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DTSTART:20241103T020000
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DESCRIPTION:NOTE: BIS 526 students are required to attend in person. Other
 s are invited to attend in person\, but may also attend via zoom. Speaker
 - Chao Huang\, Ph.D. Title- “Exploring Aging through Statistical Shape An
 alysis in Imaging Studies” Abstract Alterations in subcortical brain stru
 ctures\, such as changes in the shape of the hippocampus\, are typically 
 interconnected and associated with the natural process of brain aging. De
 tecting and comprehending the geometric distinctions within these diverse
  subcortical brain structures is crucial for monitoring brain aging. Howe
 ver\, existing learning methods encounter several challenges\, including:
  (i) the non-Euclidean representation of subcortical shapes\; (ii) the co
 mplex spatial correlation structure in local geometry\; (iii) subject-lev
 el imaging heterogeneity due to misalignment of shapes in imaging preproc
 essing steps\; (iv) group-level imaging heterogeneity arising from distin
 ct brain aging patterns\; and (v) geometric variations associated with co
 variates of interest (e.g.\, gender and education length)\, which may be 
 high-dimensional. To tackle these challenges\, we propose a Mixture of Sh
 ape-on-scalar Factor Regression Model (MS-FARM). In each cluster\, a geod
 esic regression structure\, including covariates of interest and an align
 ment step\, is established along with the Riemannian Gaussian distributio
 n in the pre-shape space. Additionally\, a latent factor model is constru
 cted in the tangent space. A penalized likelihood approach is employed fo
 r variable selection in MS-FARM. Furthermore\, to integrate multiple 2D/3
 D subcortical shapes and explore optimal Riemannian local embedding repre
 sentations of shapes across different subgroups\, we introduce a Geometri
 c Deep Multi-Autoencoders (Geo-DMAE) framework. Specifically\, in each ge
 ometric deep autoencoder\, the prealigned shapes go through a rotation & 
 embedding layer and a multilayer perceptron (MLP) based autoencoder. Subg
 roup patterns are learned with mixing proportions defined through a regre
 ssion model (e.g.\, logistic) applied to the latent features integrated f
 rom multiple autoencoders. Finally\, simulation studies and real data ana
 lysis based on multiple brain subcortical structures from the brain aging
  study are conducted to assess the finite sample performance of MS-FARM a
 nd Geo-DMAE.\n\nSpeaker:\nChao Huang\, Ph.D.\n\nAdmission:\nFree\n\nDetai
 ls URL:\nhttps://medicine.yale.edu/event/ysph-biostatistics-seminar-tba2-
 27/\n
DTEND;TZID=America/New_York:20240227T130000
DTSTAMP:20261020T143606Z
DTSTART;TZID=America/New_York:20240227T120000
GEO:41.302961;-72.931638
LOCATION:Rm. 106A&B\, 47 College Street\, New Haven\, CT\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:YSPH Biostatistics Seminar: “Exploring Aging through Statistical S
 hape Analysis in Imaging Studies”
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