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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Modelling Large- and Small-Scale Brain Networks -
Thomas Nichols (University of Warwick)
DTSTART;TZID=Europe/London:20160714T140000
DTEND;TZID=Europe/London:20160714T143000
UID:TALK66756AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/66756
DESCRIPTION:Investigations of the human brain with neuroimagin
g have recently seen a dramatic shift in focus\,
from "brain mapping"\, identifying brain regions r
elated to particular functions\, to connectivity
or "connectomics"\, identifying networks of coord
inated brain regions\, and how these networks beha
ve at rest and during tasks. In this presentation
I will discuss two quite different approaches to
modeling brain connectivity. In the first work\,
we use Bayesian time series methods to allow for
time-varying connectivity. Non-stationarity connec
tivity methods typically use a moving-window appr
oach\, while this method poses a single generativ
e model for all nodes\, all time points. Known as
a "Multiregression Dynamic Model" (MDM)\, it comp
rises an extension of a traditional Bayesian Netw
ork (or Graphical Model)\, by posing latent time-v
arying coefficients that implement a regression a
given node on its parent nodes. Intended for a mo
dest number of nodes (up to about 12)\, a MDM all
ows inference of the structure of the graph using
closed form Bayes factors (conditional on a singl
e estimated "discount factor"\, reflecting the ba
lance of observation and latent variance. While o
riginally developed for directed acyclic graphs\,
it can also accommodate directed (possibly cyclic
) graphs as well. In the second work\, we use mixt
ures of simple binary random graph models to acco
unt for complex structure in brain networks. In t
his approach\, the network is reduced to a binary
adjacency matrix. While this is invariably repres
ents a loss of information\, it avoids a Gaussian
ity assumption and allows the use of much larger g
raphs\, e.g. with 100'\;s of nodes. Daudin et
al. (2008) proposed a "Erdos-Reyni Mixture Model"\
, which assumes that\, after an unknown number of
latent node classes have been estimated\, that c
onnections arise as Bernoulli counts\, homogeneous
ly for each pair of classes. We extend this work
to account for multisubject data (where edge data
are now Binomially distributed)\, allowing

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LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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**