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Wang Y, Chen H, Wang C, Liu J, Miao P, Wei Y, Wu L, Wang X, Wang P, Zhang Y, Cheng J, Fan S, Sun G. Static and dynamic interactions within the triple-network model in stroke patients with multidomain cognitive impairments. Neuroimage Clin 2024; 43:103655. [PMID: 39146837 DOI: 10.1016/j.nicl.2024.103655] [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: 04/26/2024] [Revised: 07/23/2024] [Accepted: 08/07/2024] [Indexed: 08/17/2024]
Abstract
BACKGROUND Internal capsule strokes often result in multidomain cognitive impairments across memory, attention, and executive function, typically due to disruptions in brain network connectivity. Our study examines these impairments by analyzing interactions within the triple-network model, focusing on both static and dynamic aspects. METHODS We collected resting-state fMRI data from 62 left (CI_L) and 56 right (CI_R) internal capsule stroke patients, along with 57 healthy controls (HC). Using independent component analysis to extract the default mode (DMN), executive control (ECN), and salience networks (SAN), we conducted static and dynamic functional network connectivity analyses (DFNC) to identify differences between stroke patients and controls. For DFNC, we used k-means clustering to focus on temporal properties and multilayer network analysis to examine integration and modularity Q, where integration represents dynamic interactions between networks, and modularity Q measures how well the network is divided into distinct modules. We then calculated the correlations between SFNC/DFNC properties with significant inter-group differences and cognitive scales. RESULTS Compared to HC, both CI_L and CI_R patients showed increased static FCs between SAN and DMN and decreased dynamic interactions between ECN and other networks. CI_R patients also had heightened static FCs between SAN and ECN and maintained a state with strongly positive FNCs across all networks in the triple-network model. Additionally, CI_R patients displayed decreased modularity Q. CONCLUSION These findings highlight that stroke can result in the disruption of static and dynamic interactions in the triple network model, aiding our understanding of the neuropathological basis for multidomain cognitive deficits after internal capsule stroke.
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Affiliation(s)
- Yingying Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hongxu Chen
- Cardiff University Brain Research Imaging Centre, United Kingdom
| | - Caihong Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingchun Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Peifang Miao
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ying Wei
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Luobing Wu
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peipei Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Siyuan Fan
- Cardiovascular Center, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Guifang Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Henan Province 450052, China.
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Wang Y, Han Z, Wang C, Liu J, Guo J, Miao P, Wei Y, Wu L, Wang X, Wang P, Zhang Y, Cheng J, Fan S. Withdrawn: The altered dynamic community structure for adaptive adjustment in stroke patients with multidomain cognitive impairments: A multilayer network analysis. Comput Biol Med 2024:108712. [PMID: 38906761 DOI: 10.1016/j.compbiomed.2024.108712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/10/2024] [Accepted: 06/03/2024] [Indexed: 06/23/2024]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconveniencethis may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.
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Affiliation(s)
- Yingying Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zongli Han
- Department of Neurosurgery, Peking University Shenzhen Hospital, Futian District Shenzhen Guangdong, P.R. China
| | - Caihong Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingchun Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jun Guo
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Peifang Miao
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ying Wei
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Luobing Wu
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peipei Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Siyuan Fan
- Cardiovascular Center, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Wei Y, Wang P, Zhang Y, Miao P, Liu J, Wei S, Wang X, Wang Y, Wu L, Han S, Wei Y, Wang K, Cheng J, Wang C. Altered static and dynamic cerebellar-cerebral functional connectivity in acute pontine infarction. Cereb Cortex 2024; 34:bhae182. [PMID: 38741271 DOI: 10.1093/cercor/bhae182] [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: 03/03/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
This study investigates abnormalities in cerebellar-cerebral static and dynamic functional connectivity among patients with acute pontine infarction, examining the relationship between these connectivity changes and behavioral dysfunction. Resting-state functional magnetic resonance imaging was utilized to collect data from 45 patients within seven days post-pontine infarction and 34 normal controls. Seed-based static and dynamic functional connectivity analyses identified divergences in cerebellar-cerebral connectivity features between pontine infarction patients and normal controls. Correlations between abnormal functional connectivity features and behavioral scores were explored. Compared to normal controls, left pontine infarction patients exhibited significantly increased static functional connectivity within the executive, affective-limbic, and motor networks. Conversely, right pontine infarction patients demonstrated decreased static functional connectivity in the executive, affective-limbic, and default mode networks, alongside an increase in the executive and motor networks. Decreased temporal variability of dynamic functional connectivity was observed in the executive and default mode networks among left pontine infarction patients. Furthermore, abnormalities in static and dynamic functional connectivity within the executive network correlated with motor and working memory performance in patients. These findings suggest that alterations in cerebellar-cerebral static and dynamic functional connectivity could underpin the behavioral dysfunctions observed in acute pontine infarction patients.
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Affiliation(s)
- Ying Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
| | - Peipei Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
| | - Peifang Miao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
| | - Jingchun Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154 Anshan Road, Heping District, Tianjin 300052, China
| | - Sen Wei
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
| | - Xin Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
| | - Yingying Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, No. 37 Guoxue Lane, Wuhou District, Chengdu 610041, China
| | - Luobing Wu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
- Department of Radiology, The First Affiliated Hospital of Henan University of Science and Technology, No. 24 Jinghua Road, Jianxi District, Luoyang 471003, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
| | - Kaiyu Wang
- GE Healthcare MR Research, Tongji South Road, Daxing District, Beijing 100176, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, Erqi District, Zhengzhou 450052, China
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Stein A, Thorstensen JR, Ho JM, Ashley DP, Iyer KK, Barlow KM. Attention Please! Unravelling the Link Between Brain Network Connectivity and Cognitive Attention Following Acquired Brain Injury: A Systematic Review of Structural and Functional Measures. Brain Connect 2024; 14:4-38. [PMID: 38019047 DOI: 10.1089/brain.2023.0067] [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] [Indexed: 11/30/2023] Open
Abstract
Traumatic brain injury (TBI) and stroke are the most common causes of acquired brain injury (ABI), annually affecting 69 million and 15 million people, respectively. Following ABI, the relationship between brain network disruption and common cognitive issues including attention dysfunction is heterogenous. Using PRISMA guidelines, we systematically reviewed 43 studies published by February 2023 that reported correlations between attention and connectivity. Across all ages and stages of recovery, following TBI, greater attention was associated with greater structural efficiency within/between executive control network (ECN), salience network (SN), and default mode network (DMN) and greater functional connectivity (fc) within/between ECN and DMN, indicating DMN interference. Following stroke, greater attention was associated with greater structural connectivity (sc) within ECN; or greater fc within the dorsal attention network (DAN). In childhood ABI populations, decreases in structural network segregation were associated with greater attention. Longitudinal recovery from TBI was associated with normalization of DMN activity, and in stroke, normalization of DMN and DAN activity. Results improve clinical understanding of attention-related connectivity changes after ABI. Recommendations for future research include increased use of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to measure connectivity at the point of care, standardized attention and connectivity outcome measures and analysis pipelines, detailed reporting of patient symptomatology, and casual analysis of attention-related connectivity using brain stimulation.
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Affiliation(s)
- Athena Stein
- Child Health Research Centre, The University of Queensland, South Brisbane, Australia
| | - Jacob R Thorstensen
- Child Health Research Centre, The University of Queensland, South Brisbane, Australia
- School of Biomedical Sciences, The University of Queensland, St Lucia, Australia
| | - Jonathan M Ho
- Child Health Research Centre, The University of Queensland, South Brisbane, Australia
| | - Daniel P Ashley
- Child Health Research Centre, The University of Queensland, South Brisbane, Australia
| | - Kartik K Iyer
- Child Health Research Centre, The University of Queensland, South Brisbane, Australia
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Herston, Australia
| | - Karen M Barlow
- Child Health Research Centre, The University of Queensland, South Brisbane, Australia
- Queensland Pediatric Rehabilitation Service, Queensland Children's Hospital, South Brisbane, Australia
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Wang C, Liu J, Guo J, Han S, Miao P, Wei Y, Wang Y, Wang X, Li Z, Xue K, Wang K, Cheng J. Dynamic brain activity states of memory impairment in stroke patients with varying motor outcomes. Front Aging Neurosci 2023; 15:1294009. [PMID: 38046468 PMCID: PMC10690823 DOI: 10.3389/fnagi.2023.1294009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 11/02/2023] [Indexed: 12/05/2023] Open
Abstract
Introduction The objective of this study was to characterize the alteration patterns of dynamic spatiotemporal activity in chronic subcortical stroke patients with varying motor outcomes, while investigating the imaging indicators relevant to the assessment of potential cognitive deficits in these patients. Methods A total of 136 patients and 88 normal controls were included in the analysis of static and dynamic intrinsic brain activity, determined by amplitude of low-frequency fluctuations. Results The findings unveiled that subcortical stroke patients exhibited significantly aberrant temporal dynamics of intrinsic brain activity, involving regions within multiple brain networks. These spatiotemporal patterns were found to be contingent upon the side of the lesion. In addition, these aberrant metrics demonstrated potential in discerning cognitive deficits in stroke patients with memory impairment, with the dynamic indices exerting more influence than the static ones. The observe findings may indicate that subcortical stroke can trigger imbalances in the segregation and integration of spatiotemporal patterns across the entire brain with multi-domain networks, especially in patients with poor motor outcomes. Conclusion It suggests that the temporal dynamics indices of intrinsic brain activity could serve as potential imaging indicators for assessing cognitive impairment in patients with chronic subcortical stroke, which may be associated with the motor outcomes.
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Affiliation(s)
- Caihong Wang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan, China
| | - Jingchun Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jun Guo
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Shaoqiang Han
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan, China
| | - Peifang Miao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan, China
| | - Ying Wei
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan, China
| | - Yingying Wang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan, China
| | - Xin Wang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kangkang Xue
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan, China
| | - Kaiyu Wang
- MR Research China, GE Healthcare, Beijing, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan, China
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Wang A, Dong T, Wei T, Wu H, Yang Y, Ding Y, Li C, Yang W. Large-scale networks changes in Wilson's disease associated with neuropsychiatric impairments: a resting-state functional magnetic resonance imaging study. BMC Psychiatry 2023; 23:805. [PMID: 37924073 PMCID: PMC10623710 DOI: 10.1186/s12888-023-05236-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 09/29/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND In Wilson's disease (WD) patients, network connections across the brain are disrupted, affecting multidomain function. However, the details of this neuropathophysiological mechanism remain unclear due to the rarity of WD. In this study, we aimed to investigate alterations in brain network connectivity at the whole-brain level (both intra- and inter-network) in WD patients through independent component analysis (ICA) and the relationship between alterations in these brain network functional connections (FCs) and clinical neuropsychiatric features to understand the underlying pathophysiological and central compensatory mechanisms. METHODS Eighty-five patients with WD and age- and sex-matched 85 healthy control (HC) were recruited for resting-state functional magnetic resonance imaging (rs-fMRI) scanning. We extracted the resting-state networks (RSNs) using the ICA method, analyzed the changes of FC in these networks and the correlation between alterations in FCs and clinical neuropsychiatric features. RESULTS Compared with HC, WD showed widespread lower connectivity within RSNs, involving default mode network (DMN), frontoparietal network (FPN), somatomotor network (SMN), dorsal attention network (DAN), especially in patients with abnormal UWDRS scores. Furthermore, the decreased FCs in the left medial prefrontal cortex (L_ MPFC), left anterior cingulate gyrus (L_ACC), precuneus (PCUN)within DMN were negatively correlated with the Unified Wilson's Disease Rating Scale-neurological characteristic examination (UWDRS-N), and the decreased FCs in the L_MPFC, PCUN within DMN were negatively correlated with the Unified Wilson's Disease Rating Scale-psychiatric symptoms examination (UWDRS-P). We additionally discovered that the patients with WD exhibited significantly stronger FC between the FPN and DMN, between the DAN and DMN, and between the FPN and DAN compared to HC. CONCLUSIONS We have provided evidence that WD is a disease with widespread dysfunctional connectivity in resting networks in brain, leading to neurological features and psychiatric symptoms (e.g. higher-order cognitive control and motor control impairments). The alter intra- and inter-network in the brain may be the neural underpinnings for the neuropathological symptoms and the process of injury compensation in WD patients.
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Affiliation(s)
- Anqin Wang
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China
| | - Ting Dong
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China
| | - Taohua Wei
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China
| | - Hongli Wu
- School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, 230012, Anhui, China
| | - Yulong Yang
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China
- School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, 230012, Anhui, China
| | - Yufeng Ding
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China
- School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, 230012, Anhui, China
| | - Chuanfu Li
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China.
| | - Wenming Yang
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, Anhui, China.
- Xin 'an Institute of Medicine and Modernization of Traditional Chinese Medicine, Institute of Great Health, Hefei National Science Center, Hefei, China.
- Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, China.
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Nong H, Pang X, Jing J, Cen Y, Qin S, Jiang H. Alterations in intra- and inter-network connectivity associated with cognition impairment in insulinoma patients. Front Endocrinol (Lausanne) 2023; 14:1234921. [PMID: 37818091 PMCID: PMC10561291 DOI: 10.3389/fendo.2023.1234921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/06/2023] [Indexed: 10/12/2023] Open
Abstract
Objective Cognitive dysfunction is common in insulinoma patients, but the underlying neural mechanisms are less well understood. This study aimed to explore the alterations of intra- and inter-network connectivity patterns associated with patients with insulinoma. Methods Resting-state fMRI were acquired from 13 insulinoma patients and 13 matched healthy controls (HCs). Group Independent component analysis (ICA) was employed to capture the resting-state networks (RSNs), then the intra- and inter-network connectivity patterns, were calculated and compared. Montreal Cognitive Assessment (MoCA) was used to assess the cognitive function. The relationship between connectivity patterns and MoCA scores was also examined. Results Insulinoma patients performed significantly worse on MoCA compared to HCs. The intra-network connectivity analysis revealed that patients with insulinoma showed decreased connectivity in the left medial superior frontal gyrus within anterior default mode network (aDMN), and decreased connectivity in right lingual gyrus within the visual network (VN). The intra-network connectivity analysis showed that patients with insulinoma had an increased connectivity between the inferior-posterior default mode network (ipDMN) and right frontoparietal network (rFPN) and decreased connectivity between the ipDMN and auditory network (AUN). There was a significant negative correlation between the ipDMN-rFPN connectivity and MoCA score. Conclusion This study demonstrated significant abnormalities in the intra- and inter-network connectivity in patients with insulinoma, which may represent the neural mechanisms underlying the cognitive impairment in insulinoma patients.
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Affiliation(s)
- Hui Nong
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Xiaomin Pang
- Department of Neurology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Jie Jing
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Yu Cen
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Shanyu Qin
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Haixing Jiang
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
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Yu Z, Pang H, Liu Y, Li X, Bu S, Wang J, Zhao M, Ren K. Disrupted network communication predicts mild cognitive impairment in end-stage renal disease: an individualized machine learning study based on resting-state fMRI. Cereb Cortex 2023; 33:10098-10107. [PMID: 37492012 DOI: 10.1093/cercor/bhad269] [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: 06/03/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/27/2023] Open
Abstract
End-Stage Renal Disease (ESRD) is known to be associated with a range of brain injuries, including cognitive decline. The purpose of this study is to investigate the functional connectivity (FC) of the resting-state networks (RSNs) through resting state functional magnetic resonance imaging (MRI), in order to gain insight into the neuropathological mechanism of ESRD. A total of 48 ESRD patients and 49 healthy controls underwent resting-state functional MRI and neuropsychological tests, for which Independent Components Analysis and graph-theory (GT) analysis were utilized. With the machine learning results, we examined the connections between RSNs abnormalities and neuropsychological test scores. Combining intra/inter network FC differences and GT results, ESRD was optimally distinguished in the testing dataset, with a balanced accuracy of 0.917 and area under curve (AUC) of 0.942. Shapley additive explanations results revealed that the increased functional network connectivity between DMN and left frontoparietal network (LFPN) was the most critical predictor for ESRD associated mild cognitive impairment diagnosis. Moreover, hypoSN (salience network) was positively correlated with Attention scores, while hyperLFPN was negatively correlated with Execution scores, indicating correlations between functional disruption and cognitive impairment measurements in ESRD patients. This study demonstrated that both the loss of FC within the SN and compensatory FC within the lateral frontoparietal network coexist in ESRD. This provides a network basis for understanding the individual brain circuits and offers additional noninvasive evidence to comprehend the brain networks in ESRD.
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Affiliation(s)
- Ziyang Yu
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Huize Pang
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yu Liu
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Xiaolu Li
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Shuting Bu
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Juzhou Wang
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Mengwan Zhao
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Ke Ren
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
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Li Z, Wang Z, Cao D, You R, Hu J. Altered dynamic functional network connectivity states in patients with acute basal ganglia ischemic stroke. Brain Res 2023:148406. [PMID: 37201623 DOI: 10.1016/j.brainres.2023.148406] [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: 12/26/2022] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Dynamic functional network connectivity (dFNC) patterns are successfully able to capture the time-varying features of intrinsic fluctuations throughout a scan. We explored dFNC alterations across the entire brain in patients with acute ischemic stroke (AIS) of the basal ganglia (BG). METHOD Resting-state functional magnetic resonance imaging data were acquired from 26 patients with first-ever AIS in the BG and 26 healthy controls (HCs). Independent component analysis, the sliding window method, and the K-means clustering method were used to obtain reoccurring dynamic network connectivity patterns. Moreover, temporal features across diverse dFNC states were compared between the two groups, and the local and global efficiencies across states were analyzed to explore the characteristics of the topological networks among states. RESULTS Four dFNC states were characterized for comparison of dynamic brain network connectivity patterns. In contrast to the HC group, the AIS group spent a significantly higher fraction of time in State 1, which is characterized by a relatively weaker brain network connectome. Conversely, compared with HC, patients with AIS showed a lower mean dwell time in State 2, which was characterized by a relatively stronger brain network connectome. Additionally, functional networks exhibited variable efficiency of information transfer across 4 states. CONCLUSIONS AIS not only altered the interaction between the different dynamic networks but also promoted characteristic alterations in the temporal and topological features of large-scale dynamic network connectivity.
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Affiliation(s)
- Zhongming Li
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
| | - Zhimin Wang
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Dairong Cao
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Ruixiong You
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Jianping Hu
- Department of Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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