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PassFlow: a multimodal workflow for predicting deep brain stimulation outcomes

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Deep Brain Stimulation (DBS) is a proven therapy for Parkinson’s Disease (PD), frequently resulting in an enhancement of motor function. Nonetheless, several undesirable side effects can occur after DBS, which can worsen the quality of life of the patient. Thus, the clinical team has to carefully select patients on whom to perform DBS. Over the past decade, there have been some attempts to relate pre-operative data and DBS clinical outcomes, with most focused on the motor symptomatology. In this paper, we propose a machine learning-based method able to predict a large number of DBS clinical outcomes for PD.

Methods

We propose a multimodal pipeline, referred to as PassFlow, which predicts 84 clinical post-operative clinical scores. PassFlow is composed of an artificial neural network to compress clinical information, an image processing method from the state-of-the-art to extract morphological biomarkers our of T1 imaging, and an SVM to perform the regressions. We validated PassFlow on 196 PD patients who undergone a DBS.

Results

PassFlow showed correlation coefficients as high as 0.71 and were able to significantly predict 63 out of the 84 scores, outperforming a comparative linear method. The number of metrics that are predicted with this pre-operative information was also found to be correlated with the number of patients with this information available, indicating that the PassFlow method is still actively learning.

Conclusion

We presented a novel, machine learning-based pipeline to predict a variety of post-operative clinical outcomes of DBS for PD patients. PassFlow took into account various bio-markers, arising from different data modalities, showing high correlation coefficients for some scores from pre-operative data only. It indicates that many clinical outcomes of DBS can be predicted agnostic to the specific simulation parameters, as PassFlow has been validated without such stimulation-related information.

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Availability of data and material

Data is not available for this study.

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Funding

This work was funded by the Fondation pour la Recherche Médicale.

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Authors and Affiliations

  1. Université de Rennes 1, INSERM, LTSI - UMR 1099, 35000, Rennes, France

    Maxime Peralta, Pierre Jannin & John S. H. Baxter

  2. Department of Neurosurgery, Centre Hospitalier Universitaire de Rennes, Rennes, France

    Claire Haegelen

Authors
  1. Maxime Peralta
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  2. Claire Haegelen
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  3. Pierre Jannin
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  4. John S. H. Baxter
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Corresponding author

Correspondence to John S. H. Baxter.

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The authors have no relevant financial or non-financial interests to disclose.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Peralta, M., Haegelen, C., Jannin, P. et al. PassFlow: a multimodal workflow for predicting deep brain stimulation outcomes. Int J CARS 16, 1361–1370 (2021). https://doi.org/10.1007/s11548-021-02435-9

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  • DOI: https://doi.org/10.1007/s11548-021-02435-9

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