2019
DOI: 10.1016/j.brs.2019.09.003
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rTMS treatment of visual hallucinations using a connectivity-based targeting method - A case study

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Cited by 7 publications
(7 citation statements)
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“…Treatments based on transcranial magnetic stimulation and deep brain stimulation might consider the use of rs-fMRI data together with targeting data-driven methods (i.e. graph theory) to customize the intervention by modulating relevant neural networks 8,21,24 .…”
Section: Discussionmentioning
confidence: 99%
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“…Treatments based on transcranial magnetic stimulation and deep brain stimulation might consider the use of rs-fMRI data together with targeting data-driven methods (i.e. graph theory) to customize the intervention by modulating relevant neural networks 8,21,24 .…”
Section: Discussionmentioning
confidence: 99%
“…In this study, we used eigenvector centrality (EC) 22 derived from rs-fMRI data 23,24 together with data-driven methods to: (a) discriminate and identify shifts in centrality between WTC-PTSD and non-PTSD responders; (b) link these shifts to WTC-exposure, and (c) examine how shifts in centrality associate with PTSD symptom scales. We focused on highly connected (or disconnected) nodes (i.e.…”
Section: Mainmentioning
confidence: 99%
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“…Per group, only ROIs with EC coefficients higher than the 95th percentile (5% highest) were considered the most central and therefore used in the following analyses. Based on literature, the 95th percentile was chosen as an arbitrary threshold (Invernizzi et al, 2019 ). Furthermore, we quantify possible differences between influential hubs across groups by permuting labels with 10,000 time repetitions.…”
Section: Methodsmentioning
confidence: 99%
“…Per group, only ROIs with EC coefficients higher than the 95th percentile (5% highest) were considered the most central and therefore used in the following analyses. Based on literature, the 95th percentile was chosen as an arbitrary threshold (Invernizzi et al, 2019). Furthermore, we quantify possible differences between influential hubs across groups by permuting labels with 10000 time repetitions.…”
Section: Methodsmentioning
confidence: 99%