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Fouladivanda M, Iraji A, Wu L, van Erp TG, Belger A, Hawamdeh F, Pearlson GD, Calhoun VD. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.13.553101. [PMID: 38853973 PMCID: PMC11160563 DOI: 10.1101/2023.08.13.553101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.
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Affiliation(s)
- Mahshid Fouladivanda
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Lei Wu
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Theodorus G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior School of Medicine, University of California, Irvine, CA, USA
| | - Aysenil Belger
- Department of Psychiatry Director, Neuroimaging Research in Psychiatry Director, Clinical Translational Core, UNC Intellectual and Developmental Disabilities Research Center, University of North Carolina, Chapel Hill, NC, USA
| | - Faris Hawamdeh
- Center for Disaster Informatics and Computational Epidemiology (DICE), Georgia State University, Atlanta, GA, USA
| | - Godfrey D. Pearlson
- Olin Neuropsychiatry Research Center, Department of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, USA
| | - Vince D. Calhoun
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
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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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Xiang J, Sun Y, Wu X, Guo Y, Xue J, Niu Y, Cui X. Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia. Brain Sci 2023; 14:40. [PMID: 38248255 PMCID: PMC10813230 DOI: 10.3390/brainsci14010040] [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: 12/11/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.
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Affiliation(s)
- Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yumeng Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Xubin Wu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yuxiang Guo
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China;
| | - Jiayue Xue
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yan Niu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Xiaohong Cui
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
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Panikratova YR, Lebedeva IS, Akhutina TV, Tikhonov DV, Kaleda VG, Vlasova RM. Executive control of language in schizophrenia patients with history of auditory verbal hallucinations: A neuropsychological and resting-state fMRI study. Schizophr Res 2023; 262:201-210. [PMID: 37923596 DOI: 10.1016/j.schres.2023.10.026] [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: 03/03/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND As demonstrated by a plethora of studies, compromised executive functions (EF) and language are implicated in mechanisms of auditory verbal hallucinations (AVH), but the contribution of their interaction to AVH remains unclear. We hypothesized that schizophrenia patients with history of AVH (AVHh+) vs. without history of AVH (AVHh-) have a specific deficit of executive control of language and alterations in functional connectivity (FC) between the brain regions involved in EF and language, and these neuropsychological and neurophysiological traits are associated with each other. METHODS To explore the executive control of language and its contribution to AVH, we used an integrative approach involving analysis of neuropsychological and resting-state fMRI data of 34 AVHh+, 16 AVHh-, and 40 healthy controls. We identified the neuropsychological and FC measures that differentiated between AVHh+, AVHh-, and HC, and tested the associations between them. RESULTS AVHh+ were characterized by decreased category and phonological verbal fluency, utterance length, productivity in the planning tasks, and poorer retelling. AVHh+ had decreased FC between the left inferior frontal gyrus and the anterior cingulate cortex. Productivity in category verbal fluency was associated with the FC between these regions. CONCLUSIONS Poor executive control of word retrieval and deficient programming of sentence and narrative related to more general deficits of planning may be the neuropsychological traits specific for AVHh+. A neurophysiological trait specific for AVHh+ may be a decreased FC between regions involved in language production and differentiation between alien- vs. self-generated speech and between language production vs. comprehension.
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Affiliation(s)
- Yana R Panikratova
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, 115522, 34 Kashirskoye shosse, Moscow, Russia.
| | - Irina S Lebedeva
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, 115522, 34 Kashirskoye shosse, Moscow, Russia
| | - Tatiana V Akhutina
- Laboratory of Neuropsychology, Faculty of Psychology, Lomonosov Moscow State University, 125009, 11/9 Mokhovaya street, Moscow, Russia
| | - Denis V Tikhonov
- Department of Youth Psychiatry, Mental Health Research Center, 115522, 34 Kashirskoye shosse, Moscow, Russia
| | - Vasilii G Kaleda
- Department of Youth Psychiatry, Mental Health Research Center, 115522, 34 Kashirskoye shosse, Moscow, Russia
| | - Roza M Vlasova
- Department of Psychiatry, University of North Carolina, 101 Manning Dr # 1, Chapel Hill, NC 27514, United States of America
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Sun Y, Zhang M, Saggar M. Cross-attractor modeling of resting-state functional connectivity in psychiatric disorders. Neuroimage 2023; 279:120302. [PMID: 37579998 PMCID: PMC10515743 DOI: 10.1016/j.neuroimage.2023.120302] [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: 10/29/2022] [Revised: 07/26/2023] [Accepted: 07/29/2023] [Indexed: 08/16/2023] Open
Abstract
Resting-state functional connectivity (RSFC) is altered across various psychiatric disorders. Brain network modeling (BNM) has the potential to reveal the neurobiological underpinnings of such abnormalities by dynamically modeling the structure-function relationship and examining biologically relevant parameters after fitting the models with real data. Although innovative BNM approaches have been developed, two main issues need to be further addressed. First, previous BNM approaches are primarily limited to simulating noise-driven dynamics near a chosen attractor (or a stable brain state). An alternative approach is to examine multi(or cross)-attractor dynamics, which can be used to better capture non-stationarity and switching between states in the resting brain. Second, previous BNM work is limited to characterizing one disorder at a time. Given the large degree of co-morbidity across psychiatric disorders, comparing BNMs across disorders might provide a novel avenue to generate insights regarding the dynamical features that are common across (vs. specific to) disorders. Here, we address these issues by (1) examining the layout of the attractor repertoire over the entire multi-attractor landscape using a recently developed cross-attractor BNM approach; and (2) characterizing and comparing multiple disorders (schizophrenia, bipolar, and ADHD) with healthy controls using an openly available and moderately large multimodal dataset from the UCLA Consortium for Neuropsychiatric Phenomics. Both global and local differences were observed across disorders. Specifically, the global coupling between regions was significantly decreased in schizophrenia patients relative to healthy controls. At the same time, the ratio between local excitation and inhibition was significantly higher in the schizophrenia group than the ADHD group. In line with these results, the schizophrenia group had the lowest switching costs (energy gaps) across groups for several networks including the default mode network. Paired comparison also showed that schizophrenia patients had significantly lower energy gaps than healthy controls for the somatomotor and visual networks. Overall, this study provides preliminary evidence supporting transdiagnostic multi-attractor BNM approaches to better understand psychiatric disorders' pathophysiology.
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Affiliation(s)
- Yinming Sun
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Mengsen Zhang
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA.
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Wilkinson ID, Mahmood T, Yasmin SF, Tomlinson A, Nazari J, Alhaj H, el din SN, Neill J, Pandit C, Ashraf S, Cardno AG, Clapcote SJ, Inglehearn CF, Woodruff PW. In memory of Professor Iain Wilkinson: cognitive and neuroimaging endophenotypes in a consanguineous schizophrenia multiplex family. Psychol Med 2023; 53:3178-3186. [PMID: 35125130 PMCID: PMC10235651 DOI: 10.1017/s0033291721005250] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/24/2021] [Accepted: 12/03/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Schizophrenia endophenotypes may help elucidate functional effects of genetic risk variants in multiply affected consanguineous families that segregate recessive risk alleles of large effect size. We studied the association between a schizophrenia risk locus involving a 6.1Mb homozygous region on chromosome 13q22-31 in a consanguineous multiplex family and cognitive functioning, haemodynamic response and white matter integrity using neuroimaging. METHODS We performed CANTAB neuropsychological testing on four affected family members (all homozygous for the risk locus), ten unaffected family members (seven homozygous and three heterozygous) and ten healthy volunteers, and tested neuronal responses on fMRI during an n-back working memory task, and white matter integrity on diffusion tensor imaging (DTI) on four affected and six unaffected family members (four homozygous and two heterozygous) and three healthy volunteers. For cognitive comparisons we used a linear mixed model (Kruskal-Wallis) test, followed by posthoc Dunn's pairwise tests with a Bonferroni adjustment. For fMRI analysis, we counted voxels exceeding the p < 0.05 corrected threshold. DTI analysis was observational. RESULTS Family members with schizophrenia and unaffected family members homozygous for the risk haplotype showed attention (p < 0.01) and working memory deficits (p < 0.01) compared with healthy controls; a neural activation laterality bias towards the right prefrontal cortex (voxels reaching p < 0.05, corrected) and observed lower fractional anisotropy in the anterior cingulate cortex and left dorsolateral prefrontal cortex. CONCLUSIONS In this family, homozygosity at the 13q risk locus was associated with impaired cognition, white matter integrity, and altered laterality of neural activation.
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Affiliation(s)
- Iain D. Wilkinson
- Academic Unit of Radiology, School of Medicine, University of Sheffield, Sheffield, UK
| | - Tariq Mahmood
- Leeds & York Partnership NHS Foundation Trust, Leeds, UK
| | - Sophia Faye Yasmin
- Academic Unit of Radiology, School of Medicine, University of Sheffield, Sheffield, UK
| | | | - Jamshid Nazari
- South West Yorkshire NHS Foundation Trust, Wakefield, UK
| | - Hamid Alhaj
- University of Sharjah, UAE
- Department of Neuroscience, School of Medicine, University of Sheffield, Sheffield, UK
| | | | - Joanna Neill
- Division of Pharmacy and Optometry, University of Manchester, Manchester, UK
| | - Chhaya Pandit
- Leeds & York Partnership NHS Foundation Trust, Leeds, UK
| | - Shahzad Ashraf
- South West Yorkshire NHS Foundation Trust, Wakefield, UK
| | - Alastair G. Cardno
- Psychological & Social Medicine, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | | | - Chris F. Inglehearn
- Division of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - Peter W. Woodruff
- Department of Neuroscience, School of Medicine, University of Sheffield, Sheffield, UK
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Blanchett R, Chen Y, Aguate F, Xia K, Cornea E, Burt SA, de Los Campos G, Gao W, Gilmore JH, Knickmeyer RC. Genetic and environmental factors influencing neonatal resting-state functional connectivity. Cereb Cortex 2023; 33:4829-4843. [PMID: 36190430 PMCID: PMC10110449 DOI: 10.1093/cercor/bhac383] [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: 06/01/2021] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 11/14/2022] Open
Abstract
Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.
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Affiliation(s)
- Reid Blanchett
- Genetics and Genome Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Yuanyuan Chen
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Fernando Aguate
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - S Alexandra Burt
- Department of Psychology, Michigan State University, East Lansing, MI 48824, USA
| | - Gustavo de Los Campos
- Departments of Epidemiology and Biostatistics and Statistics and Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Wei Gao
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Rebecca C Knickmeyer
- Department of Pediatrics and Human Development, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI 48824, USA
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Kim M, Kim T, Ha M, Oh H, Moon SY, Kwon JS. Large-Scale Thalamocortical Triple Network Dysconnectivities in Patients With First-Episode Psychosis and Individuals at Risk for Psychosis. Schizophr Bull 2023; 49:375-384. [PMID: 36453986 PMCID: PMC10016393 DOI: 10.1093/schbul/sbac174] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
BACKGROUND AND HYPOTHESIS Aberrant thalamocortical connectivity and large-scale network interactions among the default mode network (DMN), salience network (SN), and executive control network (ECN) (ie, triple networks) have been regarded as critical in schizophrenia pathophysiology. Despite the importance of network properties and the role of the thalamus as an integrative hub, large-scale thalamocortical triple network functional connectivities (FCs) in different stages of the psychotic disorder have not yet been reported. STUDY DESIGN Thirty-nine first-episode psychosis (FEP) patients, 75 individuals at clinical high risk (CHR) for psychosis, 46 unaffected relatives (URs) of schizophrenia patients with high genetic loading, and 110 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Modular community detection was used to identify cortical and thalamic resting-state networks, and thalamocortical network interactions were compared across the groups. STUDY RESULTS Thalamic triple networks included higher-order thalamic nuclei. Thalamic SN-cortical ECN FC was greater in the FEP group than in the CHR, UR, and HC groups. Thalamic DMN-cortical DMN and thalamic SN-cortical DMN FCs were greater in FEP and CHR participants. Thalamic ECN-cortical DMN and thalamic ECN-cortical SN FCs were greater in FEP patients and URs. CONCLUSIONS These results highlight critical modulatory functions of thalamic triple networks and the shared and distinct patterns of thalamocortical triple network dysconnectivities across different stages of psychotic disorders. The current study findings suggest that large-scale thalamocortical triple network dysconnectivities may be used as an integrative biomarker for extending our understanding of the psychosis pathophysiology and for targeting network-based neuromodulation therapeutics.
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Affiliation(s)
- Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Taekwan Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Minji Ha
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Harin Oh
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Sun-Young Moon
- Department of Psychiatry, Hallym University Kangnam Sacred Heart Hospital, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
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Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations. Psychiatry Res 2023; 320:114974. [PMID: 36587467 DOI: 10.1016/j.psychres.2022.114974] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/10/2022] [Accepted: 11/19/2022] [Indexed: 11/22/2022]
Abstract
Auditory verbal hallucinations (AVH) are a key symptom of schizophrenia. Low-frequency repetitive transcranial magnetic stimulation (rTMS) has shown potential in the treatment of AVH. However, the underlying neural mechanismof rTMS in the treatment of AVH remains largely unknown. In this study, we used a static and dynamic functional network connectivity approach to investigate the connectivity changes among the brain functional networks in schizophrenia patients with AVH receiving 1 Hz rTMS treatment. The static functional network connectivity (sFNC) analysis revealed that patients at baseline had significantly decreased connectivity between the default mode network (DMN) and language network (LAN), and within the executive control network (ECN) as well as within the auditory network (AUD) compared to controls. However, the abnormal network connectivity patterns were normalized or restored after rTMS treatment in patients, instead of increased connectivity between the ECN and LAN, as well as within the AUD. Moreover, the dynamic functional network connectivity (dFNC) analysis showed that the patients at baseline spent more time in this state that was characterized by strongly negative connectivity between the ENC and AUD, as well as within the AUD relative to controls. While after rTMS treatment, the patients showed a higher occurrence rate in this state that was characterized by strongly positive connectivity among the LAN, DMN, and ENC, as well as within the ECN. In addition, the altered static and dynamic connectivity properties were associated with reduced severity of clinical symptoms. Both sFNC and dFNC analyses provided complementary information and suggested that low-frequency rTMS treatment could induce intrinsic functional network alternations and contribute to improvements in clinical symptoms in patients with AVH.
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Oliveira-Saraiva D, Ferreira HA. Normative model detects abnormal functional connectivity in psychiatric disorders. Front Psychiatry 2023; 14:1068397. [PMID: 36873218 PMCID: PMC9975396 DOI: 10.3389/fpsyt.2023.1068397] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
INTRODUCTION The diagnosis of psychiatric disorders is mostly based on the clinical evaluation of the patient's signs and symptoms. Deep learning binary-based classification models have been developed to improve the diagnosis but have not yet reached clinical practice, in part due to the heterogeneity of such disorders. Here, we propose a normative model based on autoencoders. METHODS We trained our autoencoder on resting-state functional magnetic resonance imaging (rs-fMRI) data from healthy controls. The model was then tested on schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD) patients to estimate how each patient deviated from the norm and associate it with abnormal functional brain networks' (FBNs) connectivity. Rs-fMRI data processing was conducted within the FMRIB Software Library (FSL), which included independent component analysis and dual regression. Pearson's correlation coefficients between the extracted blood oxygen level-dependent (BOLD) time series of all FBNs were calculated, and a correlation matrix was generated for each subject. RESULTS AND DISCUSSION We found that the functional connectivity related to the basal ganglia network seems to play an important role in the neuropathology of BD and SCZ, whereas in ADHD, its role is less evident. Moreover, the abnormal connectivity between the basal ganglia network and the language network is more specific to BD. The connectivity between the higher visual network and the right executive control and the connectivity between the anterior salience network and the precuneus networks are the most relevant in SCZ and ADHD, respectively. The results demonstrate that the proposed model could identify functional connectivity patterns that characterize different psychiatric disorders, in agreement with the literature. The abnormal connectivity patterns from the two independent SCZ groups of patients were similar, demonstrating that the presented normative model was also generalizable. However, the group-level differences did not withstand individual-level analysis implying that psychiatric disorders are highly heterogeneous. These findings suggest that a precision-based medical approach, focusing on each patient's specific functional network changes may be more beneficial than the traditional group-based diagnostic classification.
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Affiliation(s)
- Duarte Oliveira-Saraiva
- Institute of Biophysics and Biomedical Engineering, Faculty of Sciences of the University of Lisbon, Lisbon, Portugal
| | - Hugo Alexandre Ferreira
- Institute of Biophysics and Biomedical Engineering, Faculty of Sciences of the University of Lisbon, Lisbon, Portugal
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Functional connectivity directionality between large-scale resting-state networks across typical and non-typical trajectories in children and adolescence. PLoS One 2022; 17:e0276221. [PMID: 36454744 PMCID: PMC9714732 DOI: 10.1371/journal.pone.0276221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 10/04/2022] [Indexed: 12/02/2022] Open
Abstract
Mental disorders often emerge during adolescence and have been associated with age-related differences in connection strengths of brain networks (static functional connectivity), manifesting in non-typical trajectories of brain development. However, little is known about the direction of information flow (directed functional connectivity) in this period of functional brain progression. We employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 1143 participants, aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on estimates of direction flow. Across participants, we show a pattern of reciprocal information flow between visual-medial and visual-lateral connections, in line with findings in adults. Investigating directed connectivity patterns between networks, we observed a positive association for age and direction flow from the cerebellar to the auditory network, and for the auditory to the sensorimotor network. Further, higher cognitive abilities were linked to lower information flow from the visual occipital to the default mode network. Additionally, examining the degree networks overall send and receive information to each other, we identified age-related effects implicating the right frontoparietal and sensorimotor network. However, we did not find any associations with psychopathology. Our results suggest that the directed functional connectivity of large-scale resting-state brain networks is sensitive to age and cognition during adolescence, warranting further studies that may explore directed relationships at rest and trajectories in more fine-grained network parcellations and in different populations.
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12
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Bouttier V, Duttagupta S, Denève S, Jardri R. Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes. Schizophr Res 2022; 245:59-67. [PMID: 33618940 DOI: 10.1016/j.schres.2020.12.045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 12/22/2020] [Accepted: 12/26/2020] [Indexed: 12/17/2022]
Abstract
Schizophrenia is a severe mental disorder whose neural basis remains difficult to ascertain. Among the available pathophysiological theories, recent work has pointed towards subtle perturbations in the excitation-inhibition (E/I) balance within different neural circuits. Computational approaches have suggested interesting mechanisms that can account for both E/I imbalances and psychotic symptoms. Based on hierarchical neural networks propagating information through a message-passing algorithm, it was hypothesized that changes in the E/I ratio could cause a "circular belief propagation" in which bottom-up and top-down information reverberate. This circular inference (CI) was proposed to account for the clinical features of schizophrenia. Under this assumption, this paper examined the impact of CI on network dynamics in light of brain imaging findings related to psychosis. Using brain-inspired graphical models, we show that CI causes overconfidence and overactivation most specifically at the level of connector hubs (e.g., nodes with many connections allowing integration across networks). By also measuring functional connectivity in these graphs, we provide evidence that CI is able to predict specific changes in modularity known to be associated with schizophrenia. Altogether, these findings suggest that the CI framework may facilitate behavioral and neural research on the multifaceted nature of psychosis.
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Affiliation(s)
- Vincent Bouttier
- Univ Lille, INSERM U1172, CHU Lille, Lille Neurosciences & Cognition Centre (LiNC), Plasticity & SubjectivitY team, 59037 Lille, France; Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France.
| | - Suhrit Duttagupta
- Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France
| | - Sophie Denève
- Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France
| | - Renaud Jardri
- Univ Lille, INSERM U1172, CHU Lille, Lille Neurosciences & Cognition Centre (LiNC), Plasticity & SubjectivitY team, 59037 Lille, France; Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France.
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13
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Co-occurrence of schizo-obsessive traits and its correlation with altered executive control network functional connectivity. Eur Arch Psychiatry Clin Neurosci 2022; 272:301-312. [PMID: 33389057 DOI: 10.1007/s00406-020-01222-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/12/2020] [Indexed: 10/22/2022]
Abstract
The prevalence of obsessive-compulsive symptoms (OCS) in schizophrenia patients is as around 30%. Evidence suggested that mild OCS could reduce symptoms of schizophrenia, supporting the presence of compensatory functions. However, severe OCS could aggravate various impairments in schizophrenia patients, supporting the "double jeopardy hypothesis". Patients with schizo-obsessive comorbidity, schizophrenia patients and obsessive-compulsive disorder patients have been found to have similarities in executive dysfunctions and altered resting-state functional connectivity within the executive control network (ECN). Executive functions could be associated with the ECN. However, little is known as to whether such overlap exists in the subclinical populations of individuals with schizo-obsessive traits (SOT), schizotypal individuals and individuals with high levels of obsessive-compulsive symptoms (OCS). In this study, we recruited 30 schizotypal individuals, 25 individuals with OCS, 29 individuals with SOT and 29 controls for a resting-state ECN-related functional connectivity (rsFC) and a go/shift/no-go task. We found that individuals with SOT exhibited increased rsFC within the ECN compared with controls, while schizotypal individuals exhibited the opposite. Individuals with OCS exhibited decreased rsFC within the ECN and between the ECN and the default mode network (DMN), relative to controls. No significant correlational results between altered rsFC related to the ECN with executive function performance were found after corrections for multiple comparisons in three subclinical groups. Our findings showed that individuals with SOT had increased rsFC within the ECN, while schizotypal individuals and individuals with OCS showed the opposite. Our findings provide evidence for possible neural substrates of subclinical comorbidity of OCS and schizotypy.
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14
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Mastrandrea R, Piras F, Gabrielli A, Banaj N, Caldarelli G, Spalletta G, Gili T. The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia. Sci Rep 2021; 11:15400. [PMID: 34321538 PMCID: PMC8319172 DOI: 10.1038/s41598-021-94825-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 07/08/2021] [Indexed: 01/10/2023] Open
Abstract
Network neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization's basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.
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Affiliation(s)
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, 00179, Rome, Italy
| | - Andrea Gabrielli
- Dipartimento di Ingegneria, Università Roma Tre, 00146, Rome, Italy.,Istituto dei Sistemi Complessi (ISC)-CNR, UoS Sapienza, Dipartimento di Fisica, Università "Sapienza", 00185, Rome, Italy
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, 00179, Rome, Italy
| | - Guido Caldarelli
- Networks Unit, IMT School for Advanced Studies, 55100, Lucca, Italy.,Istituto dei Sistemi Complessi (ISC)-CNR, UoS Sapienza, Dipartimento di Fisica, Università "Sapienza", 00185, Rome, Italy
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, 00179, Rome, Italy. .,Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Tommaso Gili
- Networks Unit, IMT School for Advanced Studies, 55100, Lucca, Italy
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15
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Social brain network correlates with real-life social network in individuals with schizophrenia and social anhedonia. Schizophr Res 2021; 232:77-84. [PMID: 34044349 DOI: 10.1016/j.schres.2021.05.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 05/06/2021] [Accepted: 05/08/2021] [Indexed: 02/07/2023]
Abstract
Social behaviour requires the brain to efficiently integrate multiple social processes, but it is not clear what neural substrates underlie general social behaviour. While psychosis patients and individuals with subclinical symptoms are characterized by social dysfunction, the neural mechanisms underlying social dysfunctions in schizophrenia spectrum disorders remains unclear. We first constructed a general social brain network (SBN) using resting-state functional connectivity (FC) with regions of interest based on the automatic meta-analysis results from NeuroSynth. We then examined the general SBN and its relationship with social network (SN) characteristics in 30 individuals with schizophrenia (SCZ) and 33 individuals with social anhedonia (SA). We found that patients with SCZ exhibited deficits in their SN, while SA individuals did not. SCZ patients showed decreased segregation and functional connectivity in their SBN, while SA individuals showed a reversed pattern with increased segregation and functional connectivity of their SBN. Sparse canonical correlation analysis showed that both SCZ patients and SA individuals exhibited reduced correlation between SBN and SN characteristics compared with their corresponding healthy control groups. These preliminary findings suggest that both SCZ and SA participants exhibit abnormality in segregation and functional connectivity within the general SBN and reduced correlation with SN characteristics. These findings could guide the development of non-pharmacological interventions for social dysfunction in SCZ spectrum disorders.
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16
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Lin X, Deng J, Dong G, Li S, Wu P, Sun H, Liu L, Shi J, Fan Y, Lu L, Li P. Effects of Chronic Pharmacological Treatment on Functional Brain Network Connectivity in Patients with Schizophrenia. Psychiatry Res 2021; 295:113338. [PMID: 32768152 DOI: 10.1016/j.psychres.2020.113338] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 07/19/2020] [Accepted: 07/26/2020] [Indexed: 12/19/2022]
Abstract
Schizophrenia is characterized by the dysfunction of various brain networks. Previous studies suggested that pharmacological treatments for schizophrenia induce functional changes in localized brain regions. However, the effects of antipsychotic treatments on brain networks associated with symptom improvement are still elusive. The elucidation of antipsychotic-induced functional brain changes is essential for the development of biologically informed treatment strategies. Forty-five healthy controls and 44 patients with schizophrenia underwent resting-state fMRI scans at baseline. The patients underwent a second scan after 6 weeks of antipsychotic treatment. At baseline, patients exhibited a significant decrease in functional connectivity of the cingulate gyrus in the default mode network compared to healthy controls, and this decrease was negatively correlated with symptom severity. Clinical improvements were observed after 6 weeks treatment, accompanied by an increase in functional connectivity of the cingulate gyrus in the default mode network and the inferior parietal lobule in the executive control network. The changes in functional connectivity of the inferior parietal lobule were significantly correlated with symptom improvement. These longitudinal neuroimaging findings suggest that schizophrenia might be an outcome of the disruption of the optimal balance of brain networks, and reestablishing this balance through antipsychotic treatment may result in clinical symptom improvement.
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Affiliation(s)
- Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jiahui Deng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Guangheng Dong
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 311121, China
| | - Suxia Li
- National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China
| | - Ping Wu
- National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China
| | - Hongqiang Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Lin Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China.
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China.
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17
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Mi WF, Tabarak S, Wang L, Zhang SZ, Lin X, Du LT, Liu Z, Bao YP, Gao XJ, Zhang WH, Wang XQ, Fan TT, Li LZ, Hao XN, Fu Y, Shi Y, Guo LH, Sun HQ, Liu L, Si TM, Zhang HY, Lu L, Li SX. Effects of agomelatine and mirtazapine on sleep disturbances in major depressive disorder: evidence from polysomnographic and resting-state functional connectivity analyses. Sleep 2020; 43:5837058. [PMID: 32406918 DOI: 10.1093/sleep/zsaa092] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 05/04/2020] [Indexed: 11/14/2022] Open
Abstract
To investigate effects of agomelatine and mirtazapine on sleep disturbances in patients with major depressive disorder. A total of 30 depressed patients with sleep disturbances, 27 of which completed the study, took agomelatine or mirtazapine for 8 weeks. Subjective scales were administered, and polysomnography was performed at baseline and at the end of week 1 and 8. Functional magnetic resonance imaging was performed at baseline and at the end of week 8. Compared with baseline, scores on the Hamilton Depression Scale, Hamilton Anxiety Scale, Pittsburgh Sleep Quality Index, Sleep Dysfunction Rating Scale, and Insomnia Severity Index after 8 weeks of treatment significantly decreased in both groups, with no significant differences between groups, accompanied by significant increases in total sleep time, sleep efficiency, and rapid eye movement (REM) sleep and significant decrease in wake after sleep onset. Mirtazapine treatment increased N3 sleep at week 1 compared with agomelatine treatment, but this difference disappeared at week 8. The increases in the percentage and duration of N3 sleep were positively correlated with increases in connectivity between right dorsal lateral prefrontal cortex (dlPFC) and right precuneus and between left posterior cingulate cortex and right precuneus in both groups, respectively. Functional connectivity (FC) between right dlPFC and left precuneus in mirtazapine group was higher compared with agomelatine group after 8 weeks of treatment. These findings indicated that both agomelatine and mirtazapine improved sleep in depressed patients, and the effect of mirtazapine was greater than agomelatine with regard to rapidly increasing N3 sleep and gradually improving FC in the brain.
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Affiliation(s)
- Wei-Feng Mi
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Serik Tabarak
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Li Wang
- Xuanwu Hospital Capital Medical University, Xicheng, Beijing, China
| | - Su-Zhen Zhang
- Department of Psychiatry, Huzhou 3rd Hospital, Huzhou, Zhejiang, China
| | - Xiao Lin
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Lan-Ting Du
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Zhen Liu
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Yan-Ping Bao
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Xue-Jiao Gao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Wei-Hua Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Xue-Qin Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Teng-Teng Fan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Ling-Zhi Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Xiao-Nan Hao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Yi Fu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Ying Shi
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Li-Hua Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Hong-Qiang Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Lin Liu
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Tian-Mei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Hong-Yan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Lin Lu
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China
| | - Su-Xia Li
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
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18
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Soni S, Muthukrishnan SP, Sood M, Kaur S, Sharma R. Altered parahippocampal gyrus activation and its connectivity with resting-state network areas in schizophrenia: An EEG study. Schizophr Res 2020; 222:411-422. [PMID: 32534839 DOI: 10.1016/j.schres.2020.03.066] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 02/21/2020] [Accepted: 03/29/2020] [Indexed: 02/02/2023]
Abstract
Synchronized and coherent activity in resting-networks during normal brain functioning could be altered in disconnection syndrome like schizophrenia. Study of neural oscillations as assessed by EEG appears to be a promising proposition to understand the pathophysiology of schizophrenia in patients and their first-degree relatives, where disturbances in neural oscillations point towards genetic predisposition. Therefore, present study aims at establishing EEG based biomarkers for early detection and management strategies. Thirty-two patients with schizophrenia, 28 first-degree relatives and 31 healthy controls (HC) participated in the study. Resting brain activity was recorded using 128-channel electroencephalography. After pre-processing and independent component analysis (ICA), an equivalent current dipole was estimated for each IC. Total of 1551 independent and localizable EEG components across all groups were used in subsequent analysis. Power spectral density and source coherence between IC clusters were computed. Patients and first-degree relatives displayed significantly higher power spectral density (PSD) than HC for all frequency bands in left parahippocampal gyrus (PHG) (-7, -26, 8; BA 27). Another region within left deep PHG (-4, -28, 1), however, distinguished patients from first-degree relatives and HC in terms of significantly lower PSD in higher frequency bands. Functional connectivity (FC) was found to be lower in patients and higher in relatives compared to HC between different resting-state network areas. In patients, connectivity was lower compared to first-degree relatives. Altered activity within left PHG and FC of primarily this with other areas in resting-state network can serve as state and trait markers of schizophrenia.
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Affiliation(s)
- Sunaina Soni
- Stress and Cognitive Electroimaging Laboratory, Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Suriya Prakash Muthukrishnan
- Stress and Cognitive Electroimaging Laboratory, Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Mamta Sood
- Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India
| | - Simran Kaur
- Stress and Cognitive Electroimaging Laboratory, Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Ratna Sharma
- Stress and Cognitive Electroimaging Laboratory, Department of Physiology, All India Institute of Medical Sciences, New Delhi, India.
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19
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Rezaeinia P, Fairley K, Pal P, Meyer FG, Carter RM. Identifying brain network topology changes in task processes and psychiatric disorders. Netw Neurosci 2020; 4:257-273. [PMID: 32181418 PMCID: PMC7069064 DOI: 10.1162/netn_a_00122] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 12/11/2019] [Indexed: 11/04/2022] Open
Abstract
A central goal in neuroscience is to understand how dynamic networks of neural activity produce effective representations of the world. Advances in the theory of graph measures raise the possibility of elucidating network topologies central to the construction of these representations. We leverage a result from the description of lollipop graphs to identify an iconic network topology in functional magnetic resonance imaging data and characterize changes to those networks during task performance and in populations diagnosed with psychiatric disorders. During task performance, we find that task-relevant subnetworks change topology, becoming more integrated by increasing connectivity throughout cortex. Analysis of resting state connectivity in clinical populations shows a similar pattern of subnetwork topology changes; resting scans becoming less default-like with more integrated sensory paths. The study of brain network topologies and their relationship to cognitive models of information processing raises new opportunities for understanding brain function and its disorders.
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Affiliation(s)
- Paria Rezaeinia
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, USA
| | - Kim Fairley
- Department of Economics, Leiden University, Leiden, The Netherlands
| | - Piya Pal
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, USA
| | - François G Meyer
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
| | - R McKell Carter
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA
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20
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Reduced Resting-State Connectivity in the Precuneus is correlated with Apathy in Patients with Schizophrenia. Sci Rep 2020; 10:2616. [PMID: 32054907 PMCID: PMC7018974 DOI: 10.1038/s41598-020-59393-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 01/24/2020] [Indexed: 02/05/2023] Open
Abstract
A diagnosis of schizophrenia is associated with a heterogeneous psychopathology including positive and negative symptoms. The disconnection hypothesis, an early pathophysiological framework conceptualizes the diversity of symptoms as a result of disconnections in neural networks. In line with this hypothesis, previous neuroimaging studies of patients with schizophrenia reported alterations within the default mode network (DMN), the most prominent network at rest. The aim of the present study was to investigate the functional connectivity during rest in patients with schizophrenia and with healthy individuals and explore whether observed functional alterations are related to the psychopathology of patients. Therefore, functional magnetic resonance images at rest were recorded of 35 patients with schizophrenia and 41 healthy individuals. Independent component analysis (ICA) was used to extract resting state networks. Comparing ICA results between groups indicated alterations only within the network of the DMN. More explicitly, reduced connectivity in the precuneus was observed in patients with schizophrenia compared to healthy controls. Connectivity in this area was negatively correlated with the severity of negative symptoms, more specifically with the domain of apathy. Taken together, the current results provide further evidence for a role DMN alterations might play in schizophrenia and especially in negative symptoms such as apathy.
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21
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Steardo L, Carbone EA, de Filippis R, Pisanu C, Segura-Garcia C, Squassina A, De Fazio P, Steardo L. Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review. Front Psychiatry 2020; 11:588. [PMID: 32670113 PMCID: PMC7326270 DOI: 10.3389/fpsyt.2020.00588] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 06/08/2020] [Indexed: 01/06/2023] Open
Abstract
Non-invasive measurements of brain function and structure as neuroimaging in patients with mental illnesses are useful and powerful tools for studying discriminatory biomarkers. To date, functional MRI (fMRI), structural MRI (sMRI) represent the most used techniques to provide multiple perspectives on brain function, structure, and their connectivity. Recently, there has been rising attention in using machine-learning (ML) techniques, pattern recognition methods, applied to neuroimaging data to characterize disease-related alterations in brain structure and function and to identify phenotypes, for example, for translation into clinical and early diagnosis. Our aim was to provide a systematic review according to the PRISMA statement of Support Vector Machine (SVM) techniques in making diagnostic discrimination between SCZ patients from healthy controls using neuroimaging data from functional MRI as input. We included studies using SVM as ML techniques with patients diagnosed with Schizophrenia. From an initial sample of 660 papers, at the end of the screening process, 22 articles were selected, and included in our review. This technique can be a valid, inexpensive, and non-invasive support to recognize and detect patients at an early stage, compared to any currently available assessment or clinical diagnostic methods in order to save crucial time. The higher accuracy of SVM models and the new integrated methods of ML techniques could play a decisive role to detect patients with SCZ or other major psychiatric disorders in the early stages of the disease or to potentially determine their neuroimaging risk factors in the near future.
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Affiliation(s)
- Luca Steardo
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Elvira Anna Carbone
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Renato de Filippis
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Claudia Pisanu
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Faculty of Medicine and Surgery, University of Cagliari, Cagliari, Italy
| | - Cristina Segura-Garcia
- Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Italy
| | - Alessio Squassina
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Faculty of Medicine and Surgery, University of Cagliari, Cagliari, Italy.,Department of Psychiatry, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Pasquale De Fazio
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Luca Steardo
- Department of Physiology and Pharmacology, Faculty of Pharmacy and Medicine, Sapienza University of Rome, Rome, Italy.,Department of Psychiatry, Giustino Fortunato University, Benevento, Italy
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22
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Joo SW, Yoon W, Jo YT, Kim H, Kim Y, Lee J. Aberrant Executive Control and Auditory Networks in Recent-Onset Schizophrenia. Neuropsychiatr Dis Treat 2020; 16:1561-1570. [PMID: 32606708 PMCID: PMC7319504 DOI: 10.2147/ndt.s254208] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 05/27/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Despite a large number of resting-state functional MRI (rsfMRI) studies in schizophrenia, current evidence on the abnormalities of functional connectivity (FC) of resting-state networks shows high variability, and the findings on recent-onset schizophrenia are insufficient compared to those on chronic schizophrenia. PATIENTS AND METHODS We performed a rsfMRI in 46 patients with recent-onset schizophrenia and 22 healthy controls. Group independent component brainmap and dual regression were performed for voxel-wise comparisons between the groups. Correlation of the symptom severity, cognitive function, duration of illness, and a total antipsychotics dose with FC was evaluated with Spearman's rho correlation. RESULTS The patient group had areas with a significantly decreased FC compared to that of the control group in which it existed in the left supplementary motor cortex and supramarginal gyrus (the executive control network) and the right postcentral gyrus (the auditory network). The patient group had a significant correlation of the total antipsychotics dose with the FC of the cluster in the left supplementary motor cortex in the executive control network. CONCLUSION Patients with recent-onset schizophrenia have decreased FC of the executive control and auditory networks compared to healthy controls.
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Affiliation(s)
- Sung Woo Joo
- Medical Corps, Republic of Korea Navy 1st Fleet, Donghae, Republic of Korea
| | - Woon Yoon
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Harin Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yangsik Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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23
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Adhikari BM, Hong LE, Sampath H, Chiappelli J, Jahanshad N, Thompson PM, Rowland LM, Calhoun VD, Du X, Chen S, Kochunov P. Functional network connectivity impairments and core cognitive deficits in schizophrenia. Hum Brain Mapp 2019; 40:4593-4605. [PMID: 31313441 DOI: 10.1002/hbm.24723] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 07/03/2019] [Accepted: 07/08/2019] [Indexed: 12/19/2022] Open
Abstract
Cognitive deficits contribute to functional disability in patients with schizophrenia and may be related to altered functional networks that serve cognition. We evaluated the integrity of major functional networks and assessed their role in supporting two cognitive functions affected in schizophrenia: processing speed (PS) and working memory (WM). Resting-state functional magnetic resonance imaging (rsfMRI) data, N = 261 patients and 327 controls, were aggregated from three independent cohorts and evaluated using Enhancing NeuroImaging Genetics through Meta Analysis rsfMRI analysis pipeline. Meta- and mega-analyses were used to evaluate patient-control differences in functional connectivity (FC) measures. Canonical correlation analysis was used to study the association between cognitive deficits and FC measures. Patients showed consistent patterns of cognitive and resting-state FC (rsFC) deficits across three cohorts. Patient-control differences in rsFC calculated using seed-based and dual-regression approaches were consistent (Cohen's d: 0.31 ± 0.09 and 0.29 ± 0.08, p < 10-4 ). RsFC measures explained 12-17% of the individual variations in PS and WM in the full sample and in patients and controls separately, with the strongest correlations found in salience, auditory, somatosensory, and default-mode networks. The pattern of association between rsFC (within-network) and PS (r = .45, p = .07) and WM (r = .36, p = .16), and rsFC (between-network) and PS (r = .52, p = 8.4 × 10-3 ) and WM (r = .47, p = .02), derived from multiple networks was related to effect size of patient-control differences in the functional networks. No association was detected between rsFC and current medication dose or psychosis ratings. Patients demonstrated significant reduction in several FC networks that may partially underlie some of the core neurocognitive deficits in schizophrenia. The strength of connectivity-cognition relationships in different networks was strongly associated with network's vulnerability to schizophrenia.
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Affiliation(s)
- Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Hemalatha Sampath
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine of USC, Marina del Rey, California
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine of USC, Marina del Rey, California
| | - Laura M Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
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24
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Vlaskamp DRM, Bassett AS, Sullivan JE, Robblee J, Sadleir LG, Scheffer IE, Andrade DM. Schizophrenia is a later-onset feature of PCDH19
Girls Clustering Epilepsy. Epilepsia 2019; 60:429-440. [DOI: 10.1111/epi.14678] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 01/29/2019] [Accepted: 01/29/2019] [Indexed: 02/06/2023]
Affiliation(s)
- Danique R. M. Vlaskamp
- Department of Medicine; Epilepsy Research Centre; The University of Melbourne; Austin Health; Melbourne Victoria Australia
- Department of Neurology; University Medical Center Groningen; University of Groningen; Groningen The Netherlands
- Department of Genetics; University Medical Center Groningen; University of Groningen; Groningen The Netherlands
| | - Anne S. Bassett
- Clinical Genetics Research Program; Campbell Family Mental Health Research Institute; Centre for Addiction and Mental Health; Toronto Ontario Canada
- Department of Psychiatry; University of Toronto; Toronto Ontario Canada
- Dalglish Family 22q Clinic for Adults with 22q11.2 Deletion Syndrome; Toronto General Research Institute; University Health Network; Toronto Ontario Canada
| | - Joseph E. Sullivan
- Pediatric Epilepsy Center; Benioff Children's Hospital; University of California San Francisco; San Francisco California
| | - Jennifer Robblee
- Division of Neurology; Toronto Western Hospital; University of Toronto; Toronto Ontario Canada
| | - Lynette G. Sadleir
- Department of Paediatrics and Child Health; University of Otago; Wellington New Zealand
| | - Ingrid E. Scheffer
- Department of Medicine; Epilepsy Research Centre; The University of Melbourne; Austin Health; Melbourne Victoria Australia
- Department of Paediatrics; Royal Children's Hospital; The University of Melbourne; Victoria Australia
- The Florey Institute of Neurosciences and Mental Health; Melbourne Victoria Australia
| | - Danielle M. Andrade
- Division of Neurology; Toronto Western Hospital; University of Toronto; Toronto Ontario Canada
- Epilepsy Genetics Research Program; Krembil Neuroscience Centre; University of Toronto; Toronto Ontario Canada
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25
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Ning Y, Yin D, Jia W, Zhu H, Xue S, Liu J, Jia H. Cognitive impairment in patients with kidney deficiency syndrome: A resting-state fMRI study. Eur J Integr Med 2018. [DOI: 10.1016/j.eujim.2018.10.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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26
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Disinhibition of the prefrontal cortex leads to brain-wide increases in neuronal activation that are modified by spatial learning. Brain Struct Funct 2018; 224:171-190. [DOI: 10.1007/s00429-018-1769-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 10/03/2018] [Indexed: 12/30/2022]
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27
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Kottaram A, Johnston L, Ganella E, Pantelis C, Kotagiri R, Zalesky A. Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis. Hum Brain Mapp 2018; 39:3663-3681. [PMID: 29749660 PMCID: PMC6866493 DOI: 10.1002/hbm.24202] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 04/18/2018] [Accepted: 04/19/2018] [Indexed: 02/01/2023] Open
Abstract
Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding-window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting-state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting-state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting-state dynamics are not trivially attributable to sampling variability and head motion.
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Affiliation(s)
- Akhil Kottaram
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia
| | - Leigh Johnston
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia
- Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, 3010, Australia
- Florey Institute for Neurosciences and Mental health, Parkville, Victoria, 3052, Australia
| | - Eleni Ganella
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
- Cooperative Research Centre for Mental Health, Carlton, Victoria, 3053, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
- Department of Psychiatry, The University of Melbourne, Victoria, 3010, Australia
- Florey Institute for Neurosciences and Mental health, Parkville, Victoria, 3052, Australia
- North Western Mental Health, Melbourne Health, Parkville, Victoria, Australia
- Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, 3053, Australia
- Cooperative Research Centre for Mental Health, Carlton, Victoria, 3053, Australia
| | - Ramamohanarao Kotagiri
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
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28
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Singh K, Lilleväli K, Gilbert SF, Bregin A, Narvik J, Jayaram M, Rahi M, Innos J, Kaasik A, Vasar E, Philips MA. The combined impact of IgLON family proteins Lsamp and Neurotrimin on developing neurons and behavioral profiles in mouse. Brain Res Bull 2018; 140:5-18. [PMID: 29605488 DOI: 10.1016/j.brainresbull.2018.03.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 02/26/2018] [Accepted: 03/23/2018] [Indexed: 12/13/2022]
Abstract
Cell surface neural adhesion proteins are critical components in the complex orchestration of cell proliferation, apoptosis, and neuritogenesis essential for proper brain construction and behavior. We focused on the impact of two plasticity-associated IgLON family neural adhesion molecules, Neurotrimin (Ntm) and Limbic system associated membrane protein (Lsamp), on mouse behavior and its underlying neural development. Phenotyping neurons derived from the hippocampi of Lsamp-/-, Ntm-/- and Lsamp-/-Ntm-/- mice was performed in parallel with behavioral testing. While the anatomy of mutant brains revealed no gross changes, the Ntm-/- hippocampal neurons exhibited premature sprouting of neurites and manifested accelerated neurite elongation and branching. We propose that Ntm exerts an inhibitory impact on neurite outgrowth, whereas Lsamp appears to be an enhancer of the said process as premature neuritogenesis in Ntm-/- neurons is apparent only in the presence of Lsamp. We also show interplay between Lsamp and Ntm in regulating tissue homeostasis: the impact of Ntm on cellular proliferation was dependent on Lsamp, and Lsamp appeared to be a positive regulator of apoptosis in the presence of Ntm. Behavioral phenotyping indicated test-specific interactions between Lsamp and Ntm. The phenotypes of single mutant lines, such as reduced swimming speed in Morris water maze and increased activity in the elevated plus maze, were magnified in Lsamp-/-Ntm-/- mice. Altogether, evidence both from behavioral experiments and cultured hippocampal cells show combined and differential interactions between Ntm and Lsamp in the formation of hippocampal circuits and behavioral profiles. We demonstrate that mutual interactions between IgLON molecules regulate the initiation of neurite sprouting at very early ages, and even cell-autonomously, independent of their regulation of cell-cell adhesion.
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Affiliation(s)
- Katyayani Singh
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Kersti Lilleväli
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Scott F Gilbert
- Department of Biology, Swarthmore College, Swarthmore, PA, USA
| | - Aleksandr Bregin
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Jane Narvik
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Mohan Jayaram
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Märt Rahi
- Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Fr.R. Kreutzwaldi 5, 51014, Tartu, Estonia
| | - Jürgen Innos
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Allen Kaasik
- Department of Pharmacology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Eero Vasar
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia
| | - Mari-Anne Philips
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, 19 Ravila Street, 50411, Tartu, Estonia.
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29
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Karis K, Eskla KL, Kaare M, Täht K, Tuusov J, Visnapuu T, Innos J, Jayaram M, Timmusk T, Weickert CS, Väli M, Vasar E, Philips MA. Altered Expression Profile of IgLON Family of Neural Cell Adhesion Molecules in the Dorsolateral Prefrontal Cortex of Schizophrenic Patients. Front Mol Neurosci 2018; 11:8. [PMID: 29434535 PMCID: PMC5797424 DOI: 10.3389/fnmol.2018.00008] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 01/08/2018] [Indexed: 01/03/2023] Open
Abstract
Neural adhesion proteins are crucial in the development and maintenance of functional neural connectivity. Growing evidence suggests that the IgLON family of neural adhesion molecules LSAMP, NTM, NEGR1, and OPCML are important candidates in forming the susceptibility to schizophrenia (SCZ). IgLON proteins have been shown to be involved in neurite outgrowth, synaptic plasticity and neuronal connectivity, all of which have been shown to be altered in the brains of patients with the diagnosis of schizophrenia. Here we optimized custom 5'-isoform-specific TaqMan gene-expression analysis for the transcripts of human IgLON genes to study the expression of IgLONs in the dorsolateral prefrontal cortex (DLPFC) of schizophrenic patients (n = 36) and control subjects (n = 36). Uniform 5'-region and a single promoter was confirmed for the human NEGR1 gene by in silico analysis. IgLON5, a recently described family member, was also included in the study. We detected significantly elevated levels of the NEGR1 transcript (1.33-fold increase) and the NTM 1b isoform transcript (1.47-fold increase) in the DLPFC of schizophrenia patients compared to healthy controls. Consequent protein analysis performed in male subjects confirmed the increase in NEGR1 protein content both in patients with the paranoid subtype and in patients with other subtypes. In-group analysis of patients revealed that lower expression of certain IgLON transcripts, mostly LSAMP 1a and 1b, could be related with concurrent depressive endophenotype in schizophrenic patients. Additionally, our study cohort provides further evidence that cannabis use may be a relevant risk factor associated with suicidal behaviors in psychotic patients. In conclusion, we provide clinical evidence of increased expression levels of particular IgLON family members in the DLPFC of schizophrenic patients. We propose that alterations in the expression profile of IgLON neural adhesion molecules are associated with brain circuit disorganization in neuropsychiatric disorders, such as schizophrenia. In the light of previously published data, we suggest that increased level of NEGR1 in the frontal cortex may serve as molecular marker for a wider spectrum of psychiatric conditions.
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Affiliation(s)
- Karina Karis
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Kattri-Liis Eskla
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Maria Kaare
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Karin Täht
- Institute of Psychology, University of Tartu, Tartu, Estonia
| | - Jana Tuusov
- Department of Pathological Anatomy and Forensic Medicine, University of Tartu, Tartu, Estonia.,Estonian Forensic Science Institute, Tallinn, Estonia
| | - Tanel Visnapuu
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Jürgen Innos
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Mohan Jayaram
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Tõnis Timmusk
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | - Cynthia S Weickert
- Faculty of Medicine, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Schizophrenia Research Institute, Neuroscience Research Australia, Randwick, NSW, Australia
| | - Marika Väli
- Department of Pathological Anatomy and Forensic Medicine, University of Tartu, Tartu, Estonia.,Estonian Forensic Science Institute, Tallinn, Estonia
| | - Eero Vasar
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Mari-Anne Philips
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
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