2016/04/30 by Sacha Epskamp, Joost Kruis, Maarten Marsman · 3 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Neuroscience · Psychology · #Functional Brain Connectivity Studies #Mental Health Research Topics #Tryptophan and brain disorders #q-bio.NC #stat.ME
paper · pdf · doi:10.1371/journal.pone.0179891
Published in PlosOne
openalex publication_date 2017/06/23 · arxiv created 2017/09/11 · openalex created_date 2025/10/10 · arxiv updated 2026/08/04 · openalex updated_date 2026/08/04
Network models, in which psychopathological disorders are conceptualized as a complex interplay of psychological and biological components, have become increasingly popular in the recent psychopathological literature (Borsboom, et. al., 2011). These network models often contain significant numbers of unknown parameters, yet the sample sizes available in psychological research are limited. As such, general assumptions about the true network are introduced to reduce the number of free parameters. Incorporating these assumptions, however, means that the resulting network will lead to reflect the particular structure assumed by the estimation method-a crucial and often ignored aspect of psychopathological networks. For example, observing a sparse structure and simultaneously assuming a sparse structure does not imply that the true model is, in fact, sparse. To illustrate this point, we discuss recent literature and show the effect of the assumption of sparsity in three simulation studies.