Kim Jinmo, Lee Jungmin, Kim Eunho, Choi Joon Ho, Rah Jong-Cheol, Choi Ji-Woong
Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea.
Brain Engineering Convergence Research Center, DGIST, Daegu, Republic of Korea.
Neurobiol Dis. 2022 Jun 15;168:105692. doi: 10.1016/j.nbd.2022.105692. Epub 2022 Mar 16.
Electrophysiological biomarkers reflecting the pathological activities in the basal ganglia are essential to gain an etiological understanding of Parkinson's disease (PD) and develop a method of diagnosing and treating the disease. Previous studies that explored electrophysiological biomarkers in PD have focused mainly on oscillatory or periodic activities such as beta and gamma oscillations. Emerging evidence has suggested that the nonoscillatory, aperiodic component reflects the firing rate and synaptic current changes corresponding to cognitive and pathological states. Nevertheless, it has never been thoroughly examined whether the aperiodic component can be used as a biomarker that reflects pathological activities in the basal ganglia in PD. In this study, we examined the parameters of the aperiodic component in hemiparkinsonian rats and tested its practicality as an electrophysiological biomarker of pathological activity. We found that a set of aperiodic parameters, aperiodic offset and exponent, were significantly decreased by the nigrostriatal lesion. To further prove the usefulness of the parameters as biomarkers, acute levodopa treatment reverted the aperiodic offset. We then compared the aperiodic parameters with a previously established periodic biomarker of PD, beta frequency oscillation. We found a significantly low negative correlation with beta power. We showed that the performance of the machine learning-based prediction of pathological activities in the basal ganglia can be improved by using both beta power and the aperiodic component, which showed a low correlation with each other. We suggest that the aperiodic component will provide a more sensitive measurement to early diagnosis PD and have the potential to use as the feedback parameter for the adaptive deep brain stimulation.
反映基底神经节病理活动的电生理生物标志物对于深入了解帕金森病（PD）的病因以及开发该疾病的诊断和治疗方法至关重要。先前探索PD电生理生物标志物的研究主要集中在振荡或周期性活动，如β和γ振荡。新出现的证据表明，非振荡的、非周期性成分反映了与认知和病理状态相对应的放电率和突触电流变化。然而，非周期性成分是否可以作为反映PD基底神经节病理活动的生物标志物从未得到过彻底研究。在本研究中，我们检测了偏侧帕金森病大鼠非周期性成分的参数，并测试了其作为病理活动电生理生物标志物的实用性。我们发现，一组非周期性参数，即非周期性偏移和指数，因黑质纹状体损伤而显著降低。为了进一步证明这些参数作为生物标志物的有用性，急性左旋多巴治疗使非周期性偏移恢复。然后，我们将非周期性参数与先前建立的PD周期性生物标志物β频率振荡进行了比较。我们发现它与β功率呈显著低负相关。我们表明，同时使用β功率和非周期性成分可以提高基于机器学习的基底神经节病理活动预测性能，二者相互之间相关性较低。我们认为，非周期性成分将为PD的早期诊断提供更敏感的测量方法，并有潜力用作适应性深部脑刺激的反馈参数。
