Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

The menopausal transition is a complex neuroendocrine aging process affecting brain structure and metabolic function. Such changes are consistent with neurological sequelae noted following the menopausal transition, including cognitive deficits. Although studies in rodent models of the menopause revealed changes in learning and memory, little is known about the structural and metabolic changes in the brain regions serving the cognitive function in these models. The administration 4-vinylcyclohexene diepoxide (VCD) in laboratory animals results in follicular depletion, and thus, is a powerful translational tool that models the human menopause. In the studies presented here, we evaluated behavior, brain structure, and metabolism in young female rats administered with either VCD or vehicle for 15 days across the early, mid, and post-follicular depletion states at 1-, 2-, and 3-months post-final injection, respectively. Additionally, we evaluated the serum hormonal profile and ovarian follicles based on the estrous cycle pattern. Positron emission tomography (PET) was utilized to determine regional brain glucose metabolism in the hippocampus, medial prefrontal cortex, and striatum. Subsequently, the rats were euthanized for ex-vivo magnetic resonance imaging (MRI) to assess regional brain volumes. VCD-induced rats at the post-follicular depleted time points had diminished spatial learning and memory as well as reduced hippocampal glucose uptake. Additionally, VCD-induced rats at post-follicular depletion time points had marked reductions in estradiol, progesterone, and anti-mullerian hormone with an increase in follicle-stimulating hormone. These rats also exhibited fewer ovarian follicles, indicating that substantial ovarian function loss during post-follicular time points impairs the female rats' spatial learning/memory abilities and triggers the metabolic changes in the hippocampus.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.exger.2024.112607DOI Listing

Publication Analysis

Top Keywords

learning memory
12
post-follicular depletion
12
time points
12
spatial learning
8
reduced hippocampal
8
hippocampal glucose
8
glucose uptake
8
menopausal transition
8
brain structure
8
metabolic changes
8

Similar Publications

Brain ischemia is a major global cause of disability, frequently leading to psychoneurological issues. This study investigates the effects of 4-aminopyridine (4-AP) on anxiety, cognitive impairment, and potential underlying mechanisms in a mouse model of medial prefrontal cortex (mPFC) ischemia. Mice with mPFC ischemia were treated with normal saline (NS) or different doses of 4-AP (250, 500, and 1000 µg/kg) for 14 consecutive days.

View Article and Find Full Text PDF

Background: Intensive language-action therapy treats language deficits and depressive symptoms in chronic poststroke aphasia, yet the underlying neural mechanisms remain underexplored. Long-range temporal correlations (LRTCs) in blood oxygenation level-dependent signals indicate persistence in brain activity patterns and may relate to learning and levels of depression. This observational study investigates blood oxygenation level-dependent LRTC changes alongside therapy-induced language and mood improvements in perisylvian and domain-general brain areas.

View Article and Find Full Text PDF

Maximizing theoretical and practical storage capacity in single-layer feedforward neural networks.

Front Comput Neurosci

August 2025

Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.

Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks as a function of these parameters.

View Article and Find Full Text PDF

DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction.

Front Genet

August 2025

Hunan Provincial Key Laboratory of Finance and Economics Big Data Science and Technology, Hunan University of Finance and Economics, Changsha, China.

RNA N4-acetylcytidine (ac4C) is a crucial chemical modification involved in various biological processes, influencing RNA properties and functions. Accurate prediction of RNA ac4C sites is essential for understanding the roles of RNA molecules in gene expression and cellular regulation. While existing methods have made progress in ac4C site prediction, they still struggle with limited accuracy and generalization.

View Article and Find Full Text PDF

Background: Synaptic dysfunction and synapse loss occur in Alzheimer's disease (AD). The current study aimed to identify synaptic-related genes with diagnostic potential for AD.

Methods: Differentially expressed genes (DEGs) were overlapped with phenotype-associated module selected through weighted gene co-expression network analysis (WGCNA), and synaptic-related genes.

View Article and Find Full Text PDF