Measuring complex behaviors of local oscillatory networks in deep brain local field potentials

- The complex behaviors of the thalamic local field potentials were investigated and quantified based on power spectral and time-frequency analysis.
- Significant difference in the behaviors were found in thalamic local field potentials between neuropathic pain and dystonic tremor groups.
- The study provides a strategy for studying the brain states in a multi-dimensional behavior space and a framework to screen quantitative characteristics for biomarkers related to diseases or nucleus.

BackgroundMultiple oscillations emerging from the same neuronal substrate serve to construct a local oscillatory network. The network usually exhibits complex behaviors of rhythmic, balancing and coupling between the oscillations, and the quantification of these behaviors would provide valuable insight into organization of the local network related to brain states.New methodAn integrated approach to quantify rhythmic, balancing and coupling neural behaviors based upon power spectral analysis, power ratio analysis and cross-frequency power coupling analysis was presented. Deep brain local field potentials (LFPs) were recorded from the thalamus of patients with neuropathic pain and dystonic tremor. t-Test was applied to assess the difference between the two patient groups.ResultsThe rhythmic behavior measured by power spectral analysis showed significant power spectrum difference in the high beta band between the two patient groups. The balancing behavior measured by power ratio analysis showed significant power ratio differences at high beta band to 8-20 Hz, and 30-40 Hz to high beta band between the patient groups. The coupling behavior measured by cross-frequency power coupling analysis showed power coupling differences at (theta band, high beta band) and (45-55 Hz, 70-80 Hz) between the patient groups.Comparison with existing methodThe study provides a strategy for studying the brain states in a multi-dimensional behavior space and a framework to screen quantitative characteristics for biomarkers related to diseases or nuclei.ConclusionsThe work provides a comprehensive approach for understanding the complex behaviors of deep brain LFPs and identifying quantitative biomarkers for brain states related to diseases or nuclei.