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Li L, Long T, Liu Y, Ayoub M, Song Y, Shu Y, Liu X, Zeng L, Huang L, Liu Y, Deng Y, Li H, Peng D. Abnormal dynamic functional connectivity and topological properties of cerebellar network in male obstructive sleep apnea. CNS Neurosci Ther 2024; 30:e14786. [PMID: 38828694 PMCID: PMC11145370 DOI: 10.1111/cns.14786] [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: 01/04/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 06/05/2024] Open
Abstract
PURPOSE To investigate dynamic functional connectivity (dFC) within the cerebellar-whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients. METHODS Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum-whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed. RESULTS Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep. CONCLUSION Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high-order and low-level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA.
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Affiliation(s)
- Lifeng Li
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
- Department of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangHunan ProvinceChina
| | - Ting Long
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
| | - Yuting Liu
- Department of OphthalmologyHunan Children's HospitalChangshaHunan ProvinceChina
| | - Muhammad Ayoub
- School of Computer Science and Engineering, Central South UniversityChangshaHunan ProvinceChina
| | - Yucheng Song
- School of Computer Science and Engineering, Central South UniversityChangshaHunan ProvinceChina
| | - Yongqiang Shu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
| | - Xiang Liu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
| | - Li Zeng
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
| | - Ling Huang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
| | - Yumeng Liu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
| | - Yingke Deng
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
| | - Haijun Li
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
- PET Center, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
| | - Dechang Peng
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
- PET Center, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxi ProvinceChina
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Liu Y, Wang H, Sha G, Cao Y, Chen Y, Chen Y, Zhang J, Chai C, Fan Q, Xia S. The covariant structural and functional neuro-correlates of cognitive impairments in patients with end-stage renal diseases. Front Neurosci 2024; 18:1374948. [PMID: 38686326 PMCID: PMC11056510 DOI: 10.3389/fnins.2024.1374948] [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: 01/23/2024] [Accepted: 04/03/2024] [Indexed: 05/02/2024] Open
Abstract
Introduction Cognitive impairment (CI) is a common complication of end-stage renal disease (ESRD) that is associated with structural and functional changes in the brain. However, whether a joint structural and functional alteration pattern exists that is related to CI in ESRD is unclear. Methods In this study, instead of looking at brain structure and function separately, we aim to investigate the covariant characteristics of both functional and structural aspects. Specifically, we took the fusion analysis approach, namely, multimodal canonical correlation analysis and joint independent component analysis (mCCA+jICA), to jointly study the discriminative features in gray matter volume (GMV) measured by T1-weighted (T1w) MRI, fractional anisotropy (FA) in white matter measured by diffusion MRI, and the amplitude of low-frequency fluctuation (ALFF) measured by blood oxygenation-level-dependent (BOLD) MRI in 78 ESRD patients versus 64 healthy controls (HCs), followed by a mediation effect analysis to explore the relationship between neuroimaging findings, cognitive impairments and uremic toxins. Results Two joint group-discriminative independent components (ICs) were found to show covariant abnormalities across FA, GMV, and ALFF (all p < 0.05). The most dominant joint IC revealed associative patterns of alterations of GMV (in the precentral gyrus, occipital lobe, temporal lobe, parahippocampal gyrus, and hippocampus), alterations of ALFF (in the precuneus, superior parietal gyrus, and superior occipital gyrus), and of white matter FA (in the corticospinal tract and inferior frontal occipital fasciculus). Another significant IC revealed associative alterations of GMV (in the dorsolateral prefrontal and orbitofrontal cortex) and FA (in the forceps minor). Moreover, the brain changes identified by FA and GMV in the above-mentioned brain regions were found to mediate the negative correlation between serum phosphate and mini-mental state examination (MMSE) scores (all p < 0.05). Conclusion The mCCA+jICA method was demonstrated to be capable of revealing covariant abnormalities across neuronal features of different types in ESRD patients as contrasted to HCs, and joint brain changes may play an important role in mediating the relationship between serum toxins and CIs in ESRD. Our results show the mCCA+jICA fusion analysis approach may provide new insights into similar neurobiological studies.
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Affiliation(s)
- Yuefan Liu
- Department of Biomedical Engineering, Medical College, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
| | - Huiying Wang
- Department of Radiology, School of Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
| | - Guanchen Sha
- Department of Biomedical Engineering, Medical College, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
| | - Yutong Cao
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- Intelligent Medical Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, United States
| | - Yuanyuan Chen
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- Intelligent Medical Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Jingyi Zhang
- Department of Radiology, School of Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
| | - Chao Chai
- Department of Radiology, School of Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
| | - Qiuyun Fan
- Department of Biomedical Engineering, Medical College, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- Intelligent Medical Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Shuang Xia
- Department of Radiology, School of Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
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Liu T, Shi Z, Zhang J, Wang K, Li Y, Pei G, Wang L, Wu J, Yan T. Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing. Hum Brain Mapp 2022; 44:744-761. [PMID: 36214186 PMCID: PMC9842897 DOI: 10.1002/hbm.26096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/01/2022] [Accepted: 09/19/2022] [Indexed: 01/25/2023] Open
Abstract
Using group-level functional parcellations and constant-length sliding window analysis, dynamic functional connectivity studies have revealed network-specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual-level ageing-related brain dynamics are uncertain. Here, we performed individual parcellation and individual-length sliding window clustering to characterize ageing-related dynamic network changes. Healthy participants (n = 637, 18-88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven-network parcellation, varied from group-level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual-length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U-shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network-specific and individual-level targeting during neuromodulation in ageing and ageing-related diseases.
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Affiliation(s)
- Tiantian Liu
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Zhongyan Shi
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Jian Zhang
- Intelligent Robotics Institute, School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
| | - Kexin Wang
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Yuanhao Li
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Guangying Pei
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Li Wang
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Jinglong Wu
- School of Medical TechnologyBeijing Institute of TechnologyBeijingChina
| | - Tianyi Yan
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
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