1. Beta–gamma phase-amplitude coupling as a non-invasive biomarker for Parkinson’s diseas : insights from Electroencephalography studiesTisa Hodnik, Stiven Roytman, Nicolaas I. Bohnen, Uroš Marušič, 2024, pregledni znanstveni članek Povzetek: Phase-amplitude coupling (PAC) describes the interaction of two separate frequencies in which the lower frequency phase acts as a carrier frequency of the higher frequency amplitude. It is a means of carrying integrated streams of information between micro- and macroscale systems in the brain, allowing for coordinated activity of separate brain regions. A beta–gamma PAC increase over the sensorimotor cortex has been observed consistently in people with Parkinson’s disease (PD). Its cause is attributed to neural entrainment in the basal ganglia, caused by pathological degeneration characteristic of PD. Disruptions in this phenomenon in PD patients have been observed in the resting state as well as during movement recordings and have reliably distinguished patients from healthy participants. The changes can be detected non-invasively with the electroencephalogram (EEG). They correspond to the severity of the motor symptoms and the medication status of people with PD. Furthermore, a medication-induced decrease in PAC in PD correlates with the alleviation of motor symptoms measured by assessment scales. A beta–gamma PAC increase has, therefore, been explored as a possible means of quantifying motor pathology in PD. The application of this parameter to closed-loop deep brain stimulation could serve as a self-adaptation measure of such treatment, responding to fluctuations of motor symptom severity in PD. Furthermore, phase-dependent stimulation provides a new precise method for modulating PAC increases in the cortex. This review offers a comprehensive synthesis of the current EEG-based evidence on PAC fluctuations in PD, explores the potential practical utility of this biomarker, and provides recommendations for future research.
Ključne besede: neurodegenerative diseases, Parkinson’s disease, electroencephalography, phase-amplitude coupling
Objavljeno v DiRROS: 21.03.2024; Ogledov: 77; Prenosov: 43
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2. A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprostheticsArnau Dillen, Elke Lathouwers, Aleksandar Miladinović, Uroš Marušič, Fakhreddine Ghaffari, Olivier Romain, Romain Meeusen, Kevin De Pauw, 2022, izvirni znanstveni članek Povzetek: Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.
Ključne besede: neuroprosthetics, brain-computer interface, machine learning, electroencephalography, data-driven learning, lower limb amputation
Objavljeno v DiRROS: 21.07.2022; Ogledov: 541; Prenosov: 373
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3. Higher neural demands on stimulus processing after prolonged hospitalization can be mitigated by a cognitively stimulating environmentUroš Marušič, Rado Pišot, Voyko Kavcic, 2021, izvirni znanstveni članek Povzetek: Prolonge d periods of complete physical inactivity or bed rest trigger various alterations in the functional and metabolic levels of the human body. However, bed rest-related adaptations of the central nervous system are less known and thoroughly studied. The aim of this study was to investigate brain electrophysiological changes using event-related potentials (ERPs) after 14 days of bed rest and 12 consecutive sessions of computerized cognitive training (CCT). Sixteen older (Mage= 60 years) healthy volunteers were randomly divided into a CCT treatment group and an active control group. All participants performed ERP measurements based on the foveal visual presentation of a circle on a black background before and after bed rest. After 14 days of bed rest, participants in the control group showed increased peak P1 amplitude (p = .012), decreased P1 latency (p = .024), and increased P2 amplitude (p = .036), while the CCT group also showed decreased P1 latency (p = .023) and decreased P2 latency (p = .049). Our results suggest that, even from a central adaptation perspective, prolonged periods of physical inactivity or bed rest trigger additional neural recruitment and should therefore be minimized, and that CCT may serve as a tool to mitigate this. Future research should focus on other aspects of central nervous system adaptation following periods of immobilization/hospitalization to improve our knowledge of infl uence of physical inactivity and its eff ects on cortical activity and to develop appropriate countermeasures to mitigate functional dysregulation.
Ključne besede: aging, physical inactivity, immobilization, electroencephalography, EEG, computerized cognitive training
Objavljeno v DiRROS: 13.05.2021; Ogledov: 1163; Prenosov: 825
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