference.Main results.82 subject resting-state functional magnetic resonance imaging (fMRI) dataset and opening Center for Biomedical Research Excellence (COBRE) schizophrenia dataset both containing schizophrenia patients (SZs) and healthy controls (HCs) were utilized in our work. Three typical dFNC patterns between different brain functional regions were obtained. Compared to the spatial modules of HCs, the aberrant connections among auditory network, somatomotor, visual, cognitive control and cerebellar networks in 82 subject dataset and COBRE dataset were detected. Four temporal states reveal significant differences between SZs and HCs for these two datasets. Additionally, the accuracy values for SZs and HCs classification based on time-varying weights are larger than 0.96.Significance.This study significantly excavates spatio-temporal patterns for schizophrenia disease.
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Affiliation(s)
- Li-Dan Kuang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - He-Qiang Li
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - Jianming Zhang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - Yan Gui
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - Jin Zhang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
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Ellis CA, Miller RL, Calhoun VD. Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia. Front Psychiatry 2024; 15:1165424. [PMID: 38495909 PMCID: PMC10941842 DOI: 10.3389/fpsyt.2024.1165424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 01/30/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features. Methods We apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity. Results Importantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states. Conclusion We present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.
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Affiliation(s)
- Charles A. Ellis
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, GA, United States
- Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, GA, United States
- Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, GA, United States
- Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
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7
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Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [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: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
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Wang F, Liu Z, Ford SD, Deng M, Zhang W, Yang J, Palaniyappan L. Aberrant Brain Dynamics in Schizophrenia During Working Memory Task: Evidence From a Replication Functional MRI Study. Schizophr Bull 2024; 50:96-106. [PMID: 37018464 PMCID: PMC10754176 DOI: 10.1093/schbul/sbad032] [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] [Indexed: 04/07/2023]
Abstract
BACKGROUND AND HYPOTHESIS The integration of information that typifies working memory (WM) operation requires a flexible, dynamic functional relationship among brain regions. In schizophrenia, though WM capacity is prominently impaired at higher loads, the mechanistic underpinnings are unclear. As a result, we lack convincing cognitive remediation of load-dependent deficits. We hypothesize that reduced WM capacity arises from a disruption in dynamic functional connectivity when patients face cognitive demands. STUDY DESIGN We calculate the dynamic voxel-wise degree centrality (dDC) across the functional connectome in 142 patients with schizophrenia and 88 healthy controls (HCs) facing different WM loads during an n-back task. We tested associations of the altered variability in dDC and clinical symptoms and identified intermediate connectivity configurations (clustered states) across time during WM operation. These analyses were repeated in another independent dataset of 169 subjects (102 with schizophrenia). STUDY RESULTS Compared with HCs, patients showed an increased dDC variability of supplementary motor area (SMA) for the "2back vs. 0back" contrast. This instability at the SMA seen in patients correlated with increased positive symptoms and followed a limited "U-shape" pattern at rest-condition and 2 loads. In the clustering analysis, patients showed reduced centrality in the SMA, superior temporal gyrus, and putamen. These results were replicated in a constrained search in the second independent dataset. CONCLUSIONS Schizophrenia is characterized by a load-dependent reduction of stable centrality in SMA; this relates to the severity of positive symptoms, especially disorganized behaviour. Restoring SMA stability in the presence of cognitive demands may have a therapeutic effect in schizophrenia.
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Affiliation(s)
- Feiwen Wang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Sabrina D Ford
- Robarts Research Institute, Western University, London, ON, Canada
| | - Mengjie Deng
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Wen Zhang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Jie Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Lena Palaniyappan
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
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9
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Betzel RF, Cutts SA, Tanner J, Greenwell SA, Varley T, Faskowitz J, Sporns O. Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI. Netw Neurosci 2023; 7:926-949. [PMID: 37781150 PMCID: PMC10473297 DOI: 10.1162/netn_a_00321] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/03/2023] [Indexed: 10/03/2023] Open
Abstract
Edge time series decompose functional connectivity into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames (time points when the global co-fluctuation amplitude takes on its largest value), including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations (peaks in co-fluctuation time series but of lower amplitude). Here, we directly address those questions, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multiscale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge concerning the heterogeneity and richness of co-fluctuation patterns as estimated with edge time series while providing some practical guidance for future studies.
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Affiliation(s)
- Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Sarah A. Cutts
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Sarah A. Greenwell
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Thomas Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
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10
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Ye J, Sun H, Gao S, Dadashkarimi J, Rosenblatt M, Rodriguez RX, Mehta S, Jiang R, Noble S, Westwater ML, Scheinost D. Altered Brain Dynamics Across Bipolar Disorder and Schizophrenia During Rest and Task Switching Revealed by Overlapping Brain States. Biol Psychiatry 2023; 94:580-590. [PMID: 37031780 PMCID: PMC10524212 DOI: 10.1016/j.biopsych.2023.03.024] [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: 11/23/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/11/2023]
Abstract
BACKGROUND Individuals with bipolar disorder (BD) and schizophrenia (SCZ) show aberrant brain dynamics (i.e., altered recruitment or traversal through different brain states over time). Existing investigations of brain dynamics typically assume that one dominant brain state characterizes each time point. However, as multiple brain states likely are engaged at any given moment, this approach can obscure alterations in less prominent but critical brain states. Here, we examined brain dynamics in BD and SCZ by implementing a novel framework that simultaneously assessed the engagement of multiple brain states. METHODS Four recurring brain states were identified by applying nonlinear manifold learning and k-means clustering to the Human Connectome Project task-based functional magnetic resonance imaging data. We then assessed moment-to-moment state engagement in 2 independent samples of healthy control participants and patients with BD or SCZ using resting-state (N = 336) or task-based (N = 217) functional magnetic resonance imaging data. Relative state engagement and state engagement variability were extracted and compared across groups using multivariate analysis of covariance, controlling for site, medication, age, and sex. RESULTS Our framework identified dynamic alterations in BD and SCZ, while a state discretization approach revealed no significant group differences. Participants with BD or SCZ showed reduced state engagement variability, but not relative state engagement, across multiple brain states during resting-state and task-based functional magnetic resonance imaging. We found decreased state engagement variability in older participants and preliminary evidence suggesting an association with avolition. CONCLUSIONS Assessing multiple brain states simultaneously can reflect the complexity of aberrant brain dynamics in BD and SCZ, providing a more comprehensive understanding of the neural mechanisms underpinning these conditions.
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Affiliation(s)
- Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut.
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | | | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | | | - Saloni Mehta
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Margaret L Westwater
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Child Study Center, Yale School of Medicine, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut
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11
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Sendi MS, Zendehrouh E, Fu Z, Liu J, Du Y, Mormino E, Salat DH, Calhoun VD, Miller RL. Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease. Brain Connect 2023; 13:334-343. [PMID: 34102870 PMCID: PMC10442683 DOI: 10.1089/brain.2020.0847] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Background: Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods: Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Results: Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion: Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.
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Affiliation(s)
- Mohammad S.E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Yuhui Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | | | - David H. Salat
- Harvard Medical School, Cambridge, Massachusetts, USA
- Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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12
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Jarrahi B. Altered Dynamic Functional Network Connectivity in Healthy Adults with Acute Pain: Findings from the Human Connectome Project. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083512 DOI: 10.1109/embc40787.2023.10339952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Characterizing the neural signature of pain and its modulation is critical for assessing treatment efficacy and conducting translational clinical research. However, the dynamics of pain processing in the brain have remained largely unknown. In this study, we employed independent component analysis (ICA) as a data-driven clustering method on resting-state functional magnetic resonance imaging (fMRI) to obtain intrinsic connectivity networks (ICNs) in a cohort of healthy adults from the Human Connectome Project (HCP) who were identified as having acute pain. We examined the temporal dynamic functional network connectivity (dFNC) with sliding time window correlation and k-means clustering, and compared dFNC state properties and meta-state metrics between groups. Results showed that acute pain had a significant impact on dFNC in a common connectivity state (dynamic state 5) among several ICN pairs, including the salience network, default mode network, central executive, dorsal attention networks, and basal ganglia (false discovery rate [FDR]-corrected p of 0.05). Furthermore, healthy adults with and without acute pain exhibited differences in mean dwell time (dynamic state 3), which indicated that individuals with acute pain spent more time in particular states than those without pain. Meta-state dynamic analysis further indicated significant group differences in the number of states (i.e., unique time windows for each subject), changes between states (i.e., number of times each subject changes from one meta-state to other), and total travelled distances. These preliminary results provide new information about time-varying properties of pain states related to acute pain and advocate for further state-based analyses of pain for future pain biomarker discovery and development.
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13
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Jing R, Chen P, Wei Y, Si J, Zhou Y, Wang D, Song C, Yang H, Zhang Z, Yao H, Kang X, Fan L, Han T, Qin W, Zhou B, Jiang T, Lu J, Han Y, Zhang X, Liu B, Yu C, Wang P, Liu Y. Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study. Hum Brain Mapp 2023; 44:3467-3480. [PMID: 36988434 PMCID: PMC10203807 DOI: 10.1002/hbm.26291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/27/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.
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Affiliation(s)
- Rixing Jing
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Yongbin Wei
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Juanning Si
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Yuying Zhou
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Chengyuan Song
- Department of NeurologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Hongwei Yang
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical CentreNational Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Tong Han
- Department of RadiologyTianjin Huanhu HospitalTianjinChina
| | - Wen Qin
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Bo Zhou
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Jie Lu
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Beijing Institute of GeriatricsBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
| | - Xi Zhang
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijingChina
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Pan Wang
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
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14
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Zarghami TS, Zeidman P, Razi A, Bahrami F, Hossein‐Zadeh G. Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study. Hum Brain Mapp 2023; 44:2873-2896. [PMID: 36852654 PMCID: PMC10089110 DOI: 10.1002/hbm.26251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/28/2023] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.
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Affiliation(s)
- Tahereh S. Zarghami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Peter Zeidman
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
| | - Adeel Razi
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
- Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
- CIFAR Azrieli Global Scholars Program, CIFARTorontoCanada
| | - Fariba Bahrami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Gholam‐Ali Hossein‐Zadeh
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
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15
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Candelaria-Cook FT, Schendel ME, Flynn L, Cerros C, Hill DE, Stephen JM. Disrupted dynamic functional network connectivity in fetal alcohol spectrum disorders. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2023; 47:687-703. [PMID: 36880528 PMCID: PMC10281251 DOI: 10.1111/acer.15046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/23/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Prenatal alcohol exposure (PAE) can result in harmful and long-lasting neurodevelopmental changes. Children with PAE or a fetal alcohol spectrum disorder (FASD) have decreased white matter volume and resting-state spectral power compared to typically developing controls (TDC) and impaired resting-state static functional connectivity. The impact of PAE on resting-state dynamic functional network connectivity (dFNC) is unknown. METHODS Using eyes-closed and eyes-open magnetoencephalography (MEG) resting-state data, global dFNC statistics and meta-states were examined in 89 children aged 6-16 years (51 TDC, 38 with FASD). Source analyzed MEG data were used as input to group spatial independent component analysis to derive functional networks from which the dFNC was calculated. RESULTS During eyes-closed, relative to TDC, participants with FASD spent a significantly longer time in state 2, typified by anticorrelation (i.e., decreased connectivity) within and between default mode network (DMN) and visual network (VN), and state 4, typified by stronger internetwork correlation. The FASD group exhibited greater dynamic fluidity and dynamic range (i.e., entered more states, changed from one meta-state to another more often, and traveled greater distances) than TDC. During eyes-open, TDC spent significantly more time in state 1, typified by positive intra- and interdomain connectivity with modest correlation within the frontal network (FN), while participants with FASD spent a larger fraction of time in state 2, typified by anticorrelation within and between DMN and VN and strong correlation within and between FN, attention network, and sensorimotor network. CONCLUSIONS There are important resting-state dFNC differences between children with FASD and TDC. Participants with FASD exhibited greater dynamic fluidity and dynamic range and spent more time in states typified by anticorrelation within and between DMN and VN, and more time in a state typified by high internetwork connectivity. Taken together, these network aberrations indicate that prenatal alcohol exposure has a global effect on resting-state connectivity.
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Affiliation(s)
| | - Megan E. Schendel
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Lucinda Flynn
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Cassandra Cerros
- Department of Pediatrics, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Dina E. Hill
- Department of Psychiatry and Behavioral Sciences, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Julia M. Stephen
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
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16
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Hancock F, Rosas FE, McCutcheon RA, Cabral J, Dipasquale O, Turkheimer FE. Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology. PLoS One 2023; 18:e0282707. [PMID: 36952467 PMCID: PMC10035891 DOI: 10.1371/journal.pone.0282707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/21/2023] [Indexed: 03/25/2023] Open
Abstract
The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81-92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
| | - Fernando E. Rosas
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
| | - Robert A. McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Life and Health Sciences Research Institute School of Medicine, University of Minho, Braga, Portugal
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
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17
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Lahti K, Setänen S, Vorobyev V, Nyman A, Haataja L, Parkkola R. Altered temporal connectivity and reduced meta-state dynamism in adolescents born very preterm. Brain Commun 2023; 5:fcad009. [PMID: 36819939 PMCID: PMC9927875 DOI: 10.1093/braincomms/fcad009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 06/29/2022] [Accepted: 02/05/2023] [Indexed: 02/09/2023] Open
Abstract
Adolescents born very preterm have an increased risk for anxiety, social difficulties and inattentiveness, i.e. the 'preterm behavioural phenotype'. The extreme end of these traits comprises the core diagnostic features of attention and hyperactivity disorders and autism spectrum disorder, which have been reported to show aberrant dynamic resting-state functional network connectivity. This study aimed to compare this dynamism between adolescents born very preterm and controls. A resting-state functional magnetic resonance imaging was performed on 24 adolescents born very preterm (gestational age <32 weeks and/or birth weight ≤1500 g) and 32 controls born full term (≥37 weeks of gestation) at 13 years of age. Group-wise comparisons of dynamic connectivity between the resting-state networks were performed using both hard clustering and meta-state analysis of functional network connectivity. The very preterm group yielded a higher fraction of time spent in the least active connectivity state in hard clustering state functional network connectivity, even though no group differences in pairwise connectivity patterns were discovered. The meta-state analysis showed a decreased fluidity and dynamic range in the very preterm group compared with controls. Our results suggest that the 13-year-old adolescents born very preterm differ from controls in the temporal characteristics of functional connectivity. The findings may reflect the long-lasting effects of prematurity and the clinically acknowledged 'preterm behavioural phenotype'.
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Affiliation(s)
- Katri Lahti
- Correspondence to: Katri Lahti, Department of Adolescent Psychiatry, University of Turku and Turku University Hospital, Kunnallissairaalantie 20, rak4, 3.krs PL52, 20520 Turku, Finland E-mail:
| | - Sirkku Setänen
- Department of Pediatric Neurology, University of Turku and Turku University Hospital, PO Box 52, FI-20521, Turku, Finland
| | - Victor Vorobyev
- Department of Diagnostic Radiology, University of Turku, Kiinamyllynkatu 4-8, FI- 20520 Turku, Finland
| | - Anna Nyman
- Department of Social Research, 20014 University of Turku, Turku, Finland
| | - Leena Haataja
- Children’s Hospital, University of Helsinki, PO Box 22 (Stenbäckinkatu 11), 00014 Helsinki, Finland
| | - Riitta Parkkola
- Department of Diagnostic Radiology, University of Turku, Kiinamyllynkatu 4-8, FI- 20520 Turku, Finland
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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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19
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Fu Z, Abbott CC, Sui J, Calhoun VD. Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes. Front Pharmacol 2023; 14:1102413. [PMID: 36755955 PMCID: PMC9899999 DOI: 10.3389/fphar.2023.1102413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
Introduction: Electroconvulsive therapy (ECT) remains one of the most effective approaches for treatment-resistant depressive episodes, despite the potential cognitive impairment associated with this treatment. As a potent stimulator of neuroplasticity, ECT might normalize aberrant depression-related brain function via the brain's reconstruction by forming new neural connections. Multiple lines of evidence have demonstrated that functional connectivity (FC) changes are reliable indicators of antidepressant efficacy and cognitive changes from static and dynamic perspectives. However, no previous studies have directly ascertained whether and how different aspects of FC provide complementary information in terms of neuroimaging-based prediction of clinical outcomes. Methods: In this study, we implemented a fully automated independent component analysis framework to an ECT dataset with subjects (n = 50, age = 65.54 ± 8.92) randomized to three treatment amplitudes (600, 700, or 800 milliamperes [mA]). We extracted the static functional network connectivity (sFNC) and dynamic FNC (dFNC) features and employed a partial least square regression to build predictive models for antidepressant outcomes and cognitive changes. Results: We found that both antidepressant outcomes and memory changes can be robustly predicted by the changes in sFNC (permutation test p < 5.0 × 10-3). More interestingly, by adding dFNC information, the model achieved higher accuracy for predicting changes in the Hamilton Depression Rating Scale 24-item (HDRS24, t = 9.6434, p = 1.5 × 10-21). The predictive maps of clinical outcomes show a weakly negative correlation, indicating that the ECT-induced antidepressant outcomes and cognitive changes might be associated with different functional brain neuroplasticity. Discussion: The overall results reveal that dynamic FC is not redundant but reflects mechanisms of ECT that cannot be captured by its static counterpart, especially for the prediction of antidepressant efficacy. Tracking the predictive signatures of static and dynamic FC will help maximize antidepressant outcomes and cognitive safety with individualized ECT dosing.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Christopher C. Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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20
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Rafi H, Delavari F, Perroud N, Derome M, Debbané M. The continuum of attention dysfunction: Evidence from dynamic functional network connectivity analysis in neurotypical adolescents. PLoS One 2023; 18:e0279260. [PMID: 36662797 PMCID: PMC9858399 DOI: 10.1371/journal.pone.0279260] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/04/2022] [Indexed: 01/21/2023] Open
Abstract
The question of whether attention-related disorders such as attention-deficit/hyperactivity disorder (ADHD) are best understood as clinical categories or as extreme ends of a spectrum is an ongoing debate. Assessing individuals with varying degrees of attention problems and utilizing novel methodologies to assess relationships between attention and brain activity may provide key information to support the spectrum hypothesis. We scanned 91 neurotypical adolescents during rest using functional magnetic resonance imaging. We conducted static and dynamic functional network connectivity (FNC) analysis and correlated findings to behavioral metrics of ADHD, attention problems, and impulsivity. We found that dynamic FNC analysis detects significant differences in large-scale neural connectivity as a function of individual differences in attention and impulsivity that are obscured in static analysis. We show ADHD manifestations and attention problems are associated with diminished Salience Network-centered FNC and that ADHD manifestations and impulsivity are associated with prolonged periods of dynamically hyperconnected states. Importantly, our meta-state analysis results reveal a relationship between ADHD manifestations and exhibiting variable and volatile dynamic behavior such as changing meta-states more often and traveling over a greater dynamic range. These findings in non-clinical adolescents provide support for the continuum model of attention disorders.
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Affiliation(s)
- Halima Rafi
- Faculty of Psychology and Educational Sciences, Developmental Clinical Psychology Research Unit, University of Geneva, Geneva, Switzerland
- Department of Psychiatry, Developmental Neuroimaging and Psychopathology Laboratory, University of Geneva, Geneva, Switzerland
| | - Farnaz Delavari
- Department of Psychiatry, Developmental Neuroimaging and Psychopathology Laboratory, University of Geneva, Geneva, Switzerland
- Medical Image Processing Lab, Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Nader Perroud
- Department of Psychiatry, Service of Psychiatric Specialties, University Hospitals of Geneva, Geneva, Switzerland
| | - Mélodie Derome
- Faculty of Psychology and Educational Sciences, Developmental Clinical Psychology Research Unit, University of Geneva, Geneva, Switzerland
- Department of Psychiatry, Developmental Neuroimaging and Psychopathology Laboratory, University of Geneva, Geneva, Switzerland
| | - Martin Debbané
- Faculty of Psychology and Educational Sciences, Developmental Clinical Psychology Research Unit, University of Geneva, Geneva, Switzerland
- Department of Psychiatry, Developmental Neuroimaging and Psychopathology Laboratory, University of Geneva, Geneva, Switzerland
- Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
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21
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Liddell BJ, Das P, Malhi GS, Nickerson A, Felmingham KL, Askovic M, Aroche J, Coello M, Cheung J, Den M, Outhred T, Bryant RA. Refugee visa insecurity disrupts the brain's default mode network. Eur J Psychotraumatol 2023; 14:2213595. [PMID: 37289090 PMCID: PMC10251781 DOI: 10.1080/20008066.2023.2213595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 04/17/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Research has largely focused on the psychological consequences of refugee trauma exposure, but refugees living with visa insecurity face an uncertain future that also adversely affects psychological functioning and self-determination. OBJECTIVE This study aimed to examine how refugee visa insecurity affects the functional brain. METHOD We measured resting state brain activity via fMRI in 47 refugees with insecure visas (i.e. temporary visa status) and 52 refugees with secure visas (i.e. permanent visa status) residing in Australia, matched on key demographic, trauma exposure and psychopathology. Data analysis comprised independent components analysis to identify active networks and dynamic functional causal modelling tested visa security group differences in network connectivity. RESULTS We found that visa insecurity specifically affected sub-systems within the default mode network (DMN) - an intrinsic network subserving self-referential processes and mental simulations about the future. The insecure visa group showed less spectral power in the low frequency band in the anterior ventromedial DMN, and reduced activity in the posterior frontal DMN, compared to the secure visa group. Using functional dynamic causal modelling, we observed positive coupling between the anterior and posterior midline DMN hubs in the secure visa group, while the insecure visa group displayed negative coupling that correlated with self-reported fear of future deportation. CONCLUSIONS Living with visa-related uncertainty appears to undermine synchrony between anterior-posterior midline components of the DMN responsible for governing the construction of the self and making mental representations of the future. This could represent a neural signature of refugee visa insecurity, which is marked by a perception of living in limbo and a truncated sense of the future.
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Affiliation(s)
| | - Pritha Das
- Department of Psychiatry, Faculty of Medicine and Health, Northern Clinical School, The University of Sydney, Sydney, Australia
- Academic Department of Psychiatry, Royal North Shore Hospital, St Leonards, Australia
- CADE Clinic, Royal North Shore Hospital, St Leonards, Australia
| | - Gin S. Malhi
- Department of Psychiatry, Faculty of Medicine and Health, Northern Clinical School, The University of Sydney, Sydney, Australia
- Academic Department of Psychiatry, Royal North Shore Hospital, St Leonards, Australia
- CADE Clinic, Royal North Shore Hospital, St Leonards, Australia
| | | | - Kim L. Felmingham
- School of Psychological Sciences, University of Melbourne, Melbourne, Australia
| | - Mirjana Askovic
- NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), Sydney, Australia
| | - Jorge Aroche
- NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), Sydney, Australia
| | - Mariano Coello
- NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), Sydney, Australia
| | | | - Miriam Den
- School of Psychology, UNSW Sydney, Sydney, Australia
| | - Tim Outhred
- Department of Psychiatry, Faculty of Medicine and Health, Northern Clinical School, The University of Sydney, Sydney, Australia
- Academic Department of Psychiatry, Royal North Shore Hospital, St Leonards, Australia
- CADE Clinic, Royal North Shore Hospital, St Leonards, Australia
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22
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Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression. Brain Imaging Behav 2022; 16:2744-2754. [PMID: 36333522 PMCID: PMC9638404 DOI: 10.1007/s11682-022-00739-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2022] [Indexed: 11/06/2022]
Abstract
Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls.
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23
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Puvogel S, Blanchard K, Casas BS, Miller RL, Garrido-Jara D, Arizabalos S, Rehen SK, Sanhueza M, Palma V. Altered resting-state functional connectivity in hiPSCs-derived neuronal networks from schizophrenia patients. Front Cell Dev Biol 2022; 10:935360. [PMID: 36158199 PMCID: PMC9489842 DOI: 10.3389/fcell.2022.935360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/02/2022] [Indexed: 11/15/2022] Open
Abstract
Schizophrenia (SZ) is a severe mental disorder that arises from abnormal neurodevelopment, caused by genetic and environmental factors. SZ often involves distortions in reality perception and it is widely associated with alterations in brain connectivity. In the present work, we used Human Induced Pluripotent Stem Cells (hiPSCs)-derived neuronal cultures to study neural communicational dynamics during early development in SZ. We conducted gene and protein expression profiling, calcium imaging recordings, and applied a mathematical model to quantify the dynamism of functional connectivity (FC) in hiPSCs-derived neuronal networks. Along the neurodifferentiation process, SZ networks displayed altered gene expression of the glutamate receptor-related proteins HOMER1 and GRIN1 compared to healthy control (HC) networks, suggesting a possible tendency to develop hyperexcitability. Resting-state FC in neuronal networks derived from HC and SZ patients emerged as a dynamic phenomenon exhibiting connectivity configurations reoccurring in time (hub states). Compared to HC, SZ networks were less thorough in exploring different FC configurations, changed configurations less often, presented a reduced repertoire of hub states and spent longer uninterrupted time intervals in this less diverse universe of hubs. Our results suggest that alterations in the communicational dynamics of SZ emerging neuronal networks might contribute to the previously described brain FC anomalies in SZ patients, by compromising the ability of their neuronal networks for rapid and efficient reorganization through different activity patterns.
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Affiliation(s)
- Sofía Puvogel
- Laboratory of Stem Cells and Developmental Biology, Department of Biology, Faculty of Sciences. Universidad de Chile. Santiago, Chile
- Cell Physiology Laboratory, Department of Biology, Faculty of Sciences, Universidad de Chile, Santiago, Chile
| | - Kris Blanchard
- Laboratory of Stem Cells and Developmental Biology, Department of Biology, Faculty of Sciences. Universidad de Chile. Santiago, Chile
- Cell Physiology Laboratory, Department of Biology, Faculty of Sciences, Universidad de Chile, Santiago, Chile
| | - Bárbara S. Casas
- Laboratory of Stem Cells and Developmental Biology, Department of Biology, Faculty of Sciences. Universidad de Chile. Santiago, Chile
| | - Robyn L. Miller
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS Center), Atlanta, GA, United States
| | - Delia Garrido-Jara
- Laboratory of Stem Cells and Developmental Biology, Department of Biology, Faculty of Sciences. Universidad de Chile. Santiago, Chile
| | - Sebastián Arizabalos
- Laboratory of Stem Cells and Developmental Biology, Department of Biology, Faculty of Sciences. Universidad de Chile. Santiago, Chile
| | - Stevens K. Rehen
- Instituto D’Or de Pesquisa e Ensino (IDOR), Rio de Janeiro, Brazil
| | - Magdalena Sanhueza
- Cell Physiology Laboratory, Department of Biology, Faculty of Sciences, Universidad de Chile, Santiago, Chile
- *Correspondence: Verónica Palma, ; Magdalena Sanhueza,
| | - Verónica Palma
- Laboratory of Stem Cells and Developmental Biology, Department of Biology, Faculty of Sciences. Universidad de Chile. Santiago, Chile
- *Correspondence: Verónica Palma, ; Magdalena Sanhueza,
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24
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Qiao J, Wang R, Liu H, Xu G, Wang Z. Brain disorder prediction with dynamic multivariate spatio-temporal features: Application to Alzheimer’s disease and autism spectrum disorder. Front Aging Neurosci 2022; 14:912895. [PMID: 36110425 PMCID: PMC9468323 DOI: 10.3389/fnagi.2022.912895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/05/2022] [Indexed: 11/16/2022] Open
Abstract
The dynamic functional connectivity (dFC) in functional magnetic resonance imaging (fMRI) is beneficial for the analysis and diagnosis of neurological brain diseases. The dFCs between regions of interest (ROIs) are generally delineated by a specific template and clustered into multiple different states. However, these models inevitably fell into the model-driven self-contained system which ignored the diversity at spatial level and the dynamics at time level of the data. In this study, we proposed a spatial and time domain feature extraction approach for Alzheimer’s disease (AD) and autism spectrum disorder (ASD)-assisted diagnosis which exploited the dynamic connectivity among independent functional sub networks in brain. Briefly, independent sub networks were obtained by applying spatial independent component analysis (SICA) to the preprocessed fMRI data. Then, a sliding window approach was used to segment the time series of the spatial components. After that, the functional connections within the window were obtained sequentially. Finally, a temporal signal-sensitive long short-term memory (LSTM) network was used for classification. The experimental results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Autism Brain Imaging Data Exchange (ABIDE) datasets showed that the proposed method effectively predicted the disease at the early stage and outperformed the existing algorithms. The dFCs between the different components of the brain could be used as biomarkers for the diagnosis of diseases such as AD and ASD, providing a reliable basis for the study of brain connectomics.
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Affiliation(s)
- Jianping Qiao
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
- *Correspondence: Jianping Qiao,
| | - Rong Wang
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Hongjia Liu
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Guangrun Xu
- Department of Neurology, Qilu Hospital of Shandong University, Jinan, China
- Guangrun Xu,
| | - Zhishun Wang
- Department of Psychiatry, Columbia University, New York, NY, United States
- Zhishun Wang,
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25
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Schoonheim MM, Broeders TAA, Geurts JJG. The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics. Neuroimage Clin 2022; 35:103108. [PMID: 35917719 PMCID: PMC9421449 DOI: 10.1016/j.nicl.2022.103108] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/01/2022] [Accepted: 07/10/2022] [Indexed: 11/16/2022]
Abstract
Multiple sclerosis (MS) can be considered as a network disorder. This review discusses network concepts in order to understand progression in MS. Damage is hypothesized to lead to a “network collapse” and clinical progression. New concepts are discussed that will likely influence the field in the near future. These include brain wiring, how regions communicate and robustness to damage.
Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a “network collapse”. After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.
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Affiliation(s)
- Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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26
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Lan L, Liu Y, Xu JJ, Ma D, Yin X, Wu Y, Chen YC, Cai Y. Aberrant Modulations of Neurocognitive Network Dynamics in Migraine Comorbid With Tinnitus. Front Aging Neurosci 2022; 14:913191. [PMID: 35813956 PMCID: PMC9257523 DOI: 10.3389/fnagi.2022.913191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThe possible relationship between migraine and tinnitus still remains elusive although migraine is often accompanied by chronic tinnitus. Several neuroimaging studies have reinforced the cognitive network abnormality in migraine and probably as well as tinnitus. The present work aims to investigate the dynamic neurocognitive network alterations of migraine comorbid with tinnitus.Materials and MethodsParticipants included migraine patients (n = 32), tinnitus patients (n = 20), migraine with tinnitus (n = 27), and healthy controls (n = 47), matched for age and gender. Resting-state functional magnetic resonance imaging (rs-fMRI) with independent component analysis (ICA), sliding window cross-correlation, and clustering state analysis was used to detect the dynamic functional network connectivity (dFNC) of each group. Correlation analyses illustrated the association between clinical symptoms and abnormal dFNC in migraine as well as tinnitus.ResultsCompared with healthy controls, migraine patients exhibited decreased cerebellar network and visual network (CN-VN) connectivity in State 2; migraine with tinnitus patients showed not only decreased CN-VN connectivity in State 2 but also decreased cerebellar network and executive control network (CN-ECN) connectivity in State 2 and increased cerebellar network and somatomotor network (SMN-VN) connectivity in State 1. The abnormal cerebellum dFNC with the executive control network (CN-ECN) was negatively correlated with headache frequency of migraine (rho = −0.776, p = 0.005).ConclusionBrain network characteristics of migraine with tinnitus patients may indicate different mechanisms for migraine and tinnitus. Our results demonstrated a transient pathologic state with atypical cerebellar-cortical connectivity in migraine with tinnitus patients, which might be used to identify the neuro-pathophysiological mechanisms in migraine accompanied by tinnitus.
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Affiliation(s)
- Liping Lan
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yin Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jin-Jing Xu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Di Ma
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Yu-Chen Chen,
| | - Yuexin Cai
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Yuexin Cai,
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27
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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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28
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Miller RL, Vergara VM, Pearlson GD, Calhoun VD. Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs. Front Neurosci 2022; 16:770468. [PMID: 35516809 PMCID: PMC9063321 DOI: 10.3389/fnins.2022.770468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 01/24/2022] [Indexed: 11/28/2022] Open
Abstract
The study of brain network connectivity as a time-varying property began relatively recently and, to date, has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group-level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the stable identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a continuity-preserving planar embedding of high-dimensional time-varying measurements of whole-brain functional network connectivity. Planar linear exemplars summarizing dominant dynamic trends across the population are computed from local linear approximations to the two-dimensional 2D embedded trajectories. A high-dimensional representation of each 2D exemplar segment is obtained by averaging the dFNC observations corresponding to the n planar nearest neighbors of τ evenly spaced points along the 2D line segment representation (where n is the UMAP number-of-neighbors parameter and τ is the temporal duration of trajectory segments being approximated). Each of the 2D exemplars thus “lifts” to a multiframe high-dimensional dFNC trajectory of length τ. The collection of high-dimensional temporally evolving dFNC representations (EVOdFNCs) derived in this manner are employed as dynamic basis objects with which to characterize observed high-dimensional dFNC trajectories, which are then expressed as weighted combination of these basis objects. Our approach yields new insights into anomalous patterns of fluidly varying whole-brain connectivity that are significantly associated with schizophrenia as a broad diagnosis as well as with certain symptoms of this serious disorder. Importantly, we show that relative to conventional hidden Markov modeling with single-frame unvarying dFNC summary states, EVOdFNCs are more sensitive to positive symptoms of schizophrenia including hallucinations and delusions, suggesting that a more dynamic characterization is needed to help illuminate such a complex brain disorder.
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Affiliation(s)
- Robyn L. Miller
- The 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
- *Correspondence: Robyn L. Miller,
| | - Victor M. Vergara
- The 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
| | | | - Vince D. Calhoun
- The 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
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29
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Broeders TA, Douw L, Eijlers AJ, Dekker I, Uitdehaag BM, Barkhof F, Hulst HE, Vinkers CH, Geurts JJ, Schoonheim MM. A more unstable resting-state functional network in cognitively declining multiple sclerosis. Brain Commun 2022; 4:fcac095. [PMID: 35620116 PMCID: PMC9128379 DOI: 10.1093/braincomms/fcac095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/14/2022] [Accepted: 04/11/2022] [Indexed: 11/24/2022] Open
Abstract
Cognitive impairment is common in people with multiple sclerosis and strongly
affects their daily functioning. Reports have linked disturbed cognitive
functioning in multiple sclerosis to changes in the organization of the
functional network. In a healthy brain, communication between brain regions and
which network a region belongs to is continuously and dynamically adapted to
enable adequate cognitive function. However, this dynamic network adaptation has
not been investigated in multiple sclerosis, and longitudinal network data
remain particularly rare. Therefore, the aim of this study was to longitudinally
identify patterns of dynamic network reconfigurations that are related to the
worsening of cognitive decline in multiple sclerosis. Resting-state functional
MRI and cognitive scores (expanded Brief Repeatable Battery of
Neuropsychological tests) were acquired in 230 patients with multiple sclerosis
and 59 matched healthy controls, at baseline (mean disease duration: 15 years)
and at 5-year follow-up. A sliding-window approach was used for functional MRI
analyses, where brain regions were dynamically assigned to one of seven
literature-based subnetworks. Dynamic reconfigurations of subnetworks were
characterized using measures of promiscuity (number of subnetworks switched to),
flexibility (number of switches), cohesion (mutual switches) and disjointedness
(independent switches). Cross-sectional differences between cognitive groups and
longitudinal changes were assessed, as well as relations with structural damage
and performance on specific cognitive domains. At baseline, 23% of
patients were cognitively impaired (≥2/7 domains
Z < −2) and 18% were mildly
impaired (≥2/7 domains
Z < −1.5). Longitudinally,
28% of patients declined over time (0.25 yearly change on ≥2/7
domains based on reliable change index). Cognitively impaired patients displayed
more dynamic network reconfigurations across the whole brain compared with
cognitively preserved patients and controls, i.e. showing higher promiscuity
(P = 0.047), flexibility
(P = 0.008) and cohesion
(P = 0.008). Over time, cognitively
declining patients showed a further increase in cohesion
(P = 0.004), which was not seen in stable
patients (P = 0.544). More cohesion was
related to more severe structural damage (average
r = 0.166,
P = 0.015) and worse verbal memory
(r = −0.156,
P = 0.022), information processing speed
(r = −0.202,
P = 0.003) and working memory
(r = −0.163,
P = 0.017). Cognitively impaired multiple
sclerosis patients exhibited a more unstable network reconfiguration compared to
preserved patients, i.e. brain regions switched between subnetworks more often,
which was related to structural damage. This shift to more unstable network
reconfigurations was also demonstrated longitudinally in patients that showed
cognitive decline only. These results indicate the potential relevance of a
progressive destabilization of network topology for understanding cognitive
decline in multiple sclerosis.
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Affiliation(s)
- Tommy A.A. Broeders
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anand J.C. Eijlers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Iris Dekker
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bernard M.J. Uitdehaag
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK
| | - Hanneke E. Hulst
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Christiaan H. Vinkers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Departments of Psychiatry, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J.G. Geurts
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M. Schoonheim
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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30
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Rahaman MA, Damaraju E, Saha DK, Plis SM, Calhoun VD. Statelets: Capturing recurrent transient variations in dynamic functional network connectivity. Hum Brain Mapp 2022; 43:2503-2518. [PMID: 35274791 PMCID: PMC9057100 DOI: 10.1002/hbm.25799] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/23/2021] [Accepted: 01/24/2022] [Indexed: 11/17/2022] Open
Abstract
Dynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approach for analyzing dFNC models the system through a fixed sequence of connectivity states. SWC assumes connectivity patterns span throughout the brain, but they are relatively spatially constrained and temporally short‐lived in practice. Thus, SWC is neither designed to capture transient dynamic changes nor heterogeneity across subjects/time. We propose a state‐space time series summarization framework called “statelets” to address these shortcomings. It models functional connectivity dynamics at fine‐grained timescales, adapting time series motifs to changes in connectivity strength, and constructs a concise yet informative representation of the original data that conveys easily comprehensible information about the phenotypes. We leverage the earth mover distance in a nonstandard way to handle scale differences and utilize kernel density estimation to build a probability density profile for local motifs. We apply the framework to study dFNC of patients with schizophrenia (SZ) and healthy control (HC). Results demonstrate SZ subjects exhibit reduced modularity in their brain network organization relative to HC. Statelets in the HC group show an increased recurrence across the dFNC time‐course compared to the SZ. Analyzing the consistency of the connections across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced approach also enables handling dynamic information in cross‐modal and multimodal applications to study healthy and disordered brains.
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Affiliation(s)
- Md Abdur Rahaman
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Eswar Damaraju
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Debbrata K Saha
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Sergey M Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Vince D Calhoun
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
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31
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Farinha M, Amado C, Morgado P, Cabral J. Increased Excursions to Functional Networks in Schizophrenia in the Absence of Task. Front Neurosci 2022; 16:821179. [PMID: 35360175 PMCID: PMC8963765 DOI: 10.3389/fnins.2022.821179] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/10/2022] [Indexed: 01/10/2023] Open
Abstract
Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition, and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using functional magnetic resonance imaging (fMRI) data from an open-source database, this study investigated differences between the dynamic exploration of resting-state networks in 71 schizophrenia patients and 74 healthy controls. Focusing on recurrent states of phase coherence in fMRI signals, brain activity was examined for intergroup differences through the lens of dynamical systems theory. Results showed reduced fractional occupancy and dwell time of a globally synchronized state in schizophrenia. Conversely, patients exhibited increased fractional occupancy, dwell time and limiting probability of being in states during which canonical functional networks—i.e., Limbic, Dorsal Attention and Somatomotor—synchronized in anti-phase with respect to the rest of the brain. In terms of state-to-state transitions, patients exhibited increased probability of switching to Limbic, Somatomotor and Visual networks, and reduced probability of remaining in states related to the Default Mode network, the Orbitofrontal network and the globally synchronized state. All results revealed medium to large effect sizes. Combined, these findings expose pronounced differences in the temporal expression of resting-state networks in schizophrenia patients, which may relate to the pathophysiology of this disorder. Overall, these results reinforce the utility of dynamical systems theory to extend current knowledge regarding disrupted brain dynamics in psychiatric disorders.
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Affiliation(s)
- Miguel Farinha
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Centro Clínico Académico Hospital de Braga, Braga, Portugal
- *Correspondence: Miguel Farinha
| | - Conceição Amado
- Department of Mathematics and CEMAT, Instituto Superior Tècnico, University of Lisbon, Lisbon, Portugal
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Centro Clínico Académico Hospital de Braga, Braga, Portugal
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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32
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Xu N, LaGrow TJ, Anumba N, Lee A, Zhang X, Yousefi B, Bassil Y, Clavijo GP, Khalilzad Sharghi V, Maltbie E, Meyer-Baese L, Nezafati M, Pan WJ, Keilholz S. Functional Connectivity of the Brain Across Rodents and Humans. Front Neurosci 2022; 16:816331. [PMID: 35350561 PMCID: PMC8957796 DOI: 10.3389/fnins.2022.816331] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
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Affiliation(s)
- Nan Xu
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Theodore J. LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Nmachi Anumba
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Azalea Lee
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
- Emory University School of Medicine, Atlanta, GA, United States
| | - Xiaodi Zhang
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Behnaz Yousefi
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Yasmine Bassil
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
| | - Gloria P. Clavijo
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | | | - Eric Maltbie
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Lisa Meyer-Baese
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Wen-Ju Pan
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Shella Keilholz
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
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33
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Ma L, Liu M, Xue K, Ye C, Man W, Cheng M, Liu Z, Zhu D, Liu F, Wang J. Abnormal regional spontaneous brain activities in white matter in patients with autism spectrum disorder. Neuroscience 2022; 490:1-10. [PMID: 35218886 DOI: 10.1016/j.neuroscience.2022.02.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/01/2022] [Accepted: 02/18/2022] [Indexed: 10/19/2022]
Abstract
Previous studies have demonstrated patients with autism spectrum disorder (ASD) are accompanied by alterations of spontaneous brain activity in gray matter. However, whether the alterations of spontaneous brain activity exist in white matter remains largely unclear. In this study, 88 ASD patients and 87 typical controls (TCs) were included and regional homogeneity (ReHo) was calculated to characterize spontaneous brain activity in white matter. Voxel-wise two-sample t-tests were performed to investigate ReHo alterations, and cluster-level analyses were conducted to examine structural-functional coupling changes. Compared with TCs, the ASD group showed significantly decreased ReHo in the left superior corona radiata and left posterior limb of internal capsule, and decreased ReHo in the left anterior corona radiata with a trend level of significance. In addition, significantly weaker structural-functional coupling was observed in the left superior corona radiata and left posterior limb of internal capsule in ASD patients. Taken together, these findings highlighted abnormalities of white matter's regional spontaneous brain activity in ASD, which may provide new insights into the pathophysiological mechanisms of this disorder.
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Affiliation(s)
- Lin Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Mengge Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Caihua Ye
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Weiqi Man
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Meng Cheng
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zhixuan Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Dan Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China; Department of Radiology, Tianjin Medical University General Hospital Airport Hospital, Tianjin 300308, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
| | - Junping Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
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34
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Rahaman MA, Damaraju E, Turner JA, van Erp TG, Mathalon D, Vaidya J, Muller B, Pearlson G, Calhoun VD. Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals. Brain Connect 2022; 12:61-73. [PMID: 34049447 PMCID: PMC8867091 DOI: 10.1089/brain.2020.0896] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Background: Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Methods: Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. Results: Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Conclusions: Outcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run k-means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the k-means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.
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Affiliation(s)
- Md Abdur Rahaman
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Address correspondence to: Md Abdur Rahaman, Department of Computational Science and Engineering, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Eswar Damaraju
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Jessica A. Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Theo G.M. van Erp
- Center for the Neurobiology of Learning and Memory, Department of Psychiatry and Human Behavior, University of California Irvine, California, USA.,Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, California, USA
| | - Daniel Mathalon
- Department of Psychiatry, Weill Institute for Neurosciences, University of California San Francisco, California, USA
| | - Jatin Vaidya
- Department of Psychiatry, Cognitive Brain Development Laboratory, University of Iowa Health Care, Iowa, USA
| | - Bryon Muller
- Department of Psychiatry, University of Minnesota, Minnesota, USA
| | - Godfrey Pearlson
- Department of Psychiatry and Neuroscience, Yale School of Medicine, Connecticut, USA
| | - Vince D. Calhoun
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
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35
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Sambataro F, Hirjak D, Fritze S, Kubera KM, Northoff G, Calhoun VD, Meyer‐Lindenberg A, Wolf RC. Intrinsic neural network dynamics in catatonia. Hum Brain Mapp 2021; 42:6087-6098. [PMID: 34585808 PMCID: PMC8596986 DOI: 10.1002/hbm.25671] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/07/2021] [Accepted: 09/16/2021] [Indexed: 12/17/2022] Open
Abstract
Catatonia is a transnosologic psychomotor syndrome with high prevalence in schizophrenia spectrum disorders (SSD). There is mounting neuroimaging evidence that catatonia is associated with aberrant frontoparietal, thalamic and cerebellar regions. Large-scale brain network dynamics in catatonia have not been investigated so far. In this study, resting-state fMRI data from 58 right-handed SSD patients were considered. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate and test the underlying intrinsic components (ICs) in SSD patients with (NCRS total score ≥ 3; n = 30) and without (NCRS total score = 0; n = 28) catatonia. Functional network connectivity (FNC) during rest was calculated between pairs of ICs and transient changes in connectivity were estimated using sliding windowing and clustering (to capture both static and dynamic FNC). Catatonic patients showed increased static FNC in cerebellar networks along with decreased low frequency oscillations in basal ganglia (BG) networks. Catatonic patients had reduced state changes and dwelled more in a state characterized by high within-network correlation of the sensorimotor, visual, and default-mode network with respect to noncatatonic patients. Finally, in catatonic patients according to DSM-IV-TR (n = 44), there was a significant correlation between increased within FNC in cortico-striatal state and NCRS motor scores. The data support a neuromechanistic model of catatonia that emphasizes a key role of disrupted sensorimotor network control during distinct functional states.
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Affiliation(s)
- Fabio Sambataro
- Department of Neuroscience (DNS)University of PadovaPadovaItaly
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Katharina M. Kubera
- Center for Psychosocial Medicine, Department of General PsychiatryHeidelberg UniversityGermany
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal's Institute of Mental Health ResearchUniversity of OttawaOttawaOntarioCanada
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory UniversityAtlantaGeorgia
| | - Andreas Meyer‐Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Robert C. Wolf
- Center for Psychosocial Medicine, Department of General PsychiatryHeidelberg UniversityGermany
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36
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Li F, Lu L, Shang S, Chen H, Wang P, Muthaiah VP, Yin X, Chen YC. Altered static and dynamic functional network connectivity in post-traumatic headache. J Headache Pain 2021; 22:137. [PMID: 34773973 PMCID: PMC8590227 DOI: 10.1186/s10194-021-01348-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 10/28/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Post-traumatic headache (PTH) is a very common symptom following mild traumatic brain injury (mTBI), yet much remains unknown about the underlying pathophysiological mechanisms of PTH. Neuroimaging studies suggest that aberrant functional network connectivity (FNC) may be an important factor in pain disorders. The present study aimed to investigate the functional characteristics of static FNC (sFNC) and dynamic FNC (dFNC) in mTBI patients with PTH. METHODS With Institutional Review Board (IRB) approval, we prospectively recruited 50 mTBI patients with PTH, who were diagnosed with ICHD-3 beta diagnostic criteria and 39 mTBI without PTH who were well matched for age, gender and education. Resting-state functional magnetic resonance imaging (fMRI) scanning (3.0 T, Philips Medical Systems, Netherlands), Montreal Cognitive Assessment (MoCA) and headache symptom measurement (headache frequency and headache intensity) were performed. The resting-state fMRI sequence took 8 min and 10 s. Independent component analysis and sliding window method were applied to examine the FNC on the basis of nine resting-state networks, namely, default mode network (DMN), sensorimotor network (SMN), executive control network (ECN), auditory network (AuN), attention network (AN), salience network (SN), visual network (VN), and cerebellum network (CN). The differences in sFNC and dFNC were determined and correlated with clinical variables using Pearson rank correlation. RESULTS For sFNC, compared with mTBI patients without PTH, mTB with PTH group showed four altered interactions, including decreased interactions in SN-SMN and VN-DMN pairs, increased sFNC in SN-ECN and SMN-DMN pairs. For dFNC, significant group differences were found in State 2, including increased connectivity alteration in the DMN with CN, DMN with SMN, and AuN with CN. Significant reduced connectivity changes in the DMN with VN was found in State 4. Furthermore, the number of transitions (r=0.394, p=0.005) between states was positively associated with headache frequency. Additionally, dwell time (r=-0.320, p=0.025) in State 1 was negatively correlated with MoCA score. CONCLUSIONS MTBI patients with PTH are characterized with altered sFNC and dFNC, which could provide new perspective to understand the neuropathological mechanism underlying the PTH to determine more appropriate management, and may be a useful imaging biomarker for identifying and predicting mTBI with PTH.
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Affiliation(s)
- Fengfang Li
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China
| | - Liyan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China
| | - Song'an Shang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China
| | - Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China
| | - Vijaya Prakash Muthaiah
- Department of Rehabilitation Science, School of Public Health and Health Professions, University at Buffalo, Buffalo, USA
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China.
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China.
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Jarrahi B. The Influence of Spatial Smoothing Kernel Size on the Whole-brain Dynamic Functional Network Connectivity and Meta-state Parameters. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3197-3200. [PMID: 34891921 DOI: 10.1109/embc46164.2021.9629759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In functional magnetic resonance imaging (fMRI), spatial smoothing procedure is generally a stable step in the preprocessing stream. Previous research (including ours) suggested dependency of the static functional connectivity on the size of the spatial smoothing kernel size. But its impact on the time-varying patterns of functional connectivity has not been investigated. Here, we sought to identify the effects of spatial smoothing on brain dynamics by performing dynamic functional network connectivity (dFNC) and meta-state analysis, a unique approach capable of examining a higher-dimensional temporal dynamism of whole-brain functional connectivity. Gaussian smoothing kernel with different widths at half of the maximum of the height of the Gaussian (4, 8, and 12 mm FWHM) were used during preprocessing prior to the group independent component analysis (ICA) with a relatively high model order of 75. dFNC was conducted using the sliding-time window approach and k-means clustering algorithm. Meta-state dynamics method was performed by reducing the number of windowed FNC correlations using principal components analysis (PCA), temporal and spatial ICA and k-means. Results revealed robust effects of spatial smoothing on the connectivity dynamics of several network pairs including a variety of cognitive/attention networks in a connectivity state with the highest occurrence (FDR corrected-p < 0.01). Meta-state analyses indicated significant changes in meta-state metrics including the number of meta-states, meta-state changes, meta-state span, and the total distance. These changes were particularly pronounced when we compared resting state data smoothed with 8 vs. 12 mm FWHM. Our preliminary findings give insights into the effects of spatial smoothing kernel size on the dynamics of functional connectivity and its consequences on meta-state parameters. It also provides further indication of the importance of evaluating variance associated with preprocessing steps on analysis outcomes.
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Jarrahi B. An ICA Investigation into the Effect of Physiological Noise Correction on Dynamic Functional Network Connectivity and Meta-state Metrics. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3137-3140. [PMID: 34891906 DOI: 10.1109/embc46164.2021.9630968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Physiological fluctuations such as cardiac pulsations (heart rate) and respiratory rhythm (breathing) have been studied in the resting state functional magnetic resonance imaging (rs-fMRI) studies as the potential sources of confounds in functional connectivity. Independent component analysis (ICA) provides a data driven approach to investigate functional connectivity at the network level. However, the effect of physiological noise correction on the dynamic of ICA-derived networks has not yet been studied. The goal of this study was to investigate the effect of retrospective correction of cardiorespiratory artifacts on the time-varying aspects of functional network connectivity. Blood oxygenation-level dependent (BOLD) rs-fMRI data were collected from healthy subjects using a 3.0T MRI scanner. Whole-brain dynamic functional network connectivity (dFNC) was computed using sliding time window correlation, and k-means clustering of windowed correlation matrices. Results showed significant effects of physiological denoising on dFNC between several network pairs in particular the subcortical, and cognitive/attention networks (false discovery rate [FDR]-corrected p < 0.01). Meta-state dynamics further revealed significant changes in the number of unique windows for each subject, number of times each subject changes from one meta-state to other, and sum of L1 distances between successive meta-states. In conclusion, removal of artifacts is important for achieving reliable fMRI results, however a more cautious approach should be adapted in regressing such "noise" in ICA functional connectivity approach. More experiments are needed to investigate impact of denoising on dFNC especially across different datasets.
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Brain connectivity dynamics in cisgender and transmen people with gender incongruence before gender affirmative hormone treatment. Sci Rep 2021; 11:21036. [PMID: 34702875 PMCID: PMC8548343 DOI: 10.1038/s41598-021-00508-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 10/13/2021] [Indexed: 11/08/2022] Open
Abstract
Large-scale brain network interactions have been described between trans- and cis-gender binary identities. However, a temporal perspective of the brain's spontaneous fluctuations is missing. We investigated the functional connectivity dynamics in transmen with gender incongruence and its relationship with interoceptive awareness. We describe four states in native and meta-state spaces: (i) one state highly prevalent with sparse overall connections; (ii) a second with strong couplings mainly involving components of the salience, default, and executive control networks. Two states with global sparse connectivity but positive couplings (iii) within the sensorimotor network, and (iv) between salience network regions. Transmen had more dynamical fluidity than cismen, while cismen presented less meta-state fluidity and range dynamism than transmen and ciswomen. A positive association between attention regulation and fluidity and meta-state range dynamism was found in transmen. There exist gender differences in the temporal brain dynamism, characterized by distinct interrelations of the salience network as catalyst interacting with other networks. We offer a functional explanation from the neurodevelopmental cortical hypothesis of a gendered-self.
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40
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Marimpis AD, Dimitriadis SI, Goebel R. Dyconnmap: Dynamic connectome mapping-A neuroimaging python module. Hum Brain Mapp 2021; 42:4909-4939. [PMID: 34250674 PMCID: PMC8449119 DOI: 10.1002/hbm.25589] [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: 01/20/2021] [Revised: 06/10/2021] [Accepted: 06/25/2021] [Indexed: 11/16/2022] Open
Abstract
Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.
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Affiliation(s)
- Avraam D Marimpis
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Brain Innovation B.V, Maastricht, The Netherlands
| | - Stavros I Dimitriadis
- Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Rainer Goebel
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Brain Innovation B.V, Maastricht, The Netherlands
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de Lacy N, Kutz JN, Calhoun VD. Sex-related differences in brain dynamism at rest as neural correlates of positive and negative valence system constructs. Cogn Neurosci 2021; 12:131-154. [PMID: 32715898 PMCID: PMC7881523 DOI: 10.1080/17588928.2020.1793752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/22/2020] [Indexed: 10/23/2022]
Abstract
Clinical anxiety and depression are the most prevalent mental illnesses, likely representing maladaptive expressions of negative valence systems concerned with conditioned responses to fear, threat, loss, and frustrative nonreward. These conditions exhibit similar, striking sex/gender-related differences in onset, incidence, and severity for which the neural correlates are not yet established. In alarge sample of neurotypical young adults, we demonstrate that intrinsic brain dynamism metrics derived from sex-sensitive models of whole-brain network function are significantly associated with valence system traits. Surprisingly, we found that greater brain dynamism is strongly positively correlated to anxiety and depression traits in males, but almost wholly decoupled from traits for important cognitive control and reappraisal strategies associated with positive valence. Conversely, intrinsic brain dynamism is strongly positively coupled to drive, novelty-seeking and self-control in females with only rare or non-significant directional negative correlation with anxiety and depression traits. Our results suggest that the dynamic neural correlates of traits for valence, anxiety and depression are significantly different in males/men and females/women. These findings may relate to the known sex/gender-related differences in cognitive reappraisal of emotional experiences and clinical presentations of anxiety and depression, with potential relevance to gold standard therapies based on enhancing cognitive control strategies.
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Affiliation(s)
- Nina de Lacy
- Department of Psychiatry and Behavioral Sciences, University of Washington, 1959 NE Pacific St, Seattle, WA 98195
| | - J. Nathan Kutz
- Department of Applied Mathematics, University of Washington, Lewis Hall 201, Seattle WA 98195
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
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Hidalgo de la Cruz M, Valsasina P, Sangalli F, Esposito F, Rocca MA, Filippi M. Dynamic Functional Connectivity in the Main Clinical Phenotypes of Multiple Sclerosis. Brain Connect 2021; 11:678-690. [PMID: 33813839 DOI: 10.1089/brain.2020.0920] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: Dynamic functional connectivity (dFC) allows capturing recurring patterns (states) of interaction among functional networks. In this study, we investigated resting state (RS) dFC abnormalities across the different clinical phenotypes of multiple sclerosis (MS) and assessed their correlation with motor and cognitive performances. Methods: RS functional magnetic resonance imaging (fMRI) and 3D T1-weighted MRI data were acquired from 128 MS patients (69 relapsing-remitting [RR] MS, 34 secondary progressive [SP] MS, and 25 primary progressive [PP] MS) and 40 healthy controls (HC). RS fMRI data underwent independent component analysis and sliding-window correlations, to identify recurring dFC states and between-group dFC differences in the main networks. Results: dFC identified three recurring connectivity states: State 1 (frequency of appearance during fMRI acquisition = 57%, low dFC strength), State 2 (frequency = 19%, middle-high dFC strength), and State 3 (frequency = 24%, high sensorimotor and visual dFC strength). Compared to HC, MS (as a whole), RRMS, and PPMS patients exhibited lower State1/State 3 dFC (p = 0.0001, corrected) between sensorimotor, cerebellar, and cognitive networks, and some dFC increments (p = 0.001-0.05, uncorrected) in sensorimotor, visual, default-mode, and frontal/attention networks in States 2 and 3. Similar results were observed in SPMS versus RRMS patients. In MS, dFC decrease in sensorimotor, default-mode, and frontal/attention networks was correlated with worse motor and cognitive performances. Conclusions: MS patients exhibited overall lower dFC, and marginally higher dFC in sensorimotor/cognitive networks in the less-frequent middle/high-connected States. dFC abnormalities became more severe in progressive MS and correlated with motor and cognitive impairment, suggesting the presence of maladaptive mechanisms concomitant with accumulation of damage.
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Affiliation(s)
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCSS San Raffaele Scientific Institute, Milan, Italy
| | | | - Federica Esposito
- Neurology Unit, IRCSS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCSS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCSS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCSS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCSS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurophysiology Service and IRCSS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCSS San Raffaele Scientific Institute, Milan, Italy
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43
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Dynamic functional connectivity and its anatomical substrate reveal treatment outcome in first-episode drug-naïve schizophrenia. Transl Psychiatry 2021; 11:282. [PMID: 33980821 PMCID: PMC8115129 DOI: 10.1038/s41398-021-01398-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Convergent evidence has suggested a significant effect of antipsychotic exposure on brain structure and function in patients with schizophrenia, yet the characteristics of favorable treatment outcome remains largely unknown. In this work, we aimed to examine how large-scale brain networks are modulated by antipsychotic treatment, and whether the longitudinal changes could track the improvements of psychopathologic scores. Thirty-four patients with first-episode drug-naïve schizophrenia and 28 matched healthy controls were recruited at baseline from Shanghai Mental Health Center. After 8 weeks of antipsychotic treatment, 24 patients were re-scanned. Through a systematical dynamic functional connectivity (dFC) analysis, we investigated the schizophrenia-related intrinsic alterations of dFC at baseline, followed by a longitudinal study to examine the influence of antipsychotic treatment on these abnormalities by comparing patients at baseline and follow-up. A structural connectivity (SC) association analysis was further carried out to investigate longitudinal anatomical changes that underpin the alterations of dFC. We found a significant symptomatic improvement-related increase in the occurrence of a dFC state characterized by stronger inter-network integration. Furthermore, symptom reduction was correlated with increased FC variability in a unique connectomic signature, particularly in the connections within the default mode network and between the auditory, cognitive control, and cerebellar network to other networks. Additionally, we observed that the SC between the superior frontal gyrus and medial prefrontal cortex was decreased after treatment, suggesting a relaxation of normal constraints on dFC. Taken together, these findings provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network. Moreover, our identified neuroimaging markers tied to the neurobiology of schizophrenia could be used as potential indicators in predicting the treatment outcome of antipsychotics.
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Faghiri A, Damaraju E, Belger A, Ford JM, Mathalon D, McEwen S, Mueller B, Pearlson G, Preda A, Turner JA, Vaidya JG, Van Erp T, Calhoun VD. Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics. Front Neurosci 2021; 15:621716. [PMID: 33927587 PMCID: PMC8076753 DOI: 10.3389/fnins.2021.621716] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time. METHODS We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set. RESULTS We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern. CONCLUSION Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.
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Affiliation(s)
- Ashkan Faghiri
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Eswar Damaraju
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Aysenil Belger
- Department of Psychiatry, The University of North Carolina, Chapel Hill, Chapel Hill, NC, United States
| | - Judith M. Ford
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- San Francisco VA Medical Center, San Francisco, CA, United States
| | - Daniel Mathalon
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- San Francisco VA Medical Center, San Francisco, CA, United States
| | - Sarah McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bryon Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - Godfrey Pearlson
- School of Medicine, Yale University, New Haven, CT, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Jessica A. Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States
| | - Jatin G. Vaidya
- Department of Psychiatry, The University of Iowa, Iowa, IA, United States
| | - Theodorus Van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Vince D. Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Department of Psychology, Georgia State University, Atlanta, GA, United States
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45
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Faghiri A, Iraji A, Damaraju E, Turner J, Calhoun VD. A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks. Netw Neurosci 2021; 5:56-82. [PMID: 33688606 PMCID: PMC7935048 DOI: 10.1162/netn_a_00155] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/05/2020] [Indexed: 01/02/2023] Open
Abstract
Static and dynamic functional network connectivity (FNC) are typically studied separately, which makes us unable to see the full spectrum of connectivity in each analysis. Here, we propose an approach called filter-banked connectivity (FBC) to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. First, we demonstrate that FBC can estimate connectivity across multiple frequencies missed by a sliding-window approach. Next, we use FBC to estimate FNC in a resting-state fMRI dataset including schizophrenia patients (SZ) and typical controls (TC). The FBC results are clustered into different network states. Some states showed weak low-frequency strength and as such were not captured in the window-based approach. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies compared with TCs who spent more time in lower frequency states. Finally, we show that FBC enables us to analyze static and dynamic connectivity in a unified way. In summary, FBC offers a novel way to unify static and dynamic connectivity analyses and can provide additional information about the frequency profile of connectivity patterns.
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Affiliation(s)
- Ashkan Faghiri
- Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Armin Iraji
- Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Eswar Damaraju
- Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Jessica Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
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46
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Altered temporal, but intact spatial, features of transient network dynamics in psychosis. Mol Psychiatry 2021; 26:2493-2503. [PMID: 33462330 PMCID: PMC8286268 DOI: 10.1038/s41380-020-00983-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 10/09/2020] [Accepted: 12/02/2020] [Indexed: 01/03/2023]
Abstract
Contemporary models of psychosis suggest that a continuum of severity of psychotic symptoms exists, with subthreshold psychotic experiences (PEs) potentially reflecting some genetic and environmental risk factors shared with clinical psychosis. Thus, identifying abnormalities in brain activity that manifest across this continuum can shed new light on the pathophysiology of psychosis. Here, we investigated the moment-to-moment engagement of brain networks ("states") in individuals with schizophrenia (SCZ) and PEs and identified features of these states that are associated with psychosis-spectrum symptoms. Transient brain states were defined by clustering "single snapshots" of blood oxygen level-dependent images, based on spatial similarity of the images. We found that individuals with SCZ (n = 35) demonstrated reduced recruitment of three brain states compared to demographically matched healthy controls (n = 35). Of these three illness-related states, one specific state, involving primarily the visual and salience networks, also occurred at a lower rate in individuals with persistent PEs (n = 22), compared to demographically matched healthy youth (n = 22). Moreover, the occurrence rate of this marker brain state was negatively correlated with the severity of PEs (r = -0.26, p = 0.003, n = 130). In contrast, the spatial map of this state appeared to be unaffected in the SCZ or PE groups. Thus, reduced engagement of a brain state involving the visual and salience networks was demonstrated across the psychosis continuum, suggesting that early disruptions of perceptual and affective function may underlie some of the core symptoms of the illness.
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47
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Fu Z, Sui J, Turner JA, Du Y, Assaf M, Pearlson GD, Calhoun VD. Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder. Hum Brain Mapp 2021; 42:80-94. [PMID: 32965740 PMCID: PMC7721229 DOI: 10.1002/hbm.25205] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/14/2020] [Accepted: 09/05/2020] [Indexed: 02/06/2023] Open
Abstract
The dynamics of the human brain span multiple spatial scales, from connectivity associated with a specific region/network to the global organization, each representing different brain mechanisms. Yet brain reconfigurations at different spatial scales are seldom explored and whether they are associated with the neural aspects of brain disorders is far from understood. In this study, we introduced a dynamic measure called step-wise functional network reconfiguration (sFNR) to characterize how brain configuration rewires at different spatial scales. We applied sFNR to two independent datasets, one includes 160 healthy controls (HCs) and 151 patients with schizophrenia (SZ) and the other one includes 314 HCs and 255 individuals with autism spectrum disorder (ASD). We found that both SZ and ASD have increased whole-brain sFNR and sFNR between cerebellar and subcortical/sensorimotor domains. At the ICN level, the abnormalities in SZ are mainly located in ICNs within subcortical, sensory, and cerebellar domains, while the abnormalities in ASD are more widespread across domains. Interestingly, the overlap SZ-ASD abnormality in sFNR between cerebellar and sensorimotor domains was correlated with the reasoning-problem-solving performance in SZ (r = -.1652, p = .0058) as well as the Autism Diagnostic Observation Schedule in ASD (r = .1853, p = .0077). Our findings suggest that dynamic reconfiguration deficits may represent a key intersecting point for SZ and ASD. The investigation of brain dynamics at different spatial scales can provide comprehensive insights into the functional reconfiguration, which might advance our knowledge of cognitive decline and other pathophysiology in brain disorders.
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Affiliation(s)
- Zening Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Jing Sui
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
- Chinese Academy of Sciences (CAS) Centre for Excellence in Brain Science and Intelligence TechnologyUniversity of Chinese Academy of SciencesBeijingChina
| | | | - Yuhui Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - Michal Assaf
- Olin Neuropsychiatry Research Center, The Institute of LivingHartfordConnecticutUSA
| | - Godfrey D. Pearlson
- Olin Neuropsychiatry Research Center, The Institute of LivingHartfordConnecticutUSA
- Department of PsychiatryYale University School of MedicineNew HavenConnecticutUSA
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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48
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Premi E, Cristillo V, Gazzina S, Benussi A, Alberici A, Cotelli MS, Calhoun VD, Iraji A, Magoni M, Cotelli M, Micheli A, Gasparotti R, Padovani A, Borroni B. Expanding the role of education in frontotemporal dementia: a functional dynamic connectivity (the chronnectome) study. Neurobiol Aging 2020; 93:35-43. [DOI: 10.1016/j.neurobiolaging.2020.04.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 12/14/2022]
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49
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Kottaram A, Johnston LA, Tian Y, Ganella EP, Laskaris L, Cocchi L, McGorry P, Pantelis C, Kotagiri R, Cropley V, Zalesky A. Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors. Hum Brain Mapp 2020; 41:3342-3357. [PMID: 32469448 PMCID: PMC7375115 DOI: 10.1002/hbm.25020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 01/13/2020] [Accepted: 04/13/2020] [Indexed: 12/25/2022] Open
Abstract
In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.
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Affiliation(s)
- Akhil Kottaram
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ye Tian
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia
| | - Eleni P Ganella
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia.,Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia
| | - Liliana Laskaris
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia.,Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Luca Cocchi
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Patrick McGorry
- Orygen, Parkville, Victoria, Australia.,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia.,Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia.,Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,North Western Mental Health, Melbourne Health, Parkville, Victoria, Australia.,Florey Institute for Neurosciences and Mental Health, Parkville, Victoria, Australia
| | - Ramamohanarao Kotagiri
- Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Vanessa Cropley
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia.,Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia
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50
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Iraji A, Faghiri A, Lewis N, Fu Z, Rachakonda S, Calhoun VD. Tools of the trade: estimating time-varying connectivity patterns from fMRI data. Soc Cogn Affect Neurosci 2020; 16:849-874. [PMID: 32785604 PMCID: PMC8343585 DOI: 10.1093/scan/nsaa114] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/24/2020] [Accepted: 08/05/2020] [Indexed: 01/04/2023] Open
Abstract
Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.
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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
| | - 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 30303, USA
| | - Noah Lewis
- 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
| | - 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 30303, 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 30303, 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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