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Analysis of Brain States from Multi-Region LFP Time-Series
Kyle R Ulrich · David Carlson · Wenzhao Lian · Jana S Borg · Kafui Dzirasa · Lawrence Carin

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D

The local field potential (LFP) is a source of information about the broad patterns of brain activity, and the frequencies present in these time-series measurements are often highly correlated between regions. It is believed that these regions may jointly constitute a ``brain state,'' relating to cognition and behavior. An infinite hidden Markov model (iHMM) is proposed to model the evolution of brain states, based on electrophysiological LFP data measured at multiple brain regions. A brain state influences the spectral content of each region in the measured LFP. A new state-dependent tensor factorization is employed across brain regions, and the spectral properties of the LFPs are characterized in terms of Gaussian processes (GPs). The LFPs are modeled as a mixture of GPs, with state- and region-dependent mixture weights, and with the spectral content of the data encoded in GP spectral mixture covariance kernels. The model is able to estimate the number of brain states and the number of mixture components in the mixture of GPs. A new variational Bayesian split-merge algorithm is employed for inference. The model infers state changes as a function of external covariates in two novel electrophysiological datasets, using LFP data recorded simultaneously from multiple brain regions in mice; the results are validated and interpreted by subject-matter experts.

Author Information

Kyle R Ulrich (Duke)
David Carlson (Duke University)
Wenzhao Lian (Duke University)
Jana S Borg (Duke University)
Kafui Dzirasa (Duke University)

Kafui Dzirasa completed a PhD in Neurobiology at Duke University. His research interests focus on understanding how changes in the brain produce neurological and mental illness, and his graduate work has led to several distinctions including: the Somjen Award for Most Outstanding Dissertation Thesis, the Ruth K. Broad Biomedical Research Fellowship, the UNCF·Merck Graduate Science Research Fellowship, and the Wakeman Fellowship. Kafui obtained an MD from the Duke University School of Medicine in 2009, and he completed residency training in General Psychiatry in 2016. Kafui received the Charles Johnson Leadership Award in 2007, and he was recognized as one of Ebony magazine’s 30 Young Leaders of the Future in February 2008. He has also been awarded the International Mental Health Research Organization Rising Star Award, the Sydney Baer Prize for Schizophrenia Research, and his laboratory was featured on CBS 60 Minutes in 2011. In 2016, he was awarded the inaugural Duke Medical Alumni Emerging Leader Award and the Presidential Early Career Award for Scientists and Engineers: The Nation’s highest award for scientists and engineers in the early stages of their independent research careers. In 2017, he was recognized as 40 under 40 in Health by the National Minority Quality Forum, and the Engineering Alumni of the Year from UMBC. He was induced into the American Society for Clinical Investigation in 2019. Kafui has served as an Associate Scientific Advisor for the journal Science Translational Medicine, and he was a member of the Congressional-mandated Next Generation Research Initiative. He currently serves on the Editorial Advisory Board for TEDMED, and the NIH Director’s guiding committee for the BRAIN Initiative. Kafui is an Associate Professor at Duke University with appointments in the Departments of Psychiatry and Behavioral Sciences, Neurobiology, Biomedical Engineering, and Neurosurgery. His ultimate goal is to combine his research, medical training, and community experience to improve outcomes for diverse communities suffering from Neurological and Psychiatric illness.

Lawrence Carin (KAUST)

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