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Ju M, Zhang Z, Gao F, Chen G, Zhao S, Wang D, Wang H, Jia Y, Shen L, Yuan Y, Yao H. Intranasal Delivery of circATF7IP siRNA via Lipid Nanoparticles Alleviates LPS-induced Depressive-Like Behaviors. Adv Healthc Mater 2024:e2402219. [PMID: 39254274 DOI: 10.1002/adhm.202402219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 08/30/2024] [Indexed: 09/11/2024]
Abstract
Major depressive disorder (MDD) is a prevalent mental disorder that significantly impacts social and psychological function, but no effective medication is currently available. Circular RNAs (circRNAs) have been reported to participate in the pathogenesis of MDD which are envisioned as promising therapeutic targets. However, nonviral-based delivery strategies targeting circRNA against MDD are not thoroughly investigated. Here, it is identified that circATF7IP is significantly upregulated in plasma samples and positively correlated with 24-Hamilton Depression Scale (HAMD-24) scores of MDD patients. Synergistic amine lipid nanoparticles (SALNPs) are designed to deliver siRNA targeting circATF7IP (si-circATF7IP) into the hippocampus brain region by intranasal administration. Intranasal delivery of SALNP-si-circATF7IP successfully alleviated the depressive-like behaviors in the LPS-induced mouse depression model via decreasing CD11b+CD45dim microglia population and pro-inflammatory cytokine productions (TNF-α and IL-6). These results indicate that the level of circATF7IP positively correlates with MDD pathogenesis, and SALNP delivery of si-circATF7IP via intranasal administration is an effective strategy to ameliorate LPS-induced depressive-like behaviors.
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Affiliation(s)
- Minzi Ju
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Zhongkun Zhang
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Feng Gao
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Gang Chen
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
- Institute of Psychosomatics, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
- Department of Psychiatry, the Third People's Hospital of Huai'an, Huai'an, Jiangsu, 223001, China
| | - Sibo Zhao
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Dan Wang
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Huijuan Wang
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Yanpeng Jia
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Ling Shen
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
- Institute of Psychosomatics, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Honghong Yao
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu, 226019, China
- Institute of Life Sciences, Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu, 210009, China
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Zu Z, Choi S, Zhao Y, Gao Y, Li M, Schilling KG, Ding Z, Gore JC. The missing third dimension-Functional correlations of BOLD signals incorporating white matter. SCIENCE ADVANCES 2024; 10:eadi0616. [PMID: 38277462 PMCID: PMC10816695 DOI: 10.1126/sciadv.adi0616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 12/27/2023] [Indexed: 01/28/2024]
Abstract
Correlations between magnetic resonance imaging (MRI) blood oxygenation level-dependent (BOLD) signals from pairs of gray matter areas are used to infer their functional connectivity, but they are unable to describe how white matter is engaged in brain networks. Recently, evidence that BOLD signals in white matter are robustly detectable and are modulated by neural activities has accumulated. We introduce a three-way correlation between BOLD signals from pairs of gray matter volumes (nodes) and white matter bundles (edges) to define the communication connectivity through each white matter bundle. Using MRI images from publicly available databases, we show, for example, that the three-way connectivity is influenced by age. By integrating functional MRI signals from white matter as a third component in network analyses, more comprehensive descriptions of brain function may be obtained.
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Affiliation(s)
- Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Soyoung Choi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
- Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
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3
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Schilling KG, Li M, Rheault F, Gao Y, Cai L, Zhao Y, Xu L, Ding Z, Anderson AW, Landman BA, Gore JC. Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis. Proc Natl Acad Sci U S A 2023; 120:e2219666120. [PMID: 37824529 PMCID: PMC10589709 DOI: 10.1073/pnas.2219666120] [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: 11/28/2022] [Accepted: 08/11/2023] [Indexed: 10/14/2023] Open
Abstract
Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.
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Affiliation(s)
- Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
| | - Francois Rheault
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN37235
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - Leon Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN37235
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
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Sheng W, Cui Q, Jiang K, Chen Y, Tang Q, Wang C, Fan Y, Guo J, Lu F, He Z, Chen H. Individual variation in brain network topology is linked to course of illness in major depressive disorder. Cereb Cortex 2022; 32:5301-5310. [PMID: 35152289 DOI: 10.1093/cercor/bhac015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 12/09/2021] [Accepted: 01/13/2022] [Indexed: 12/27/2022] Open
Abstract
Major depressive disorder (MDD) is a chronic and highly recurrent disorder. The functional connectivity in depression is affected by the cumulative effect of course of illness. However, previous neuroimaging studies on abnormal functional connection have not mainly focused on the disease duration, which is seen as a secondary factor. Here, we used a data-driven analysis (multivariate distance matrix regression) to examine the relationship between the course of illness and resting-state functional dysconnectivity in MDD. This method identified a region in the anterior cingulate cortex, which is most linked to course of illness. Specifically, follow-up seed analyses show this phenomenon resulted from the individual differences in the topological distribution of three networks. In individuals with short-duration MDD, the connection to the default mode network was strong. By contrast, individuals with long-duration MDD showed hyperconnectivity to the ventral attention network and the frontoparietal network. These results emphasized the centrality of the anterior cingulate cortex in the pathophysiology of the increased course of illness and implied critical links between network topography and pathological duration. Thus, dissociable patterns of connectivity of the anterior cingulate cortex is an important dimension feature of the disease process of depression.
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Affiliation(s)
- Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation, High Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Kexing Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuyan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chong Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yunshuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation, High Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
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5
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He E, Liu M, Gong S, Fu X, Han Y, Deng F. White Matter Alterations in Depressive Disorder. Front Immunol 2022; 13:826812. [PMID: 35634314 PMCID: PMC9133348 DOI: 10.3389/fimmu.2022.826812] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Depressive disorder is the most prevalent affective disorder today. Depressive disorder has been linked to changes in the white matter. White matter changes in depressive disorder could be a result of impaired cerebral blood flow (CBF) and CBF self-regulation, impaired blood-brain barrier function, inflammatory factors, genes and environmental factors. Additionally, white matter changes in patients with depression are associated with clinical variables such as differential diagnosis, severity, treatment effect, and efficacy assessment. This review discusses the characteristics, possible mechanisms, clinical relevance, and potential treatment of white matter alterations caused by depressive disorders.
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6
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Guo B, Zhou F, Li M, Gore JC, Ding Z. Correlated functional connectivity and glucose metabolism in brain white matter revealed by simultaneous MRI/positron emission tomography. Magn Reson Med 2022; 87:1507-1514. [PMID: 34825730 PMCID: PMC9299712 DOI: 10.1002/mrm.29107] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 10/19/2021] [Accepted: 11/12/2021] [Indexed: 01/06/2023]
Abstract
PURPOSE There has been converging evidence of reliable detections of blood oxygenation level dependent (BOLD) signals evoked by neural stimulation and in a resting state in white matter (WM), within which few studies examined the relationship between BOLD functional signals and tissue metabolism. The purpose of the present study was to explore whether such relationship exists using combined functional MRI and positron emission tomography (PET) measurements of glucose uptake. METHODS Functional and metabolic imaging data from 25 right-handed healthy human adults (aged 18-23 years, 18 females) were analyzed. Measures, including average resting state functional connectivity (FC) with respect to 82 Brodmann areas, fractional amplitude of low-frequency fluctuations (FALFF), and average fluorodeoxyglucose (FDG) uptake by PET, were computed for 48 predefined WM bundles. Pearson correlations across the bundles and 25 subjects studied were calculated among these measures. Linear mixed effects models were used to estimate the variance explainable by a predictor variable in the absence of inter-subject variations. RESULTS Analysis of six separate imaging intervals found that average FC the bundles was significantly correlated with local FDG uptake (r = 0.25, p < 0.001), and the FC also covaried significantly with FALFF (r = 0.41, p < 0.001). When random effects from inter-subject variations were controlled, these correlations appeared to be medium to strong (r = 0.41 for FC vs. FDG uptake, and r = 0.65 for FALFF vs. FC). CONCLUSION This study indicates that BOLD signals in WM are directly related to variations in metabolic demand and engagement with cortical processing and suggests they should be incorporated into more complete models of brain function.
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Affiliation(s)
- Bin Guo
- Image Processing CenterSchool of AstronauticsBeihang UniversityBeijingChina,Vanderbilt University Institute of Imaging ScienceNashvilleTennesseeUSA
| | - Fugen Zhou
- Image Processing CenterSchool of AstronauticsBeihang UniversityBeijingChina
| | - Muwei Li
- Vanderbilt University Institute of Imaging ScienceNashvilleTennesseeUSA,Department of Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - John C. Gore
- Vanderbilt University Institute of Imaging ScienceNashvilleTennesseeUSA,Department of Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA,Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging ScienceNashvilleTennesseeUSA,Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA,Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
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Guo Q, Duan J, Cai S, Zhang J, Chen T, Yang H. Desynchronized white matter function and structure in drug-naive first-episode major depressive disorder patients. Front Psychiatry 2022; 13:1082052. [PMID: 36713909 PMCID: PMC9874158 DOI: 10.3389/fpsyt.2022.1082052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/21/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly prevalent mental disease. Using magnetic resonance imaging (MRI), although numerous studies have revealed the alterations in structure and function of grey matter (GM), few studies focused on the synchronization of white matter (WM) structure and function in MDD. The aim of this study was to investigate whether functional and structural abnormalities of WM play an essential role in the neurobiological mechanisms of MDD. METHODS Gradient-echo imaging sequences at 3.0T were used to gather resting state functional MRI (rsfMRI) data, which were performed on 33 drug-naive first-episode MDD patients and 34 healthy controls (HCs). After data preprocessed, amplitude of low frequency fluctuation (ALFF) of WM was calculated. ALFF values in different frequency bands were analyzed, including typical (0.01-0.15 Hz) band, slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) bands. In addition, the fractional anisotropy (FA) values in WM in 23 patients and 26 HCs were examined using tract-based spatial statistics (TBSS) and tractography based on diffusion tensor imaging (DTI). Pearson correlation analysis was applied to analyze the relationships between ALFF values and Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA). RESULTS Compared with the HCs, MDD patients showed decreased ALFF values in posterior thalamic radiation (PTR) and superior longitudinal fasciculus (SLF) in slow-5 frequency band, no significant differences of ALFF values were found in typical and slow-4 frequency bands. In addition, there were no significant differences in FA values with TBSS analysis as well as the number of fibers in PTR and SLF with tractography analysis between two groups. Further correlation analysis showed that the ALFF value in SLF was negatively correlated with HAMA-2 score (r = -0.548, p FDR = 0.037) in patients. CONCLUSION Our results indicated that WM dysfunction may be associated with the pathophysiological mechanism of depression. Our study also suggested that the functional damage of the WM may precedes the structural damage in first-episode MDD patients. Furthermore, for mental disorders, slow-5 frequency band may be a more sensitive functional indicator for early detection of abnormal spontaneous brain activity in WM.
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Affiliation(s)
- Qinger Guo
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jingfeng Duan
- Department of Psychiatry, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Shuyang Cai
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jiaxi Zhang
- School of Teacher Education, Zhejiang Normal University, Jinhua, China.,Key Laboratory of Intelligent Education Technology and Application of Zhejiang, Zhejiang Normal University, Jinhua, China
| | - Tao Chen
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Hong Yang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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