Choice Behaviors and Prefrontal-Hippocampal Coupling Are Disrupted in a Rat Model of Fetal Alcohol Spectrum Disorders

被引:0
作者
Rosenblum, Hailey L. [1 ]
Kim, Suhyeong [1 ]
Stout, John J. [2 ]
Klintsova, Anna Y. [1 ]
Griffin, Amy L. [1 ]
机构
[1] Univ Delaware, Dept Psychol & Brain Sci, Newark, DE 19716 USA
[2] Univ Connecticut Hlth, Dept Neurosci, Farmington, CT 06030 USA
关键词
disrupts oscillatory brain growth spurt; local field potential; machine learning; oscillatory synchronization; spatial working memory; vicarious trial and error; SPATIAL WORKING-MEMORY; TRIAL-AND-ERROR; VICARIOUS TRIAL; THETA OSCILLATIONS; ETHANOL EXPOSURE; DEFICITS; CORTEX; NUMBER; GROWTH; PERFORMANCE;
D O I
10.1523/JNEUROSCI.1241-24.2025
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Fetal alcohol spectrum disorders (FASDs) are characterized by a range of physical, cognitive, and behavioral impairments. Determining how temporally specific alcohol exposure (AE) affects neural circuits is crucial to understanding the FASD phenotype. Third trimester AE can be modeled in rats by administering alcohol during the first two postnatal weeks, which damages the medial prefrontal cortex (mPFC) and hippocampus (HPC), structures whose functional interactions are required for working memory and executive function. Therefore, we hypothesized that AE during this period would impair working memory, disrupt choice behaviors, and alter mPFC-HPC oscillatory synchrony. To test this hypothesis, we recorded local field potentials from the mPFC and dorsal HPC as male and female AE and sham-intubated (SI) rats performed a spatial working memory task in adulthood and implemented algorithms to detect vicarious trial and errors (VTEs), behaviors associated with deliberative decision-making. We found that, compared with the SI group, the AE group performed fewer VTEs and demonstrated a disturbed relationship between VTEs and choice outcomes, while spatial working memory was unimpaired. This behavioral disruption was accompanied by alterations to mPFC and HPC oscillatory activity in the theta and beta bands, respectively, and a reduced prevalence of mPFC-HPC synchronous events. When trained on multiple behavioral variables, a machine learning algorithm could accurately predict whether rats were in the AE or SI group, thus characterizing a potential phenotype following third trimester AE. Together, these findings indicate that third trimester AE disrupts mPFC-HPC oscillatory interactions and choice behaviors.
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页数:14
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