Cerebral cortical functional hyperconnectivity in a mouse model of spinocerebellar ataxia type 8 (SCA8).

Autor:	Nietz AK; Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA., Popa LS; Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA., Carter RE; Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA., Gerhart ML; Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA., Manikonda K; Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA., Ranum LPW; Department of Molecular Genetics & Microbiology, College of Medicine, University of Florida, Gainesville, FL 32610, USA., Ebner TJ; Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Jun 20. Date of Electronic Publication: 2024 Jun 20.
DOI:	10.1101/2024.06.20.599947
Abstrakt:	Spinocerebellar Ataxia Type 8 (SCA8) is an inherited neurodegenerative disease caused by a bidirectionally expressed CTG●CAG expansion mutation in the ATXN-8 and ATXN8-OS genes. While primarily a motor disorder, psychiatric and cognitive symptoms have been reported. It is difficult to elucidate how the disease alters brain function in areas with little or no degeneration producing both motor and cognitive symptoms. Using transparent polymer skulls and CNS-wide GCaMP6f expression, we studied neocortical networks throughout SCA8 progression using wide-field Ca 2+ imaging in a transgenic mouse model of SCA8. We observed that neocortical networks in SCA8+ mice were hyperconnected globally which led to network configurations with increased global efficiency and centrality. At the regional level, significant network changes occurred in nearly all cortical regions, however mainly involved sensory and association cortices. Changes in functional connectivity in anterior motor regions worsened later in the disease. Near perfect decoding of animal genotype was obtained using a generalized linear model based on canonical correlation strengths between activity in cortical regions. The major contributors to decoding were concentrated in the somatosensory, higher visual and retrosplenial cortices and occasionally extended into the motor regions, demonstrating that the areas with the largest network changes are predictive of disease state. Competing Interests: Conflict of Interest Statement: The authors have declared that there are no conflicts of interest.
Databáze:	MEDLINE
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