Abstract:
:Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy - generative embedding (GE) - which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.
journal_name
Neuroimage Clinjournal_title
NeuroImage. Clinicalauthors
Frässle S,Marquand AF,Schmaal L,Dinga R,Veltman DJ,van der Wee NJA,van Tol MJ,Schöbi D,Penninx BWJH,Stephan KEdoi
10.1016/j.nicl.2020.102213subject
Has Abstractpub_date
2020-01-01 00:00:00pages
102213issn
2213-1582pii
S2213-1582(20)30050-4journal_volume
26pub_type
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journal_title:NeuroImage. Clinical
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doi:10.1016/j.nicl.2018.11.005
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journal_title:NeuroImage. Clinical
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journal_title:NeuroImage. Clinical
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journal_title:NeuroImage. Clinical
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journal_title:NeuroImage. Clinical
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journal_title:NeuroImage. Clinical
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journal_title:NeuroImage. Clinical
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journal_title:NeuroImage. Clinical
pub_type: 杂志文章
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journal_title:NeuroImage. Clinical
pub_type: 杂志文章,随机对照试验
doi:10.1016/j.nicl.2018.07.006
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journal_title:NeuroImage. Clinical
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doi:10.1016/j.nicl.2014.01.009
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journal_title:NeuroImage. Clinical
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journal_title:NeuroImage. Clinical
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doi:10.1016/j.nicl.2020.102331
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journal_title:NeuroImage. Clinical
pub_type: 杂志文章
doi:10.1016/j.nicl.2017.09.012
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journal_title:NeuroImage. Clinical
pub_type: 杂志文章
doi:10.1016/j.nicl.2019.102033
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journal_title:NeuroImage. Clinical
pub_type: 杂志文章
doi:10.1016/j.nicl.2015.10.009
更新日期:2015-10-19 00:00:00
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journal_title:NeuroImage. Clinical
pub_type: 杂志文章
doi:10.1016/j.nicl.2015.08.016
更新日期:2015-09-01 00:00:00
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journal_title:NeuroImage. Clinical
pub_type: 杂志文章
doi:10.1016/j.nicl.2016.06.011
更新日期:2016-06-15 00:00:00
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journal_title:NeuroImage. Clinical
pub_type: 杂志文章
doi:10.1016/j.nicl.2017.06.034
更新日期:2017-06-30 00:00:00
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journal_title:NeuroImage. Clinical
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doi:10.1016/j.nicl.2018.10.017
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journal_title:NeuroImage. Clinical
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doi:10.1016/j.nicl.2019.101853
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journal_title:NeuroImage. Clinical
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journal_title:NeuroImage. Clinical
pub_type: 杂志文章,多中心研究
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journal_title:NeuroImage. Clinical
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doi:10.1016/j.nicl.2015.10.017
更新日期:2015-10-31 00:00:00
abstract::[This corrects the article DOI: 10.1016/j.nicl.2015.05.014.]. ...
journal_title:NeuroImage. Clinical
pub_type: 已发布勘误
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更新日期:2016-05-06 00:00:00
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journal_title:NeuroImage. Clinical
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doi:10.1016/j.nicl.2018.04.036
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journal_title:NeuroImage. Clinical
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doi:10.1016/j.nicl.2018.02.035
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