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Zhu Y, Lai X, Wang M, Tang X, Wan T, Li B, Liu X, Wu J, He L, He Y. Abnormal Functional Connectivity Intra- and Inter-Network in Resting-State Brain Networks of Patients with Toothache. J Pain Res 2024; 17:2111-2120. [PMID: 38903397 PMCID: PMC11189307 DOI: 10.2147/jpr.s456437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 06/05/2024] [Indexed: 06/22/2024] Open
Abstract
Objective To separate the resting-state network of patients with dental pain using independent component analysis (ICA) and analyze abnormal changes in functional connectivity within as well as between the networks. Patients and Methods Twenty-three patients with dental pain and 30 healthy controls participated in this study. We extracted the resting-state functional network components of both using ICA. Functional connectivity differences within 14 resting-state brain networks were analyzed at the voxel level. Directional interactions between networks were analyzed using Granger causality analysis. Subsequently, functional connectivity values and causal coefficients were assessed for correlations with clinical parameters. Results Compared to healthy controls, we found enhanced functional connectivity in the left superior temporal gyrus of anterior protrusion network and the right Rolandic operculum of auditory network in patients with dental pain (p<0.01 and cluster-level p<0.05, Gaussian random field corrected). In contrast, functional connectivity of the right precuneus in the precuneus network was reduced, and were significantly as well as negatively correlated to those of the Visual Analogue Scale (r=-4.93, p=0.017), Hamilton Anxiety Scale (r=-0.46, p=0.027), and Hamilton Depression Scale (r=-0.563, p<0.01), using the Spearman correlation analysis. Regarding the causal relationship between resting-state brain networks, we found increased connectivity from the language network to the precuneus in patients with dental pain (p<0.05, false discovery rate corrected). However, the increase in causal coefficients from the verbal network to the precuneus network was independent of clinical parameters. Conclusion Patients with toothache exhibited abnormal functional changes in cognitive-emotion-related brain networks, such as the salience, auditory, and precuneus networks, thereby offering a new imaging basis for understanding central neural mechanisms in dental pain patients.
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Affiliation(s)
- Yuping Zhu
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Xunfu Lai
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Mengting Wang
- Department of Radiology, Yichang Central People’s Hospital, Yichang, People’s Republic of China
| | - Xin Tang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Tianyi Wan
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, People’s Republic of China
| | - Bin Li
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Xiaoming Liu
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Jialin Wu
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Lei He
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Yulin He
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
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2
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Bogler C, Zangrossi A, Miller C, Sartori G, Haynes J. Have you been there before? Decoding recognition of spatial scenes from fMRI signals in precuneus. Hum Brain Mapp 2024; 45:e26690. [PMID: 38703117 PMCID: PMC11069338 DOI: 10.1002/hbm.26690] [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: 07/19/2023] [Revised: 01/23/2024] [Accepted: 04/08/2024] [Indexed: 05/06/2024] Open
Abstract
One potential application of forensic "brain reading" is to test whether a suspect has previously experienced a crime scene. Here, we investigated whether it is possible to decode real life autobiographic exposure to spatial locations using fMRI. In the first session, participants visited four out of eight possible rooms on a university campus. During a subsequent scanning session, subjects passively viewed pictures and videos from these eight possible rooms (four old, four novel) without giving any responses. A multivariate searchlight analysis was employed that trained a classifier to distinguish between "seen" versus "unseen" stimuli from a subset of six rooms. We found that bilateral precuneus encoded information that can be used to distinguish between previously seen and unseen rooms and that also generalized to the two stimuli left out from training. We conclude that activity in bilateral precuneus is associated with the memory of previously visited rooms, irrespective of the identity of the room, thus supporting a parietal contribution to episodic memory for spatial locations. Importantly, we could decode whether a room was visited in real life without the need of explicit judgments about the rooms. This suggests that recognition is an automatic response that can be decoded from fMRI data, thus potentially supporting forensic applications of concealed information tests for crime scene recognition.
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Affiliation(s)
- Carsten Bogler
- Bernstein Center for Computational NeuroscienceCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Andrea Zangrossi
- Department of General PsychologyUniversity of PadovaPadovaItaly
- Padova Neuroscience Center (PNC)University of PadovaPadovaItaly
| | - Chantal Miller
- Berlin School of Mind and BrainHumboldt‐Universität zu BerlinBerlinGermany
| | | | - John‐Dylan Haynes
- Bernstein Center for Computational NeuroscienceCharité‐Universitätsmedizin BerlinBerlinGermany
- Berlin School of Mind and BrainHumboldt‐Universität zu BerlinBerlinGermany
- Max Planck School of CognitionLeipzigGermany
- Berlin Center for Advanced NeuroimagingCharité‐Universitätsmedizin BerlinBerlinGermany
- Clinic of NeurologyCharité‐Universitätsmedizin BerlinBerlinGermany
- Institute of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
- Cluster of Excellence “Science of Intelligence”Berlin Institute of TechnologyBerlinGermany
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3
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Xiao M, Luo Y, Ding C, Chen X, Liu Y, Tang Y, Chen H. Social support and overeating in young women: The role of altering functional network connectivity patterns and negative emotions. Appetite 2023; 191:107069. [PMID: 37837769 DOI: 10.1016/j.appet.2023.107069] [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: 03/16/2023] [Revised: 09/20/2023] [Accepted: 09/29/2023] [Indexed: 10/16/2023]
Abstract
Research suggests that social support has a protective effect on emotional health and emotionally induced overeating. Women are especially more sensitive to benefits from social support when facing eating problems. Although it has been demonstrated that social support can affect the neural processes of emotion regulation and reward perception, it is unclear how social support alters synergistic patterns in large-scale brain networks associated with negative emotions and overeating. We used a large sample of young women aged 17-22 years (N = 360) to examine how social support influences the synchrony of five intrinsic networks (executive control network [ECN], default mode network, salience network [SN], basal ganglia network, and precuneus network [PN]) and how these networks influence negative affect and overeating. Additionally, we explored these analyses in another sample of males (N = 136). After statistically controlling for differences in age and head movement, we observed significant associations of higher levels of social support with increased intra- and inter-network functional synchrony, particularly for ECN-centered network connectivity. Subsequent chain-mediated analyses showed that social support predicted overeating through the ECN-SN and ECN-PN network connectivity and negative emotions. However, these results were not found in men. These findings suggest that social support influences the synergistic patterns within and between intrinsic networks related to inhibitory control, emotion salience, self-referential thinking, and reward sensitivity. Furthermore, they reveal that social support and its neural markers may play a key role in young women's emotional health and eating behavior.
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Affiliation(s)
- Mingyue Xiao
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yijun Luo
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Cody Ding
- Department of Educational Psychology, Research, and Evaluation, University of Missouri, St. Louis, USA
| | - Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yutian Tang
- Faculty of Arts, University of British Columbia, Canada
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China; Research Center of Psychology and Social Development, Southwest University, Chongqing, China.
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4
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Wang D, Xu K, Dang M, Sang F, Chen K, Zhang Z, Li X. Multi-domain cognition dysfunction accompanies frontoparietal and temporal amyloid accumulation in the elderly. Cereb Cortex 2023; 33:11329-11338. [PMID: 37859548 DOI: 10.1093/cercor/bhad369] [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: 08/15/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/21/2023] Open
Abstract
It is helpful to understand the pathology of Alzheimer's disease by exploring the relationship between amyloid-β accumulation and cognition. The study explored the relationship between regional amyloid-β accumulation and multiple cognitions and study their application value in the Alzheimer's disease diagnosis. 135 participants completed 18F-florbetapir Positron Emission Tomography (PET), structural MRI, and a cognitive battery. Partial correlation was used to examine the relationship between global and regional amyloid-β accumulation and cognitions. Then, a support vector machine was applied to determine whether cognition-related accumulation regions can adequately distinguish the cognitively normal controls (76 participants) and mild cognitive impairment (30 participants) groups or mild cognitive impairment and Alzheimer's disease (29 participants) groups. The result showed that amyloid-β accumulation regions were mainly located in the frontoparietal cortex, calcarine fissure, and surrounding cortex and temporal pole regions. Episodic memory-related regions included the frontoparietal cortices; executive function-related regions included the frontoparietal, temporal, and occipital cortices; and processing speed-related regions included the frontal and occipital cortices. Support vector machine analysis showed that only episodic memory-related amyloid-β accumulation regions had better classification performance during the progression of Alzheimer's disease. Assessing regional changes in amyloid, particularly in frontoparietal regions, can aid in the early detection of amyloid-related decline in cognitive function.
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Affiliation(s)
- Dandan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Kai Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- School of Artificial Intelligence, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Mingxi Dang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Kewei Chen
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Banner Alzheimer's Institute, Phoenix, AZ 85006, United States
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
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5
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Jackson RL, Humphreys GF, Rice GE, Binney RJ, Lambon Ralph MA. A network-level test of the role of the co-activated default mode network in episodic recall and social cognition. Cortex 2023; 165:141-159. [PMID: 37285763 PMCID: PMC10284259 DOI: 10.1016/j.cortex.2022.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 10/10/2022] [Accepted: 12/19/2022] [Indexed: 06/09/2023]
Abstract
Resting-state network research is extremely influential, yet the functions of many networks remain unknown. In part, this is due to typical (e.g., univariate) analyses independently testing the function of individual regions and not examining the full set of regions that form a network whilst co-activated. Connectivity is dynamic and the function of a region may change based on its current connections. Therefore, determining the function of a network requires assessment at this network-level. Yet popular theories implicating the default mode network (DMN) in episodic memory and social cognition, rest principally upon analyses performed at the level of individual brain regions. Here we use independent component analysis to formally test the role of the DMN in episodic and social processing at the network level. As well as an episodic retrieval task, two independent datasets were employed to assess DMN function across the breadth of social cognition; a person knowledge judgement and a theory of mind task. Each task dataset was separated into networks of co-activated regions. In each, the co-activated DMN, was identified through comparison to an a priori template and its relation to the task model assessed. This co-activated DMN did not show greater activity in episodic or social tasks than high-level baseline conditions. Thus, no evidence was found to support hypotheses that the co-activated DMN is involved in explicit episodic or social tasks at a network-level. The networks associated with these processes are described. Implications for prior univariate findings and the functional significance of the co-activated DMN are considered.
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Affiliation(s)
- Rebecca L Jackson
- Department of Psychology & York Biomedical Research Institute, University of York, York, UK; MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Gina F Humphreys
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Grace E Rice
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK
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6
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Messina A, Cuccì G, Crescimanno C, Signorelli MS. Clinical anatomy of the precuneus and pathogenesis of the schizophrenia. Anat Sci Int 2023:10.1007/s12565-023-00730-w. [PMID: 37340095 DOI: 10.1007/s12565-023-00730-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/12/2023] [Indexed: 06/22/2023]
Abstract
Recent evidence has shown that the precuneus plays a role in the pathogenesis of schizophrenia. The precuneus is a structure of the parietal lobe's medial and posterior cortex, representing a central hub involved in multimodal integration processes. Although neglected for several years, the precuneus is highly complex and crucial for multimodal integration. It has extensive connections with different cerebral areas and is an interface between external stimuli and internal representations. In human evolution, the precuneus has increased in size and complexity, allowing the development of higher cognitive functions, such as visual-spatial ability, mental imagery, episodic memory, and other tasks involved in emotional processing and mentalization. This paper reviews the functions of the precuneus and discusses them concerning the psychopathological aspects of schizophrenia. The different neuronal circuits, such as the default mode network (DMN), in which the precuneus is involved and its alterations in the structure (grey matter) and the disconnection of pathways (white matter) are described.
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Affiliation(s)
- Antonino Messina
- Department of Clinical and Experimental Medicine, Psychiatry Unit, University of Catania, Catania, Italy.
| | | | | | - Maria Salvina Signorelli
- Department of Clinical and Experimental Medicine, Psychiatry Unit, University of Catania, Catania, Italy
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7
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Specific and common functional connectivity deficits in drug-free generalized anxiety disorder and panic disorder: A data-driven analysis. Psychiatry Res 2023; 319:114971. [PMID: 36459805 DOI: 10.1016/j.psychres.2022.114971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 11/17/2022] [Accepted: 11/19/2022] [Indexed: 11/22/2022]
Abstract
Evidence of comparing neural network differences between anxiety disorder subtypes is limited, while it is crucial to reveal the pathogenesis of anxiety disorders. The present study aimed to investigate specific and common resting-state functional connectivity (FC) networks in generalized anxiety disorder (GAD), panic disorder (PD), and healthy controls (HC). We employed the gRAICAR algorithm to decompose the resting-state fMRI into independent components and align the components across 61 subjects (22 GAD, 18 PD and 21 HC). The default mode network and precuneus network exhibited GAD-specific aberrance, the anterior default mode network showed atypicality specific to PD, and the right fronto-parietal network showed aberrance common to GAD and PD. Between GAD-specific networks, FC between bilateral dorsolateral prefrontal cortex (DLPFC) was positively correlated with interoceptive sensitivity. In the common network, altered FCs between DLPFC and angular gyrus, and between orbitofrontal cortex and precuneus, were positively correlated with anxiety severity and interoceptive sensitivity. The pathological mechanism of PD could closely relate to the dysfunction of prefrontal cortex, while GAD could involve more extensive brain areas, which may be related to fear generalization.
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Li H, Jiang S, Dong D, Hu J, He C, Hou C, He H, Huang H, Shen D, Pei H, Zhao G, Dong L, Yao D, Luo C. Vascular feature as a modulator of the aging brain. Cereb Cortex 2022; 32:5609-5621. [PMID: 35174854 DOI: 10.1093/cercor/bhac039] [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: 12/10/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 01/25/2023] Open
Abstract
The cerebral functional reorganization and declined cognitive function of aging might associate with altered vascular features. Here, we explored the altered cerebral hierarchical functional network of 2 conditions (task-free and naturalistic stimuli) in older adults and its relationship with vascular features (systemic microvascular and perfusion features, measured by magnetic resonance imaging) and behavior. Using cerebral gradient analysis, we found that compressive gradient of resting-state mainly located on the primary sensory-motor system and transmodal regions in aging, and further compress in these regions under the continuous naturalistic stimuli. Combining cerebral functional gradient, vascular features, and cognitive performance, the more compressive gradient in the resting-state, the worse vascular state, the lower cognitive function in older adults. Further modulation analysis demonstrated that both vascular features can regulate the relationship between gradient scores in the insula and behavior. Interestingly, systemic microvascular oxygenation also can modulate the relationship between cerebral gradient and cerebral perfusion. Furthermore, the less alteration of the compressive gradient with naturalistic stimuli came with lower cognitive function. Our findings demonstrated that the altered cerebral hierarchical functional structure in aging was linked with changed vascular features and behavior, offering a new framework for studying the physiological mechanism of functional connectivity in aging.
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Affiliation(s)
- Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
| | - Debo Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jian Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Chuan He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Changyue Hou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Dai Shen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Guocheng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Cancer Hospital affiliate to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610042, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Cancer Hospital affiliate to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610042, P. R. China
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Wang ZW, Yin ZH, Wang X, Zhang YT, Xu T, Du JR, Wen Y, Liao HQ, Zhao Y, Liang FR, Zhao L. Brain structural and functional changes during menstrual migraine: Relationships with pain. Front Mol Neurosci 2022; 15:967103. [PMID: 36187356 PMCID: PMC9515315 DOI: 10.3389/fnmol.2022.967103] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/25/2022] [Indexed: 12/21/2022] Open
Abstract
Objectives Menstrual migraine (MM) is a special type of migraine associated with the ovarian cycle, which imposes a marked burden on female patients. However, the pathogenesis of MM is not completely understood. We investigated gray matter volume (GMV) and functional connectivity (FC) alterations in patients with MM to explore whether there are changes in resting-state FC (rsFC) in brain regions with structural GMV abnormalities and investigated their relevance to pain and concomitant symptoms. Methods Seventy-five patients with MM and 54 female healthy controls underwent functional magnetic resonance imaging and examination. The patients completed a patient’s headache diary, which included the frequency of migraine attacks, a visual analog scale for pain, a self-rating anxiety scale, and a self-rating depression scale. We used voxel-based morphometry (VBM) to examine the GMV differences between the MM and healthy control groups. The identified brain areas were selected as seeds to assess functional changes in the MM group. Correlation analysis between the altered VBM/rsFC and clinical outcomes was performed. Results Compared with healthy controls, patients with MM showed decreased GMV in the right anterior cingulum cortex (ACC) and increased GMV in the right superior parietal cortex. Pearson’s correlation analysis illustrated that only GMV in the right ACC was associated with visual analogue scale pain scores in the MM group. RsFC with the ACC as the seed showed that patients with MM exhibited increased FC between the ACC and the left inferior temporal gyrus, bilateral angular gyrus, and right precuneus. Correlation analysis showed that the change in FC between the right ACC and the right precuneus was positively correlated with headache frequency, and the change in FC between the right ACC and the right angular gyrus was positively correlated with the depression score. Conclusion Our results suggested that the ACC may be an important biomarker in MM, and its structural and functional impairments are significantly associated with the severity of pain and pain-related impairment of emotion in patients with MM. These findings demonstrated that headache-associated structural and functional abnormalities in the ACC may can provide integrative evidence on the physiological mechanisms of MM.
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Affiliation(s)
- Zi-wen Wang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Sichuan Provincial Acupuncture Clinical Medicine Research Center, Chengdu, China
| | - Zi-han Yin
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Sichuan Provincial Acupuncture Clinical Medicine Research Center, Chengdu, China
| | - Xiao Wang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yu-tong Zhang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Sichuan Provincial Acupuncture Clinical Medicine Research Center, Chengdu, China
| | - Tao Xu
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Sichuan Provincial Acupuncture Clinical Medicine Research Center, Chengdu, China
| | - Jia-rong Du
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yi Wen
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hua-qiang Liao
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yu Zhao
- Chengdu Integrated Traditional Chinese Medicine and Western Medicine Hospital, Chengdu, China
| | - Fan-rong Liang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Sichuan Provincial Acupuncture Clinical Medicine Research Center, Chengdu, China
- Fan-rong Liang,
| | - Ling Zhao
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Sichuan Provincial Acupuncture Clinical Medicine Research Center, Chengdu, China
- *Correspondence: Ling Zhao,
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10
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Wu Q, Huang Q, Liu C, Wu H. Oxytocin modulates social brain network correlations in resting and task state. Cereb Cortex 2022; 33:3607-3620. [PMID: 36005833 DOI: 10.1093/cercor/bhac295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 11/13/2022] Open
Abstract
The effects of oxytocin (OT) on the social brain can be tracked upon assessing the neural activity in resting and task states, and developing a system-level framework for characterizing the state-based functional relationships of its distinct effect. Here, we contribute to this framework by examining how OT modulates social brain network correlations during resting and task states, using fMRI. First, we investigated network activation, followed by an analysis of the relationships between networks and individual differences. Subsequently, we evaluated the functional connectivity in both states. Finally, the relationship between networks across states was represented by the predictive power of networks in the resting state for task-evoked activities. The differences in the predicted accuracy between the subjects displayed individual variations in this relationship. Our results showed that the activity of the dorsal default mode network in the resting state had the largest predictive power for task-evoked activation of the precuneus network (PN) only in the OT group. The results also demonstrated that OT reduced the individual variation in PN in the prediction process. These findings suggest a distributed but modulatory effect of OT on the association between resting and task-dependent brain networks.
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Affiliation(s)
- Qingyuan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau 999078, China.,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qi Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau 999078, China
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11
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Zhang Q, Li B, Jin S, Liu W, Liu J, Xie S, Zhang L, Kang Y, Ding Y, Zhang X, Cheng W, Yang Z. Comparing the Effectiveness of Brain Structural Imaging, Resting-state fMRI, and Naturalistic fMRI in Recognizing Social Anxiety Disorder in Children and Adolescents. Psychiatry Res Neuroimaging 2022; 323:111485. [PMID: 35567906 DOI: 10.1016/j.pscychresns.2022.111485] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/07/2022] [Accepted: 04/16/2022] [Indexed: 01/11/2023]
Abstract
Social anxiety disorder (SAD) is a common anxiety disorder in childhood and adolescence. Studies on SAD in adults have reported both structural and functional aberrancies of the brain at the group level. However, evidence has shown differences in anxiety-related brain abnormalities between adolescents and adults. Since children and adolescents can afford limited scan time, optimizing the scan tasks is essential for SAD research in children and adolescents. Thus, we need to address whether brain structure, resting-state fMRI, and naturalistic imaging enable individualized identification of SAD in children and adolescents, which measurement is more effective, and whether pooling multi-modal features can improve the identification of SAD. We comprehensively addressed these questions by building machine learning models based on parcel-wise brain features. We found that naturalistic fMRI yielded higher classification accuracy (69.17%) than the other modalities and the classification performance showed dependence on the contents of the movie. The classification models also identified contributing brain regions, some of which exhibited correlations with the symptoms scores of SAD. However, pooling brain features from the three modalities did not help enhance the classification accuracy. These results support the application of carefully designed naturalistic imaging in recognizing children and adolescents at risk of SAD.
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Affiliation(s)
- Qinjian Zhang
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Baobin Li
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Shuyu Jin
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenjing Liu
- Department of Child and Adolescent Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingjing Liu
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuqi Xie
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lei Zhang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinzhi Kang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yue Ding
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaochen Zhang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenhong Cheng
- Department of Child and Adolescent Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Zhi Yang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China.
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12
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Wei J, Wang B, Yang Y, Niu Y, Yang L, Guo Y, Xiang J. Effects of virtual lesions on temporal dynamics in cortical networks based on personalized dynamic models. Neuroimage 2022; 254:119087. [PMID: 35364277 DOI: 10.1016/j.neuroimage.2022.119087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/02/2022] [Accepted: 03/08/2022] [Indexed: 11/19/2022] Open
Abstract
The human brain dynamically shifts between a predominantly integrated state and a predominantly segregated state, each with different roles in supporting cognition and behavior. However, no studies to date have investigated lesions placed in different regions of the cerebral cortex to determine the effects on the temporal balance of integration and segregation. Here, we used the integrated state occurrence rate to measure the temporal balance of integration and segregation in the resting brain. Based on dynamic mean-field models coupled through the individual's structural white matter connections, neural activity was modeled. By lesioning different individual nodes from the model, our results implied that the impact of lesions reaches far beyond focal damage and could impair cognition by affecting system-level dynamics. Lesions in a portion of the nodes in the visual, somatomotor, limbic, and default networks significantly compromised the temporal balance of integration and segregation. Crucially, the effects of lesions in different regions could be predicted based on the hierarchical axis inferred from the T1w/T2w map and specific graph measures of structural brain networks. Taken together, our findings indicate the possibility of significant contributions of anatomical heterogeneity to the dynamics of functional network topology. Although still in its infancy, computational modeling may provide an entry point for understanding brain disorders at a causal mechanistic level, possibly leading to novel, more effective therapeutic interventions.
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Affiliation(s)
- Jing Wei
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China; School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China; Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Lan Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yuxiang Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
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13
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Mechanisms of Repetitive Transcranial Magnetic Stimulation on Post-stroke Depression: A Resting-State Functional Magnetic Resonance Imaging Study. Brain Topogr 2022; 35:363-374. [PMID: 35286526 DOI: 10.1007/s10548-022-00894-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 02/21/2022] [Indexed: 12/25/2022]
Abstract
We aimed to identify neural mechanisms underlying clinical response to repetitive transcranial magnetic stimulation (rTMS) in post-stroke depression (PSD) by the Resting-state functional magnetic resonance imaging (rs-fMRI). Thirty-two depressed patients after ischemic stroke were randomized in a 1:1 ratio to receive 20 min of 5 Hz rTMS or sham over left dorsolateral prefrontal cortex (DLPFC) in addition to routine supportive treatments. The clinical outcome was measured by the 17-item Hamilton Depression Rating Scale (HDRS-17), while the imaging results were acquired from rs-fMRI, including regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuation (fALFF) and seed-based dynamic functional connection (dFC). HRSD-17 scores were improved in the two groups after treatment (P < 0.01), while greater mood improvement was observed in the rTMS group (P < 0.05). Compared with the sham group, the rTMS group demonstrated regions with higher ReHo and fALFF values locating mainly in the left hemisphere and highly consistent with the default mode network (DMN) (p < 0.05). Using the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) as seeds, significant difference between the two groups in dFC within the DMN was found after treatment, including 10 connections with increased connectivity strength and 2 connections with reduced connectivity strength. The ReHo, fALFF and dFC values within DMN in the rTMS group were negatively correlated with the HDRS scores after treatment (P < 0.05). Our results indicated reductions in depressive symptoms following rTMS in PSD are associated with functional alterations of different depression-related areas within the DMN.
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14
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mGluR5 binding changes during a mismatch negativity task in a multimodal protocol with [ 11C]ABP688 PET/MR-EEG. Transl Psychiatry 2022; 12:6. [PMID: 35013095 PMCID: PMC8748790 DOI: 10.1038/s41398-021-01763-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/22/2021] [Accepted: 11/30/2021] [Indexed: 02/08/2023] Open
Abstract
Currently, the metabotropic glutamate receptor 5 (mGluR5) is the subject of several lines of research in the context of neurology and is of high interest as a target for positron-emission tomography (PET). Here, we assessed the feasibility of using [11C]ABP688, a specific antagonist radiotracer for an allosteric site on the mGluR5, to evaluate changes in glutamatergic neurotransmission through a mismatch-negativity (MMN) task as a part of a simultaneous and synchronized multimodal PET/MR-EEG study. We analyzed the effect of MMN by comparing the changes in nondisplaceable binding potential (BPND) prior to (baseline) and during the task in 17 healthy subjects by applying a bolus/infusion protocol. Anatomical and functional regions were analyzed. A small change in BPND was observed in anatomical regions (posterior cingulate cortex and thalamus) and in a functional network (precuneus) after the start of the task. The effect size was quantified using Kendall's W value and was 0.3. The motor cortex was used as a control region for the task and did not show any significant BPND changes. There was a significant ΔBPND between acquisition conditions. On average, the reductions in binding across the regions were - 8.6 ± 3.2% in anatomical and - 6.4 ± 0.5% in the functional network (p ≤ 0.001). Correlations between ΔBPND and EEG latency for both anatomical (p = 0.008) and functional (p = 0.022) regions were found. Exploratory analyses suggest that the MMN task played a role in the glutamatergic neurotransmission, and mGluR5 may be indirectly modulated by these changes.
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15
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Riemer F, Grüner R, Beresniewicz J, Kazimierczak K, Ersland L, Hugdahl K. Dynamic switching between intrinsic and extrinsic mode networks as demands change from passive to active processing. Sci Rep 2020; 10:21463. [PMID: 33293637 PMCID: PMC7722921 DOI: 10.1038/s41598-020-78579-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 11/23/2020] [Indexed: 11/10/2022] Open
Abstract
In this study we report on the relationship between default and extrinsic mode networks across alternating brief periods of rest and active task processing. Three different visual tasks were used in a classic fMRI ON-OFF block design where task (ON) blocks alternated with equal periods of rest (OFF) blocks: mental rotation, working memory and mental arithmetic. We showed the existence of a generalized task-positive network, labelled the extrinsic mode network (EMN) that is anti-correlated with the default mode network (DMN) as processing demands shifted from rest to active processing. We then identified two key regions of interest (ROIs) in the supplementary motor area (SMA) and precuneus/posterior cingulate cortex (PCC) regions as hubs for the extrinsic and intrinsic networks, and extracted the time-course from these ROIs. The results showed a close to perfect anti-correlation for the SMA and Precuneus/PCC time-courses for ON- and OFF-blocks. We suggest the existence of two large-scale networks, an extrinsic mode network and an intrinsic mode network, which are up- and down-regulated as environmental demands change from active to passive processing.
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Affiliation(s)
- Frank Riemer
- Mohn Medical Imaging and Visualization Centre, University of Bergen and, Haukeland University Hospital, Bergen, Norway. .,Department of Radiology, Haukeland University Hospital, Bergen, Norway.
| | - Renate Grüner
- Mohn Medical Imaging and Visualization Centre, University of Bergen and, Haukeland University Hospital, Bergen, Norway.,Department of Radiology, Haukeland University Hospital, Bergen, Norway.,Department of Physics and Technology, University of Bergen, Bergen, Norway
| | - Justyna Beresniewicz
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Katarzyna Kazimierczak
- Mohn Medical Imaging and Visualization Centre, University of Bergen and, Haukeland University Hospital, Bergen, Norway.,Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Lars Ersland
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Kenneth Hugdahl
- Department of Radiology, Haukeland University Hospital, Bergen, Norway.,Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
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16
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Reliability map of individual differences reflected in inter-subject correlation in naturalistic imaging. Neuroimage 2020; 223:117277. [DOI: 10.1016/j.neuroimage.2020.117277] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 07/21/2020] [Accepted: 08/11/2020] [Indexed: 12/30/2022] Open
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17
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Shoukry RS, Waugh R, Bartlett D, Raitcheva D, Floeter MK. Longitudinal changes in resting state networks in early presymptomatic carriers of C9orf72 expansions. NEUROIMAGE-CLINICAL 2020; 28:102354. [PMID: 32769055 PMCID: PMC7406915 DOI: 10.1016/j.nicl.2020.102354] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 06/26/2020] [Accepted: 07/16/2020] [Indexed: 01/10/2023]
Abstract
Previous cross-sectional imaging studies found differences in brain structure and in resting state networks between presymptomatic carriers of mutations in C9orf72 (C9+) and healthy controls. We carried out a prospective longitudinal study of clinical and resting state functional imaging in a cohort of 15 presymptomatic C9+ carriers to determine whether differences in resting state connectivity prior to developing symptoms reflect static developmental differences or ongoing low-grade degenerative changes. Presymptomatic C9+ carriers were scanned at baseline with follow-up scanning at 6- and 18-months and compared to a cohort of 14 healthy controls scanned longitudinally. Resting state networks associated with manifest disease were visualized by comparing 27 symptomatic C9+ carriers to 34 healthy controls. Motor, salience, thalamic, and speech production networks were visualized using a seed-based analysis. Neurofilament light chain was measured in serum obtained at the time of the scans. Neither clinical measures of motor, cognitive, and behavioral function nor neurofilament levels changed over follow-up in presymptomatic C9+ carriers. In thalamic networks, there was a reduction in connectivity in presymptomatic carriers at all timepoints with a constant difference compared to healthy controls. In contrast, precuneus/posterior cingulate regions exhibited declining functional connectivity compared to controls over the 18-month follow-up, particularly in motor networks. These were regions that also exhibited reduced functional connectivity in symptomatic C9+ carriers. Reduced connectivity over time also occurred in small regions of frontal and temporal cortex within salience and thalamic networks in presymptomatic C9+ carriers. A few areas of increased connectivity occurred, including cortex near the motor seed and within the speech production network. Overall, changes in functional connectivity over time favor the explanation of ongoing low-grade alterations in presymptomatic C9+ carriers in most networks, with the exception of thalamic networks where functional connectivity reductions were stable over time. The loss of connectivity to parietal cortex regions in several different networks may be a distinct feature of C9orf72-related degeneration. Longitudinal studies of carriers who phenoconvert will be important to determine the prognostic significance of presymptomatic functional connectivity alterations.
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Affiliation(s)
- Rachel Smallwood Shoukry
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 10 Center Drive, 20892-1140, USA
| | - Rebecca Waugh
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 10 Center Drive, 20892-1140, USA.
| | - Dan Bartlett
- Biogen, 225 Binney Street, Cambridge, MA 02142, USA.
| | | | - Mary Kay Floeter
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 10 Center Drive, 20892-1140, USA.
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18
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Guo Q, Hu Y, Zeng B, Tang Y, Li G, Zhang T, Wang J, Northoff G, Li C, Goff D, Wang J, Yang Z. Parietal memory network and default mode network in first-episode drug-naïve schizophrenia: Associations with auditory hallucination. Hum Brain Mapp 2020; 41:1973-1984. [PMID: 32112506 PMCID: PMC7267906 DOI: 10.1002/hbm.24923] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 11/29/2019] [Accepted: 01/01/2020] [Indexed: 12/17/2022] Open
Abstract
Atypical spontaneous activities in resting‐state networks may play a role in auditory hallucinations (AHs), but networks relevant to AHs are not apparent. Given the debating role of the default mode network (DMN) in AHs, a parietal memory network (PMN) may better echo cognitive theories of AHs in schizophrenia, because PMN is spatially adjacent to the DMN and more relevant to memory processing or information integration. To examine whether PMN is more relevant to AHs than DMN, we characterized these intrinsic networks in AHs with 59 first‐episode, drug‐naïve schizophrenics (26 AH+ and 33 AH−) and 60 healthy participants in resting‐state fMRI. We separated the PMN, DMN, and auditory network (AN) using independent component analysis, and compared their functional connectivity across the three groups. We found that only AH+ patients displayed dysconnectivity in PMN, both AH+ and AH− patients exhibited dysfunctions of AN, but neither patient group showed abnormal connectivity within DMN. The connectivity of PMN significantly correlated with memory performance of the patients. Further region‐of‐interest analyses confirmed that the connectivity between the core regions of PMN, the left posterior cingulate gyrus and the left precuneus, was significantly lower only in the AH+ group. In exploratory correlation analysis, this functional connectivity metric significantly correlated with the severity of AH symptoms. The results implicate that compared to the DMN, the PMN is more relevant to the AH symptoms in schizophrenia, and further provides a more precise potential brain modulation target for the intervention of AH symptoms.
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Affiliation(s)
- Qian Guo
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.,Department of Early Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Botao Zeng
- Department of Psychiatry, Qingdao Mental Health Center, Qingdao, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guanjun Li
- Department of Early Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinhong Wang
- Department of Medical Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Georg Northoff
- University of Ottawa Brain and Mind Research Institute, and Mind Brain Imaging and Neuroethics Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.,Institute of Psychology and Behaviour Science, Shanghai Jiao Tong University, Shanghai, China
| | - Donald Goff
- Department of Psychiatry, New York University School of Medicine, New York, New York
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.,Institute of Psychology and Behaviour Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi Yang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.,Institute of Psychology and Behaviour Science, Shanghai Jiao Tong University, Shanghai, China
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