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In Alzheimer's disease (AD), the formation of amyloid beta (A ß ) 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. Since in LEC, neurons fire in response to objects (object cells) and 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 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 the 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.

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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 subject's 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.

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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. Two responses per clinical presentation were generated and averaged for the final clinical presentation score. Clinical presentation scores for each topic area were averaged as its final score. The average of the topic scores within a panel determined the final score of each panel. LLM responses were on a scale of 0 to 3. Partial scores were given for nonspecific answers. Pearson correlation coefficient (R-value) was calculated for each panel to determine a context-specific performance. RESULTS: Glass AI scored significantly higher than ChatGPT (2.32 ± 0.67 versus 2.08 ± 0.74, P = .002). Both LLMs performed the best in the Polytrauma, Breast, and Vascular panels, and performed the worst in the Neurologic, Musculoskeletal, and Cardiac panels. Glass AI outperformed ChatGPT in 10 of 11 panels, except Obstetrics and Gynecology. Maximum agreement was in the Pediatrics, Neurologic, and Thoracic panels, and the most disagreement occurred in the Vascular, Breast, and Urologic panels. CONCLUSION: LLMs can be used to predict imaging studies, with Glass AI's superior performance indicating the benefits of extra medical-text training. This supports the potential of LLMs in radiologic decision making.

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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. 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 Mover's 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 EMD's 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.

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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. METHOD: We conducted a scoping review following the Joanna Briggs Institute (JBI) guidelines. We conducted descriptive analysis of the general characteristics of the included papers and thematic analysis of the key findings of included studies categorized by targeted primary users of different digital health intervention. RESULTS: Out of the 10,724 articles identified, we included 93 studies. The included studies mainly focused on digital health interventions targeting healthcare providers, clients and data services, while few studies focused on health systems or organizations managers. Most of the included studies were observational studies (49%). We identified no systematic reviews. Most of the studies were conducted in Lebanon (32%) followed by Afghanistan (13%) and Palestine (12%). The first authors were mainly affiliated with institutions from countries outside the MENA region (57%), mainly United Kingdom and United States. Digital health interventions provided a platform for training, supervision, and consultation for health care providers, continuing education for medical students, and disease self-management. The review also highlighted some implementation considerations for the adoption of digital health such as computer literacy, weak technological infrastructure, and privacy concerns. CONCLUSION: This review showed that digital health technologies can provide promising solutions in addressing health needs in fragile and conflict-affected states. However, rigorous evaluation of digital technologies in fragile settings and humanitarian crises are needed to inform their design and deployment.

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Introduction: 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. This limitation hinders our understanding and quantification of remapping, a key aspect of spatial memory function. Methods: We propose a family of metrics based on the Earth Mover's Distance (EMD) as a versatile framework for characterizing remapping. Results: 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 EMD's 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. Discussion: 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.