Nooshin Abbasi1,2, Bahram Mohajer1,2, Sima Abbasi3, Payam Hasanabadi3, Amirhussein Abdolalizadeh1,2, Reza Rajimehr4. 1. Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran. 2. Multiple Sclerosis Research Center (MSRC), Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran. 3. Mashhad University of Medical Sciences, Mashhad, Iran. 4. McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA.
Abstract
BACKGROUND: Pathological accumulation of α-synuclein, amyloid-β42 , and tau proteins in the brain is considered critical for development of various neurodegenerative diseases. OBJECTIVES: We investigated the association between CSF levels of these biomarkers, brain structural connectivity, and the UPDRS in PD. METHODS: Diffusion tensor images and CSF biomarkers (α-synuclein, amyloid-β42 , total tau, and phosphorylated tau181) from 132 drug-naïve, nondemented PD patients and 61 healthy controls were obtained from the Parkinson's Progression Markers Initiative database. After network reconstruction of structural connectivity patterns, global interconnectivity measures (including global efficiency, clustering coefficient, and characteristic path length) and local efficiency were calculated. Network properties and CSF biomarkers were compared between PD patients and healthy controls. The association of CSF biomarkers with network properties and UPDRS-III score was investigated. RESULTS: Global measures (but not local efficiency) and CSF α-synuclein were significantly lower in PD patients. Global efficiency and clustering coefficient correlated positively with α-synuclein, Aβ42 , and total tau CSF levels. Furthermore, these CSF biomarkers showed no significant association with the UPDRS-III score. CONCLUSIONS: This study examined the association of CSF biomarkers that reflect the brain pathology, with structural brain connectivity and UPDRS-III in PD. Our results revealed an association between the abnormal aggregation of α-synuclein, Aβ42 , and tau proteins and structural connectivity disruption in PD patients. In summary, a combination of structural imaging and measurement of CSF biomarkers provide a better understanding of the pathogenesis of PD.
BACKGROUND: Pathological accumulation of α-synuclein, amyloid-β42 , and tau proteins in the brain is considered critical for development of various neurodegenerative diseases. OBJECTIVES: We investigated the association between CSF levels of these biomarkers, brain structural connectivity, and the UPDRS in PD. METHODS: Diffusion tensor images and CSF biomarkers (α-synuclein, amyloid-β42 , total tau, and phosphorylated tau181) from 132 drug-naïve, nondemented PDpatients and 61 healthy controls were obtained from the Parkinson's Progression Markers Initiative database. After network reconstruction of structural connectivity patterns, global interconnectivity measures (including global efficiency, clustering coefficient, and characteristic path length) and local efficiency were calculated. Network properties and CSF biomarkers were compared between PDpatients and healthy controls. The association of CSF biomarkers with network properties and UPDRS-III score was investigated. RESULTS: Global measures (but not local efficiency) and CSF α-synuclein were significantly lower in PDpatients. Global efficiency and clustering coefficient correlated positively with α-synuclein, Aβ42 , and total tauCSF levels. Furthermore, these CSF biomarkers showed no significant association with the UPDRS-III score. CONCLUSIONS: This study examined the association of CSF biomarkers that reflect the brain pathology, with structural brain connectivity and UPDRS-III in PD. Our results revealed an association between the abnormal aggregation of α-synuclein, Aβ42 , and tau proteins and structural connectivity disruption in PDpatients. In summary, a combination of structural imaging and measurement of CSF biomarkers provide a better understanding of the pathogenesis of PD.
Authors: Diego Castillo-Barnes; Javier Ramírez; Fermín Segovia; Francisco J Martínez-Murcia; Diego Salas-Gonzalez; Juan M Górriz Journal: Front Neuroinform Date: 2018-08-14 Impact factor: 4.081
Authors: Xueling Suo; Du Lei; Nannan Li; Wenbin Li; Graham J Kemp; John A Sweeney; Rong Peng; Qiyong Gong Journal: Brain Struct Funct Date: 2021-04-07 Impact factor: 3.270