DBS in tremor with dystonia: VIM, GPi or both? A review of the literature and considerations from a single-center experience

Paoli, Davide; Mills, Russell; Brechany, Una; Pavese, Nicola; Nicholson, Claire

doi:10.1007/s00415-023-11569-6

Cited by 15 publications

(17 citation statements)

References 42 publications

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“…These results imply that the kinematic features are, at least partially, encoded by different neurobiological substrates. In terms of oscillatory features, this finding aligns with recent work on diverging symptom-specific circuit components for tremor versus dystonia [49]. Moreover, it suggests that clinicians may unintentionally incorporate more complex kinematic aspects from a patient’s dystonic symptomatology into their clinical scores to more accurately reflect the global clinical impression.…”

Section: Discussionsupporting

confidence: 82%

Quantitative assessment of head movement dynamics in dystonia using visual perceptive deep learning: a multi-centre retrospective longitudinal cohort study

Peach,

Friedrich,

Fronemann

et al. 2023

Preprint

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BackgroundDystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation. This framework overcomes the limitations of traditional rating scales and offers an efficient and accurate method that is rater-independent for evaluating and monitoring dystonia patients.MethodsTo evaluate the framework, we leveraged semi-standardized clinical video data collected in three retrospective, longitudinal cohort studies across seven academic centres in Germany. We extracted static head angle excursions for clinical validation and derived kinematic variables reflecting naturalistic head dynamics to predict dystonia severity, subtype, and neuromodulation effects. The framework was validated in a fully independent cohort of generalised dystonia patients.FindingsComputer vision-derived measurements of head angle excursions showed a strong correlation with clinically assigned scores, outperforming previous approaches employing specialised camera equipment. Across comparisons, we discovered a consistent set of kinematic features derived from full video assessments, which encoded information relevant to disease severity, subtype, and effects of neural circuit intervention more strongly and independently of static head angle deviations predominantly used for scoring.InterpretationThe proposed visual perceptive machine learning framework reveals kinematic pathosignatures of dystonia which may be utilized to augment clinical management, facilitate scientific translation and inform personalised and precision approaches in Neurology.Research in contextEvidence before this studyClinical assessment of dystonia, a neurological movement disorder, has traditionally relied on rating scales that aim to simplify complex phenomenology into lowerdimensional rating items. However, these score-based assessments have significant clinimetric limitations and do not fully capture the rich spatiotemporal dynamics of dystonic phenomena, which are crucial for clinical judgment and pathophysiological understanding. In contrast, recent investigations in animal models of dystonia have already demonstrated the utility and relevance of quantitative methods for phenotyping, which gradually supersedes previous observer-dependent behavioural analyses. Taken together, this has led to a need for more objective and detailed clinical evaluation methods of dystonia.We performed a PubMed search up to July 2023 combining the terms “dystonia” AND (”deep learning” OR “machine learning” or “computer vision” OR “vision-based” OR “video-based”) AND (”angle” OR “kinematic” OR “rating” OR “scoring” OR “movement analysis”) including abstracts in English or German. The search yielded three studies that validated vision-based frameworks for automating the assessment of cervical dystonia severity compared to clinician-annotated ratings. Two of these studies focused on deriving head angle deviations from specialised camera setups, while the third study utilised computer vision in a retrospective video dataset recorded using conventional equipment. These studies reported fair to moderately strong correlations between vision-based head angle measurements and clinical scores. Additionally, two studies investigated computer vision for assessing head tremor in the context of cervical dystonia: one single case report demonstrated the clinical validity of computer vision-derived head angle and head tremor metrics, while a retrospective cross-sectional study reported moderately strong clinical agreement of computer vision-derived head oscillation metrics across different dystonia subgroups. Two additional studies used computer visionbased kinematics to quantify dystonia-like phenomena in rodent models of monogenetic dystonia, demonstrating utility in both phenotype and genotype predictions.However, most of the clinical studies were limited to static task conditions, where patients attempted to hold a neutral position of the head, thus not providing a naturalistic account of dystonia. Moreover, beyond head angular deviations and oscillation metrics, no study explored a broader kinematic feature space that reflects the true spatiotemporal complexity of dystonic movements. Additionally, the studies assessed patients at single time points without considering different therapy conditions, particularly the effects of deep brain stimulation, which is a highly effective intervention targeting brain circuits. Nor did they compare dystonia sub-types, such as cervical and generalised systonia.Added value of this studyIn this study, we present a comprehensive visual perceptive deep learning framework that addresses the gaps in current dystonia assessments. We use this framework to retrospectively analyse a unique dataset from three multi-centric, studies encompassing video examinations of patients along the dystonic severity continuum, including different deep brain stimulation states. Our framework goes beyond the automation of suboptimal symptom severity assessments by reverse engineering a set of clinically inspired kinematic features. The resulting high dimensional, yet intuitively interpretable kinematic feature space enabled us to explore disease states and effects of brain circuit therapies in a level of detail comparable to experimental neuroscientific investigations. Through a data-driven approach, we have identified a consistent set of only four dynamic parameters that encode dystonia severity, subtype, and the efficacy of brain circuit interventions. Notably, these features are independent of static head angle deviations, which play a central role in dystonia severity scores, pointing to the involvement of partially distinct neurobiological processes not captured by these scores. Our findings align with emerging concepts of symptom-specific brain circuits and findings in rodent models of dystonia, thereby exemplifying the visual perceptive framework’s potential to augment clinical management and bridge translational gaps in movement disorders research. By providing a more comprehensive and precise assessment of the disorder, our study offers valuable insights for improved treatment strategies and further understanding of dystonia’s complex neurobiology.Implications of all the available evidenceThe available evidence collectively underscores the limitations of traditional rating scales in capturing the informative spatiotemporal dynamics of dystonic movements, emphasizing the need for more objective and granular evaluation methods. In line with recent animal studies using computer vision for dystonia quantification, recent clinical studies have shown the potential of computer vision-based frameworks in automating cervical dystonia severity assessment and capturing head tremor metrics. However, their underlying study designs may inadvertently reinforce limitations associated with the clinical scoring process.In this study, we introduce a comprehensive visual perceptive deep learning framework that serves as a powerful platform to augment clinical judgement and generate valuable pathophysiological insights by extracting a set of clinically inspired, interpretable kinematic features. Our findings have implications beyond dystonia, showcasing the utility of visual perceptive frameworks in enhancing clinical management and fostering integration with advanced neuroimaging and neurotechnological methods. This study opens doors for future translational research to explore the broader application of computer vision and deep learning techniques to derive kinematic signatures of movement disorders across species and experimental conditions, promising more precise and personalised assessments that can significantly improve therapeutic strategies and patient outcomes.

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Section: Discussionsupporting

confidence: 82%

Quantitative assessment of head movement dynamics in dystonia using visual perceptive deep learning: a multi-centre retrospective longitudinal cohort study

Peach,

Friedrich,

Fronemann

et al. 2023

Preprint

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“…The improvement in Patient 1’s axial symptomatology with PPN-DBS could be attributed to its widespread connections to motor regions of the brainstem and spinal cord ( 50 ). Conversely, the identification of the VIM as optimal for Patient 2’s appendicular symptomatology is possibly attributable to the modulation of cerebellothalamic projections by DBS, such as those thought to participate in tremor genesis ( 30 ). The interplay between GPi-PPN and GPi-VIM stimulation also requires further understanding to ascertain if combined DBS may provide benefits across varying presentations of childhood-onset dystonia related to metabolic conditions and in which conditions each of these combinations may be optimal.…”

Section: Discussionmentioning

confidence: 99%

“…For example, combinations of GPi and pedunculopontine nucleus (PPN) DBS have been used to treat gait disturbances in a late-stage Parkinson’s Disease patient, where single-target stimulation showed only mild improvement ( 28 ). Combinations of STN and PPN dual-target stimulation have also been reported in Parkinson’s disease ( 28 ), and combined thalamic and pallidal DBS has shown efficacy in complex ipsilateral dystonia ( 29 ), dystonic tremor ( 30 ), and clinically complex movement disorders with profound functional impairment ( 31 ). The use of deep brain stimulation in other neurometabolic conditions is largely unknown.…”

Section: Introductionmentioning

confidence: 99%

Application of deep brain stimulation for the treatment of childhood-onset dystonia in patients with MEPAN syndrome

Nataraj,

MacLean,

Davies

et al. 2024

Front. Neurol.

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IntroductionMitochondrial Enoyl CoA Reductase Protein-Associated Neurodegeneration (MEPAN) syndrome is a rare inherited metabolic condition caused by MECR gene mutations. This gene encodes a protein essential for fatty acid synthesis, and defects cause progressively worsening childhood-onset dystonia, optic atrophy, and basal ganglia abnormalities. Deep brain stimulation (DBS) has shown mixed improvement in other childhood-onset dystonia conditions. To the best of our knowledge, DBS has not been investigated as a treatment for dystonia in patients with MEPAN syndrome.MethodsTwo children with MEPAN were identified as possible DBS candidates due to severe generalized dystonia unresponsive to pharmacotherapy. Temporary depth electrodes were placed in six locations bilaterally and tested during a 6-day hospitalization to determine the best locations for permanent electrode placement. The Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS) and Barry-Albright Dystonia Scale (BADS) were used for preoperative and postoperative testing to quantitatively assess dystonia severity changes. Patient 1 had permanent electrodes placed at the globus pallidus internus (GPi) and pedunculopontine nucleus (PPN). Patient 2 had permanent electrodes placed at the GPi and ventralis intermedius nucleus of the thalamus (VIM).ResultsBoth patients successfully underwent DBS placement with no perioperative complications and significant improvement in their BFMDRS score. Patient 2 also demonstrated improvement in the BADS.DiscussionWe demonstrated a novel application of DBS in MEPAN syndrome patients with childhood-onset dystonia. These patients showed clinically significant improvements in dystonia following DBS, indicating that DBS can be considered for dystonia in patients with rare metabolic disorders that currently have no other proven treatment options.

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“…There is no generally accepted optimal target for dystonic tremor despite many short‐ 11,32–38 and long‐term 27 case series in the field. There is one recent review of the available cases 31 . Many of the case reports show a better response of tremor than for dystonia with Vim‐stimulation 11,38 .…”

Section: Discussionmentioning

confidence: 99%

The Challenge of Choosing the Right Stimulation Target for Dystonic Tremor—A Series of Instructive Cases

Paschen

Becktepe

Hobert

et al. 2023

Movement Disord Clin Pract

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BackgroundThalamic deep brain stimulation (Th‐DBS) is established for medically refractory tremor syndromes and globus pallidus stimulation (GPi‐DBS) for medically refractory dystonia syndromes. For combined tremor and dystonia syndromes, the best target is unclear.ObjectivesWe present four patients with two different profiles whose clinical course demonstrates that our current analysis of clinical symptomatology is not a sufficient predictor of surgical success.MethodsOutcome parameters were assessed with observer‐blinded video ratings and included the Fahn‐Tolosa‐Marin‐Tremor Rating Scale (FTM‐TRS) and the Unified Dystonia Rating Scale (UDRS).ResultsTwo patients with ‘predominant lateralized action tremor’ of the hands and mild cervical dystonia showed no relevant tremor improvement after GPi‐DBS, but UDRS improved (mean: 45%). Rescue Vim‐DBS electrodes were implanted and both patients benefited significantly with a mean tremor reduction of 51%.Two other patients with ‘axial‐predominant action tremor of the trunk and head’ associated with cervical dystonia underwent bilateral Vim DBS implantation with little effect on tremor (24% reduction in mean FTM‐TRS total score) and no effect on dystonic symptoms. GPi rescue DBS was implanted and showed a significant effect on tremor (63% reduction in mean FTM‐TRS) and dystonia (49% reduction in UDRS).ConclusionsThe diagnosis of dystonic tremor alone is not a sufficient predictor to establish the differential indication of Gpi‐ or Vim‐DBS. Further criteria (e.g., proximal‐distal distribution of tremor/dystonia) are needed to avoid rescue surgery in the future. On the other hand, the course of our patients encourages rescue surgery in such severely disabled patients if the first target fails.This article is protected by copyright. All rights reserved.

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DBS in tremor with dystonia: VIM, GPi or both? A review of the literature and considerations from a single-center experience

Cited by 15 publications

References 42 publications

Quantitative assessment of head movement dynamics in dystonia using visual perceptive deep learning: a multi-centre retrospective longitudinal cohort study

Quantitative assessment of head movement dynamics in dystonia using visual perceptive deep learning: a multi-centre retrospective longitudinal cohort study

Application of deep brain stimulation for the treatment of childhood-onset dystonia in patients with MEPAN syndrome

The Challenge of Choosing the Right Stimulation Target for Dystonic Tremor—A Series of Instructive Cases

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