Left prefrontal impact links subthalamic stimulation with depressive symptoms

Irmen, Friederike; Horn, Andreas; Mosley, Philip; Perry, Alistair; Petry‐Schmelzer, Jan Niklas; Dafsari, Haidar S.; Barbe, Michael T.; Visser‐Vandewalle, Veerle; Schneider, Gerd‐Helge; Li, Ningfei; Kübler, Dorothee; Wenzel, Gregor; Kühn, Andrea A.

doi:10.1101/665976

Cited by 9 publications

(16 citation statements)

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“…43 Regarding the concept of predicting effects of DBS in relation to the anatomical target area, there have been several approaches in the past, such as investigating lead positions, 1 the creation of probabilistic sweet spots, 30,42 and the estimation of beneficial connectivity profiles. 21,31 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 a well-established approach introduced by Baldermann et al 31,32,44,45 In these studies including clinical stimulation settings only, t tests and T statistics were employed to define discriminative fibers and to predict the respective outcome parameter. In contrast, in the present study, determination of discriminative fibers was based on linear mixed effect models to control for multiple testing per patient.…”

Section: Methodological Considerations and Limitationsmentioning

confidence: 99%

Network Fingerprint of Stimulation‐Induced Speech Impairment in Essential Tremor

Petry‐Schmelzer

Jergas

Thies

et al. 2020

Annals of Neurology

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Objective This study was undertaken to gain insights into structural networks associated with stimulation‐induced dysarthria (SID) and to predict stimulation‐induced worsening of intelligibility in essential tremor patients with bilateral thalamic deep brain stimulation (DBS). Methods Monopolar reviews were conducted in 14 essential tremor patients. Testing included determination of SID thresholds, intelligibility ratings, and a fast syllable repetition task. Volumes of tissue activated (VTAs) were calculated to identify discriminative fibers for stimulation‐induced worsening of intelligibility in a structural connectome. The resulting fiber‐based atlas structure was then validated in a leave‐one‐out design. Results Fibers determined as discriminative for stimulation‐induced worsening of intelligibility were mainly connected to the ipsilateral precentral gyrus as well as to both cerebellar hemispheres and the ipsilateral brain stem. In the thalamic area, they ran laterally to the thalamus and posteromedially to the subthalamic nucleus, in close proximity, mainly anterolaterally, to fibers beneficial for tremor control as published by Al‐Fatly et al in 2019. The overlap of the respective clinical stimulation setting's VTAs with these fibers explained 62.4% (p < 0.001) of the variance of stimulation‐induced change in intelligibility in a leave‐one‐out analysis. Interpretation This study demonstrates that SID in essential tremor patients is associated with both motor cortex and cerebellar connectivity. Furthermore, the identified fiber‐based atlas structure might contribute to future postoperative programming strategies to achieve optimal tremor control without speech impairment in essential tremor patients with thalamic DBS. ANN NEUROL 2021;89:315–326

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Section: Methodological Considerations and Limitationsmentioning

confidence: 99%

Network Fingerprint of Stimulation‐Induced Speech Impairment in Essential Tremor

Petry‐Schmelzer

Jergas

Thies

et al. 2020

Annals of Neurology

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“…As such, connectomic DBS studies have explained a maximum of 30–40% of variance in clinical improvement across independent datasets (e.g. R = 0.55–0.69 in ( Baldermann et al, 2019b ), also see Table 1 ).This variance compares favorably with other predictors of DBS outcomes in independent datasets, including L-dopa response ( Horn et al, 2017 ; Irmen et al, 2020 ), but may still fall short of clinical utility. Reasons why explained variance is not higher include limitations of the connectome, nectome, but also limitations of our clinical outcome measures and the fact that clinical outcomes are dependent on many factors besides the neuromodulation site including disease-subtype, specific symptoms, comorbidities, age, etc.…”

Section: Limitations Of Connectomic Neuromodulationmentioning

confidence: 98%

“…In many of the aforementioned studies, network targets were identified using normative connectome data that was not derived from the individual patient ( Al-Fatly et al, 2019 ; Baldermann etal., 2019b ; Calabrese, 2016 ; Cash et al, 2019 ; Horn et al, 2017 ; Irmen et al, 2020 ; Petersen et al, 2019 ; Weigand et al, 2018 ). Normative connectomes have been derived from several different sources including ultra high-resolution postmortem MRI data ( Aggarwal et al, 2013 ; Calabrese et al, 2015b ), data from specialized MRI hardware optimized for connectome nectome imaging ( Holmes et al, 2015 ; Setsompop et al, 2013 ; Van Essen et al, 2012 ; Yeo et al, 2011 ), and even tract atlases derived using augmented reality environments ( Petersen et al, 2019 ) or from histological datasets ( Alho et al, 2019 ).…”

Section: Eight Opportunities Of Connectomic Neuromodulationmentioning

confidence: 99%

Opportunities of connectomic neuromodulation

2020

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The process of altering neural activity – neuromodulation – has long been used to treat patients with brain disorders and answer scientific questions. Deep brain stimulation in particular has provided clinical benefit to over 150,000 patients. However, our understanding of how neuromodulation impacts the brain is evolving. Instead of focusing on the local impact at the stimulation site itself, we are considering the remote impact on brainregions connected to the stimulation site. Brain connectivity information derived from advanced magnetic resonance imaging data can be used to identify these connections and better understand clinical and behavioral effectsof neuromodulation. In this article, we review studies combining neuromodulation and brain connectomics, highlighting opportunities where this approach may prove particularly valuable. We focus on deep brain stimulation, but show that the same principles can be applied to other forms of neuromodulation, such as transcranial magnetic stimulation and MRI-guided focused ultrasound. We outline future perspectives and provide testable hypotheses for future work.

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“…Fourthly, networks that lead to side-effects when stimulated could be identified. For instance, Irmen and colleagues recently reported a connectivity profile that was associated with depressive symptoms following STN-DBS in PD (Irmen et al, 2019).…”

Section: The Case For Using Brain Connectivity Studies In Stn-dbsmentioning

confidence: 99%

Normative vs. patient-specific brain connectivity in Deep Brain Stimulation

Wang

Akram

González‐Escamilla

et al. 2020

Preprint

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Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, most studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and retrospective estimation of clinical improvement that they may generate. Data from 33 patients suffering from Parkinson Disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely either patient-specific diffusion-MRI data, disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were constructed and used to retrospectively estimate the clinical improvement in out of sample data. All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on a novel multi-center cohort. Connectivity estimates seeding from electrodes when using either patient-specific or normative connectomes correlated significantly to primary motor cortex (R = 0.57, p = 0.001, R=0.73, p=0.001), supplementary motor area (R = 0.40, p = 0.005, R = 0.43, p = 0.003), pre-supplementary motor area (R = 0.33, p = 0.022, R = 0.33, p = 0.031), but not to more frontal regions such as the dorsomedial prefrontal cortex (R = 0.21, p = 0.17, R = 0.18, p = 0.17). However, in a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample estimation of clinical improvements were made and ranged within a similar magnitude when applying either of the three modalities (R = 0.43 at p = 0.001 for patient-specific connectivity; R = 0.25, p = 0.048 for the age- and disease-matched group connectome; R = 0.31 at p = 0.028 for healthy-/young connectome). Conclusions: The use of patient-specific connectivity and normative connectomes lead to identical main conclusions about which brain areas are associated with clinical improvement. Still, although results were not significantly different, they hint at the fact that patient-specific connectivity may bear the potential of estimating slightly more variance when compared to group connectomes. Our findings further support the role of DBS electrode connectivity profiles as a promising method to guide surgical targeting and DBS programming.

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Left prefrontal impact links subthalamic stimulation with depressive symptoms

Cited by 9 publications

References 58 publications

Network Fingerprint of Stimulation‐Induced Speech Impairment in Essential Tremor

Network Fingerprint of Stimulation‐Induced Speech Impairment in Essential Tremor

Opportunities of connectomic neuromodulation

Normative vs. patient-specific brain connectivity in Deep Brain Stimulation

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