Myelination- and immune mediated MR-based brain network correlates
Background: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS), characterized by inflammatory and neurodegenerative processes. Despite demyelination being a hallmark of the disease, how it relates to neurodegeneration has still not been completely unraveled, and research is still ongoing into how these processes can be tracked non-invasively. Magnetic resonance imaging (MRI) derived brain network characteristics, which closely mirror disease processes and relate to functional impairment, recently became important variables for characterizing immune-mediated neurodegeneration, however their histopathological basis remains unclear.
Methods: In order to determine the MRI-derived correlates of myelin dynamics and to test if brain network characteristics derived from diffusion tensor imaging reflect microstructural tissue reorganization, we took advantage of the cuprizone model of general demyelination in mice and performed longitudinal histological and imaging analyses with behavioral tests. By introducing cuprizone into the diet, we induced targeted and consistent demyelination of oligodendrocytes, over a period of 5 weeks. Subsequent myelin synthesis was enabled by reintroduction of normal food.
Results: Using specific immune-histological markers, we demonstrated that two weeks of cuprizone diet induced a 52% reduction of myelin content in the corpus callosum (CC) and a 35% reduction in the neocortex. An extended cuprizone diet increased myelin loss in the CC, while remyelination commenced in the neocortex. These histologically determined dynamics were reflected by MRI measurements from diffusion tensor imaging. Demyelination was associated with decreased fractional anisotropy (FA) values and increased modularity and clustering at the network level. MRI-derived modularization of the brain network and FA reduction in key anatomical regions, including the hippocampus, thalamus and analyzed cortical areas, were closely related to impaired memory function and anxiety-like behavior.
Conclusion: Network specific remyelination, shown by histology and MRI metrics, determined amelioration of functional performance and neuropsychiatric symptoms. Taken together, we illustrate the histological basis for the MRI-driven network responses to demyelination, where increased modularity leads to evolving damage and abnormal behavior in MS. Quantitative information about in vivo myelination processes is mirrored by diffusion based imaging of microstructural integrity and network characteristics.
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Posted 16 Apr, 2020
On 14 Apr, 2020
On 13 Apr, 2020
On 13 Apr, 2020
Received 02 Apr, 2020
On 02 Apr, 2020
On 27 Mar, 2020
Received 27 Mar, 2020
Invitations sent on 26 Mar, 2020
On 26 Mar, 2020
On 26 Mar, 2020
On 26 Mar, 2020
On 25 Mar, 2020
Received 09 Mar, 2020
On 09 Mar, 2020
Received 24 Feb, 2020
On 20 Feb, 2020
On 15 Feb, 2020
Invitations sent on 05 Feb, 2020
On 24 Jan, 2020
On 23 Jan, 2020
On 23 Jan, 2020
On 23 Jan, 2020
Myelination- and immune mediated MR-based brain network correlates
Posted 16 Apr, 2020
On 14 Apr, 2020
On 13 Apr, 2020
On 13 Apr, 2020
Received 02 Apr, 2020
On 02 Apr, 2020
On 27 Mar, 2020
Received 27 Mar, 2020
Invitations sent on 26 Mar, 2020
On 26 Mar, 2020
On 26 Mar, 2020
On 26 Mar, 2020
On 25 Mar, 2020
Received 09 Mar, 2020
On 09 Mar, 2020
Received 24 Feb, 2020
On 20 Feb, 2020
On 15 Feb, 2020
Invitations sent on 05 Feb, 2020
On 24 Jan, 2020
On 23 Jan, 2020
On 23 Jan, 2020
On 23 Jan, 2020
Background: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS), characterized by inflammatory and neurodegenerative processes. Despite demyelination being a hallmark of the disease, how it relates to neurodegeneration has still not been completely unraveled, and research is still ongoing into how these processes can be tracked non-invasively. Magnetic resonance imaging (MRI) derived brain network characteristics, which closely mirror disease processes and relate to functional impairment, recently became important variables for characterizing immune-mediated neurodegeneration, however their histopathological basis remains unclear.
Methods: In order to determine the MRI-derived correlates of myelin dynamics and to test if brain network characteristics derived from diffusion tensor imaging reflect microstructural tissue reorganization, we took advantage of the cuprizone model of general demyelination in mice and performed longitudinal histological and imaging analyses with behavioral tests. By introducing cuprizone into the diet, we induced targeted and consistent demyelination of oligodendrocytes, over a period of 5 weeks. Subsequent myelin synthesis was enabled by reintroduction of normal food.
Results: Using specific immune-histological markers, we demonstrated that two weeks of cuprizone diet induced a 52% reduction of myelin content in the corpus callosum (CC) and a 35% reduction in the neocortex. An extended cuprizone diet increased myelin loss in the CC, while remyelination commenced in the neocortex. These histologically determined dynamics were reflected by MRI measurements from diffusion tensor imaging. Demyelination was associated with decreased fractional anisotropy (FA) values and increased modularity and clustering at the network level. MRI-derived modularization of the brain network and FA reduction in key anatomical regions, including the hippocampus, thalamus and analyzed cortical areas, were closely related to impaired memory function and anxiety-like behavior.
Conclusion: Network specific remyelination, shown by histology and MRI metrics, determined amelioration of functional performance and neuropsychiatric symptoms. Taken together, we illustrate the histological basis for the MRI-driven network responses to demyelination, where increased modularity leads to evolving damage and abnormal behavior in MS. Quantitative information about in vivo myelination processes is mirrored by diffusion based imaging of microstructural integrity and network characteristics.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6