UMBC CSEE Seminar Series

Phase synchrony in heart-brain interactions predicts personality and emotions

Ehsan Shokri Kojori
NIH, National Institute on Alcoloh Abuse and Alcoholism

1:00-2:00pm Friday, 17 March 2017, ITE 231

Despite the historical interest in the link between brain and heart, it is unknown whether brain and heart interactions provide meaningful information about emotions and personality. Here we studied the phase and amplitude of coherence between cardiac pulse and resting state fMRI signals in 203 subjects. We show low-frequency (LF, < 0.1 Hz) components of the resting-state networks (RSN) share significant content with corresponding components in physiological recordings. We found LF cardiovascular components precede those in RSNs, and LF respiratory components follow those in RSNs. Phase dispersion (in LF) between cardiac (but not respiratory) and RSN signals predicted a main positivity-negativity dimension of personality (r = 0.31, p < 0.0001) and emotions (r = 0.24, p = 0.001). Specifically, higher phase dispersion between cardiac and brain RSNs predicted higher tendency toward negative inclinations. In summary, these results provide evidence that brain-wide sensitivity to cardiovascular signaling predicts a main dimension of personality and emotions. Finally, our analysis of phase dispersion may have diagnostic value in specific neuropsychiatric disorders.

Dr. Ehsan Shokri Kojori joined the Laboratory of Neuroimaging at the NIH National Institute on Alcohol Abuse and Alcoholism
 as a postdoctoral IRTA fellow in August 2014 and became a Research Fellow in May 2016. He earned a PhD degree in cognitive neuroscience from the University of Texas at Dallas in Spring 2014. Ehsan also has a background in electrical engineering and signal processing. His interests include combining brain imaging modalities (e.g., fMRI, DTI, and PET) and behavioral measurements to understand the neurocognitive underpinnings of goal directed behavior. His current work involves studying how addiction and alcohol abuse affect efficiency and energetic cost of the brain networks. He is also working on developing novel methodologies to better characterize anatomical and functional brain connectivity indices.