Associations between maternal depression and infant fronto-limbic connectivity

Emily L. Dennis, Ananya Singh, Conor K. Corbin, Neda Jahanshad, Tiffany C. Ho, Lucy S. King, Lauren R. Borchers, Kathryn L. Humphreys, Paul M. Thompson, Ian H. Gotlib

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


Maternal depression is a well-documented risk factor for psychopathology in children. The origins of this association, however, are not well understood. Noninvasive imaging methods such as MRI can be used to assess infant brain development and examine associations between maternal depression and neurobiological correlates of risk for psychopathology in early life. Here, we present preliminary analyses on 24 6 month old infants using a multi-shell diffusion MRI sequence optimized for imaging infant white matter, along with a novel tract clustering and identification workflow, TractStat. We examine the association between maternal depressive symptoms, measured by the Edinburgh Postnatal Depression Scale, and infant white matter organization in the uncinate fasciculus (UF). Infants whose mothers report experiencing more severe depressive symptoms have lower fractional anisotropy of the right UF, suggesting a possible neurobiological marker of the intergenerational transmission of risk for depression.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538636411
StatePublished - Apr 2019
Externally publishedYes
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019


  • Diffusion MRI
  • Infant
  • Maternal depression
  • Multi-shell
  • Tract clustering


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