Connectome-based prediction of functional impairment in experimental stroke models.
Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal population...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0310743 |
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| author | Oliver Schmitt Peter Eipert Yonggang Wang Atsushi Kanoke Gratianne Rabiller Jialing Liu |
| author_facet | Oliver Schmitt Peter Eipert Yonggang Wang Atsushi Kanoke Gratianne Rabiller Jialing Liu |
| author_sort | Oliver Schmitt |
| collection | DOAJ |
| description | Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication. |
| format | Article |
| id | doaj-art-3c481849633c49729e2295a39f773529 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3c481849633c49729e2295a39f7735292025-01-08T05:32:58ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031074310.1371/journal.pone.0310743Connectome-based prediction of functional impairment in experimental stroke models.Oliver SchmittPeter EipertYonggang WangAtsushi KanokeGratianne RabillerJialing LiuExperimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.https://doi.org/10.1371/journal.pone.0310743 |
| spellingShingle | Oliver Schmitt Peter Eipert Yonggang Wang Atsushi Kanoke Gratianne Rabiller Jialing Liu Connectome-based prediction of functional impairment in experimental stroke models. PLoS ONE |
| title | Connectome-based prediction of functional impairment in experimental stroke models. |
| title_full | Connectome-based prediction of functional impairment in experimental stroke models. |
| title_fullStr | Connectome-based prediction of functional impairment in experimental stroke models. |
| title_full_unstemmed | Connectome-based prediction of functional impairment in experimental stroke models. |
| title_short | Connectome-based prediction of functional impairment in experimental stroke models. |
| title_sort | connectome based prediction of functional impairment in experimental stroke models |
| url | https://doi.org/10.1371/journal.pone.0310743 |
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