The neurophysiological brain-fingerprint of Parkinson’s disease

Abstract

AbstractIn this study, we investigate the clinical potential of brain-fingerprints derived from electrophysiological brain activity for diagnostics and progression monitoring of Parkinson’s disease (PD). We obtained brain-fingerprints from PD patients and age-matched healthy controls using short, task-free magnetoencephalographic recordings. The rhythmic components of the individual brain-fingerprint distinguished between patients and healthy participants with approximately 90% accuracy. The most prominent cortical features of the Parkinson’s brain-fingerprint mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also show that Parkinson’s disease stages can be decoded directly from cortical neurophysiological activity. Additionally, our study reveals that the cortical topography of the Parkinson’s brain-fingerprint aligns with that of neurotransmitter systems affected by the disease’s pathophysiology. We further demonstrate that the arrhythmic components of cortical activity are more variable over short periods of time in patients with Parkinson’s disease than in healthy controls, making individual differentiation between patients based on these features more challenging and explaining previous negative published results. Overall, we outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and clinical staging of Parkinson’s disease. For this reason, the proposed definition of a rhythmic brain-fingerprint of Parkinson’s disease may contribute to novel, refined approaches to patient stratification and to the improved identification and testing of therapeutic neurostimulation targets.Lay summaryWe propose a new method to help diagnose and monitor Parkinson’s disease (PD) using patients’ uniquebrain-fingerprint. These fingerprints are based on the brain’s electrical activity, which we measured without any specific tasks, using a technique called magnetoencephalography. Remarkably, we found that these brain-fingerprints can differentiate between people with Parkinson’s and those without, with about 90% accuracy. Specifically, we noticed that certain rhythmic patterns in the brain, particularly in areas involved in sensory and motor functions, were key indicators of Parkinson’s. Interestingly, these patterns also helped us identify the different stages of the disease.Additionally, our research shows that the arrangement of these brain-fingerprints in Parkinson’s patients corresponds to how the neurochemistry of the brain is impacted by the disease. We also observed that certain irregular patterns in the brain’s activity, which vary more from moment to moment in Parkinson’s patients, make it harder to distinguish between individuals based on these features alone. This finding sheds light on why previous studies reported challenges with similar approaches.Overall, our study offers new insights into the unique brain activity patterns in Parkinson’s disease and suggests that individual brain-fingerprints could be valuable in tailoring treatment plans and developing new therapies for this condition.