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BEGIN:VEVENT
SUMMARY:Bayesian nonparametric modeling of populations of brain networks
DTSTART:20181205T150000
DTEND:20181205T160000
DTSTAMP:20260503T022029Z
UID:37c0c33be428c38133e3fa9aebb4eec6098f18da0f644ebf2b29d8fd
CATEGORIES:Conferences - Seminars
DESCRIPTION:Daniel Durante (Bocconi)\nIn neuroscience there is increasing 
 interest in relating the structural connectivity network of white matter f
 ibers in the human brain to cognitive traits and neuropsychiatric disorder
 s. In fact\, there is evidence that the structural brain network is an imp
 ortant driver of variability in cognitive traits and disorders. Recent con
 nectomics pipelines can estimate the brain network based on diffusion tens
 or imaging and structural MRI. This produces a realization from a network-
 valued random variable for each individual in a study. I will present rece
 nt nonparametric Bayes advances for analyzing network-valued data\, and fo
 r performing inference on the relationship between brain networks and cogn
 itive traits. These methods are provably flexible\, reduce dimension adapt
 ively and can be used for formal inferences on group differences adjusting
  for multiple comparisons automatically. I will discuss key improvements r
 elative to current approaches and illustrate the methods through applicati
 on to creative reasoning and Alzheimer’s disease data.
LOCATION:B1.03 Campus Biotech
STATUS:CONFIRMED
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