Subthalamic Deep Brain Stimulation: Mapping Non‐Motor Outcomes to Structural Connections

In Parkinson's Disease (PD), deep brain stimulation of the subthalamic nucleus (STN-DBS) reliably improves motor symptoms, and the circuits mediating these effects have largely been identified. However, non-motor outcomes are more variable, and it remains unclear which specific brain circuits need to be modulated or avoided to improve them. Since numerous non-motor symptoms potentially respond to DBS, it is challenging to independently identify the circuits mediating each one of them. Data compression algorithms such as principal component analysis (PCA) may provide a powerful alternative. This study aimed at providing a proof of concept for this approach by mapping changes along extensive score batteries to a few anatomical fiber bundles and, in turn, estimating changes in individual scores based on stimulation of these tracts. Retrospective data from 56 patients with PD and bilateral STN-DBS was included. The patients had undergone comprehensive clinical assessments covering changes in appetitive behaviors, mood, anxiety, impulsivity, cognition, and empathy. PCA was implemented to identify the main dimensions of neuropsychiatric and neuropsychological outcomes. Using DBS fiber filtering, we identified the structural connections whose stimulation was associated with change along these dimensions. Then, estimates of individual symptom outcomes were derived based on the stimulation of these connections by inverting the PCA. Finally, changes along a specific non-motor score were estimated in an independent validation dataset (N = 68) using the tract model. Four principal components were retained, which could be interpreted to reflect (i) general non-motor improvement; (ii) improvement of mood and cognition and worsening of trait impulsivity; (iii) improvement of cognition; and (iv) improvement of empathy and worsening of impulsive-compulsive behaviors. Each component was associated with the stimulation of spatially segregated fiber bundles connecting regions of the frontal cortex with the subthalamic nucleus. The extent of stimulation of these tracts was able to explain significant amounts of variance in outcomes for individual symptoms in the original cohort (circular analysis), as well as in the rank of depression outcomes in the independent validation cohort. Our approach represents an innovative concept for mapping changes along extensive score batteries to a few anatomical fiber bundles and could pave the way toward personalized deep brain stimulation.

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Journal
Human Brain Mapping
Published
2025-04-01
DOI
https://doi.org/10.1002/hbm.70207
Citations
2
Primary Topic
Neurological disorders and treatments
Type
article
Field-Weighted Citation Impact
2.78

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article

Subthalamic Deep Brain Stimulation: Mapping Non‐Motor Outcomes to Structural Connections

Clemens Neudorfer, Veerle E. R. M. Visser-Vandewalle, Haidar S. Dafsari, Konstantin Butenko et al.
2 citations
Human Brain Mapping
Neurological disorders and treatments
2.78
article

Subthalamic Deep Brain Stimulation: Mapping Non‐Motor Outcomes to Structural Connections

Clemens Neudorfer, Veerle E. R. M. Visser-Vandewalle, Haidar S. Dafsari, Konstantin Butenko, Nanditha Rajamani, Jan Niklas Petry‐Schmelzer, Barbara Hollunder, Garance M. Meyer, Ilkem Aysu Sahin, Philip Mosley, Michael T. Barbe, Lauren A. Hart, Andreas Horn
article en
2 citations

Abstract

In Parkinson's Disease (PD), deep brain stimulation of the subthalamic nucleus (STN-DBS) reliably improves motor symptoms, and the circuits mediating these effects have largely been identified. However, non-motor outcomes are more variable, and it remains unclear which specific brain circuits need to be modulated or avoided to improve them. Since numerous non-motor symptoms potentially respond to DBS, it is challenging to independently identify the circuits mediating each one of them. Data compression algorithms such as principal component analysis (PCA) may provide a powerful alternative. This study aimed at providing a proof of concept for this approach by mapping changes along extensive score batteries to a few anatomical fiber bundles and, in turn, estimating changes in individual scores based on stimulation of these tracts. Retrospective data from 56 patients with PD and bilateral STN-DBS was included. The patients had undergone comprehensive clinical assessments covering changes in appetitive behaviors, mood, anxiety, impulsivity, cognition, and empathy. PCA was implemented to identify the main dimensions of neuropsychiatric and neuropsychological outcomes. Using DBS fiber filtering, we identified the structural connections whose stimulation was associated with change along these dimensions. Then, estimates of individual symptom outcomes were derived based on the stimulation of these connections by inverting the PCA. Finally, changes along a specific non-motor score were estimated in an independent validation dataset (N = 68) using the tract model. Four principal components were retained, which could be interpreted to reflect (i) general non-motor improvement; (ii) improvement of mood and cognition and worsening of trait impulsivity; (iii) improvement of cognition; and (iv) improvement of empathy and worsening of impulsive-compulsive behaviors. Each component was associated with the stimulation of spatially segregated fiber bundles connecting regions of the frontal cortex with the subthalamic nucleus. The extent of stimulation of these tracts was able to explain significant amounts of variance in outcomes for individual symptoms in the original cohort (circular analysis), as well as in the rank of depression outcomes in the independent validation cohort. Our approach represents an innovative concept for mapping changes along extensive score batteries to a few anatomical fiber bundles and could pave the way toward personalized deep brain stimulation.

Human Brain MappingVol. 46(5)
Harvard University (US), The University of Queensland (AU), University of Cologne (DE), QIMR Berghofer Medical Research Institute (AU), Humboldt-Universität zu Berlin (DE), Massachusetts General Hospital (US), Circuit Therapeutics (United States) (US), St Andrew’s Hospital (AU), Australian e-Health Research Centre (AU), CSIRO Health and Biosecurity (AU), University Hospital Cologne (DE), Einstein Center for Neurosciences Berlin (DE), Charité - Universitätsmedizin Berlin (DE)
Boston Scientific Corporation, EU Joint Programme – Neurodegenerative Disease Research, New Venture Fund, Deutsche Forschungsgemeinschaft, Deutsches Zentrum für Luft- und Raumfahrt, Deutsche Gesellschaft für Neurologie, Universität zu Köln, EVER Neuro Pharma, Thiemann Stiftung, National Institutes of Health
Quality Education
Openalex Percentile: Top 11%
Neurological disorders and treatments
2.78
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