Lab website: Human Affective and Motivational Neuroscience Lab
Dr Narun Pat (Pornpattananangkul) studies brain bases of individual differences in cognition, emotion and motivation. His laboratory employs cognitive neuroscience methods (such as, fMRI, EEG and polygenic scores) along with modern data science tools (such as, big data, machine learning and computational modeling). His research has been supported by Health Research Council of New Zealand, Oakley Mental Health Research Foundation, Otago Medical Research Foundation and University of Otago.
Narun joined the Department in 2019. He was trained by clinical and social neuroscientists during his PhD in Brain Behavior and Cognition at Northwestern University in the US. He was then trained by a neuroeconomist during his first post-doctoral position at National University of Singapore. He later worked with psychiatrists and neuroimagers during his second post-doctoral training at the National Institute of Mental Health in the US. Accordingly, he has developed an interdisciplinary research program to study individual differences and has collaborated with people across fields of study locally and internationally. He obtained several academic awards from agencies such as Fulbright and the US National Institute of Health.
- human cognitive neuroscience techniques (fMRI and EEG)
- individual differences in cognition, emotion and motivation
- mental and neurodevelopmental health (e.g., mood disorders and ADHD)
Pat, N., Wang, Y., Bartonicek, A., Candia, J., & Stringaris, A. (2023). Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition. Cerebral Cortex, 33, 2682-2703. doi: 10.1093/cercor/bhac235
Huang, Y., Pat, N., Kok, B. C., Chai, J., Feng, L., & Yu, R. (2023). Getting over past mistakes: Prospective and retrospective regret in older adults. Journals of Gerontology Series B, 78(3), 469-478. doi: 10.1093/geronb/gbac159
Tetereva, A., Li, J., Deng, J. D., Stringaris, A., & Pat, N. (2022). Capturing brain-cognition relationship: Integrating task-based fMRI across tasks markedly boosts prediction and test-retest reliability. NeuroImage, 263, 119588. doi: 10.1016/j.neuroimage.2022.119588
Pat, N., Wang, Y., Anney, R., Riglin, L., Thapar, A., & Stringaris, A. (2022). Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors. Human Brain Mapping, 43, 5520-5542. doi: 10.1002/hbm.26027
Pat, N., Riglin, L., Anney, R., Wang, Y., Barch, D. M., Thapar, A., & Stringaris, A. (2022). Motivation and cognitive abilities as mediators between polygenic scores and psychopathology in children. Journal of the American Academy of Child & Adolescent Psychiatry, 61(6), 782-795. doi: 10.1016/j.jaac.2021.08.019