Objective: Utilize a novel Network Physiology approach to identify new biomarkers of Parkinson’s Disease (PD) based on the dynamics on individual physiological systems, and their network interactions derived from continuous polysomnographic (PSG) recordings.

Background: Patients with PD have changes in autonomic function and sleep regulation early in the disease and prior to the onset of motor symptoms. . Identifying and quantifying PD-related patterns in brain dynamics and organ network interactions will lead to establishing parameters that may serve as novel diagnostic and prognostic biomarkers of PD.

Methods: We analyze PSGs from 50 PD patients and study changes in brain, cardiac, respiratory and locomotor dynamics across physiologic states (sleep/wake, sleep stages), including aspects of pair-wise coupling and network interactions. Specifically, we investigate alterations in individual brain rhythms, in the synchronous activation of the same brain rhythm across brain areas, network interactions among different brain rhythms and alterations in brain functional connectivity; cardiac neuroautonomic regulation and patterns in heart rate variability; respiratory function and patterns of respiratory dynamics; changes in cardio-respiratory coupling and interactions across sleep stages. We correlate changes in systems’ dynamics and organ network interactions with PD alterations in sleep micro-architecture, with focus on arousals, sleep-stage transitions and networks in brain-cardiac-respiratory interactions. In the last years we have developed a concept of self-organized criticality (SOC) in sleep micro-architecture (in contrast to sleep homeostasis at large time scales) and found that SOC features change with ventrolateral preoptic nucleus (VLPO) and locus coeruleus (LC) brain lesions (respective models of insomnia and narcolepsy). We compare how SOC features change PD.

Results: We find that linear and nonlinear scale-invariant characteristics in the dynamics of individual systems (brain, cardiac, respiratory, locomotor) significantly change with PD, reflecting alterations in autonomic control. Further, coupling parameters based on phase synchronization and time delay stability dramatically change, leading to reorganization and breakdown of organ network interactions in PD patients.

Conclusions: Focusing on the interactions among key physiological systems, Network Physiology opens promising new avenues to investigate autonomic regulation, principles of integration in networks of organ systems and their breakdown with PD.

References: 1.Common scale-invariant patterns of sleep–wake transitions across mammalian species. Chung-Chuan Lo et al. PNAS, December 14,1004, vol101, 17545-17548. 2. Asymmetry and basic pathways in sleep-stage transitions. Chung-Chuan Lo, et al, EPL, 102 (2013) 10008. 3.Network physiology reveals relations between network topology and physiological function, Amir Bashan et al, nature communications | 3:702 | DOI: 10.1038/ncomms1705. 4.Phase transitions in physiologic coupling. Ronny P. Bartscha,b, et al, PNAS June 12, 2012. 5.Old Brains Come Uncoupled in Sleep: Slow Wave-Spindle Synchrony, Brain Atrophy, and Forgetting, Helfrich et al., 2018, Neuron 97, 1–10.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/novel-biomarkers-of-autonomic-regulation-and-sleep-in-parkinsons-disease-derived-from-network-physiology/