This study aims to determine the effect of structural racism on cognitive aging. We are looking at many different aspects of structural racism (civics, education, employment, environment, healthcare, income/credit/wealth, media/marketing, neighborhood factors, and policing), and several variables to measure each aspect. We will be acquiring several large data sets that have data from multiple years. We will be linking all these datasets to determine exposure to structural racism based on geographic location in the United States over the years. We will then link this to a longitudinal dataset with participants’ residences over their lifetime as well as measures of cognitive aging. Our analysis will primarily employ Structural Equation Modeling, but we will also conduct factor analyses and psychometric analyses. We will be analyzing each aspect individually, and as part of a larger model.
We are constantly exposed to input from the outside world, but we do not perceive nor remember everything we encounter. The state of our brains right before we receive such sensory inputs influences whether or not we process them. Brain oscillations are proposed to play a key role in setting these brain states; however, how exactly these brain rhythms influence perception and other cognitive processes remains a topic of active research. The Brain Rhythms Lab investigates how brain rhythms gate information through the brain, how they facilitate interactions with the rest of the body, and how these rhythms influence cognitive functions.
Unsupervised clustering of the human gut microbiome: revealing the biases, optimizing the parameters
Background: The human gut microbiome is a heterogeneous community of bacterial species. Many human diseases are associated with changes in the microbiome, and understanding the interaction between gut bacteria and human health is therefore expected to revolutionize healthcare.
By helping you find and keep your dependencies up to date, you can focus on your code, and we will take care of the rest.
The brain is the most complex organ in the body, composed of billions of neurons and trillions of connections between those neurons. Those connections are known as synapses and have been for many years the subject of intense study. What is less clear, however, is how synapses are organized at a population level throughout the brain. To start to address this, we developed a method that analyzes individual synapses using spatial and intensity metrics and scaled this approach to analyze hundreds of thousands of synapses concurrently. By doing so, we found that synapses fall into previously unknown subgroups. The proposed project, which is a collaboration between 2 groups (the Au lab in Pathology and Cell Biology and Menon lab in Neurology), will be to develop the robustness of our approach by working to normalize our findings across batches and conditions.
Adverse pregnancy outcomes (APOs), such as preeclampsia and preterm birth, are common and devastating. The human and economic costs of APOs are tremendous, and the United States has among the highest APO rates among developed nations. APOs are especially common in non-White and low-income communities. For example, in the United States Black women are 50% more likely to deliver preterm compared to White women. Research has shown that the increased risk of adverse outcomes in overburdened populations is not fully explained by socioeconomic status or other socio-demographic factors. In addition to having elevated risk for adverse outcomes, non-White women in the United States may be less likely to receive certain interventions, such as treatment for postpartum depression, but are more likely to receive others, such as cesarean section, suggesting that there may be unwarranted and discriminatory variation in pregnancy care.
Call for Faculty Participation- Fall 2022.
The Data Science Institute is calling for faculty submissions of research projects for the Fall 2022 sessions of the Data Science Institute (DSI) Scholars Program. The goal of the DSI Scholars Program is to engage undergraduate and master students to work with Columbia faculty, through the creation of data science research internships. Last year, we worked with over 40 projects and received more than 350 unique applications from Columbia Students. The program’s unique enrichment activities foster a learning and collaborative community in data science at Columbia. Apply here.