Biology subjects

Aoun, A.

Publications and source records attributed to Aoun, A..

4 recordsLinked to original sources

bioRxiv2024

Impaired spatial coding and neuronal hyperactivity in the medial entorhinal cortex of APP NL-G-F mice

The progressive accumulation of amyloid beta (A{beta}) pathology in the brain has been associated with aberrant neuronal network activity and poor cognitive performance in preclinical mouse models of Alzheimers disease (AD). Presently, our understanding of the mechanisms driving pathology-associated neuronal dysfunction and impaired information processing in the brain remains incomplete. Here, we assessed the impact of advanced A{beta} pathology on spatial information processing in the medial entorhinal cortex (MEC) of 18-month App NL-G-F/NL- G-F knock-in (APP KI) mice as they explored contextually novel and familiar open field arenas in a two-day, four-session recording paradigm. We tracked single unit firing activity across all sessions and found that spatial information scores were decreased in MEC neurons from APP KI mice versus those in age-matched C57BL/6J controls. MEC single unit spatial representations were also impacted in APP KI mice. Border cell firing preferences were unstable across sessions and spatial periodicity in putative grid cells was disrupted. In contrast, MEC border cells and grid cells in Control mice were intact and stable across sessions. We then quantified the stability of MEC spatial maps across sessions by utilizing a metric based on the Earth Movers Distance (EMD). We found evidence for increased instability in spatially-tuned APP KI MEC neurons versus Controls when mice were re-exposed to familiar environments and exposed to a novel environment. Additionally, spatial decoding analysis of MEC single units revealed deficits in position and speed coding in APP KI mice in all session comparisons. Finally, MEC single unit analysis revealed a mild hyperactive phenotype in APP KI mice that appeared to be driven by narrow-spiking units (putative interneurons). These findings tie A{beta}-associated dysregulation in neuronal firing to disruptions in spatial information processing that may underlie certain cognitive deficits associated with AD.

neuroscience↗

bioRxiv2024

Lateral Entorhinal Cortex Dysfunction in Alzheimers Disease Mice

In Alzheimers disease (AD), the formation of amyloid beta (A{beta}) and neurofibrillary tangles (NFTs) leads to neuronal loss in entorhinal cortex (EC), a crucial brain region for memory and navigation. These pathological changes are concurrent with the onset of memory impairments in AD patients with symptoms of forgetfulness such as misplacing items, disorientation in familiar environments etc. The lateral EC (LEC) is associated with non-spatial memory processing including object recognition. Since some LEC neurons fire in response to objects (object cells) while others fire at locations previously occupied by objects (trace cells), pathology in this region could lead to dysfunction in object-location coding. In this paper we show that a transgenic AD mouse model, EC-App/Tau, which expresses both APP and tau primarily in the EC region, have deficits in LEC-specific memory tasks. Using in vivo single-unit electrophysiology recordings we show that LEC neurons are hyperactive with low information content and high sparsity compared to the controls indicating poor firing fidelity. We finally show that object cells and trace cells fire less precisely in the EC-App/Tau mice compared to controls indicating poor encoding of objects. Overall, we show that AD pathology causes erratic firing of LEC neurons and object coding defects leading to LEC-specific memory impairment.

neuroscience↗

bioRxiv2024

Open-Source Tools to Analyze Temporal and Spatial Properties of Local Field Potentials

Analysis of local field potentials (LFPs) is important for understanding how ensemble neurons function as a network in a specific region of the brain. Despite the availability of tools for analyzing LFP data, there are some missing features such as analysis of high frequency oscillations (HFOs) and spatial properties. In addition, accessibility of most tools is restricted due to closed source code and/or high costs. To overcome these issues, we have developed two freely available tools that make temporal and spatial analysis of LFP data easily accessible. The first tool, hfoGUI (High Frequency Oscillation Graphical User Interface), allows temporal analysis of LFP data and scoring of HFOs such as ripples and fast ripples which are important in understanding memory function and neurological disorders. To complement the temporal analysis tool, a second tool, SSM (Spatial Spectral Mapper), focuses on the spatial analysis of LFP data. The SSM tool maps the spectral power of LFPs as a function of subjects position in a given environment allowing investigation of spatial properties of LFP signal. Both hfoGUI and SSM are open-source tools that have unique features not offered by any currently available tools, and allow visualization and spatio-temporal analysis of LFP data.

neuroscience↗

bioRxiv2023

Beyond Correlation: Optimal Transport Metrics For Characterizing Representational Stability and Remapping in Neurons Encoding Spatial Memory

Spatial representations in the entorhinal cortex (EC) and hippocampus (HPC) are fundamental to cognitive functions like navigation and memory. These representations, embodied in spatial field maps, dynamically remap in response to environmental changes. However, current methods, such as Pearsons correlation coefficient, struggle to capture the complexity of these remapping events, especially when fields do not overlap, or transformations are non-linear. This limitation hinders our understanding and quantification of remapping, a key aspect of spatial memory function. To address this, we propose a family of metrics based on the Earth Movers Distance (EMD) as a versatile framework for characterizing remapping. Applied to both normalized and unnormalized distributions, the EMD provides a granular, noise-resistant, and rate-robust description of remapping. This approach enables the identification of specific cell types and the characterization of remapping in various scenarios, including disease models. Furthermore, the EMDs properties can be manipulated to identify spatially tuned cell types and to explore remapping as it relates to alternate information forms such as spatiotemporal coding. By employing approximations of the EMD, we present a feasible, lightweight approach that complements traditional methods. Our findings underscore the potential of the EMD as a powerful tool for enhancing our understanding of remapping in the brain and its implications for spatial navigation, memory studies and beyond.

neuroscience↗