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SUMMARY:Neuro-X Seminar: Biological Subtyping in Psychiatry: Hope\, Contro
 versy\, and A New Method
DTSTART:20230817T110000
DTEND:20230817T120000
DTSTAMP:20260428T184719Z
UID:0461939780d6bdbb79367a71e7277f0edafa0d62c42dce3e11e68601
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Logan Grosenick\nAbstract: Recent years have seen a flur
 ry of new developments using machine learning for biological subtyping in 
 psychiatry\, promising potentially transformational improvements in indivi
 dualized care by enabling data-driven personalized diagnosis\, prognosis\,
  and treatment. Biological subtypes of major depressive disorder [1] and\,
  more recently\, autism spectrum disorder [2]\, have shown that distinct n
 eurobiology may be at the root of heterogeneity in psychiatric and neurode
 velopmental conditions. However\, such biological subtyping methods and cr
 oss-sectional neuroimaging approaches have also raised controversy\, mainl
 y centered around their feasibility and reproducibility. In this talk\, I 
 will provide supporting evidence for robust and reproducible biological su
 btypes in psychiatry\, offer insight as to why some groups may have failed
  to find them\, and present a new approach [3] that improves significantly
  on existing methods for identifying patient subgroups in multiomics and n
 euroimaging data. \n\n \n\n[1] Drysdale\, Andrew T.\, et al. "Resting-st
 ate connectivity biomarkers define neurophysiological subtypes of depressi
 on." Nature Medicine 23.1 (2017): 28-38. \n\n[2] Buch\, Amanda M.\, et al
 . "Molecular and network-level mechanisms explaining individual difference
 s in autism spectrum disorder." Nature Neuroscience 26.4 (2023): 650-663.
  \n\n[3] Buch\, Amanda M.\, Conor Liston\, and Logan Grosenick. " Simple 
 and scalable algorithms for cluster-aware precision medicine." arXiv prepr
 int arXiv:2211.16553 v3 (2023).  
LOCATION:H8-1-D https://plan.epfl.ch/?room==H8%201%20144.167 https://epfl.
 zoom.us/j/66252926905?pwd=NW00dmN4bHpsZW43MUNnS2ViSDlUUT09
STATUS:CONFIRMED
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