Objective: To integrate [¹⁸F]-FDG PET (fPET) and objective motor performance to unravel metabolic networks associated with deep brain stimulation (DBS) outcomes in Parkinson’s disease (PD).

Background: A new technique enables deriving seed-based metabolic networks (SBN) on subject-level by utilising a constant infusion dynamic functional fPET protocol. We applied preoperative fPET imaging and quantitative motor assessments to elucidate neurobiological correlates of DBS.

Method: We analysed multimodal functional imaging data from five mid-stage PD patients undergoing DBS from the fPET-fMRI cohort at Philipps University of Marburg. Motor performance was assessed by extracting power at the dominant movement frequency from accelerometer data, recorded during a standardised monopolar contact review. Metabolic networks were generated using individual volumes of activated tissue (VATs) as seeds. Group-level maps of metabolic networks of the most and least effective VATs were examined using a one-sample t-test.

Results: The metabolic connectivity profile of most effective left lateralised VATs showed larger clusters in the right precentral gyrus, left superior temporal gyrus, and right thalamus in comparison to the network of least effective VATs. Right-lateralised VATs exhibited larger clusters in the left precentral gyrus and midcingulum. fMRI-based networks of the most effective VATs revealed a widespread connectivity profile, including the left superior frontal cortex. Additionally, the most effective right-lateralised VATs were linked to a bilateral network involving the right precentral gyrus, superior frontal cortex, and inferior parietal cortex.

Conclusion: These preliminary findings highlight distinct metabolic and hemodynamic network patterns associated with changes in quantitative motor performance under DBS. SBN analysis, informed by subject-level network properties, provides a promising framework for investigating patient-specific neurobiological markers of stimulation effects.

References: These preliminary findings highlight distinct metabolic and hemodynamic network patterns associated with changes in quantitative motor performance under DBS. SBN analysis, informed by subject-level network properties, provides a promising framework for investigating patient-specific neurobiological markers of stimulation effects.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/metabolic-network-signature-of-quantitative-motor-effects-following-deep-brain-stimulation-in-parkinsons-disease/