How statistical methods, hemispheric data and masking approaches shape probabilistic sweet spots in deep brain stimulation
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Autor:in (Körperschaft)
Publikationsdatum
2026
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
IEEE Transactions on Biomedical Engineering
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DOI der Originalpublikation
Link
Zugehörige Forschungsdaten
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
1-8
Patentnummer
Verlag / Herausgebende Institution
IEEE
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
OBJECTIVE: Probabilistic mapping is increasingly used to identify optimal stimulation regions (Probabilistic Sweet Spots, PSS) in Deep Brain Stimulation (DBS). Outcomes, however, depend on workflow parameters. This study examined how methodological and data-handling choices affect PSS stability and spatial consistency across varying sample sizes. METHODS: Intraoperative stimulation test data from 36 Parkinson's Disease patients were analyzed. PSS were computed across increasing sample sizes using four statistical approaches: Bayesian t-test (BAYES), Logistic Regression Model (LRM), Wilcoxon test with FDR correction (WFDR), and Wilcoxon test with permutation correction (WPERM). We assessed the effects of statistical tests, hemispheric data handling, and masking parameters (i.e., minimum number of patients and stimulations per voxel) on PSS stability and consistency, evaluated in terms of size and spatial location. RESULTS: BAYES was more robust at small to intermediate sample sizes, while WFDR and LRM stabilized only in larger cohorts (∼25-30 patients). WPERM consistently underperformed. Stability was higher in the left hemisphere. Combining hemispheres did not improve stability, suggesting asymmetries in stimulation effects. Masking parameters mainly affected PSS volume, with stricter thresholds reducing absolute size, but did not alter stability patterns. CONCLUSION: Statistical test choice, hemispheric analysis, and masking parameters strongly influence PSS outcomes. The Bayesian t-test is recommended for small to intermediate cohorts, and hemispheres should be analyzed separately to avoid masking clinically relevant asymmetries. SIGNIFICANCE: By highlighting the interplay between sample size, statistical methods, hemispheric data, and masking strategies, this work contributes to standardizing probabilistic mapping practices and improving their reliability for clinical translation.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
0018-9294
1558-2531
1558-2531
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Closed
Zitation
Bucciarelli, V., Vogel, D., Nordin, T., Coste, J., Lemaire, J.-J., Wårdell, K., Guzman, R., & Hemm-Ode, S. (2026). How statistical methods, hemispheric data and masking approaches shape probabilistic sweet spots in deep brain stimulation. IEEE Transactions on Biomedical Engineering, 1–8. https://doi.org/10.1109/tbme.2026.3690018