Jenna Reinen

Jenna Reinen is a research scientist trained in cognitive, computational, and clinical neuroscience. She current works in the Computational Psychiatry Group in the Digital Health Division at IBM TJ Watson Research Center. Her research focuses on chronic pain, brain imaging, language, and prediction and identification of psychosis and affective disorders. She works with both industry-sponsored and academic clinical trials.

Selected Publications: 

Reinen JM, Polosecki P, Castro E, Corcoran CM, Cecchi G, Colibazzi T. (2024) Multimodal fusion of brain signals for robust prediction of psychosis transition. Accepted, Schizophrenia.

Reinen JM et al (2022). Definition and clinical validation of Pain Patient States from high-dimensional mobile data: application to a chronic pain cohort. IEEE International Conference on Digital Health, 47-53.

Reinen JM et al (2021). Differential reinforcement learning responses to positive and negative information in unmedicated individuals with depression. European Neuropsychopharmacology, 53, 89-100.

Reinen JM et al (2018). The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis. Nature Communications, 9(1): 1157.

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.