Journal Home > Volume 9 , Issue 2
Brain Science Advances 2023, 9(2): 136-154 https://doi.org/10.26599/BSA.2023.9050015
Research Article | Open Access | Issue | Published: 05 June 2023

Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms

Show Author's Information Zhongyan Shi1,§,  Lei Ding2,§,  Xingyu Han1,  Bo Jiang1,  Jiangtao Zhang1,  Dingjie Suo1,  Jinglong Wu3,  Guangying Pei1( ),  Boyan Fang4( ),  Tianyi Yan1( )
School of Life Science, Beijing Institute of Technology, Beijing 100081, China
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Anesthesiology, Peking University Cancer Hospital and Institute, Beijing 100142, China
School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
Department of Neurology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100081, China
Keywords:
Parkinson’s disease, electroencephalogram, multi biofeedback, neurofeedback, phase lag index
Cite this article:
Shi Z, Ding L, Han X, et al. Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms. Brain Science Advances, 2023, 9(2): 136-154. https://doi.org/10.26599/BSA.2023.9050015
Download citation
EndNote(RIS)
BibTeX

705

Views

85

Downloads

Citations

2

Crossref

N/A

WoS

N/A

Scopus

N/A

CSCD

Abstract Full text Electronic supplementary material About this article

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well as nonmotor symptoms such as anxiety and depression. Biofeedback improves motor and nonmotor functions of patients by regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or other physiological signals. Given that multimodal signals are closely related to PD states, the clinical effect of multimodal biofeedback on patients with PD is worth exploring. Twenty-one patients with PD in Beijing Rehabilitation Hospital were enrolled and divided into three groups: multimodal (EEG, ECG, PPG, and RSP feedback signal), EEG (EEG feedback signal), and sham (random feedback signal), and they received biofeedback training five times in two weeks. The combined clinical scale and multimodal signal analysis results revealed that the EEG group significantly improved motor symptoms and increased Berg balance scale scores by regulating β band activity; the multimodal group significantly improved nonmotor symptoms and reduced Hamilton rating scale for depression scores by improving θ band activity. Our preliminary results revealed that multimodal biofeedback can improve the clinical symptoms of PD, but the regulation effect on motor symptoms is weaker than that of EEG biofeedback.


menu
Abstract
Full text
Outline
Electronic supplementary material
About this article

Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms

Show Author's information Zhongyan Shi1,§, Lei Ding2,§, Xingyu Han1, Bo Jiang1, Jiangtao Zhang1, Dingjie Suo1, Jinglong Wu3, Guangying Pei1( ), Boyan Fang4( ), Tianyi Yan1( )
School of Life Science, Beijing Institute of Technology, Beijing 100081, China
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Anesthesiology, Peking University Cancer Hospital and Institute, Beijing 100142, China
School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
Department of Neurology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100081, China

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well as nonmotor symptoms such as anxiety and depression. Biofeedback improves motor and nonmotor functions of patients by regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or other physiological signals. Given that multimodal signals are closely related to PD states, the clinical effect of multimodal biofeedback on patients with PD is worth exploring. Twenty-one patients with PD in Beijing Rehabilitation Hospital were enrolled and divided into three groups: multimodal (EEG, ECG, PPG, and RSP feedback signal), EEG (EEG feedback signal), and sham (random feedback signal), and they received biofeedback training five times in two weeks. The combined clinical scale and multimodal signal analysis results revealed that the EEG group significantly improved motor symptoms and increased Berg balance scale scores by regulating β band activity; the multimodal group significantly improved nonmotor symptoms and reduced Hamilton rating scale for depression scores by improving θ band activity. Our preliminary results revealed that multimodal biofeedback can improve the clinical symptoms of PD, but the regulation effect on motor symptoms is weaker than that of EEG biofeedback.

Keywords: Parkinson’s disease, electroencephalogram, multi biofeedback, neurofeedback, phase lag index

References(67)

[1]
Rabelo A, Folador JP, Cabral AM, et al. Identification and characterization of short-term motor patterns in rest tremor of individuals with Parkinson’s disease. Healthcare 2022, 10(12): 2536.
DOI Google Scholar
[2]
Kurihara K, Nakagawa R, Ishido M, et al. Impact of motor and nonmotor symptoms in Parkinson disease for the quality of life: the Japanese Quality-of-Life Survey of Parkinson Disease (JAQPAD) study. J Neurol Sci 2020, 419: 117172.
DOI Google Scholar
[3]
Ryan M, Eatmon CV, Slevin JT. Drug treatment strategies for depression in Parkinson disease. Expert Opin Pharmacother 2019, 20(11): 1351–1363.
DOI Google Scholar
[4]
Ehgoetz Martens KA, Silveira CRA, Intzandt BN, et al. State anxiety predicts cognitive performance in patients with Parkinson’s disease. Neuropsychology 2018, 32(8): 950–957.
DOI Google Scholar
[5]
Chen YR, Tan CH, Su HC, et al. Investigating the interaction between neuropsychiatry features and daily activities on social function in patients with Parkinson’s disease with mild cognitive impairment. BJPsych Open 2022, 8(6): e205.
DOI Google Scholar
[6]
Dauwan M, Hoff JI, Vriens EM, et al. Aberrant resting-state oscillatory brain activity in Parkinson’s disease patients with visual hallucinations: an MEG source-space study. Neuroimage Clin 2019, 22: 101752.
DOI Google Scholar
[7]
Bosboom JL, Stoffers D, Stam CJ, et al. Cholinergic modulation of MEG resting-state oscillatory activity in Parkinson’s disease related dementia. Clin Neurophysiol 2009, 120(5): 910–915.
DOI Google Scholar
[8]
Wang Q, Meng L, Pang J, et al. Characterization of EEG data revealing relationships with cognitive and motor symptoms in Parkinson’s disease: a systematic review. Front Aging Neurosci 2020, 12: 587396.
DOI Google Scholar
[9]
Mak MK, Wong-Yu IS, Shen X, et al. Long-term effects of exercise and physical therapy in people with Parkinson disease. Nat Rev Neurol 2017, 13(11): 689–703.
DOI Google Scholar
[10]
Iznak AF, Iznak EV, Sorokin SA. Changes in EEG and reaction time in the treatment of apathic depression. Zh Nevrol Psikhiatr Im S S Korsakova 2011, 111(7): 49–53.
Google Scholar
[11]
Iyer KK, Au TR, Angwin AJ, et al. Theta and gamma connectivity is linked with affective and cognitive symptoms in Parkinson’s disease. J Affect Disord 2020, 277: 875–884.
DOI Google Scholar
[12]
Zhang BT, Yan GH, Yang ZF, et al. Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification. IEEE Trans Neural Syst Rehabil Eng 2021, 29: 215–229.
DOI Google Scholar
[13]
Fernández-Palleiro P, Rivera-Baltanás T, Rodrigues-Amorim D, et al. Brainwaves oscillations as a potential biomarker for major depression disorder risk. Clin EEG Neurosci 2020, 51(1): 3–9.
DOI Google Scholar
[14]
Li Y, Wang JJ, Li XY, et al. Association between heart rate variability and Parkinson’s disease: a meta-analysis. Curr Pharm Des 2021, 27(17): 2056–2067.
DOI Google Scholar
[15]
Akbilgic O, Kamaleswaran R, Mohammed A, et al. Electrocardiographic changes predate Parkinson’s disease onset. Sci Rep 2020, 10(1): 11319.
DOI Google Scholar
[16]
Chen ZC, Li GL, Liu J. Autonomic dysfunction in Parkinson’s disease: implications for pathophysiology, diagnosis, and treatment. Neurobiol Dis 2020, 134: 104700.
DOI Google Scholar
[17]
Tambasco N, Murgia N, Nigro P, et al. Levodopa-responsive breathing discomfort in Parkinson’s disease patients. J Neural Transm 2018, 125(7): 1033–1036.
DOI Google Scholar
[18]
Guilherme EM, de Fátima Carreira Moreira R, de Oliveira A, et al. Respiratory disorders in Parkinson’s disease. J Parkinsons Dis 2021, 11(3): 993–1010.
DOI Google Scholar
[19]
Zhu YX, Wang XL, Lv DF, et al. Effect of biofeedback therapy on sleep disorders in patients with Parkinson's disease (in Chinese). Shandong Med J 2018, 58(37): 69–71.
Google Scholar
[20]
Gustafsson JK, Södersten M, Ternström S, et al. Treatment of hypophonia in Parkinson’s disease through biofeedback in daily life administered with A portable voice accumulator. J Voice 2021: S0892–S1997(21)00342–8.
DOI Google Scholar
[21]
Kotani N, Morishita T, Yatsugi A, et al. Biofeedback core exercise using hybrid assistive limb for physical frailty patients with or without Parkinson’s disease. Front Neurol 2020, 11: 215.
DOI Google Scholar
[22]
Kearney E, Shellikeri S, Martino R, et al. Augmented visual feedback-aided interventions for motor rehabilitation in Parkinson’s disease: a systematic review. Disabil Rehabil 2019, 41(9): 995–1011.
DOI Google Scholar
[23]
van Gelder LMA, Barnes A, Wheat JS, et al. The use of biofeedback for gait retraining: a mapping review. Clin Biomech 2018, 59: 159–166.
DOI Google Scholar
[24]
Ginis P, Nackaerts E, Nieuwboer A, et al. Cueing for people with Parkinson’s disease with freezing of gait: a narrative review of the state-of-the-art and novel perspectives. Ann Phys Rehabil Med 2018, 61(6): 407–413.
DOI Google Scholar
[25]
Ji LL. Effects of biofeedback therapy combined with rehabilitation nursing intervention on gait function and sleep quality in patients with Parkinson's disease (in Chinese). Nursing Pract Res 2020, 17(14): 39–41.
Google Scholar
[26]
Thompson M, Thompson L. Biofeedback for movement disorders (dystonia with Parkinson’s disease): theory and preliminary results. J Neurother 2002, 6(4): 51–70.
DOI Google Scholar
[27]
Erickson-Davis CR, Anderson JS, Wielinski CL, et al. Evaluation of neurofeedback training in the treatment of Parkinson’s disease: a pilot study. J Neurother 2012, 16(1): 4–11.
DOI Google Scholar
[28]
Mikos V, Heng CH, Tay A, et al. A wearable, patient-adaptive freezing of gait detection system for biofeedback cueing in Parkinson’s disease. IEEE Trans Biomed Circuits Syst 2019, 13(3): 503–515.
DOI Google Scholar
[29]
Davelaar EJ. Mechanisms of neurofeedback: a computation-theoretic approach. Neuroscience 2018, 378: 175–188.
DOI Google Scholar
[30]
Fukuma R, Yanagisawa T, Tanaka M, et al. Real-time neurofeedback to modulate β-band power in the subthalamic nucleus in Parkinson’s disease patients. eNeuro 2018, 5(6): ENEURO.0246–ENEURO.0218.2018.
DOI Google Scholar
[31]
Tinaz S, Para K, Vives-Rodriguez A, et al. Insula as the interface between body awareness and movement: a neurofeedback-guided kinesthetic motor imagery study in Parkinson’s disease. Front Hum Neurosci 2018, 12: 496.
DOI Google Scholar
[32]
Biundo R, Weis L, Bostantjopoulou S, et al. MMSE and MoCA in Parkinson’s disease and dementia with Lewy bodies: a multicenter 1-year follow-up study. J Neural Transm 2016, 123(4): 431–438.
DOI Google Scholar
[33]
Politis M, Wu K, Loane C, et al. Depressive symptoms in PD correlate with higher 5-HTT binding in raphe and limbic structures. Neurology 2010, 75(21): 1920–1927.
DOI Google Scholar
[34]
Su W, Liu HJ, Jiang YY, et al. Correlation between depression and quality of life in patients with Parkinson’s disease. Clin Neurol Neurosurg 2021, 202: 106523.
DOI Google Scholar
[35]
Movement Disorder Society Task Force on Rating Scales for Parkinson's Disease. The Unified Parkinson’s Disease Rating Scale (UPDRS): status and recommendations. Mov Disord 2003, 18(7): 738–750.
DOI Google Scholar
[36]
Babaei-Ghazani A, Mohammadi H, Shahidi GA, et al. Reliability and validity of the Persian translation of Berg Balance Scale in Parkinson disease. Aging Clin Exp Res 2017, 29(5): 857–862.
DOI Google Scholar
[37]
Son M, Youm C, Cheon S, et al. Evaluation of the turning characteristics according to the severity of Parkinson disease during the timed up and go test. Aging Clin Exp Res 2017, 29(6): 1191–1199.
DOI Google Scholar
[38]
Shi ZY, Han XY, Jiang B, et al. Wearable multimodule bio-signal acquisition system: brain multi-plus. In 2022 16th ICME International Conference on Complex Medical Engineering (CME). Zhongshan, China, 2023, pp 77–80.
DOI Google Scholar
[39]
Cook AJ, Pfeifer KJ, Tass PA. A single case feasibility study of sensorimotor rhythm neurofeedback in Parkinson’s disease. Front Neurosci 2021, 15: 623317.
DOI Google Scholar
[40]
Jester DJ, Rozek EK, McKelley RA. Heart rate variability biofeedback: implications for cognitive and psychiatric effects in older adults. Aging Ment Health 2019, 23(5): 574–580.
DOI Google Scholar
[41]
Li F, Yang LC, Shi HY, et al. Differences in photoplethysmography morphological features and feature time series between two opposite emotions: happiness and sadness. Artery Res 2017, 18: 7–13.
DOI Google Scholar
[42]
Nakamura NH, Fukunaga M, Oku Y. Respiratory modulation of cognitive performance during the retrieval process. PLoS One 2018, 13(9): e0204021.
DOI Google Scholar
[43]
Guo GX, Wang SJ, Wang SB, et al. Diagnosing Parkinson’s disease using multimodal physiological signals[C]//International Workshop on Human Brain and Artificial Intelligence. Singapore: Springer, 2021: 125–136.
DOI
[44]
Liu ZD, Chen J, Tang MF. Pulse transit time detection based on waveform time domain feature and dynamic difference threshold. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2017, 34(3): 329–334.
Google Scholar
[45]
Stam CJ, Nolte G, Daffertshofer A. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 2007, 28(11): 1178–1193.
DOI Google Scholar
[46]
Alexander-Bloch A, Lambiotte R, Roberts B, et al. The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia. NeuroImage 2012, 59(4): 3889–3900.
DOI Google Scholar
[47]
Stoffers D, Bosboom JL, Deijen JB, et al. Slowing of oscillatory brain activity is a stable characteristic of Parkinson’s disease without dementia. Brain 2007, 130(Pt 7): 1847–1860.
DOI Google Scholar
[48]
Ghilardi MF, Tatti E, Quartarone A. Beta power and movement-related beta modulation as hallmarks of energy for plasticity induction: implications for Parkinson’s disease. Parkinsonism Relat Disord 2021, 88: 136–139.
DOI Google Scholar
[49]
Wang DD, de Hemptinne C, Miocinovic S, et al. Pallidal deep-brain stimulation disrupts pallidal beta oscillations and coherence with primary motor cortex in Parkinson’s disease. J Neurosci 2018, 38(19): 4556–4568.
DOI Google Scholar
[50]
Jain S, Ton TG, Perera S, et al. Cardiovascular physiology in premotor Parkinson’s disease: a neuroepidemiologic study. Mov Disord 2012, 27(8): 988–995.
DOI Google Scholar
[51]
Johansson C, Lindström B, Forsgren L, et al. Balance and mobility in patients with newly diagnosed Parkinson’s disease - a five-year follow-up of a cohort in northern Sweden. Disabil Rehabil 2020, 42(6): 770–778.
DOI Google Scholar
[52]
Yoo JE, Jang W, Shin DW, et al. Timed up and go test and the risk of Parkinson’s disease: a nation-wide retrospective cohort study. Mov Disord 2020, 35(7): 1263–1267.
DOI Google Scholar
[53]
Boon LI, Hillebrand A, Potters WV, et al. Motor effects of deep brain stimulation correlate with increased functional connectivity in Parkinson’s disease: an MEG study. Neuroimage Clin 2020, 26: 102225.
DOI Google Scholar
[54]
Laansma MA, Bright JK, Al-Bachari S, et al. International multicenter analysis of brain structure across clinical stages of Parkinson’s disease. Mov Disord 2021, 36(11): 2583–2594.
DOI Google Scholar
[55]
Olde Dubbelink KT, Hillebrand A, Stoffers D, et al. Disrupted brain network topology in Parkinson’s disease: a longitudinal magnetoencephalography study. Brain 2014, 137(Pt 1): 197–207.
DOI Google Scholar
[56]
Shi ZY, Jiang B, Liu TT, et al. Individual-level functional connectomes predict the motor symptoms of Parkinson’s disease. Cereb Cortex 2023, 33(10): 6282–6290.
DOI Google Scholar
[57]
Chen KH, Okerstrom KL, Kingyon JR, et al. Startle habituation and midfrontal Theta activity in parkinson disease. J Cogn Neurosci 2016, 28(12): 1923–1932.
DOI Google Scholar
[58]
Chen W, de Hemptinne C, Leibbrand M, et al. Altered prefrontal Theta and gamma activity during an emotional face processing task in parkinson disease. J Cogn Neurosci 2019, 31(11): 1768–1776.
DOI Google Scholar
[59]
Lefaucheur JP, Aleman A, Baeken C, et al. Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): an update (2014-2018). Clin Neurophysiol 2020, 131(2): 474–528.
DOI Google Scholar
[60]
Chander BS, Witkowski M, Braun C, et al. tACS phase locking of frontal midline Theta oscillations disrupts working memory performance. Front Cell Neurosci 2016, 10: 120.
DOI Google Scholar
[61]
Piccinni A, Marazziti D, Veltri A, et al. Depressive symptoms in Parkinson’s disease. Compr Psychiatry 2012, 53(6): 727–731.
DOI Google Scholar
[62]
Spironelli C, Fusina F, Angrilli A. Delta and theta eeg activity during resting state is altered in patients affected by major depression. European Psychiatry, 2021, 64(S1): S338–S339.
DOI Google Scholar
[63]
Chaturvedi M, Bogaarts JG, Kozak Cozac VV, et al. Phase lag index and spectral power as QEEG features for identification of patients with mild cognitive impairment in Parkinson’s disease. Clin Neurophysiol 2019, 130(10): 1937–1944.
DOI Google Scholar
[64]
Abraham DS, Gruber-Baldini AL, Magder LS, et al. Sex differences in Parkinson’s disease presentation and progression. Parkinsonism Relat Disord 2019, 69: 48–54.
DOI Google Scholar
[65]
Tosserams A, Mazaheri M, Vart P, et al. Sex and freezing of gait in Parkinson’s disease: a systematic review and meta-analysis. J Neurol 2021, 268(1): 125–132.
DOI Google Scholar
[66]
Reekes TH, Higginson CI, Ledbetter CR, et al. Sex specific cognitive differences in Parkinson disease. NPJ Parkinsons Dis 2020, 6: 7.
DOI Google Scholar
[67]
Schumann A, Helbing N, Rieger K, et al. Depressive rumination and heart rate variability: a pilot study on the effect of biofeedback on rumination and its physiological concomitants. Front Psychiatry 2022, 13: 961294.
DOI Google Scholar
File
BSA20230015_Supplementary.pdf (309.8 KB)
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 14 March 2023
Revised: 08 May 2023
Accepted: 15 May 2023
Published: 05 June 2023
Issue date: June 2023

Copyright

© The authors 2023.

Acknowledgements

All the authors thank the participants for their contribution to the research.

Rights and permissions

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Return