NeuralCipher Research overview
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An emerging perspective describes beta-band (15-28 Hz) activity as consisting of short-lived high-amplitude events that only appear sustained in conventional measures of trial-average power. This has important implications for characterising abnormalities observed in beta-band activity in disorders like Parkinson's disease. Measuring parameters associated with beta-event dynamics may yield more sensitive measures, provide more selective diagnostic neural markers, and provide greater mechanistic insight into the breakdown of brain dynamics in this disease. Here, we used magnetoencephalography in eighteen Parkinson's disease participants off dopaminergic medication and eighteen healthy control participants to investigate beta-event dynamics during timed movement preparation. We used the Hidden Markov Model to classify event dynamics in a data-driven manner and derived three parameters of beta events: (1) beta-state amplitude, (2) beta-state lifetime, and (3) beta-state interval time. Of these, changes in beta-state interval time explained the overall decreases in beta power during timed movement preparation and uniquely captured the impairment in such preparation in patients with Parkinson's disease. Thus, the increased granularity of the Hidden Markov Model analysis (compared with conventional analysis of power) provides increased sensitivity and suggests a possible reason for impairments of timed movement preparation in Parkinson's disease.
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