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Radlicka-Borysewska A, Jabłońska J, Lenarczyk M, Szumiec Ł, Harda Z, Bagińska M, Barut J, Pera J, Kreiner G, Wójcik DK, Rodriguez Parkitna J. Non-motor symptoms associated with progressive loss of dopaminergic neurons in a mouse model of Parkinson's disease. Front Neurosci 2024; 18:1375265. [PMID: 38745938 PMCID: PMC11091341 DOI: 10.3389/fnins.2024.1375265] [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: 04/09/2024] [Indexed: 05/16/2024] Open
Abstract
Parkinson's disease (PD) is characterized by three main motor symptoms: bradykinesia, rigidity and tremor. PD is also associated with diverse non-motor symptoms that may develop in parallel or precede motor dysfunctions, ranging from autonomic system dysfunctions and impaired sensory perception to cognitive deficits and depression. Here, we examine the role of the progressive loss of dopaminergic transmission in behaviors related to the non-motor symptoms of PD in a mouse model of the disease (the TIF-IADATCreERT2 strain). We found that in the period from 5 to 12 weeks after the induction of a gradual loss of dopaminergic neurons, mild motor symptoms became detectable, including changes in the distance between paws while standing as well as the swing speed and step sequence. Male mutant mice showed no apparent changes in olfactory acuity, no anhedonia-like behaviors, and normal learning in an instrumental task; however, a pronounced increase in the number of operant responses performed was noted. Similarly, female mice with progressive dopaminergic neuron degeneration showed normal learning in the probabilistic reversal learning task and no loss of sweet-taste preference, but again, a robustly higher number of choices were performed in the task. In both males and females, the higher number of instrumental responses did not affect the accuracy or the fraction of rewarded responses. Taken together, these data reveal discrete, dopamine-dependent non-motor symptoms that emerge in the early stages of dopaminergic neuron degeneration.
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Affiliation(s)
- Anna Radlicka-Borysewska
- Department of Molecular Neuropharmacology, Maj Institute of Pharmacology of the Polish Academy of Sciences, Kraków, Poland
| | - Judyta Jabłońska
- Department of Molecular Neuropharmacology, Maj Institute of Pharmacology of the Polish Academy of Sciences, Kraków, Poland
| | - Michał Lenarczyk
- Faculty of Management and Social Communication, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Łukasz Szumiec
- Department of Molecular Neuropharmacology, Maj Institute of Pharmacology of the Polish Academy of Sciences, Kraków, Poland
| | - Zofia Harda
- Department of Molecular Neuropharmacology, Maj Institute of Pharmacology of the Polish Academy of Sciences, Kraków, Poland
| | - Monika Bagińska
- Department of Brain Biochemistry, Maj Institute of Pharmacology of the Polish Academy of Sciences, Kraków, Poland
| | - Justyna Barut
- Department of Brain Biochemistry, Maj Institute of Pharmacology of the Polish Academy of Sciences, Kraków, Poland
| | - Joanna Pera
- Department of Neurology, Jagiellonian University Medical College, Kraków, Poland
| | - Grzegorz Kreiner
- Department of Brain Biochemistry, Maj Institute of Pharmacology of the Polish Academy of Sciences, Kraków, Poland
| | - Daniel K. Wójcik
- Faculty of Management and Social Communication, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
| | - Jan Rodriguez Parkitna
- Department of Molecular Neuropharmacology, Maj Institute of Pharmacology of the Polish Academy of Sciences, Kraków, Poland
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Quattrone A, Calomino C, Sarica A, Caligiuri ME, Bianco MG, Vescio B, Arcuri PP, Buonocore J, De Maria M, Vaccaro MG, Quattrone A. Neuroimaging correlates of postural instability in Parkinson's disease. J Neurol 2024; 271:1910-1920. [PMID: 38108896 DOI: 10.1007/s00415-023-12136-9] [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: 06/21/2023] [Revised: 10/23/2023] [Accepted: 11/23/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Postural instability (PI) is a common disabling symptom in Parkinson's disease (PD), but little is known on its pathophysiological basis. OBJECTIVE In this study, we aimed to identify the brain structures associated with PI in PD patients, using different MRI approaches. METHODS We consecutively enrolled 142 PD patients and 45 control subjects. PI was assessed using the MDS-UPDRS-III pull-test item (PT). A whole-brain regression analysis identified brain areas where grey matter (GM) volume correlated with the PT score in PD patients. Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) were also used to compare unsteady (PT ≥ 1) and steady (PT = 0) PD patients. Associations between GM volume in regions of interest (ROI) and several clinical features were then investigated using LASSO regression analysis. RESULTS PI was present in 44.4% of PD patients. The whole-brain approach identified the bilateral inferior frontal gyrus (IFG) and superior temporal gyrus (STG) as the only regions associated with the presence of postural instability. VBM analysis showed reduced GM volume in fronto-temporal areas (superior, middle, medial and inferior frontal gyrus, and STG) in unsteady compared with steady PD patients, and the GM volume of these regions was selectively associated with the PT score and not with any other motor or non-motor symptom. CONCLUSIONS This study demonstrates a significant atrophy of fronto-temporal regions in unsteady PD patients, suggesting that these brain areas may play a role in the pathophysiological mechanisms underlying postural instability in PD. This result paves the way for further studies on postural instability in Parkinsonism.
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Affiliation(s)
- Andrea Quattrone
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Viale Europa, Germanetox, 88100, Catanzaro, Italy
| | - Camilla Calomino
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Viale Europa, Germanetox, 88100, Catanzaro, Italy
| | - Alessia Sarica
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Viale Europa, Germanetox, 88100, Catanzaro, Italy
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Viale Europa, Germanetox, 88100, Catanzaro, Italy
| | - Maria Giovanna Bianco
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Viale Europa, Germanetox, 88100, Catanzaro, Italy
| | | | - Pier Paolo Arcuri
- Institute of Radiology, Azienda Ospedaliero-Universitaria Dulbecco, Catanzaro, Italy
| | - Jolanda Buonocore
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Marida De Maria
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Viale Europa, Germanetox, 88100, Catanzaro, Italy
| | - Maria Grazia Vaccaro
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Viale Europa, Germanetox, 88100, Catanzaro, Italy
| | - Aldo Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Viale Europa, Germanetox, 88100, Catanzaro, Italy.
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Yang K, Wu Z, Long J, Li W, Wang X, Hu N, Zhao X, Sun T. White matter changes in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:150. [PMID: 37907554 PMCID: PMC10618166 DOI: 10.1038/s41531-023-00592-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 10/17/2023] [Indexed: 11/02/2023] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease (AD). It is characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) and the formation of Lewy bodies (LBs). Although PD is primarily considered a gray matter (GM) disease, alterations in white matter (WM) have gained increasing attention in PD research recently. Here we review evidence collected by magnetic resonance imaging (MRI) techniques which indicate WM abnormalities in PD, and discuss the correlations between WM changes and specific PD symptoms. Then we summarize transcriptome and genome studies showing the changes of oligodendrocyte (OLs)/myelin in PD. We conclude that WM abnormalities caused by the changes of myelin/OLs might be important for PD pathology, which could be potential targets for PD treatment.
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Affiliation(s)
- Kai Yang
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, People's Republic of China.
| | - Zhengqi Wu
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, People's Republic of China
| | - Jie Long
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, People's Republic of China
| | - Wenxin Li
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, People's Republic of China
| | - Xi Wang
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, People's Republic of China
| | - Ning Hu
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, People's Republic of China
| | - Xinyue Zhao
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, People's Republic of China
| | - Taolei Sun
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, People's Republic of China.
- State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, People's Republic of China.
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Wang T, Chen X, Zhang J, Feng Q, Huang M. Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases. Med Image Anal 2023; 88:102842. [PMID: 37247468 DOI: 10.1016/j.media.2023.102842] [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: 11/02/2022] [Revised: 03/01/2023] [Accepted: 05/15/2023] [Indexed: 05/31/2023]
Abstract
Imaging genetics is a crucial tool that is applied to explore potentially disease-related biomarkers, particularly for neurodegenerative diseases (NDs). With the development of imaging technology, the association analysis between multimodal imaging data and genetic data is gradually being concerned by a wide range of imaging genetics studies. However, multimodal data are fused first and then correlated with genetic data in traditional methods, which leads to an incomplete exploration of their common and complementary information. In addition, the inaccurate formulation in the complex relationships between imaging and genetic data and information loss caused by missing multimodal data are still open problems in imaging genetics studies. Therefore, in this study, a deep multimodality-disentangled association analysis network (DMAAN) is proposed to solve the aforementioned issues and detect the disease-related biomarkers of NDs simultaneously. First, the imaging data are nonlinearly projected into a latent space and imaging representations can be achieved. The imaging representations are further disentangled into common and specific parts by using a multimodal-disentangled module. Second, the genetic data are encoded to achieve genetic representations, and then, the achieved genetic representations are nonlinearly mapped to the common and specific imaging representations to build nonlinear associations between imaging and genetic data through an association analysis module. Moreover, modality mask vectors are synchronously synthesized to integrate the genetic and imaging data, which helps the following disease diagnosis. Finally, the proposed method achieves reasonable diagnosis performance via a disease diagnosis module and utilizes the label information to detect the disease-related modality-shared and modality-specific biomarkers. Furthermore, the genetic representation can be used to impute the missing multimodal data with our learning strategy. Two publicly available datasets with different NDs are used to demonstrate the effectiveness of the proposed DMAAN. The experimental results show that the proposed DMAAN can identify the disease-related biomarkers, which suggests the proposed DMAAN may provide new insights into the pathological mechanism and early diagnosis of NDs. The codes are publicly available at https://github.com/Meiyan88/DMAAN.
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Affiliation(s)
- Tao Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Xiumei Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Jiawei Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
| | - Meiyan Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
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5
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Sarasso E, Filippi M, Agosta F. Clinical and MRI features of gait and balance disorders in neurodegenerative diseases. J Neurol 2023; 270:1798-1807. [PMID: 36577818 DOI: 10.1007/s00415-022-11544-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022]
Abstract
Gait and balance disorders are common signs in several neurodegenerative diseases such as Parkinson's disease, atypical parkinsonism, idiopathic normal pressure hydrocephalus, cerebrovascular disease, dementing disorders and multiple sclerosis. According to each condition, patients present with different gait and balance alterations depending on the structural and functional brain changes through the disease course. In this review, we will summarize the main clinical characteristics of gait and balance disorders in the major neurodegenerative conditions, providing an overview of the significant structural and functional MRI brain alterations underlying these deficits. We also will discuss the role of neurorehabilitation strategies in promoting brain plasticity and gait/balance improvements in these patients.
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Affiliation(s)
- Elisabetta Sarasso
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genoa, Genoa, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
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Imaging the Limbic System in Parkinson's Disease-A Review of Limbic Pathology and Clinical Symptoms. Brain Sci 2022; 12:brainsci12091248. [PMID: 36138984 PMCID: PMC9496800 DOI: 10.3390/brainsci12091248] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 01/09/2023] Open
Abstract
The limbic system describes a complex of brain structures central for memory, learning, as well as goal directed and emotional behavior. In addition to pathological studies, recent findings using in vivo structural and functional imaging of the brain pinpoint the vulnerability of limbic structures to neurodegeneration in Parkinson's disease (PD) throughout the disease course. Accordingly, dysfunction of the limbic system is critically related to the symptom complex which characterizes PD, including neuropsychiatric, vegetative, and motor symptoms, and their heterogeneity in patients with PD. The aim of this systematic review was to put the spotlight on neuroimaging of the limbic system in PD and to give an overview of the most important structures affected by the disease, their function, disease related alterations, and corresponding clinical manifestations. PubMed was searched in order to identify the most recent studies that investigate the limbic system in PD with the help of neuroimaging methods. First, PD related neuropathological changes and corresponding clinical symptoms of each limbic system region are reviewed, and, finally, a network integration of the limbic system within the complex of PD pathology is discussed.
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Shih YC, Tseng WYI, Montaser-Kouhsari L. Recent advances in using diffusion tensor imaging to study white matter alterations in Parkinson's disease: A mini review. Front Aging Neurosci 2022; 14:1018017. [PMID: 36910861 PMCID: PMC9992993 DOI: 10.3389/fnagi.2022.1018017] [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: 08/12/2022] [Accepted: 12/26/2022] [Indexed: 02/24/2023] Open
Abstract
Parkinson's disease (PD) is the second most common age-related neurodegenerative disease with cardinal motor symptoms. In addition to motor symptoms, PD is a heterogeneous disease accompanied by many non-motor symptoms that dominate the clinical manifestations in different stages or subtypes of PD, such as cognitive impairments. The heterogeneity of PD suggests widespread brain structural changes, and axonal involvement appears to be critical to the pathophysiology of PD. As α-synuclein pathology has been suggested to cause axonal changes followed by neuronal degeneration, diffusion tensor imaging (DTI) as an in vivo imaging technique emerges to characterize early detectable white matter changes due to PD. Here, we reviewed the past 5-year literature to show how DTI has helped identify axonal abnormalities at different PD stages or in different PD subtypes and atypical parkinsonism. We also showed the recent clinical utilities of DTI tractography in interventional treatments such as deep brain stimulation (DBS). Mounting evidence supported by multisite DTI data suggests that DTI along with the advanced analytic methods, can delineate dynamic pathophysiological processes from the early to late PD stages and differentiate distinct structural networks affected in PD and other parkinsonism syndromes. It indicates that DTI, along with recent advanced analytic methods, can assist future interventional studies in optimizing treatments for PD patients with different clinical conditions and risk profiles.
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Affiliation(s)
- Yao-Chia Shih
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan, Taiwan
| | - Wen-Yih Isaac Tseng
- AcroViz Inc., Taipei, Taiwan.,Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
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