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Updated: May 6, 2026

Deep Brain Stimulation with Simultaneous fMRI in Rodents
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How Statistical Methods, Hemispheric Data and Masking Approaches Shape Probabilistic Sweet Spots in Deep Brain

Vittoria Bucciarelli, Dorian Vogel, Teresa Nordin

    IEEE Transactions on Bio-Medical Engineering
    |May 4, 2026
    PubMed
    Summary
    This summary is machine-generated.

    The Bayesian t-test offers robust probabilistic sweet spot (PSS) mapping in deep brain stimulation (DBS) for small to intermediate patient cohorts. Analyzing hemispheres separately improves PSS stability and reliability for clinical use.

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    Area of Science:

    • Neuroscience
    • Medical Technology
    • Computational Biology

    Background:

    • Probabilistic mapping is crucial for identifying optimal Deep Brain Stimulation (DBS) targets, known as Probabilistic Sweet Spots (PSS).
    • The accuracy and reliability of PSS identification are sensitive to various workflow parameters, including statistical methods and data handling.
    • Understanding these influences is key to standardizing practices and enhancing the clinical translation of DBS.

    Purpose of the Study:

    • To investigate how different methodological choices impact the stability and spatial consistency of PSS.
    • To evaluate the effect of varying sample sizes on PSS computation using different statistical approaches.
    • To assess the influence of hemispheric data handling and masking parameters on PSS outcomes.

    Main Methods:

    • Analysis of intraoperative stimulation test data from 36 Parkinson's Disease patients.
    • Computation of PSS across increasing sample sizes using Bayesian t-test (BAYES), Logistic Regression Model (LRM), Wilcoxon test with FDR correction (WFDR), and Wilcoxon test with permutation correction (WPERM).
    • Assessment of PSS stability and consistency based on statistical tests, hemispheric data handling, and masking parameters.

    Main Results:

    • The Bayesian t-test demonstrated superior robustness, especially with small to intermediate sample sizes.
    • WFDR and LRM methods required larger cohorts (∼25-30 patients) for stabilization; WPERM consistently underperformed.
    • Hemispheric stability was higher when analyzed separately, indicating potential asymmetries; masking parameters primarily affected PSS volume, not stability patterns.

    Conclusions:

    • The choice of statistical test significantly influences PSS outcomes, with the Bayesian t-test recommended for smaller cohorts.
    • Separate analysis of brain hemispheres is advised to prevent masking clinically relevant asymmetries.
    • Standardizing probabilistic mapping through careful consideration of these parameters enhances reliability for clinical translation.