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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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Wei HL, Wei C, Yu YS, Yu X, Chen Y, Li J, Zhang H, Chen X. Dysfunction of the triple-network model is associated with cognitive impairment in patients with cerebral small vessel disease. Heliyon 2024; 10:e24701. [PMID: 38298689 PMCID: PMC10828708 DOI: 10.1016/j.heliyon.2024.e24701] [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: 05/05/2023] [Revised: 11/29/2023] [Accepted: 01/12/2024] [Indexed: 02/02/2024] Open
Abstract
Purpose This study aimed to demonstrate the correlations between the altered functional connectivity patterns in the triple-network model and cognitive impairment in patients with cerebral small vascular disease (CSVD). Methods Resting-state functional magnetic resonance imaging data were obtained from 22 patients with CSVD and 20 healthy controls. The resting-state data were analyzed using independent component analysis and functional network connectivity (FNC) analysis to explore the functional alterations in the intrinsic triple-network model including the salience network (SN), default mode network (DMN), and central executive network (CEN), and their correlations with the cognitive deficits and clinical observations in the patients with CSVD. Results Compared to the healthy controls, the patients with CSVD exhibited increased connectivity patterns in the CEN-DMN and decreased connectivity patterns in the DMN-SN, CEN-SN, intra-SN, and intra-DMN. Significant negative correlations were detected between the intra-DMN connectivity pattern and the Montreal Cognitive Assessment (MoCA) total scores (r = -0.460, p = 0.048) and MoCA abstraction scores (r = -0.565, p = 0.012), and a positive correlation was determined between the intra-SN connectivity pattern and the MoCA abstraction scores (r = 0.491, p = 0.033). Conclusions Our study findings suggest that the functional alterations in the triple-network model are associated with the cognitive deficits in patients with CSVD and shed light on the importance of the triple-network model in the pathogenesis of CSVD.
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Affiliation(s)
- Heng-Le Wei
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Cunsheng Wei
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Yu-Sheng Yu
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Xiaorong Yu
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Yuan Chen
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Junrong Li
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Hong Zhang
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Xuemei Chen
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
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Li D, Hao J, Hao J, Cui X, Niu Y, Xiang J, Wang B. Enhanced Dynamic Laterality Based on Functional Subnetworks in Patients with Bipolar Disorder. Brain Sci 2023; 13:1646. [PMID: 38137094 PMCID: PMC10741828 DOI: 10.3390/brainsci13121646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/07/2023] [Accepted: 11/10/2023] [Indexed: 12/24/2023] Open
Abstract
An ocean of studies have pointed to abnormal brain laterality changes in patients with bipolar disorder (BD). Determining the altered brain lateralization will help us to explore the pathogenesis of BD. Our study will fill the gap in the study of the dynamic changes of brain laterality in BD patients and thus provide new insights into BD research. In this work, we used fMRI data from 48 BD patients and 48 normal controls (NC). We constructed the dynamic laterality time series by extracting the dynamic laterality index (DLI) at each sliding window. We then used k-means clustering to partition the laterality states and the Arenas-Fernandez-Gomez (AFG) community detection algorithm to determine the number of states. We characterized subjects' laterality characteristics using the mean laterality index (MLI) and laterality fluctuation (LF). Compared with NC, in all windows and state 1, BD patients showed higher MLI in the attention network (AN) of the right hemisphere, and AN in the left hemisphere showed more frequent laterality fluctuations. AN in the left hemisphere of BD patients showed higher MLI in all windows and state 3 compared to NC. In addition, in the AN of the right hemisphere in state 1, higher MLI in BD patients was significantly associated with patient symptoms. Our study provides new insights into the understanding of BD neuropathology in terms of brain dynamic laterality.
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Affiliation(s)
- Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong 030600, China; (J.H.)
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Wilton DK, Mastro K, Heller MD, Gergits FW, Willing CR, Fahey JB, Frouin A, Daggett A, Gu X, Kim YA, Faull RLM, Jayadev S, Yednock T, Yang XW, Stevens B. Microglia and complement mediate early corticostriatal synapse loss and cognitive dysfunction in Huntington's disease. Nat Med 2023; 29:2866-2884. [PMID: 37814059 PMCID: PMC10667107 DOI: 10.1038/s41591-023-02566-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 08/24/2023] [Indexed: 10/11/2023]
Abstract
Huntington's disease (HD) is a devastating monogenic neurodegenerative disease characterized by early, selective pathology in the basal ganglia despite the ubiquitous expression of mutant huntingtin. The molecular mechanisms underlying this region-specific neuronal degeneration and how these relate to the development of early cognitive phenotypes are poorly understood. Here we show that there is selective loss of synaptic connections between the cortex and striatum in postmortem tissue from patients with HD that is associated with the increased activation and localization of complement proteins, innate immune molecules, to these synaptic elements. We also found that levels of these secreted innate immune molecules are elevated in the cerebrospinal fluid of premanifest HD patients and correlate with established measures of disease burden.In preclinical genetic models of HD, we show that complement proteins mediate the selective elimination of corticostriatal synapses at an early stage in disease pathogenesis, marking them for removal by microglia, the brain's resident macrophage population. This process requires mutant huntingtin to be expressed in both cortical and striatal neurons. Inhibition of this complement-dependent elimination mechanism through administration of a therapeutically relevant C1q function-blocking antibody or genetic ablation of a complement receptor on microglia prevented synapse loss, increased excitatory input to the striatum and rescued the early development of visual discrimination learning and cognitive flexibility deficits in these models. Together, our findings implicate microglia and the complement cascade in the selective, early degeneration of corticostriatal synapses and the development of cognitive deficits in presymptomatic HD; they also provide new preclinical data to support complement as a therapeutic target for early intervention.
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Affiliation(s)
- Daniel K Wilton
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, US.
| | - Kevin Mastro
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, US
| | - Molly D Heller
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, US
| | - Frederick W Gergits
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, US
| | - Carly Rose Willing
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, US
| | - Jaclyn B Fahey
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, US
| | - Arnaud Frouin
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, US
| | - Anthony Daggett
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
| | - Xiaofeng Gu
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
| | - Yejin A Kim
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, US
| | - Richard L M Faull
- Department of Anatomy with Radiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Suman Jayadev
- Department of Neurology, University of Washington, Seattle, WA, USA
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ted Yednock
- Annexon Biosciences, South San Francisco, CA, USA
| | - X William Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
| | - Beth Stevens
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, US.
- Stanley Center, Broad Institute, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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Deming P, Cook CJ, Meyerand ME, Kiehl KA, Kosson DS, Koenigs M. Impaired salience network switching in psychopathy. Behav Brain Res 2023; 452:114570. [PMID: 37421987 PMCID: PMC10527938 DOI: 10.1016/j.bbr.2023.114570] [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: 02/10/2023] [Revised: 06/22/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023]
Abstract
Growing evidence suggests that psychopathy is related to altered connectivity within and between three large-scale brain networks that support core cognitive functions, including allocation of attention. In healthy individuals, default mode network (DMN) is involved in internally-focused attention and cognition such as self-reference. Frontoparietal network (FPN) is anticorrelated with DMN and is involved in externally-focused attention to cognitively demanding tasks. A third network, salience network (SN), is involved in detecting salient cues and, crucially, appears to play a role in switching between the two anticorrelated networks, DMN and FPN, to efficiently allocate attentional resources. Psychopathy has been related to reduced anticorrelation between DMN and FPN, suggesting SN's role in switching between these two networks may be diminished in the disorder. To test this hypothesis, we used independent component analysis to derive DMN, FPN, and SN activity in resting-state fMRI data in a sample of incarcerated men (N = 148). We entered the activity of the three networks into dynamic causal modeling to test SN's switching role. The previously established switching effect of SN among young, healthy adults was replicated in a group of low psychopathy participants (posterior model probability = 0.38). As predicted, SN's switching role was significantly diminished in high psychopathy participants (t(145) = 26.39, p < .001). These findings corroborate a novel theory of brain function in psychopathy. Future studies may use this model to test whether disrupted SN switching is related to high psychopathy individuals' abnormal allocation of attention.
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Affiliation(s)
- Philip Deming
- Department of Psychology, Northeastern University, 360 Huntington Ave., Boston, MA 02115, USA.
| | - Cole J Cook
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave., Rm 1005, Madison, WI 53705, USA
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave., Rm 1005, Madison, WI 53705, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, WI 53706, USA
| | - Kent A Kiehl
- The Mind Research Network and Lovelace Biomedical, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA
| | - David S Kosson
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, USA
| | - Michael Koenigs
- Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Blvd., Madison, WI 53719, USA
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Allen CH, Maurer JM, Gullapalli AR, Edwards BG, Aharoni E, Harenski CL, Anderson NE, Harenski KA, Calhoun VD, Kiehl KA. Psychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girls. FRONTIERS IN NEUROIMAGING 2023; 2:1216494. [PMID: 37554634 PMCID: PMC10406221 DOI: 10.3389/fnimg.2023.1216494] [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: 05/03/2023] [Accepted: 07/19/2023] [Indexed: 08/10/2023]
Abstract
Previous work in incarcerated boys and adult men and women suggest that individuals scoring high on psychopathic traits show altered resting-state limbic/paralimbic, and default mode functional network properties. However, it is unclear whether similar results extend to high-risk adolescent girls with elevated psychopathic traits. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist: Youth Version (PCL:YV)] were associated with altered inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of low-frequency fluctuations (ALFFs) across resting-state networks among high-risk incarcerated adolescent girls (n = 40). Resting-state networks were identified by applying group independent component analysis (ICA) to resting-state fMRI scans, and a priori regions of interest included limbic, paralimbic, and default mode network components. We tested the association of psychopathic traits (PCL:YV Factor 1 measuring affective/interpersonal traits and PCL:YV Factor 2 assessing antisocial/lifestyle traits) to these three resting-state measures. PCL:YV Factor 1 scores were associated with increased low-frequency and decreased high-frequency fluctuations in components corresponding to the default mode network, as well as increased intra-network FNC in components corresponding to cognitive control networks. PCL:YV Factor 2 scores were associated with increased low-frequency fluctuations in sensorimotor networks and decreased high-frequency fluctuations in default mode, sensorimotor, and visual networks. Consistent with previous analyses in incarcerated adult women, our results suggest that psychopathic traits among incarcerated adolescent girls are associated with altered intra-network ALFFs-primarily that of increased low-frequency and decreased high-frequency fluctuations-and connectivity across multiple networks including paralimbic regions. These results suggest stable neurobiological correlates of psychopathic traits among women across development.
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Affiliation(s)
- Corey H. Allen
- The Mind Research Network, Albuquerque, NM, United States
| | | | | | | | - Eyal Aharoni
- Department of Psychology, Georgia State University, Atlanta, GA, United States
| | | | | | | | - Vince D. Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, 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
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Kent A. Kiehl
- The Mind Research Network, Albuquerque, NM, United States
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
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Dugré JR, Potvin S. Altered functional connectivity of the amygdala across variants of callous-unemotional traits: A resting-state fMRI study in children and adolescents. J Psychiatr Res 2023; 163:32-42. [PMID: 37201236 DOI: 10.1016/j.jpsychires.2023.05.002] [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: 07/24/2022] [Revised: 02/28/2023] [Accepted: 05/01/2023] [Indexed: 05/20/2023]
Abstract
Over the past years, research has shown that primary (high callousness and low anxiety) and secondary (high callousness and anxiety) variants of CU traits may be associated with opposite amygdala activity (hypo- and hyper-reactivity, respectively). However, their differences in amygdala functional connectivity remains largely unexplored. We conducted a Latent Profile Analysis on a large sample of adolescents (n = 1416) to identify homogeneous subgroups with different levels of callousness and anxiety. We then performed a seed-to-voxel connectivity analysis on resting-state fMRI data to compare subgroups on connectivity patterns of the amygdala. We examined the results in relation to conduct problems to identify potential neural risk factors. The Latent Profile Analysis revealed four subgroups, including the primary and secondary variants, anxious, and typically developing adolescents. The seed-to-voxel analyses showed that the primary variant was mainly characterized by increased connectivity between the left amygdala and left thalamus. The secondary variant exhibited deficient connectivity between the amygdala and the dorsomedial prefrontal cortex, temporo-parietal junction, premotor, and postcentral gyrus. Both variants showed increased connectivity between the left amygdala and the right thalamus but exhibited opposite functional connectivity between the left amygdala and the parahippocampal gyrus. Dimensional analyses indicated that conduct problems may play a mediating role between callousness and amygdala-dmPFC functional connectivity across youths with already high levels of callousness. Our study highlights that both variants differ in the functional connectivity of the amygdala. Our results support the importance of disentangling the heterogeneity of adolescents at risk for conduct problems in neuroimaging.
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Affiliation(s)
- Jules R Dugré
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, Montreal, Canada; Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
| | - Stéphane Potvin
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, Montreal, Canada; Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
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Shunkai L, Chen P, Zhong S, Chen G, Zhang Y, Zhao H, He J, Su T, Yan S, Luo Y, Ran H, Jia Y, Wang Y. Alterations of insular dynamic functional connectivity and psychological characteristics in unmedicated bipolar depression patients with a recent suicide attempt. Psychol Med 2023; 53:3837-3848. [PMID: 35257645 DOI: 10.1017/s0033291722000484] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Mounting evidence showed that insula contributed to the neurobiological mechanism of suicidal behaviors in bipolar disorder (BD). However, no studies have analyzed the dynamic functional connectivity (dFC) of insular Mubregions and its association with personality traits in BD with suicidal behaviors. Therefore, we investigated the alterations of dFC variability in insular subregions and personality characteristics in BD patients with a recent suicide attempt (SA). METHODS Thirty unmedicated BD patients with SA, 38 patients without SA (NSA) and 35 demographically matched healthy controls (HCs) were included. The sliding-window analysis was used to evaluate whole-brain dFC for each insular subregion seed. We assessed between-group differences of psychological characteristics on the Minnesota Multiphasic Personality Inventory-2. Finally, a multivariate regression model was adopted to predict the severity of suicidality. RESULTS Compared to NSA and HCs, the SA group exhibited decreased dFC variability values between the left dorsal anterior insula and the left anterior cerebellum. These dFC variability values could also be utilized to predict the severity of suicidality (r = 0.456, p = 0.031), while static functional connectivity values were not appropriate for this prediction. Besides, the SA group scored significantly higher on the schizophrenia clinical scales (p < 0.001) compared with the NSA group. CONCLUSIONS Our findings indicated that the dysfunction of insula-cerebellum connectivity may underlie the neural basis of SA in BD patients, and highlighted the dFC variability values could be considered a neuromarker for predictive models of the severity of suicidality. Moreover, the psychiatric features may increase the vulnerability of suicidal behavior.
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Affiliation(s)
- Lai Shunkai
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Pan Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Yiliang Zhang
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hui Zhao
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jiali He
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Ting Su
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Shuya Yan
- School of Management, Jinan University, Guangzhou, China
| | - Yange Luo
- School of Management, Jinan University, Guangzhou, China
| | - Hanglin Ran
- School of Management, Jinan University, Guangzhou, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
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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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Feng Y, Kang X, Wang H, Cong J, Zhuang W, Xue K, Li F, Yao D, Xu P, Zhang T. The relationships between dynamic resting-state networks and social behavior in autism spectrum disorder revealed by fuzzy entropy-based temporal variability analysis of large-scale network. Cereb Cortex 2023; 33:764-776. [PMID: 35297491 DOI: 10.1093/cercor/bhac100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 02/03/2023] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by a core deficit in social processes. However, it is still unclear whether the core clinical symptoms of the disorder can be reflected by the temporal variability of resting-state network functional connectivity (FC). In this article, we examined the large-scale network FC temporal variability at the local region, within-network, and between-network levels using the fuzzy entropy technique. Then, we correlated the network FC temporal variability to social-related scores. We found that the social behavior correlated with the FC temporal variability of the precuneus, parietal, occipital, temporal, and precentral. Our results also showed that social behavior was significantly negatively correlated with the temporal variability of FC within the default mode network, between the frontoparietal network and cingulo-opercular task control network, and the dorsal attention network. In contrast, social behavior correlated significantly positively with the temporal variability of FC within the subcortical network. Finally, using temporal variability as a feature, we construct a model to predict the social score of ASD. These findings suggest that the network FC temporal variability has a close relationship with social behavioral inflexibility in ASD and may serve as a potential biomarker for predicting ASD symptom severity.
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Affiliation(s)
- Yu Feng
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Xiaodong Kang
- The Department of Sichuan 81 Rehabilitation Center, Chengdu University of TCM, No.37, Twelfth Bridge Road,Chengdu 610075, China
| | - Hesong Wang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Institute of Gastroenterology of Guangdong Province, Nanfang Hospital, Southern Medical University, No. 1023-1063, Shatai South Road, Baiyun District, Guangzhou 510515, China
| | - Jing Cong
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Kaiqing Xue
- School of Computer and Software Engineering, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
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11
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Allen CH, Shold J, Michael Maurer J, Reynolds BL, Anderson NE, Harenski CL, Harenski KA, Calhoun VD, Kiehl KA. Aberrant resting-state functional connectivity associated with childhood trauma among juvenile offenders. Neuroimage Clin 2023; 37:103343. [PMID: 36764058 PMCID: PMC9929859 DOI: 10.1016/j.nicl.2023.103343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/20/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023]
Abstract
Individuals with history of childhood trauma are characterized by aberrant resting-state limbic and paralimbic functional network connectivity. However, it is unclear whether specific subtypes of trauma (i.e., experienced vs observed or community) showcase differential effects. This study examined whether subtypes of childhood trauma (assessed via the Trauma Checklist [TCL] 2.0) were associated with aberrant intra-network amplitude of fluctuations and connectivity (i.e., functional coherence within a network), and inter-network connectivity across resting-state networks among incarcerated juvenile males (n = 179). Subtypes of trauma were established via principal component analysis of the TCL 2.0 and resting-state networks were identified by applying group independent component analysis to resting-state fMRI scans. We tested the association of subtypes of childhood trauma (i.e., TCL Factor 1 measuring experienced trauma and TCL Factor 2 assessing community trauma), and TCL Total scores to the aforementioned functional connectivity measures. TCL Factor 2 scores were associated with increased high-frequency fluctuations and increased intra-network connectivity in cognitive control, auditory, and sensorimotor networks, occurring primarily in paralimbic regions. TCL Total scores exhibited similar neurobiological patterns to TCL Factor 2 scores (with the addition of aberrant intra-network connectivity in visual networks), and no significant associations were found for TCL Factor 1. Consistent with previous analyses of community samples, our results suggest that childhood trauma among incarcerated juvenile males is associated with aberrant intra-network amplitude of fluctuations and connectivity across multiple networks including predominately paralimbic regions. Our results highlight the importance of accounting for traumatic loss, observed trauma, and community trauma in assessing neurobiological aberrances associated with adverse experiences in childhood, as well as the value of trained-rater trauma assessments compared to self-report.
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Affiliation(s)
- Corey H Allen
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA.
| | - Jenna Shold
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - J Michael Maurer
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - Brooke L Reynolds
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA; School of Graduate Psychology, Pacific University, Hillsboro, OR, USA
| | | | - Carla L Harenski
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - Keith A Harenski
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Place NE, 18th Floor, Atlanta, GA 30303, USA; Department of Computer Science, Georgia State University, Atlanta, USA
| | - Kent A Kiehl
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA; Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
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12
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Abnormal Dynamic Functional Network Connectivity in Adults with Autism Spectrum Disorder. Clin Neuroradiol 2022; 32:1087-1096. [PMID: 35543744 DOI: 10.1007/s00062-022-01173-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/12/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE This study sought to explore changes of brain dynamic functional network connectivity (dFNC) in adults with autism spectrum disorder (ASD) and investigate their relationship with clinical manifestations. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 78 adult ASD patients from autism brain imaging data exchange datasets, and 65 age-matched healthy controls subjects from the local community. Independent component analysis was conducted to evaluate dFNC patterns on the basis of 13 independent components (ICs) within 11 resting-state networks (RSN), namely, auditory network (AUDN), basal ganglia network (BGN), language network (LN), sensorimotor network (SMN), precuneus network (PUCN), salience network (SN), visuospatial network (VSN), dorsal default mode network (dDMN), high visual network (hVIS), primary visual network (pVIS), ventral default mode network (vDMN). Fraction time, mean dwell time, number of transitions, and RSN connectivity were calculated for group comparisons. Correlation analyses were performed between abnormal metrics and autism diagnostic observation schedule (ADOS) scores. RESULTS Compared with controls, ASD patients had higher fraction time and mean dwell time in state 2 (P = 0.017, P = 0.014). Reduced dFNC was found in the SMN with PUCN, SMN with hVIS, and increased dFNC was observed in the dDMN with SN in state 2 in the ASD group. Fraction time and mean dwell time was positively correlated with stereotyped behavior scores of ADOS. CONCLUSION The findings demonstrated the importance of evaluating transient alterations in specific neural networks of adult ASD patients. The abnormal metrics and connectivity may be related to symptoms such as stereotyped behavior.
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13
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Allen CH, Maurer JM, Edwards BG, Gullapalli AR, Harenski CL, Harenski KA, Calhoun VD, Kiehl KA. Aberrant resting-state functional connectivity in incarcerated women with elevated psychopathic traits. FRONTIERS IN NEUROIMAGING 2022; 1:971201. [PMID: 37555166 PMCID: PMC10406317 DOI: 10.3389/fnimg.2022.971201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/20/2022] [Indexed: 08/10/2023]
Abstract
Previous work in incarcerated men suggests that individuals scoring high on psychopathy exhibit aberrant resting-state paralimbic functional network connectivity (FNC). However, it is unclear whether similar results extend to women scoring high on psychopathy. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist - Revised (PCL-R)] were associated with aberrant inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of fluctuations across limbic and surrounding paralimbic regions among incarcerated women (n = 297). Resting-state networks were identified by applying group Independent Component Analysis to resting-state fMRI scans. We tested the association of psychopathic traits (PCL-R Factor 1 measuring interpersonal/affective psychopathic traits and PCL-R Factor 2 assessing lifestyle/antisocial psychopathic traits) to the three FNC measures. PCL-R Factor 1 scores were associated with increased low-frequency fluctuations in executive control and attentional networks, decreased high-frequency fluctuations in executive control and visual networks, and decreased intra-network FNC in default mode network. PCL-R Factor 2 scores were associated with decreased high-frequency fluctuations and default mode networks, and both increased and decreased intra-network functional connectivity in visual networks. Similar to previous analyses in incarcerated men, our results suggest that psychopathic traits among incarcerated women are associated with aberrant intra-network amplitude fluctuations and connectivity across multiple networks including limbic and surrounding paralimbic regions.
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Affiliation(s)
- Corey H. Allen
- The Mind Research Network, Albuquerque, NM, United States
| | | | - Bethany G. Edwards
- The Mind Research Network, Albuquerque, NM, United States
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | | | | | | | - Vince D. Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- 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 Computer Science, Georgia State University, Atlanta, GA, United States
| | - Kent A. Kiehl
- The Mind Research Network, Albuquerque, NM, United States
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
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14
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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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15
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Ye S, Li W, Zhu B, Lv Y, Yang Q, Krueger F. Altered effective connectivity from the posterior insula to the amygdala mediates the relationship between psychopathic traits and endorsement of the Harm foundation. Neuropsychologia 2022; 170:108216. [PMID: 35339504 DOI: 10.1016/j.neuropsychologia.2022.108216] [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: 11/17/2021] [Revised: 03/20/2022] [Accepted: 03/20/2022] [Indexed: 10/18/2022]
Abstract
Psychopathic traits have been demonstrated to be associated with different moral foundations. However, the neuropsychological mechanism underlying the relationship between psychopathic traits and moral foundations remains obscure. Our study examined the effective connectivity (EC) of psychopathy-related brain regions and its association with endorsement to moral foundations (Harm, Fairness, Loyalty, Authority, and Purity)-combining questionnaire measures, resting-state fMRI (RS-fMRI), and Granger causality analysis. We administered the Levenson Self-Report Psychopathy Scale and Moral Foundation Questionnaire to 78 college students after RS-fMRI scanning. Our results showed that total and primary psychopathy negatively predicted endorsement of the Harm foundation. The EC from the posterior insula to the amygdala was negatively associated with primary psychopathy but positively associated with endorsement of the Harm foundation. Altered posterior insula-amygdala EC partially mediated the relationship between primary psychopathy and endorsement of the Harm foundation. Our findings demonstrated that individuals with elevated psychopathic traits may have atypical processes in recognizing and integrating bodily state information into emotional responses -leading to less concern for harm-related morality. Our findings deepen the understanding of the neuropsychological mechanism underlying the relationship between psychopathic traits and morality, providing potential neurobiological explanations for increased moral transgressions, especially those harm-related transgressions, committed by psychopathic individuals.
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Affiliation(s)
- Shuer Ye
- Department of Psychology, School of Education, Hangzhou Normal University, Hangzhou, China
| | - Wei Li
- Department of Psychology, School of Education, Hangzhou Normal University, Hangzhou, China
| | - Bing Zhu
- School of Marxism, Zhejiang Yuexiu University, China
| | - Yating Lv
- Centre for Cognition and Brain Disorder, The Affiliated Hospital of Hangzhou Normal University, China.
| | - Qun Yang
- Department of Psychology, School of Education, Hangzhou Normal University, Hangzhou, China.
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, VA, USA
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16
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Zheng Y, Dong H, Wang M, Zhou W, Lin X, Dong G. Similarities and differences between internet gaming disorder and tobacco use disorder: A large-scale network study. Addict Biol 2022; 27:e13119. [PMID: 34913220 DOI: 10.1111/adb.13119] [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: 11/19/2020] [Revised: 10/13/2021] [Accepted: 11/04/2021] [Indexed: 11/28/2022]
Abstract
Studies have shown that internet gaming disorder (IGD) has the potential to be a type of addiction; however, direct comparisons (similarities and differences) between IGD and traditional addictions remain scarce, especially at the neuroimaging level. Resting-state functional magnetic resonance imaging (fMRI) data were collected from 92 individuals with IGD, 96 individuals with tobacco use disorders (TUDs) and 107 individuals who served as healthy controls (HCs). Independent component analysis (ICA) was performed to explore the similarities and differences among these three groups; Granger causality analysis (GCA) was further performed based on the ICA results to determine potential neural features underlying the differences and similarities among the groups. The ICA results indicated significant differences in the subcortical network and cerebellar network. GCA results found that significant differences in bilateral caudate among three groups, and the efferents of dorsal frontostriatal circuit showed significant differences in insula among three groups, whereas efferents of ventral frontostriatal circuit showed significant differences in the medial prefrontal cortex (mPFC). Two kinds of addiction showed differences in thalamus and frontostriatal circuits, and similar changes found in cerebellum and mPFC regions. It suggested that addiction disorders have psychopathology features, and the craving and reward dysfunctions may be the key reasons. Although both substance addiction and behaviour addiction showed craving dysfunction in cerebellum, however, the key reward dysfunction of substance addiction was found in subcortical regions, whereas behaviour addiction located in cortical regions.
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Affiliation(s)
- Yan‐Bin Zheng
- Centre for Cognition and Brain disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou China
- Institute of Psychological Science Hangzhou Normal University Hangzhou China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou Normal University Hangzhou China
| | - Hao‐Hao Dong
- Department of Psychology Zhejiang Normal University Zhejiang China
| | - Min Wang
- Institute of Psychological Science Hangzhou Normal University Hangzhou China
| | - Weiran Zhou
- Centre for Cognition and Brain disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou China
- Institute of Psychological Science Hangzhou Normal University Hangzhou China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou Normal University Hangzhou China
| | - Xiao Lin
- National Clinical Research Center for Mental Disorders Peking University Sixth Hospital Beijing China
| | - Guang‐Heng Dong
- Centre for Cognition and Brain disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou China
- Institute of Psychological Science Hangzhou Normal University Hangzhou China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou Normal University Hangzhou China
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17
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Baskin-Sommers A, Brazil IA. The importance of an exaggerated attention bottleneck for understanding psychopathy. Trends Cogn Sci 2022; 26:325-336. [PMID: 35120814 DOI: 10.1016/j.tics.2022.01.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 12/22/2022]
Abstract
The psychopath has long captured the imagination. A name such as Ted Bundy evokes a morbid curiosity. The crimes committed by Bundy are so cruel that it is hard to imagine how someone could do such things. In this review we discuss evidence that exaggeration in an attention bottleneck is one mechanism that makes it possible for psychopathic individuals to be adept at focusing on a single stimulus feature or goal but struggle to process multiple streams of information simultaneously. This exaggeration may partly explain the behavioral, affective, and social deficits that are apparent among psychopathic individuals. Further research on this attentional mechanism may promote a science that adequately captures the complexity of psychopathic behavior and offers new avenues for intervention.
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Affiliation(s)
| | - Inti A Brazil
- Radboud University, Donders Institute for Brain, Cognition, and Behavior, Nijmegen, The Netherlands
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18
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Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer. PERSONALITY NEUROSCIENCE 2021; 4:e6. [PMID: 34909565 PMCID: PMC8640675 DOI: 10.1017/pen.2021.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/30/2021] [Accepted: 04/12/2021] [Indexed: 12/13/2022]
Abstract
By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.
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19
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Dynamic functional network connectivity reveals the brain functional alterations in lung cancer patients after chemotherapy. Brain Imaging Behav 2021; 16:1040-1048. [PMID: 34718941 DOI: 10.1007/s11682-021-00575-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 09/28/2021] [Indexed: 10/19/2022]
Abstract
This study aimed to investigate alterations of brain functional network connectivity (FNC) in lung cancer patients after chemotherapy and explore links between these FNC differences and cognitive impairment. Twenty-two lung cancer patients receiving chemotherapy and 26 healthy controls (HCs) underwent resting-state functional MRI (rs-fMRI) and neuropsychological testing. Group independent component analysis (GICA) was applied to rs-fMRI data to extract whole-brain resting state networks (RSNs). Static and dynamic FNC (dFNC) were constructed to reveal RSNs connectivity alterations between lung cancer patients and HCs group, and the correlations between the group differences in RSNs and cognitive performance were analyzed. Our findings revealed that chemotherapeutics can produce widespread connectivity abnormalities in RSNs, mainly focused on default mode network (DMN) and executive control network. Furthermore, the dFNC analysis help identify network configurations of each state and capture more chemotherapy-induced disorders of interactions between and within RSNs, which mainly includes sensorimotor network, attentional network and auditory network. In addition, after chemotherapy, the lung cancer patients spend shorter mean dwell time (MDT) in state 2. The decreased dFNC between DMN [independent component 5 (IC5)] and DMN (IC6) in the lung cancer patients after chemotherapy in state 4 was negatively correlated with Montreal Cognitive Assessment (MoCA) scores (r=-0.447, p=0.042). The dFNC analysis enrich our understanding of the neural mechanisms underlying the chemobrain, and suggested that the temporal dynamics of FNC could be a potential effective method to detect cognitive changes in lung cancer patients receiving chemotherapy.
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20
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Peng Z, Guo Y, Wu X, Yang Q, Wei Z, Seger CA, Chen Q. Abnormal brain functional network dynamics in obsessive-compulsive disorder patients and their unaffected first-degree relatives. Hum Brain Mapp 2021; 42:4387-4398. [PMID: 34089285 PMCID: PMC8356985 DOI: 10.1002/hbm.25555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/14/2021] [Accepted: 05/26/2021] [Indexed: 01/01/2023] Open
Abstract
We utilized dynamic functional network connectivity (dFNC) analysis to compare participants with obsessive–compulsive disorder (OCD) with their unaffected first‐degree relative (UFDR) and healthy controls (HC). Resting state fMRI was performed on 46 OCD, 24 UFDR, and 49 HCs, along with clinical assessments. dFNC analyses revealed two distinct connectivity states: a less frequent, integrated state characterized by the predominance of between‐network connections (State I), and a more frequent, segregated state with strong within‐network connections (State II). OCD patients spent more time in State II and less time in State I than HC, as measured by fractional windows and mean dwell time. Time in each state for the UFDR were intermediate between OCD patients and HC. Within the OCD group, fractional windows of time spent in State I was positively correlated with OCD symptoms (as measured by the obsessive compulsive inventory‐revised [OCI‐R], r = .343, p<.05, FDR correction) and time in State II was negatively correlated with symptoms (r = −.343, p<.05, FDR correction). Within each state we also examined connectivity within and between established intrinsic connectivity networks, and found that UFDR were similar to the OCD group in State I, but more similar to the HC groups in State II. The similarities between OCD and UFDR groups in temporal properties and State I connectivity indicate that these features may reflect the endophenotype for OCD. These results indicate that the temporal dynamics of functional connectivity could be a useful biomarker to identify those at risk.
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Affiliation(s)
- Ziwen Peng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Ya Guo
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Xiangshu Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Qiong Yang
- Department of Psychiatry, Southern Medical University, Guangzhou, China.,Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhen Wei
- Department of Child Psychiatry and Rehabilitation, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Carol A Seger
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.,Department of Psychology, Colorado State University, Fort Collins, Colorado, USA
| | - Qi Chen
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
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21
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Othman EA. Editorial for "Progressive Deterioration of Dynamic Functional Network Connectivity in Patients With HBV-Related Cirrhosis". J Magn Reson Imaging 2021; 54:1841-1842. [PMID: 34021675 DOI: 10.1002/jmri.27742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 05/04/2021] [Indexed: 11/09/2022] Open
Affiliation(s)
- Elza Azri Othman
- Department of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia
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22
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Wang Q, Qu X, Chen W, Wang H, Huang C, Li T, Wang N, Xian J. Altered coupling of cerebral blood flow and functional connectivity strength in visual and higher order cognitive cortices in primary open angle glaucoma. J Cereb Blood Flow Metab 2021; 41:901-913. [PMID: 32580669 PMCID: PMC7983497 DOI: 10.1177/0271678x20935274] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 05/08/2020] [Accepted: 05/26/2020] [Indexed: 01/30/2023]
Abstract
Primary open-angle glaucoma (POAG) has been suggested to be a neurodegenerative disease associated with altered cerebral vascular hemodynamics and widespread disruption of neuronal activity within the visual, working memory, attention and executive networks. We hypothesized that disturbed neurovascular coupling in visual and higher order cognitive cortices exists in POAG patients and correlates with glaucoma stage and visual field defects. Through multimodal magnetic resonance imaging, we evaluated the cerebral blood flow (CBF)-functional connectivity strength (FCS) correlations of the whole gray matter and CBF/FCS ratio per voxel for all subjects. Compared with normal controls, POAG patients showed reduced global CBF-FCS coupling and altered CBF/FCS ratios, predominantly in regions in the visual cortex, salience network, default mode network, and dorsal attentional network. The CBF/FCS ratio was negatively correlated with glaucoma stage, and positively correlated with visual field defects in the lingual gyrus in POAG patients. Moreover, early brain changes were detected in early POAG. These findings indicate neurovascular coupling dysfunction might exist in the visual and higher order cognitive cortices in POAG patients and its clinical relevance. The results may contribute to the monitoring of POAG progression and provide insight into the pathophysiology of the neurodegenerative process in POAG.
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Affiliation(s)
- Qian Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xiaoxia Qu
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Weiwei Chen
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University
| | - Huaizhou Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University
| | - Caiyun Huang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ting Li
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ningli Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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Chen HJ, Zou ZY, Zhang XH, Shi JY, Huang NX, Lin YJ. Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity. J Magn Reson Imaging 2021; 54:239-248. [PMID: 33559360 DOI: 10.1002/jmri.27521] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/28/2020] [Accepted: 12/29/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Aberrant static functional connectivity (FC) has been well demonstrated in amyotrophic lateral sclerosis (ALS); however, ALS-related alterations in FC dynamic properties remain unclear, although dynamic FC analyses contribute to uncover mechanisms underlying neurodegenerative disorders. PURPOSE To explore dynamic functional network connectivity (dFNC) in ALS and its correlation with disease severity. STUDY TYPE Prospective. SUBJECTS Thirty-two ALS patients and 45 healthy controls. FIELD STRENGTH/SEQUENCE Multiband resting-state functional images using gradient echo echo-planar imaging and T1-weighted images were acquired at 3.0 T. ASSESSMENT Disease severity was evaluated with the revised ALS Functional Rating Scale (ALSFRS-R) and patients were stratified according to diagnostic category. Independent component analysis was conducted to identify the components of seven intrinsic brain networks (ie, visual/sensorimotor (SMN)/auditory/cognitive-control (CCN)/default-mode (DMN)/subcortical/cerebellar networks). A sliding-window correlation approach was used to compute dFNC. FNC states were determined by k-mean clustering, and state-specific FNC and dynamic indices (fraction time/mean dwell time/transition number) were calculated. STATISTICAL TESTS Two-sample t test used for comparisons on dynamic measures and Spearman's correlation analysis. RESULTS ALS patients showed increased FNC between DMN-SMN in state 1 and between CCN-SMN in state 4. Patients remained in state 2 (showing the weakest FNC) for a significantly longer time (mean dwell time: 49.8 ± 40.1 vs. 93.6 ± 126.3; P < 0.05) and remained in state 1 (showing a relatively strong FNC) for a shorter time (fraction time: 0.27 ± 0.25 vs. 0.13 ± 0.20; P < 0.05). ALS patients exhibited less temporal variability in their FNC (transition number: 10.2 ± 4.4 vs. 7.8 ± 3.8; P < 0.05). A significant correlation was observed between ALSFRS-R and mean dwell time in state 2 (r = -0.414, P < 0.05) and transition number (r = 0.452, P < 0.05). No significant between-subgroup difference in dFNC properties was found (all P > 0.05). DATA CONCLUSION Our findings suggest aberrant dFNC properties in ALS, which is associated with disease severity. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhang-Yu Zou
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xiao-Hong Zhang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Yan Shi
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Nao-Xin Huang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yan-Juan Lin
- Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of Nursing, Fujian Medical University Union Hospital, Fuzhou, China
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24
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Zhou Z, Cai B, Zhang G, Zhang A, Calhoun VD, Wang YP. Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI. Neuroimage 2020; 221:117190. [PMID: 32711063 DOI: 10.1016/j.neuroimage.2020.117190] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 07/15/2020] [Accepted: 07/19/2020] [Indexed: 12/15/2022] Open
Abstract
Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.
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Affiliation(s)
- Zhongxing Zhou
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States; Tianjin University, School of Precision Instruments and Optoelectronics Engineering, Tianjin, China
| | - Biao Cai
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Gemeng Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Aiying Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - 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, Georgia, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States.
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25
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Zarghami TS, Hossein-Zadeh GA, Bahrami F. Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia. Front Neurosci 2020; 14:214. [PMID: 32292324 PMCID: PMC7118690 DOI: 10.3389/fnins.2020.00214] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 02/27/2020] [Indexed: 12/30/2022] Open
Abstract
Itinerant dynamics of the brain generates transient and recurrent spatiotemporal patterns in neuroimaging data. Characterizing metastable functional connectivity (FC) - particularly at rest and using functional magnetic resonance imaging (fMRI) - has shaped the field of dynamic functional connectivity (DFC). Mainstream DFC research relies on (sliding window) correlations to identify recurrent FC patterns. Recently, functional relevance of the instantaneous phase synchrony (IPS) of fMRI signals has been revealed using imaging studies and computational models. In the present paper, we identify the repertoire of whole-brain inter-network IPS states at rest. Moreover, we uncover a hierarchy in the temporal organization of IPS modes. We hypothesize that connectivity disorder in schizophrenia (SZ) is related to the (deep) temporal arrangement of large-scale IPS modes. Hence, we analyze resting-state fMRI data from 68 healthy controls (HC) and 51 SZ patients. Seven resting-state networks (and their sub-components) are identified using spatial independent component analysis. IPS is computed between subject-specific network time courses, using analytic signals. The resultant phase coupling patterns, across time and subjects, are clustered into eight IPS states. Statistical tests show that the relative expression and mean lifetime of certain IPS states have been altered in SZ. Namely, patients spend (45%) less time in a globally coherent state and a subcortical-centered state, and (40%) more time in states reflecting anticoupling within the cognitive control network, compared to the HC. Moreover, the transition profile (between states) reveals a deep temporal structure, shaping two metastates with distinct phase synchrony profiles. A metastate is a collection of states such that within-metastate transitions are more probable than across. Remarkably, metastate occupation balance is altered in SZ, in favor of the less synchronous metastate that promotes disconnection across networks. Furthermore, the trajectory of IPS patterns is less efficient, less smooth, and more restricted in SZ subjects, compared to the HC. Finally, a regression analysis confirms the diagnostic value of the defined IPS measures for SZ identification, highlighting the distinctive role of metastate proportion. Our results suggest that the proposed IPS features may be used for classification studies and for characterizing phase synchrony modes in other (clinical) populations.
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Affiliation(s)
- Tahereh S. Zarghami
- Bio-Electric Department, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Gholam-Ali Hossein-Zadeh
- Bio-Electric Department, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Fariba Bahrami
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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