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Medial temporal lobe (MTL) function is well established as necessary for memory of facts and events. It is likely that lateral cortical regions critically guide cognitive control processes to tune in high-fidelity details that are most relevant for memory retrieval. Here, convergent results from functional and structural MRI show that retrieval of detailed episodic memory arises from lateral cortical-MTL networks, including regions of inferior frontal and angular gyrii. Results also suggest that recognition of items based on low-fidelity, generalized information, rather than memory arising from retrieval of relevant episodic details, is not associated with functional connectivity between MTL and lateral cortical regions. Additionally, individual differences in microstructural properties in white matter pathways, associated with distributed MTL-cortical networks, are positively correlated with better performance on a mnemonic discrimination task.

Medial temporal lobe (MTL) function is well established as necessary for memory of facts and events. It is likely that lateral cortical regions critically guide cognitive control processes to tune in high-fidelity details that are most relevant for memory retrieval. Here, convergent results from functional and structural MRI show that retrieval of detailed episodic memory arises from lateral cortical-MTL networks, including regions of inferior frontal and angular gyrii. Results also suggest that recognition of items based on low-fidelity, generalized information, rather than memory arising from retrieval of relevant episodic details, is not associated with functional connectivity between MTL and lateral cortical regions. Additionally, individual differences in microstructural properties in white matter pathways, associated with distributed MTL-cortical networks, are positively correlated with better performance on a mnemonic discrimination task.

As the COVID-19 pandemic began spreading worldwide in late 2019 and early 2020, many vaccine candidates were developed to combat the disease. However, new COVID-19 variants such as Omicron and Delta continue to emerge globally despite advancements in vaccine technology, leaving certain countries and variants more vulnerable than others to future outbreaks of these variants. This research aims to analyze the susceptibility of different countries to a COVID-19 outbreak, present the first visualization of the spread of COVID-19, and predict which countries are at greater risk for future outbreaks of new variants based on various factors. We created interactive maps to understand the pandemic's spread and identify high-risk countries based on their vaccination percentages. Then we employed binary classification, K-nearest neighbors (KNN), and neural network machine learning models to predict each country's risk factor. The risk factor determines whether a country is safe from a new COVID-19 variant based on vaccine percentage and government stringency. The neural network achieved the highest accuracy, classifying countries as high risk or low risk with 94% accuracy. Inspired by the Albert Barabasi model, we graphed connections between countries based on vaccination percentages. These graphs illustrate the correlation between the two countries and better demonstrate how their vaccination rates relate to the probability of a new COVID-19 outbreak. [ABSTRACT FROM AUTHOR]