Beta Power in Local Networks as Early Markers for Freezing of Gait Prediction in Parkinson's Disease
Poster No:
169
Submission Type:
Abstract Submission
Authors:
Yanqiu Tian1,2, Elie Matar2,3,4, Simon Lewis1
Institutions:
1Parkinson’s Disease Research Clinic, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia, 2Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia, 3Centre for Integrated Research and Understanding of Sleep (CIRUS), Woolcock Institute of Medical Research, Sydney, New South Wales, Australia, 4Department of Neurology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
First Author:
Yanqiu Tian
Parkinson’s Disease Research Clinic, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University|Central Clinical School, Faculty of Medicine and Health, University of Sydney
Sydney, New South Wales, Australia|Sydney, New South Wales, Australia
Parkinson’s Disease Research Clinic, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University|Central Clinical School, Faculty of Medicine and Health, University of Sydney
Sydney, New South Wales, Australia|Sydney, New South Wales, Australia
Co-Author(s):
Elie Matar
Central Clinical School, Faculty of Medicine and Health, University of Sydney|Centre for Integrated Research and Understanding of Sleep (CIRUS), Woolcock Institute of Medical Research|Department of Neurology, Royal Prince Alfred Hospital
Sydney, New South Wales, Australia|Sydney, New South Wales, Australia|Sydney, New South Wales, Australia
Central Clinical School, Faculty of Medicine and Health, University of Sydney|Centre for Integrated Research and Understanding of Sleep (CIRUS), Woolcock Institute of Medical Research|Department of Neurology, Royal Prince Alfred Hospital
Sydney, New South Wales, Australia|Sydney, New South Wales, Australia|Sydney, New South Wales, Australia
Simon Lewis
Parkinson’s Disease Research Clinic, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University
Sydney, New South Wales, Australia
Parkinson’s Disease Research Clinic, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University
Sydney, New South Wales, Australia
Introduction:
Freezing of gait (FOG) is an incapacitating, sudden and involuntary cessation of gait commonly observed in advanced stages of Parkinson's disease (PD) (Giladi et al., 2001). It often leads to falls and increased morbidity, significantly affecting patients' quality of life and daily functioning (Rahman, Griffin, Quinn, & Jahanshahi, 2008). Therefore, understanding the neural correlates underlying FOG is crucial for its early detection and prediction. While previous research has linked FOG to disruptions in static functional connectivity between distant brain regions (Asher et al., 2021), the specific changes in local brain network dynamics during the transition to FOG remain unclear. This study investigates the neural oscillation changes within local networks during transitions to FOG compared to voluntary stopping (VS) events using ambulatory electroencephalography (EEG).
Methods:
Eighteen patients (mean age: 69.6 ± 11.5 years) with varying severity and frequency of FOG, recruited from the Parkinson's Disease Research Clinic at the Brain and Mind Centre, University of Sydney, were included in the study. All participants were assessed during their "off" medication period (MDS Unified Parkinson's Disease Rating Scale III score of 48.6 ± 13.8 and Hoehn and Yahr stage of 2.8 ± 0.9). Ambulatory EEG signals were acquired whilst participants performed Timed Up and Go (TUG) tasks (Cao et al., 2021; Zampieri et al., 2010). In the standard TUG task, participants walked to a target under conditions provoking FOG episodes. In the voluntary stopping (VS) TUG task, participants stopped voluntarily upon a verbal "stop" command. Two types of trials were analysed: FOG and VS trials. Each trial consisted of three events, with 2-second epochs extracted for analysis: Normal Walking (2 seconds preceding the transition), Transition (2 seconds before the freezing episode, trFOG, or 2 seconds before the voluntary "stop" command trVS), and FOG or Voluntary Stopping (VS) (Fig 1A). A total of 277 FOG trials and 51 VS trials were included in the analysis. Functional brain networks were estimated at the source level across four EEG frequency bands and broadband signals. Graph theory-based modularity metrics were applied to compare network properties during transition states (trFOG/trVS) preceding FOG and VS events (Fig1B).
Results:
The results revealed that networks during the trFOG exhibited increased local information processing (modularity) compared to the trVS. This was observed in the right frontoparietal (FPR) (p = 0.009), right visual (VISR) (p = 0.005) and insula (INS) (p = 0.007) networks within the beta frequency band (Fig 1C, 1D). Additionally, increased beta segregation was observed in VS compared to trVS within the FPR network (p = 0.016, FDR-corrected). However, no significant differences were found in other frequency bands (delta, theta, alpha and broadband) across these networks or other networks. Functional integration, measured via participation coefficient, also showed no significant differences across the transitions.
Conclusions:
Our findings suggest that beta oscillations in specific brain networks, driven by local reorganization processes, can be distinctively identified as early detection markers for predicting FOG episodes during transitions to FOG. Beta burst oscillations, observed in the subthalamic nucleus and the occipital-parietal region during episodes of FOG (Handojoseno et al., 2014; Toledo et al., 2014), reflect pathological neural mechanisms associated with motor dysfunction. These results provide insights into the potential of real-time close-loop EEG monitoring as a novel therapeutic intervention for predicting the onset of freezing episodes prior to their occurrence in individuals with Parkinson's disease.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
EEG/MEG Modeling and Analysis
Motor Behavior:
Motor Behavior Other
Novel Imaging Acquisition Methods:
EEG
Keywords:
Data analysis
Degenerative Disease
Electroencephaolography (EEG)
Movement Disorder
1|2Indicates the priority used for review
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Provide references using APA citation style.
Cao, Z., John, A. R., Chen, H.-T., Martens, K. E., Georgiades, M., Gilat, M., . . . Engineering, R. (2021). Identification of EEG dynamics during freezing of gait and voluntary stopping in patients with Parkinson’s disease. 29, 1774-1783.
Giladi, N., Treves, T., Simon, E., Shabtai, H., Orlov, Y., Kandinov, B., . . . Korczyn, A. J. J. o. n. t. (2001). Freezing of gait in patients with advanced Parkinson's disease. 108, 53-61.
Handojoseno, A. A., Shine, J. M., Nguyen, T. N., Tran, Y., Lewis, S. J., Nguyen, H. T. J. I. t. o. n. s., & engineering, r. (2014). Analysis and prediction of the freezing of gait using EEG brain dynamics. 23(5), 887-896.
Rahman, S., Griffin, H. J., Quinn, N. P., & Jahanshahi, M. J. M. d. o. j. o. t. M. D. S. (2008). Quality of life in Parkinson's disease: the relative importance of the symptoms. 23(10), 1428-1434.
Toledo, J. B., Lopez-Azcarate, J., Garcia-Garcia, D., Guridi, J., Valencia, M., Artieda, J., . . . Rodriguez-Oroz, M. (2014). High beta activity in the subthalamic nucleus and freezing of gait in Parkinson's disease. Neurobiol Dis, 64, 60-65. doi:10.1016/j.nbd.2013.12.005
Zampieri, C., Salarian, A., Carlson-Kuhta, P., Aminian, K., Nutt, J. G., Horak, F. B. J. J. o. N., Neurosurgery, & Psychiatry. (2010). The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson's disease. 81(2), 171-176.