Impaired spatial coding and neuronal hyperactivity in the medial entorhinal cortex of aged mice.

The progressive accumulation of amyloid beta (Aβ) pathology in the brain has been associated with aberrant neuronal network activity and poor cognitive performance in preclinical mouse models of Alzheimer's 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 Aβ pathology on spatial information processing in the medial entorhinal cortex (MEC) of 18-month knock-in (APP KI) mice as they explored contextually novel and familiar open field arenas in a two-day, four-session recording paradigm.

Lateral Entorhinal Cortex Dysfunction in Alzheimer's Disease Mice.

In Alzheimer's disease (AD), the formation of amyloid beta and neurofibrillary tangles (NFTs) leads to neuronal loss in entorhinal cortex (EC), a crucial brain region involved in memory and navigation. These pathological changes are concurrent with the onset of memory-related issues 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.

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.

The Application of Large Language Models for Radiologic Decision Making.

Background And Purpose: Large language models (LLMs) have seen explosive growth, but their potential role in medical applications remains underexplored. Our study investigates the capability of LLMs to predict the most appropriate imaging study for specific clinical presentations in various subspecialty areas in radiology.

Methods And Materials: Chat Generative Pretrained Transformer (ChatGPT), by OpenAI and Glass AI by Glass Health were tested on 1,075 clinical scenarios from 11 ACR expert panels to determine the most appropriate imaging study, benchmarked against the ACR Appropriateness Criteria.

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 Pearson's correlation coefficient, struggle to capture the complexity of these remapping events, especially when fields do not overlap, or transformations are non-linear.

Digital health in fragile states in the Middle East and North Africa (MENA) region: A scoping review of the literature.

Introduction: Conflict, fragility and political violence, that are taking place in many countries in the Middle East and North Africa (MENA) region have devastating effects on health. Digital health technologies can contribute to enhancing the quality, accessibility and availability of health care services in fragile and conflict-affected states of the MENA region. To inform future research, investments and policy processes, this scoping review aims to map out the evidence on digital health in fragile states in the MENA region.