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Using person‐specific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones

dc.contributor.authorBeltz, Adriene M.
dc.contributor.authorMoser, Jason S.
dc.contributor.authorZhu, David C.
dc.contributor.authorBurt, S. Alexandra
dc.contributor.authorKlump, Kelly L.
dc.date.accessioned2018-11-20T15:33:29Z
dc.date.available2019-09-04T20:15:39Zen
dc.date.issued2018-07
dc.identifier.citationBeltz, Adriene M.; Moser, Jason S.; Zhu, David C.; Burt, S. Alexandra; Klump, Kelly L. (2018). "Using person‐specific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones." International Journal of Eating Disorders 51(7): 730-740.
dc.identifier.issn0276-3478
dc.identifier.issn1098-108X
dc.identifier.urihttps://hdl.handle.net/2027.42/146371
dc.description.abstractObjectiveEmotional eating has been linked to ovarian hormone functioning, but no studies to‐date have considered the role of brain function. This knowledge gap may stem from methodological challenges: Data are heterogeneous, violating assumptions of homogeneity made by between‐subjects analyses. The primary aim of this paper is to describe an innovative within‐subjects analysis that models heterogeneity and has potential for filling knowledge gaps in eating disorder research. We illustrate its utility in an application to pilot neuroimaging, hormone, and emotional eating data across the menstrual cycle.MethodGroup iterative multiple model estimation (GIMME) is a person‐specific network approach for estimating sample‐, subgroup‐, and individual‐level connections between brain regions. To illustrate its potential for eating disorder research, we apply it to pilot data from 10 female twins (N = 5 pairs) discordant for emotional eating and/or anxiety, who provided two resting state fMRI scans and hormone assays. We then demonstrate how the multimodal data can be linked in multilevel models.ResultsGIMME generated person‐specific neural networks that contained connections common across the sample, shared between co‐twins, and unique to individuals. Illustrative analyses revealed positive relations between hormones and default mode connectivity strength for control twins, but no relations for their co‐twins who engage in emotional eating or who had anxiety.DiscussionThis paper showcases the value of person‐specific neuroimaging network analysis and its multimodal associations in the study of heterogeneous biopsychosocial phenomena, such as eating behavior.
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.otherprecision healthcare
dc.subject.otherprogesterone
dc.subject.otherresting state
dc.subject.othertwin study
dc.subject.otherheterogeneity
dc.subject.otherconnectivity
dc.subject.otherestrogen
dc.subject.otheremotional eating
dc.subject.otherperson‐specific
dc.titleUsing person‐specific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146371/1/eat22902.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146371/2/eat22902_am.pdf
dc.identifier.doi10.1002/eat.22902
dc.identifier.sourceInternational Journal of Eating Disorders
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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