2022
DOI: 10.1016/j.brs.2021.11.008
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Anatomical and fMRI-network comparison of multiple DLPFC targeting strategies for repetitive transcranial magnetic stimulation treatment of depression

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Cited by 40 publications
(19 citation statements)
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“…Using functional MRI as an example, functional connectivity-based DLPFC targeting for the treatment of major depressive disorders has been well developed and evaluated [ 23 , 34 , 35 ]. Although there were significant differences in target-based functional connectivity among DLPFC targets [ 25 , 36 , 37 ], the heterogeneity in targeting highlights the necessity and importance of pinpointing the optimal stimulation sites within the DLPFC rather than a general anatomical area. Despite these informative findings, a pertinent question is concerned with the more efficient strategy for determining the precise targets for TMS treatment or, alternatively, whether optimal targeting within a TMS target is a quick and plausible way for clinical populations, particularly those with neurodegenerative diseases.…”
Section: Discussionmentioning
confidence: 99%
“…Using functional MRI as an example, functional connectivity-based DLPFC targeting for the treatment of major depressive disorders has been well developed and evaluated [ 23 , 34 , 35 ]. Although there were significant differences in target-based functional connectivity among DLPFC targets [ 25 , 36 , 37 ], the heterogeneity in targeting highlights the necessity and importance of pinpointing the optimal stimulation sites within the DLPFC rather than a general anatomical area. Despite these informative findings, a pertinent question is concerned with the more efficient strategy for determining the precise targets for TMS treatment or, alternatively, whether optimal targeting within a TMS target is a quick and plausible way for clinical populations, particularly those with neurodegenerative diseases.…”
Section: Discussionmentioning
confidence: 99%
“…Our computational approach with a substantial sample size underlies the utility of head models for uncovering potentially stimulated brain regions based on EF peaks. As investigated in previous brain stimulation studies [33], the efficacy of the stimulation protocol may vary depending on the brain region being modulated. However, systematic analysis of EF distribution patterns and finding the association between tES-induced EFs and stimulation outcomes requires a decision about which EF measures (e.g., maximum (99 th percentile to remove outliers), mean, median, or a binary thresholded EF map) are appropriate to use.…”
Section: Importance Of Peak Efs In Brain Stimulation Studiesmentioning
confidence: 99%
“…Our computational approach with a substantial sample size underlies the utility of head models for uncovering potentially stimulated brain regions based on EF peaks. As investigated in previous brain stimulation studies [33], the e cacy of the stimulation protocol may vary depending on the brain region being modulated. However, systematic analysis of EF distribution patterns and nding the association between tES-induced EFs and stimulation outcomes requires a decision about which EF measures (e.g., maximum (99th percentile to remove outliers), mean, median, or a binary thresholded EF map) are appropriate to use.…”
Section: Importance Of Peak Efs In Brain Stimulation Studiesmentioning
confidence: 99%