Cognitive Neuroscience Research

The Division of Cognitive Neuroscience focuses on epidemiologic, neuropsychological, cognitive experimental and neuroimaging approaches to cognitive function across the life span. There is an emphasis on normal and abnormal aging and neurological conditions.

Areas of Research

  • Cognitive experimental and traditional neuropsychological battery-based studies of cognition and metacognition
  • Multimodal structural and functional brain imaging
  • Genetics, CSF and blood measures
  • Cognitive reserve
  • Influences on brain health and cognition including literacy, education, ethnicity, culture, social networks, sleep hygiene, physical activity, nutrition, lifestyle, personality
  • Innovative image processing and analytic approaches
  • Cognitive and exercise interventions

Studies

Current Cognitive Experimental Studies

  • Cognitive experimental and traditional neuropsychological battery-based studies of cognition in normal aging and our diseases of interest, including Alzheimer's, Parkinson's, and Huntington's disease.
  • Metacognition and cognitive control processes
  • Effects of literacy, education, ethnicity, and acculturation on cognition
  • Cognitive and exercise interventions for cognition
  • Cognition and functional capacity in Parkinson's and Huntington's diseases
  • Cognitive reserve
  • Social determinants of brain health including social isolation and loneliness, sleep hygiene, and sedentary behaviors / physical activity
  • Attentional bias in multiple sclerosis
  • Impact of diagnosis concealment vs. disclosure on neuropsychological function (cognition, mood) in multiple sclerosis

Current Cognitive Neuroimaging Studies

  • Networks underlying recognition and working memory in young adults, normal aging and Alzheimer's disease
  • Correlates of cognitive improvement in intervention studies
  • Correlates of aging and Alzheimer's disease in structural and resting CBF scans
  • Improved analytic methods for functional imaging
  • Developing a fMRI biomarker to predict memory impairment in adults with multiple sclerosis
  • Utilizing machine learning approaches to identify cognitive phenotypes in multiple sclerosis

Associate Research Scientists

  • Miguel Arce, PhD
  • Patrick Lao, PhD
  • Jet Vonk, PhD