NIA T32 Current Trainees

Alejandro investigates novel computational modelling and machine learning techniques to develop individualized neuromodulatory interventions.

Tara is interested in mechanisms of learning & memory and neurodegeneration such as Alzheimer’s Disease and other forms of dementia.

Nicole will be employing machine learning methods to determine the best predictive model of working memory performance, using multimodal measures of whole-brain structure and function. Nicole’s future projects will continue to incorporate her interests in utilizing multi-modal imaging and other translational research methods to better characterize inter-individual physiological differences underlying age-associated cognitive impairments that could potentially serve as targets for intervention strategies.

Kailey aims to conduct research that adds to an understanding of the efficacy of interventions and the factors that contribute to inter-individual differences in observed improvements.

Andrea’s research interests include mechanisms that delay and/or prevent the progression of cognitive decline, cognitive and psychosocial outcomes of behavioral interventions for cognitive decline, and risk and resilience factors of cognitive aging among Spanish and English-speaking populations.

Rebecca’s research investigates the neural activation (during a mentalizing task) and connectivity changes in the temporoparietal junction (TPJ) after a 4-week oxytocin administration intervention in older adults. She is also involved in studying a real-time neurofeedback approach to cognitive and emotional regulation abilities in older adults.

Brad’s chief interests are in the predictors of effectiveness, and functional consequences of behavioral cognitive interventions for older adults.