Journal of Neuroscience MethodsNeurosciencePeer-reviewed journal article
Methodological determinants of signal quality in electrobulbogram recordings
Frans NordéniD, Irene Zanettin, Tora OlssoniD, Artin ArshamianiD, Mikael LundqvistiD, Fahimeh DarkiiD, Johan N. LundströmiD
Abstract
The electrobulbogram (EBG) is a new, non-invasive method for measuring the functional activity of the human olfactory bulb (OB). To date, the EBG has been used to assess how the OB process odor identity, valence, intensity, and it has shown promise as an early biomarker for Parkinsons disease. However, current implementation of the EBG method depends on several methodological components, including subject specific co-registration of electrode positions through neuronavigation and EEG source reconstruction, which may limit accessibility for many research groups. In this study, we test the quality and reliability of the OB signal under different configurations to potentially remedy this. Specifically, we compare six EBG setups that vary in the use of subject-specific T1 scans versus a template head model, co-registered versus template electrode positions, and individualized versus template-based OB location. Our results indicate that strongest EBG signals are obtained when using subject-specific T1 scans in combination with co-registered electrode positions. However, we obtained significant EBG activity even when using a fully template-based configuration. Our anatomical analysis of OB location of 941 individuals reveals that in 86% of cases, the OB is centered within the spatial resolution bounds of the EEG source dipole, supporting the feasibility of detecting olfactory bulb signals without precise individual anatomical mapping using template coordinates. These findings suggest that while subject-specific configurations enhance signal quality, the EBG method remains robust enough to yield meaningful results even with less complex setups. This enables a broader adoption of the EBG method in both clinical and research settings.
- organism: human
- assay: electronic health records
- research focus: benchmarking
- approach: statistical model
- Citations
- 3
- FWCI
- 1.90
- Field percentile
- top 15%
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