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Liu X, Li Y, Mo Y, Chen B, Hou X, Zhu J, Xu Y, Xue J, Wen H, Wang X, Wen Z. GABAergic imbalance in Parkinson's disease-related depression determined with MEGA-PRESS. Neuroimage Clin 2024; 43:103641. [PMID: 39032208 DOI: 10.1016/j.nicl.2024.103641] [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: 03/18/2024] [Revised: 07/10/2024] [Accepted: 07/10/2024] [Indexed: 07/22/2024]
Abstract
OBJECTIVE The pathogenesis of depression in patients with Parkinson's disease (PD) is poorly understood. Therefore, this study aimed to explore the changes in γ-aminobutyric acid (GABA) and glutamate plus glutamine (Glx) levels in patients with PD with or without depression determined using MEscher-GArwood Point Resolved Spectroscopy (MEGA-PRESS). MATERIALS AND METHODS A total of 83 patients with primary PD and 24 healthy controls were included. Patients with PD were categorized into depressed PD (DPD, n = 19) and nondepressed PD (NDPD, n = 64) based on the 17-item Hamilton Depression Rating Scale. All participants underwent T1-weighted imaging and MEGA-PRESS sequence to acquire GABA+ and Glx values. The MEGA-PRESS sequence was conducted using 18.48 mL voxels in the left thalamus and medial frontal cortex. The GABA+, Glx, and creatine values were quantified using Gannet 3.1 software. RESULTS The GABA+ and Glx values were not significantly disparate between patients with PD and controls in the thalamus and medial frontal cortex. However, the levels of N-acetyl aspartate/creatine and choline/creatine in the left thalamus were significantly lower in patients with PD than in controls (P = .031, P = .009). The GABA+/Water and GABA+/Creatine in the medial frontal cortex were higher in DPD than in NDPD (P = .001, P = .004). The effects of depression on Glx or other metabolite levels were not evident, and no significant difference in metabolite values was noted in the left thalamus among all groups (P > .05). CONCLUSIONS GABA+ levels increased in the medial frontal cortex in DPD, which may be more closely related to depressive pathology. Thus, alterations in GABAergic function in special brain structures may be related to the clinical manifestations of PD symptoms, and hence mediating this function might help in treating depression in PD.
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Affiliation(s)
- Xinzi Liu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuxin Li
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yixiang Mo
- Department of Functional Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Baoling Chen
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Xusheng Hou
- Department of Functional Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Jianbin Zhu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | | | - Jingyue Xue
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Haitao Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Xianlong Wang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
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Yuan J, Liu Y, Liao H, Tan C, Cai S, Shen Q, Liu Q, Wang M, Tang Y, Li X, Liu J, Zi Y. Alterations in cortical volume and complexity in Parkinson's disease with depression. CNS Neurosci Ther 2024; 30:e14582. [PMID: 38421103 PMCID: PMC10851315 DOI: 10.1111/cns.14582] [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: 08/05/2023] [Revised: 11/09/2023] [Accepted: 12/17/2023] [Indexed: 03/02/2024] Open
Abstract
AIMS The aim of this study is to investigate differences in gray matter volume and cortical complexity between Parkinson's disease with depression (PDD) patients and Parkinson's disease without depression (PDND) patients. METHODS A total of 41 PDND patients, 36 PDD patients, and 38 healthy controls (HC) were recruited and analyzed by Voxel-based morphometry (VBM) and surface-based morphometry (SBM). Differences in gray matter volume and cortical complexity were compared using the one-way analysis of variance (ANOVA) and correlated with the Hamilton Depression Scale-17 (HAMD-17) scores. RESULTS PDD patients exhibited significant cortical atrophy in various regions, including bilateral medial parietal-occipital-temporal lobes, right dorsolateral temporal lobes, bilateral parahippocampal gyrus, and bilateral hippocampus, compared to HC and PDND groups. A negative correlation between the GMV of left precuneus and HAMD-17 scores in the PDD group tended to be significant (r = -0.318, p = 0.059). Decreased gyrification index was observed in the bilateral insular and dorsolateral temporal cortex. However, there were no significant differences found in fractal dimension and sulcal depth. CONCLUSION Our research shows extensive cortical structural changes in the insular cortex, parietal-occipital-temporal lobes, and hippocampal regions in PDD. This provides a morphological perspective for understanding the pathophysiological mechanism underlying depression in Parkinson's disease.
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Affiliation(s)
- Jiaying Yuan
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Yujing Liu
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Haiyan Liao
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
- Clinical Research Center For Medical Imaging in Hunan ProvinceChangshaChina
| | - Changlian Tan
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Sainan Cai
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Qin Shen
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Qinru Liu
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Min Wang
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Yuqing Tang
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Xu Li
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Jun Liu
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
- Clinical Research Center For Medical Imaging in Hunan ProvinceChangshaChina
| | - Yuheng Zi
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
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3
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Chen P, Zhang S, Zhao K, Kang X, Rittman T, Liu Y. Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review. Brain Res 2024; 1823:148675. [PMID: 37979603 DOI: 10.1016/j.brainres.2023.148675] [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: 08/02/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023]
Abstract
Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Shirui Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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4
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Zhou C, Wang L, Cheng W, Lv J, Guan X, Guo T, Wu J, Zhang W, Gao T, Liu X, Bai X, Wu H, Cao Z, Gu L, Chen J, Wen J, Huang P, Xu X, Zhang B, Feng J, Zhang M. Two distinct trajectories of clinical and neurodegeneration events in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:111. [PMID: 37443179 PMCID: PMC10344958 DOI: 10.1038/s41531-023-00556-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
Increasing evidence suggests that Parkinson's disease (PD) exhibits disparate spatial and temporal patterns of progression. Here we used a machine-learning technique-Subtype and Stage Inference (SuStaIn) - to uncover PD subtypes with distinct trajectories of clinical and neurodegeneration events. We enrolled 228 PD patients and 119 healthy controls with comprehensive assessments of olfactory, autonomic, cognitive, sleep, and emotional function. The integrity of substantia nigra (SN), locus coeruleus (LC), amygdala, hippocampus, entorhinal cortex, and basal forebrain were assessed using diffusion and neuromelanin-sensitive MRI. SuStaIn model with above clinical and neuroimaging variables as input was conducted to identify PD subtypes. An independent dataset consisting of 153 PD patients and 67 healthy controls was utilized to validate our findings. We identified two distinct PD subtypes: subtype 1 with rapid eye movement sleep behavior disorder (RBD), autonomic dysfunction, and degeneration of the SN and LC as early manifestations, and cognitive impairment and limbic degeneration as advanced manifestations, while subtype 2 with hyposmia, cognitive impairment, and limbic degeneration as early manifestations, followed later by RBD and degeneration of the LC in advanced disease. Similar subtypes were shown in the validation dataset. Moreover, we found that subtype 1 had weaker levodopa response, more GBA mutations, and poorer prognosis than subtype 2. These findings provide new insights into the underlying disease biology and might be useful for personalized treatment for patients based on their subtype.
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Affiliation(s)
- Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Linbo Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - JinChao Lv
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China.
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5
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Rashidi F, Khanmirzaei MH, Hosseinzadeh F, Kolahchi Z, Jafarimehrabady N, Moghisseh B, Aarabi MH. Cingulum and Uncinate Fasciculus Microstructural Abnormalities in Parkinson's Disease: A Systematic Review of Diffusion Tensor Imaging Studies. BIOLOGY 2023; 12:biology12030475. [PMID: 36979166 PMCID: PMC10045759 DOI: 10.3390/biology12030475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/12/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023]
Abstract
Diffusion tensor imaging (DTI) is gaining traction in neuroscience research as a tool for evaluating neural fibers. The technique can be used to assess white matter (WM) microstructure in neurodegenerative disorders, including Parkinson disease (PD). There is evidence that the uncinate fasciculus and the cingulum bundle are involved in the pathogenesis of PD. These fasciculus and bundle alterations correlate with the symptoms and stages of PD. PRISMA 2022 was used to search PubMed and Scopus for relevant articles. Our search revealed 759 articles. Following screening of titles and abstracts, a full-text review, and implementing the inclusion criteria, 62 papers were selected for synthesis. According to the review of selected studies, WM integrity in the uncinate fasciculus and cingulum bundles can vary according to symptoms and stages of Parkinson disease. This article provides structural insight into the heterogeneous PD subtypes according to their cingulate bundle and uncinate fasciculus changes. It also examines if there is any correlation between these brain structures' structural changes with cognitive impairment or depression scales like Geriatric Depression Scale-Short (GDS). The results showed significantly lower fractional anisotropy values in the cingulum bundle compared to healthy controls as well as significant correlations between FA and GDS scores for both left and right uncinate fasciculus regions suggesting that structural damage from disease progression may be linked to cognitive impairments seen in advanced PD patients. This review help in developing more targeted treatments for different types of Parkinson's disease, as well as providing a better understanding of how cognitive impairments may be related to these structural changes. Additionally, using DTI scans can provide clinicians with valuable information about white matter tracts which is useful for diagnosing and monitoring disease progression over time.
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Affiliation(s)
- Fatemeh Rashidi
- School of Medicine, Tehran University of Medical Science, Tehran 1417613151, Iran
| | | | - Farbod Hosseinzadeh
- School of Medicine, Tehran University of Medical Science, Tehran 1417613151, Iran
| | - Zahra Kolahchi
- School of Medicine, Tehran University of Medical Science, Tehran 1417613151, Iran
| | - Niloofar Jafarimehrabady
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
| | - Bardia Moghisseh
- School of Medicine, Arak University of Medical Science, Arak 3848176941, Iran
| | - Mohammad Hadi Aarabi
- Department of Neuroscience (DNS), Padova Neuroscience Center, University of Padova, 35128 Padua, Italy
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6
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Lucas-Jiménez O, Ibarretxe-Bilbao N, Diez I, Peña J, Tijero B, Galdós M, Murueta-Goyena A, Del Pino R, Acera M, Gómez-Esteban JC, Gabilondo I, Ojeda N. Brain Degeneration in Synucleinopathies Based on Analysis of Cognition and Other Nonmotor Features: A Multimodal Imaging Study. Biomedicines 2023; 11:biomedicines11020573. [PMID: 36831109 PMCID: PMC9953265 DOI: 10.3390/biomedicines11020573] [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: 12/27/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND We aimed to characterize subtypes of synucleinopathies using a clustering approach based on cognitive and other nonmotor data and to explore structural and functional magnetic resonance imaging (MRI) brain differences between identified clusters. METHODS Sixty-two patients (n = 6 E46K-SNCA, n = 8 dementia with Lewy bodies (DLB) and n = 48 idiopathic Parkinson's disease (PD)) and 37 normal controls underwent nonmotor evaluation with extensive cognitive assessment. Hierarchical cluster analysis (HCA) was performed on patients' samples based on nonmotor variables. T1, diffusion-weighted, and resting-state functional MRI data were acquired. Whole-brain comparisons were performed. RESULTS HCA revealed two subtypes, the mild subtype (n = 29) and the severe subtype (n = 33). The mild subtype patients were slightly impaired in some nonmotor domains (fatigue, depression, olfaction, and orthostatic hypotension) with no detectable cognitive impairment; the severe subtype patients (PD patients, all DLB, and the symptomatic E46K-SNCA carriers) were severely impaired in motor and nonmotor domains with marked cognitive, visual and bradykinesia alterations. Multimodal MRI analyses suggested that the severe subtype exhibits widespread brain alterations in both structure and function, whereas the mild subtype shows relatively mild disruptions in occipital brain structure and function. CONCLUSIONS These findings support the potential value of incorporating an extensive nonmotor evaluation to characterize specific clinical patterns and brain degeneration patterns of synucleinopathies.
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Affiliation(s)
- Olaia Lucas-Jiménez
- Department of Psychology, Faculty of Health Sciences, University of Deusto, 48007 Bilbao, Spain
- Correspondence: ; Tel./Fax: +34-944-139000 (ext. 3231)
| | - Naroa Ibarretxe-Bilbao
- Department of Psychology, Faculty of Health Sciences, University of Deusto, 48007 Bilbao, Spain
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114-1107, USA
| | - Javier Peña
- Department of Psychology, Faculty of Health Sciences, University of Deusto, 48007 Bilbao, Spain
| | - Beatriz Tijero
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Neurology, Cruces University Hospital, 48903 Barakaldo, Spain
| | - Marta Galdós
- Ophthalmology Department, Cruces University Hospital, 48903 Barakaldo, Spain
| | - Ane Murueta-Goyena
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Neurosciences, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
| | - Rocío Del Pino
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
| | - Marian Acera
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
| | - Juan Carlos Gómez-Esteban
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Neurology, Cruces University Hospital, 48903 Barakaldo, Spain
- Department of Neurosciences, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
| | - Iñigo Gabilondo
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Neurology, Cruces University Hospital, 48903 Barakaldo, Spain
- IKERBASQUE, The Basque Foundation for Science, 48009 Bilbao, Spain
| | - Natalia Ojeda
- Department of Psychology, Faculty of Health Sciences, University of Deusto, 48007 Bilbao, Spain
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7
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Huang P, Zhang M. Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. Neurosci Bull 2023; 39:99-112. [PMID: 35771383 PMCID: PMC9849544 DOI: 10.1007/s12264-022-00905-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/07/2022] [Indexed: 01/22/2023] Open
Abstract
Neurodegenerative diseases (NDs) have become a significant threat to an aging human society. Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers. Magnetic resonance imaging (MRI) is a powerful tool for investigating structural and functional brain alterations in NDs. With the advantages of being non-invasive and non-radioactive, it has been frequently used in both animal research and large-scale clinical investigations. MRI may serve as a bridge connecting micro- and macro-level analysis and promoting bench-to-bed translational research. Nevertheless, due to the abundance and complexity of MRI techniques, exploiting their potential is not always straightforward. This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
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8
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Wang L, Zhou C, Cheng W, Rolls ET, Huang P, Ma N, Liu Y, Zhang Y, Guan X, Guo T, Wu J, Gao T, Xuan M, Gu Q, Xu X, Zhang B, Gong W, Du J, Zhang W, Feng J, Zhang M. Dopamine depletion and subcortical dysfunction disrupt cortical synchronization and metastability affecting cognitive function in Parkinson's disease. Hum Brain Mapp 2021; 43:1598-1610. [PMID: 34904766 PMCID: PMC8886656 DOI: 10.1002/hbm.25745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/28/2021] [Accepted: 11/29/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large-scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting-state blood-oxygen-level-dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical-cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia.
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Affiliation(s)
- Linbo Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ningning Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Yuchen Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Yajuan Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weikang Gong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Jingnan Du
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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9
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Janiri D, Petracca M, Moccia L, Tricoli L, Piano C, Bove F, Imbimbo I, Simonetti A, Di Nicola M, Sani G, Calabresi P, Bentivoglio AR. COVID-19 Pandemic and Psychiatric Symptoms: The Impact on Parkinson's Disease in the Elderly. Front Psychiatry 2020; 11:581144. [PMID: 33329124 PMCID: PMC7728715 DOI: 10.3389/fpsyt.2020.581144] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/28/2020] [Indexed: 12/14/2022] Open
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic represents a condition of increased vulnerability and frailty for elderly patients with Parkinson's disease (PD). Social isolation may worsen the burden of the disease and specifically exacerbate psychiatric symptoms, often comorbid with PD. This study aimed at identifying risk/protective factors associated with subjective worsening of psychiatric symptomatology during the COVID-19 outbreak in a sample of individuals with PD aged 65 years or older. Methods: Patients with PD routinely followed at the outpatient clinic of Gemelli University Hospital, Rome, were assessed for subjective worsening of psychiatric symptoms through a dedicated telephone survey, after Italy COVID-19 lockdown. Patients' medical records were reviewed to collect sociodemographic and clinical data, including lifetime psychiatric symptoms and pharmacological treatment. Results: Overall, 134 individuals were assessed and 101 (75.4%) reported lifetime psychiatric symptoms. Among those, 23 (22.8%) presented with subjective worsening of psychiatric symptomatology during the COVID-19 outbreak. In this group, the most frequent symptom was depression (82.6%), followed by insomnia (52.2%). Subjective worsening of neurological symptoms (Wald = 24.03, df = 1, p = 0.001) and lifetime irritability (Wald = 6.35, df = 1, p = 0.020), together with younger age (Wald = 5.06, df = 1, p = 0.038) and female sex (Wald = 9.07 df = 1, p = 0.007), resulted as specific risk factors for ingravescence of psychiatric presentation. Lifetime pre-existing delusions, having received antipsychotics, and not having received mood stabilizer were also associated with subjective worsening of psychiatric symptomatology due to the COVID-19 pandemic. Conclusions: Individuals with PD and lifetime history of psychiatric symptoms may be exposed to increased vulnerability to the stressful effect of COVID-19 outbreak. Interventions aimed at reducing irritability and mood instability might have an indirect effect on the health of patients with PD during the COVID-19 pandemic.
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Affiliation(s)
- Delfina Janiri
- Department of Psychiatry, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Department of Psychiatry and Neurology, Sapienza University of Rome, Rome, Italy
| | - Martina Petracca
- Dipartimento Scienze dell'Invecchiamento, Neurologiche, Ortopediche e della Testa-Collo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Lorenzo Moccia
- Department of Psychiatry, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Institute of Neurology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Tricoli
- Dipartimento Scienze dell'Invecchiamento, Neurologiche, Ortopediche e della Testa-Collo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Carla Piano
- Dipartimento Scienze dell'Invecchiamento, Neurologiche, Ortopediche e della Testa-Collo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Francesco Bove
- Dipartimento Scienze dell'Invecchiamento, Neurologiche, Ortopediche e della Testa-Collo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Institute of Neurology, Università Cattolica del Sacro Cuore, Rome, Italy
| | | | - Alessio Simonetti
- Department of Psychiatry and Neurology, Sapienza University of Rome, Rome, Italy
| | - Marco Di Nicola
- Department of Psychiatry, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Institute of Neurology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gabriele Sani
- Department of Psychiatry, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Institute of Neurology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Paolo Calabresi
- Dipartimento Scienze dell'Invecchiamento, Neurologiche, Ortopediche e della Testa-Collo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Institute of Neurology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Anna Rita Bentivoglio
- Dipartimento Scienze dell'Invecchiamento, Neurologiche, Ortopediche e della Testa-Collo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Institute of Neurology, Università Cattolica del Sacro Cuore, Rome, Italy
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10
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Liu J, Xu F, Nie Z, Shao L. Gut Microbiota Approach-A New Strategy to Treat Parkinson's Disease. Front Cell Infect Microbiol 2020; 10:570658. [PMID: 33194809 PMCID: PMC7643014 DOI: 10.3389/fcimb.2020.570658] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/30/2020] [Indexed: 12/12/2022] Open
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by neuronal loss and dysfunction of dopaminergic neurons located in the substantia nigra, which contain a variety of misfolded α-synuclein (α-syn). Medications that increase or substitute for dopamine can be used for the treatment of PD. Recently, numerous studies have shown gut microbiota plays a crucial role in regulating and maintaining multiple aspects of host physiology including host metabolism and neurodevelopment. In this review article, the role of gut microbiota in the etiological mechanism of PD will be reviewed. Furthermore, we discussed current pharmaceutical medicine-based methods to prevent and treat PD, followed by describing specific strains that affect the host brain function through the gut-brain axis. We explained in detail how gut microbiota directly produces neurotransmitters or regulate the host biosynthesis of neurotransmitters. The neurotransmitters secreted by the intestinal lumen bacteria may induce epithelial cells to release molecules that, in turn, can regulate neural signaling in the enteric nervous system and subsequently control brain function and behavior through the brain-gut axis. Finally, we proved that the microbial regulation of the host neuronal system. Endogenous α-syn can be transmitted long distance and bidirectional between ENS and brain through the circulatory system which gives us a new option that the possibility of altering the community of gut microbiota in completely new medication option for treating PD.
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Affiliation(s)
- Jing Liu
- Department of Microbiology and Immunity, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
- Microbial Pharmacology Laboratory, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Fei Xu
- Department of Microbiology and Immunity, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
- Microbial Pharmacology Laboratory, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Zhiyan Nie
- Department of Microbiology and Immunity, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Lei Shao
- Microbial Pharmacology Laboratory, Shanghai University of Medicine & Health Sciences, Shanghai, China
- State Key Laboratory of New Drug and Pharmaceutical Process, Shanghai Institute of Pharmaceutical Industry, Shanghai, China
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11
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Guo T, Guan X, Zhou C, Gao T, Wu J, Song Z, Xuan M, Gu Q, Huang P, Pu J, Zhang B, Cui F, Xia S, Xu X, Zhang M. Clinically relevant connectivity features define three subtypes of Parkinson's disease patients. Hum Brain Mapp 2020; 41:4077-4092. [PMID: 32588952 PMCID: PMC7469787 DOI: 10.1002/hbm.25110] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 05/23/2020] [Accepted: 06/14/2020] [Indexed: 12/23/2022] Open
Abstract
Parkinson's disease (PD) is characterized by complex clinical symptoms, including classic motor and nonmotor disturbances. Patients with PD vary in clinical manifestations and prognosis, which point to the existence of subtypes. This study aimed to find the fiber connectivity correlations with several crucial clinical symptoms and identify PD subtypes using unsupervised clustering analysis. One hundred and thirty-four PD patients and 77 normal controls were enrolled. Canonical correlation analysis (CCA) was performed to define the clinically relevant connectivity features, which were then used in the hierarchical clustering analysis to identify the distinct subtypes of PD patients. Multimodal neuroimaging analyses were further used to explore the neurophysiological basis of these subtypes. The methodology was validated in an independent data set. CCA revealed two significant clinically relevant patterns (motor-related pattern and depression-related pattern; r = .94, p < .001 and r = .926, p = .001, respectively) among PD patients, and hierarchical clustering analysis identified three neurophysiological subtypes ("mild" subtype, "severe depression-dominant" subtype and "severe motor-dominant" subtype). Multimodal neuroimaging analyses suggested that the patients in the "severe depression-dominant" subtype exhibited widespread disruptions both in function and structure, while the other two subtypes exhibited relatively mild abnormalities in brain function. In the independent validation, three similar subtypes were identified. In conclusion, we revealed heterogeneous subtypes of PD patients according to their distinct clinically relevant connectivity features. Importantly, depression symptoms have a considerable impact on brain damage in patients with PD.
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Affiliation(s)
- Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Gao
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhe Song
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Cui
- Department of Radiology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, China
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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