Jenna Reinen is a post-doctoral scientist in neuroimaging in the Computational Biology Center at IBM TJ Watson Research Center, where her research focuses on the cognitive neuroscience of learning, memory, decision making, affective processing, and dynamic brain connectivity. She also aims to develop technology for use with the identification and intervention of brain-based disorders.
After graduating from Duke University with an undergraduate degree in biology, she worked in clinical research for several years. She completed her PhD at Columbia University, where she studied the interaction of affective processing and learning, memory, and decision making in the human brain, with a special focus on the motivational symptoms in schizophrenia. She recently completed a post doc with at Yale University where she studied how functional brain imaging can predict psychiatric illness, as well as used computational modeling and behavioral measures to address questions related to the neural mechanisms underlying emotion-cognition interactions.
Selected Journal Publications:
Reinen JM et al (2016). Motivational context modulates prediction error response in schizophrenia. Schizophrenia Bulletin, 42 (6), 1467-1475.
Reinen JM et al (2014). Patients with schizophrenia are impaired when learning in the context of pursuing rewards. Schizohprenia Research, 152(1):309-10
Selected Conference Presentations:
Reinen JM et al. (2017) Major depressive disorder is associated with blunted learning signals in medial prefrontal cortex and putamen when seeking monetary reward. American College of Neuropsychopharmacology, Palm Springs, CA.
Tejwani R, Liska A, You H, Reinen JM, Das P. (2017) Autism classification using brain functional connectivity dynamics and machine learning. Conference on Neural Information Processing Systems, Long Beach, CA.
Reinen JM et al.(2016) Exploring the functional dynamics of large scale brain networks within and across individuals. Federation of European Neuroscience Societies, Copenhagen, Denmark.
Reinen JM et al. (2013) Dissociations in reward network activation during informative and affective feedback. Reinforcement Learning and Decision Making, Princeton University, NJ.