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Boundary complexity of cortical and subcortical areas predicts deep brain stimulation outcomes in Parkinson’s disease

Author

Listed:
  • Devin Schoen

    (University of California
    UCSF-UC Berkeley Joint PhD Program in Bioengineering)

  • Skyler Deutsch

    (University of California)

  • Juhi Mehta

    (University of California)

  • Sarah Wang

    (University of California)

  • John Kornak

    (University of California)

  • Philip A. Starr

    (UCSF-UC Berkeley Joint PhD Program in Bioengineering
    University of California)

  • Doris D. Wang

    (UCSF-UC Berkeley Joint PhD Program in Bioengineering
    University of California)

  • Jill L. Ostrem

    (University of California)

  • Ian O. Bledsoe

    (University of California)

  • Melanie A. Morrison

    (University of California
    UCSF-UC Berkeley Joint PhD Program in Bioengineering)

Abstract

While deep brain stimulation (DBS) remains an effective therapy for Parkinson’s disease (PD), sources of variance in patient outcomes are still not fully understood, underscoring a need for better prognostic criteria. Here, we leveraged routinely collected T1-weighted (T1-w) magnetic resonance imaging (MRI) data to derive patient-specific measures of brain structure and evaluate their usefulness in predicting changes in PD medications in response to DBS. Preoperative T1-w MRI data from 231 patients with PD were used to extract regional measures of fractal dimension (FD), sensitive to the structural complexities of cortical and subcortical brain. FD was validated as a biomarker of PD progression through comparison of patients with PD and healthy controls (HCs). This analysis revealed significant group differences in FD across nine brain regions, including frontal, occipital, insular, and basal ganglia areas, which supports its utility as a marker of PD. We evaluated the impact of adding imaging features (FD) to a clinical model that included demographics and clinical parameters (age, sex, total number and location of DBS electrodes), and preoperative motor response to levodopa. This model aimed to explain variance and predict changes in medication following DBS. Regression analysis revealed that inclusion of the FD of distributed brain areas correlated with post-DBS reductions in medication burden, explaining an additional 13.6% of outcome variance (R2 = 0.388) compared to clinical features alone (R2 = 0.252). Hypergraph-based classification learning tasks achieved an area under the receiver operating characteristic curve of 0.64 when predicting with clinical features alone, versus 0.76 when combining clinical and imaging features. These findings demonstrate that PD effects on brain morphology linked to disease progression influence DBS outcomes. The work also highlights FD as a potentially useful imaging biomarker to enhance DBS candidate selection criteria for optimized treatment planning.

Suggested Citation

  • Devin Schoen & Skyler Deutsch & Juhi Mehta & Sarah Wang & John Kornak & Philip A. Starr & Doris D. Wang & Jill L. Ostrem & Ian O. Bledsoe & Melanie A. Morrison, 2025. "Boundary complexity of cortical and subcortical areas predicts deep brain stimulation outcomes in Parkinson’s disease," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60695-4
    DOI: 10.1038/s41467-025-60695-4
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    References listed on IDEAS

    as
    1. Liviu Badea & Mihaela Onu & Tao Wu & Adina Roceanu & Ovidiu Bajenaru, 2017. "Exploring the reproducibility of functional connectivity alterations in Parkinson’s disease," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-21, November.
    2. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    3. repec:plo:pone00:0178984 is not listed on IDEAS
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