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DTSTART;TZID=America/Toronto:20200127T100000
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URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/department
 -seminar-james-wilson-university-san-francisco
LOCATION:M3 - Mathematics 3 200 University Avenue West Room 3127 Waterloo O
 N N2L 3G1 Canada
SUMMARY:Department seminar by James Wilson\, University of San Francisco
CLASS:PUBLIC
DESCRIPTION:NETWORK ANALYSIS OF THE BRAIN: FROM GENERATIVE MODELING TO MULT
 ILAYER\nNETWORK EMBEDDING OF FUNCTIONAL CONNECTIVITY DATA\n\n-------------
 ------------\n\nRecent large-scale projects in neuroscience\, such as the 
 Human\nConnectome Project and the BRAIN initiative\, emphasize the need of
  new\nstatistical and computational techniques for analyzing functional\nc
 onnectivity within and across populations. Network-based models have\ngrea
 tly improved our understanding of brain structure and function\,\nyet many
  important challenges remain. In this talk\, I will consider\ntwo particul
 arly important challenges: i) how does one characterize\nthe generative me
 chanisms of functional connectivity\, and ii) how does\none identify discr
 iminatory features among connectivity scans over\ndisparate populations? T
 o address the first challenge\, I propose and\ndescribe a generative netwo
 rk model\, called the correlation\ngeneralized exponential random graph mo
 del (cGERGM)\, that flexibly\ncharacterizes the joint network topology of 
 correlation networks\narising in functional connectivity. The model is the
  first of its kind\nto directly assess the network structure of a correlat
 ion network\nwhile simultaneously handling the mathematical constraints of
  a\ncorrelation matrix. I apply the cGERGM to resting state fMRI data from
 \nhealthy individuals in the Human Connectome Project. The cGERGM\nreveals
  remarkably consistent organizational properties guiding\nsubnetwork archi
 tecture\, suggesting a fundamental organizational basis\nfor subnetwork co
 mmunication that differs from previous beliefs.\n\nFor the second challeng
 e\, I focus on learning interpretable features\nfrom complex multilayer ne
 tworks arising in population studies of\nfunctional connectivity. I will i
 ntroduce the multi-node2vec\nalgorithm\, an efficient and scalable feature
  engineering method that\nlearns continuous node feature representations f
 rom multilayer\nnetworks. The multi-node2vec algorithm identifies maximum 
 likelihood\nestimators of nodal features through the use of the Skip-gram 
 neural\nnetwork model. Asymptotic analysis of the algorithm reveals that i
 t is\na fast approximation to a multi-dimensional non-negative matrix\nfac
 torization applied to a weighted average of the layers in the\nmultilayer 
 network. I apply multi-node2vec to a multilayer functional\nbrain network 
 from resting state fMRI scans over a population of 74\nhealthy individuals
  and 70 patients with varying degrees of\nschizophrenia. The identified fu
 nctional embeddings closely associate\nwith the functional organization of
  the brain and offer important\ninsights into the differences between pati
 ent and healthy groups that\nis well-supported by theory.
DTSTAMP:20260430T092551Z
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