Cognitive Neuroscience of Aging Laboratory - Yaakov Stern
This lab explores cognitive changes throughout aging and their neural basis, with a strong focus on state-of-the-art cognitive approaches and multi-modal imaging. We deal with two main themes: understanding cognitive aging and understanding individual differences in susceptibility to aging and Alzheimer's disease. We are also conducting other studies, including a study of the natural history of Alzheimer’s disease, and cognitive intervention trials in healthy elders that focus on both exercise and cognitive stimulation
Disparities in Cognitive Aging and Dementia Research Laboratory - Jennifer Manly
Disparities in cognitive aging and risk for Alzheimer’s disease and related dementias are well-established, but the mechanisms that maintain these disparities are not well-understood. The Disparities in Cognitive Aging and Dementia Research Laboratory aims to identify causal relationships between sociocultural, economic, educational, linguistic, biological, and genetic factors and cognitive function and cognitive decline among racially and ethnically diverse older adults. This is accomplished through careful examination of brain health and neuropsychological function among diverse individuals, and assessment of potential mechanisms throughout the lifecourse using longitudinal data from prospectively followed cohorts. One focus of this work is to identify factors that promote cognitive resilience in populations with early life disadvantage. The goal of this work is to identify potential interventions for promoting brain health among diverse people, and reducing disparities in cognitive aging and dementia.
Laboratory of Aging and Translational Neuropsychology - Adam Brickman
Our laboratory integrates neuropsychology, neuroimaging, basic science, and epidemiology to understand the determinants and cognitive consequences of aging and neurodegenerative disease. With a special focus on structural neuroimaging, we have been particularly interested in the role of white matter and cerebrovascular factors in cognitive aging in general and in Alzheimer’s disease specifically, in addition to age and disease-associated changes in brain structure.
Subjective Cognition and Metacognition in Aging and Dementia - Stephanie Cosentino
Subjective cognition and metacognition (the accuracy of subjective cognition) are critical determinants of everyday behavior and experiences. The overarching goals of this lab are to characterize the cognitive, affective, and neuroanatomic factors that explain variability in subjective cognition across the spectrum of healthy to pathological aging, and to identify the practical consequences of disordered subjective cognition (metacognitive impairment). A related goal is the examination, refinement and development of tools to assess subjective cognition and metacognition in cognitively diverse older adults in order to examine the accuracy and prognostic relevance of cognitive complaints for typical versus pathologic aging.
Quantitative Neuroimaging Laboratory (QNL) - Ray Razlighi
The Quantitative Neuroimaging Laboratory (QNL) is an engineering-based research lab housed in the Cognitive Neuroscience Division of the Columbia University Medical Center Neurology Department. The main focus of the QNL is to develop techniques to quantify structural and functional brain images, which allow researchers and clinicians to detect brain-based effects that are normally beyond the sensitivity and specificity of commonly-used detection methods in the field. We aim to achieve this goal by utilizing state-of-the-art signal and image processing tools as well as mathematical and statistical methods.
Brain Networks Laboratory (BNet) - Chris Habeck & Yunglin Gazes
Our lab features both methodological and applied neuroimaging analysis using multivariate and multimodal machine-learning techniques. A major focus of the applied and developed tools is the practical utility for clinical and basic cognitive neuroscience. Multivariate decompositions (like principal/independent component analysis) are used in conjunction with simple machine learning tools (naïve Bayes, k-nearest-neighbor classifier, linear and quadratic discriminant classifier, support vector machines) and resampling techniques (permutation test, bootstrap test) for statistical inference.