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Enhanced Bidirectional Connectivity of the Subthalamo-pallidal Pathway in 6-OHDA-mouse Model of Parkinson’s Disease Revealed by Probabilistic Tractography of Diffusion-weighted MRI at 9.4T

An important challenge in Parkinson’s disease (PD) based neuroscience and neuroimaging is mapping the neuronal connectivity of the basal ganglia to understand how the disease affects brain circuitry. However, a majority of diffusion tractography studies have shown difficulties in revealing connectio...

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Detalles Bibliográficos
Autores principales: Kim, A-Yoon, Oh, Chiwoo, Im, Hyung-Jun, Baek, Hyeon-Man
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society for Brain and Neural Sciences 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7075660/
https://www.ncbi.nlm.nih.gov/pubmed/32122110
http://dx.doi.org/10.5607/en.2020.29.1.80
Descripción
Sumario:An important challenge in Parkinson’s disease (PD) based neuroscience and neuroimaging is mapping the neuronal connectivity of the basal ganglia to understand how the disease affects brain circuitry. However, a majority of diffusion tractography studies have shown difficulties in revealing connections between distant anatomic brain regions and visualizing basal ganglia connectome. In this current study, we investigated the differences in basal ganglia connectivity between 6-OHDA induced ex-vivo PD mouse model and normal ex-vivo mouse model by using diffusion tensor imaging tractography from diffusion-weighted images obtained with a high resolution 9.4 T MR scanner. Connectivity pattern of the basal ganglia were compared between five 6-OHDA and five control ex-vivo mouse brains using results of probabilistic tractography generated with PROBTRACKX. When compared with control mouse, 6-OHDA mouse showed significant enhancements to motor territory-related subthalamo-pallidal and pallido-subthalamic connectivity. Multi-fiber tractography combined with diffusion MRI data has the potential to help recognize the abnormalities found in connectivity of psychiatric and neurologic disease models.