Identifying risk and protective factors for suicide

Dickhoff, Justine

doi:10.33612/diss.240460137

Cited by 2 publications

(1 citation statement)

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“…A further investigating of resting-state fMRI and the changes in the flexibility during rest could provide more insight to the different aspects of this method. A study of resting-state fMRI from 95 subjects meeting criteria for Major Depressive Disorder and/or common anxiety disorders from the Netherlands Study of Depression and Anxiety (NESDA) is in preparation by a collaborator team (Dickhoff, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility

Chinichian

Kruschwitz²,

Reinhardt³

et al. 2023

Front. Neurosci.

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Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks.

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Section: Discussionmentioning

confidence: 99%