Biology subjects

Kastanenka, K. V.

Publications and source records attributed to Kastanenka, K. V..

4 recordsLinked to original sources

bioRxiv2025

Zolpidem restores sleep and slows Alzheimer's progression in a mouse model

STRUCTURED ABSTRACTO_ST_ABSINTRODUCTIONC_ST_ABSDeficits in Non-Rapid Eye Movement (NREM) sleep facilitate Alzheimers disease (AD) progression. Enhancing GABAergic signaling can restore sleep. Unbiased computational analysis identified zolpidem as high-affinity GABA receptor modulator facilitating chloride transport that could slow AD. METHODSZolpidems effects on sleep and Alzheimers progression were evaluated in young APP/PS1 mice. Sleep was monitored with EEG/EMG telemetry. Widefield imaging with voltage-sensitive dyes was used to track sleep-dependent brain rhythms. Multiphoton microscopy allowed assessments of amyloid plaque load and basal neuronal calcium levels. Behavioral assays were used to measure memory and cognitive function. RESULTSZolpidem restored NREM sleep and rescued sleep-dependent brain rhythm, slow oscillation. Zolpidem administration reduced cortical amyloid plaque burden, mitigated neuronal calcium overload, and enhanced sleep-dependent memory consolidation without adverse effects on locomotion. DISCUSSIONZolpidem effectively slowed Alzheimers progression in young APP/PS1 mice. This supports zolpidems therapeutic promise as an intervention strategy at early stages of AD.

neuroscience↗

bioRxiv2025

Transplantation of GABAergic Interneuron Progenitors Restores Cortical Circuit Function in an Alzheimer's Disease Mouse Model

In addition to dementia, Alzheimers patients suffer from sleep impairments and aberrations in sleep-dependent brain rhythms. Deficits in inhibitory GABAergic interneuron function disrupt one of those rhythms, slow oscillation in particular, and actively contribute to Alzheimers progression. We tested the degree to which transplantation of healthy donor interneuron progenitors would restore slow oscillation rhythm in young APP mice. We harvested medial ganglionic eminence (MGE) progenitors from mouse embryos and transplanted them into host APP mutant cortices. 3D light-sheet and structured illumination microscopy revealed that transplanted MGE progenitors survived and matured into healthy interneurons. In vivo multiphoton calcium imaging and voltage-sensitive dye imaging showed functional integration and slow oscillation rescue in absence or presence of optogenetic stimulation. Our work provides proof-of-concept evidence that stem cell therapy may serve as a viable strategy to rescue functional impairments in cortical circuits of APP mice and potentially those of Alzheimers patients.

neuroscience↗

bioRxiv2022

Building Transformers from Neurons and Astrocytes

Glial cells account for roughly 90% of all human brain cells, and serve a variety of important developmental, structural, and metabolic functions. Recent experimental efforts suggest that astrocytes, a type of glial cell, are also directly involved in core cognitive processes such as learning and memory. While it is well-established that astrocytes and neurons are connected to one another in feedback loops across many time scales and spatial scales, there is a gap in understanding the computational role of neuron-astrocyte interactions. To help bridge this gap, we draw on recent advances in artificial intelligence (AI) and astrocyte imaging technology. In particular, we show that neuron-astrocyte networks can naturally perform the core computation of a Transformer, a particularly successful type of AI architecture. In doing so, we provide a concrete and experimentally testable account of neuron-astrocyte communication. Because Transformers are so successful across a wide variety of task domains, such as language, vision, and audition, our analysis may help explain the ubiquity, flexibility, and power of the brains neuron-astrocyte networks. Significance StatementTransformers have become the default choice of neural architecture for many machine learning applications. Their success across multiple domains such as language, vision, and speech raises the question: how can one build Transformers using biological computational units? At the same time, in the glial community there is a gradually accumulating evidence that astrocytes, formerly believed to be passive house-keeping cells in the brain, in fact play important role in brains information processing and computation. In this work we hypothesize that neuron-astrocyte networks can naturally implement the core computation performed by the Transformer block in AI. The omnipresence of astrocytes in almost any brain area may explain the success of Transformers across a diverse set of information domains and computational tasks.

neuroscience↗

bioRxiv2022

Hyperactive somatostatin interneurons near amyloid plaque and cell-type-specific firing deficits in a mouse model of Alzheimer's disease

Alzheimers disease (AD) is characterized by synaptic loss and neuronal network dysfunction. These network deficits are mediated by early alterations in neuronal firing rates that coincide with amyloid plaque accumulation. Mounting evidence supports that inhibitory networks are impaired in AD, but the mechanisms driving these inhibitory deficits are poorly understood. Here we use in vivo multiphoton calcium imaging to determine the relationship between amyloid accumulation and the spontaneous activity of excitatory neurons and inhibitory interneurons in an APP/PS1 mouse model of Alzheimers disease. We show that somatostatin-expressing (SOM) interneurons are hyperactive, while parvalbumin-expressing interneurons are hypoactive in APP/PS1 mice. Only SOM interneuron hyperactivity correlated with proximity to amyloid plaque. These inhibitory deficits were accompanied by decreased excitatory neurons activity and decreased pairwise activity correlations in APP/PS1 mice. Our study identifies cell-specific interneuronal firing deficits driven by amyloid pathology in APP/PS1 mice and provides new insights for targeting inhibitory circuits in Alzheimers disease.

neuroscience↗