2020
DOI: 10.1101/2020.11.04.368274
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Deep brain stimulation modulates the dynamics of resting-state networks in patients with Parkinson’s Disease

Abstract: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is increasingly used for the treatment of Parkinson's Disease (PD) but, despite its success, the neural mechanisms behind this surgical procedure remain partly unclear. As one working hypothesis, it was proposed that DBS works by restoring the balance of the brain's resting-state networks (RSNs), which is reported to be disrupted in people with PD. Hence, to elucidate the effects that STN-DBS induces on disseminated networks, we analyzed an fMRI dat… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(2 citation statements)
references
References 89 publications
0
2
0
Order By: Relevance
“…We chose a LEiDA processing pipeline, as it provides time-resolved metrics of brain states and has been successfully applied to study brain dynamics in adults (Cabral et al, 2017; Figueroa et al, 2019; Gomes et al, 2020; Lord et al, 2019; Vohryzek et al, 2020). Although a majority of prior studies of dynamic brain functional connectivity have adopted sliding window approaches, it necessitates the choice of arbitrary parameters in the analysis, such as window and step sizes.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…We chose a LEiDA processing pipeline, as it provides time-resolved metrics of brain states and has been successfully applied to study brain dynamics in adults (Cabral et al, 2017; Figueroa et al, 2019; Gomes et al, 2020; Lord et al, 2019; Vohryzek et al, 2020). Although a majority of prior studies of dynamic brain functional connectivity have adopted sliding window approaches, it necessitates the choice of arbitrary parameters in the analysis, such as window and step sizes.…”
Section: Discussionmentioning
confidence: 99%
“…This results in a symmetric dynamic functional connectivity matrix for each fMRI volume. We then obtain a lower-dimensional representation with the LEiDA approach (Cabral et al, 2017; Gomes et al, 2020; Lord et al, 2019; Vohryzek et al, 2020), whereby the LEiDA vector corresponds to the first eigenvector of the decomposition of the matrix Δ φ ij ( t ). This method has been previously shown to reveal information on the community structure of networks and graphs (Newman, 2006a, 2006b).…”
Section: Methodsmentioning
confidence: 99%