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UID:20251107T011713EST-9063UlOO2N@132.216.98.100
DTSTAMP:20251107T061713Z
DESCRIPTION:Title: Bayesian Methods for Studying Heterogeneity in Brain Ima
 ging Experiments.\n\nAbstract: Dr. Guindani is a Professor in the Departme
 nt of Statistics\, University of California\, Irvine. Before joining UCI\,
  he has held faculty positions in the Department of Biostatistics\, Univer
 sity of Texas MD Anderson Cancer Center and the Department of Mathematics 
 and Statistics at the University of New Mexico. He has received his Ph.D. 
 in Statistics from Università Bocconi\, Milan\, Italy in Spring 2005. He i
 s currently a Co-Editor for Bayesian Analysis\, the official journal of th
 e International Society for Bayesian Analysis (ISBA) and he has been servi
 ng as Editor-in-Chief of the same journal from January 2019 to December 20
 21. He is also an Associate Editor for Biometrics.\n\n\nAn improved unders
 tanding of the heterogeneity of brain mechanisms is considered key for ena
 bling the development of interventions based on imaging features. In this 
 talk\, we will discuss some examples of heterogeneity in animals’ and huma
 ns’ experiments. More specifically\, we will first discuss the analysis of
  neuronal responses to external stimuli in awake behaving animals through 
 the analysis of intra-cellular calcium signals. We propose a nested Bayesi
 an finite mixture specification that allows for the estimation of spiking 
 activity and\, simultaneously\, reconstructs the distributions of the calc
 ium transient spikes' amplitudes under different experimental conditions. 
 The proposed model borrows information between experiments and discovers s
 imilarities in the distributional patterns of neuronal responses to differ
 ent stimuli. In the second part of the talk\, we will discuss a computatio
 nally efficient time-varying Bayesian VAR approach for studying dynamic ef
 fective connectivity in functional magnetic resonance imaging (fMRI). The 
 proposed framework employs a tensor decomposition for the VAR coefficient 
 matrices at different lags. Dynamically varying connectivity patterns are 
 captured by assuming that at any given time the VAR coefficient matrices a
 re obtained as a mixture of only an active subset of components in the ten
 sor decomposition. We show the performances of our model formulation via s
 imulation studies and data from real fluorescence microscopy and fMRI stud
 ies.\n\n \n\n \n\nVia Zoom: https://mcgill.zoom.us/j/85978187693?pwd=WWtJZ
 Upnb0JXK3o5SStnOFcxK3FFUT09\n\nWeb site : www.mcgill.ca/epi-biostat-occh/n
 ews-events/seminars/biostatistics\n
DTSTART:20211110T203000Z
DTEND:20211110T213000Z
SUMMARY:Michele Guindani. (University of California\, Irvine)
URL:/mathstat/channels/event/michele-guindani-universi
 ty-california-irvine-334645
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