2019
DOI: 10.1093/brain/awz046
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Probabilistic mapping of the antidystonic effect of pallidal neurostimulation: a multicentre imaging study

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Cited by 114 publications
(172 citation statements)
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“…The largest coverage of VTAs was observed for the subpallidal area below the lateral GPi and medial GPe. This is in line with recently published results where the optimal antidystonic spot also covered the subpallidal area [15]. It is likely that the stimulation of efferent pallidothalamic fibres brings about most of the therapeutic effects observed in dystonia patients [21][22][23].…”
Section: Discussionsupporting
confidence: 91%
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“…The largest coverage of VTAs was observed for the subpallidal area below the lateral GPi and medial GPe. This is in line with recently published results where the optimal antidystonic spot also covered the subpallidal area [15]. It is likely that the stimulation of efferent pallidothalamic fibres brings about most of the therapeutic effects observed in dystonia patients [21][22][23].…”
Section: Discussionsupporting
confidence: 91%
“…Another study correlating theta oscillations with clinical outcome in CD patients localized the optimal spot for stimulation within the posterior third of the GPi [19]. A recent multicenter study in patients with cervical and generalized dystonia applied a probabilistic approach and suggested that the optimal spot for antidystonic effects was the ventro-posterior GPi and the adjacent subpallidal white matter [15]. In our study, the average commissure-based lead location is slightly distant from the target reported by Reich et al with a more lateral electrode localization in the present study (x = 21.4 ± 1.5 mm vs. Reich et al 19.8 ± 1.8 mm) [15].…”
Section: Discussionmentioning
confidence: 99%
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“…However, the theoretical concept of VTA modelling constitutes a simplification that neglects factors like fiber orientation (Anderson et al, 2019). Regarding the concept of predicting effects of deep brain stimulation in relation to the anatomical target area there have been several approaches in the past, such as investigating lead positions (Delcour et al, 2011;Camalier et al, 2014;Barbe et al, 2018;Dafsari et al, 2018), the creation of probabilistic sweet spots (Eisenstein et al, 2014;Dembek et al, 2017;Dembek et al, 2019b;Reich et al, 2019), and the estimation of beneficial Running head: Networks in DBS-induced dysarthria connectivity profiles (Horn et al, 2017;Al-Fatly et al, 2019). In this study, we focused on providing an atlas structure that is easy to implement in planning and programming software and therefore decided to base our predictive model on an approach introduced by Baldermann et al (Baldermann et al, 2019).…”
Section: Methodological Considerations and Limitationsmentioning
confidence: 99%