Exploring fNIRS-guided neurofeedback for alleviating motor symptoms in Parkinson's disease
Poster No:
1684
Submission Type:
Abstract Submission
Authors:
Franziska Klein1, Michael Lührs2, Stefanie Topp3, Stefan Debener4, Karsten Witt3, Cornelia Kranczioch4
Institutions:
1OFFIS e.V. - Institute for Computer Science, Oldenburg, Germany, 2Maastricht University, Maastricht, Netherlands, 3Evangelical Hospital Oldenburg, Oldenburg, Germany, 4University of Oldenburg, Oldenburg, Germany
First Author:
Co-Author(s):
Introduction:
Parkinson's disease (PD) is the second most common neurodegenerative disorder, affecting up to 3% of people over 80 years of age (Tysnes & Storstein, 2017; Mehler, 2022). Its prevalence and societal costs are expected to increase significantly by 2030 (Kouli et al., 2018). The motor symptoms of PD, including tremor, bradykinesia, and rigidity, arise from complex neuropathology, including degeneration of the substantia nigra and cortical motor areas such as the supplementary motor area (SMA) (Rahimpour et al., 2022). Current therapies, including pharmacological treatments, physical exercise, and deep brain stimulation, can have limitations such as side effects, accessibility issues, and variable efficacy (Mehler, 2022).
Neurofeedback (NFB) combined with motor imagery (MI) offers a promising complementary for PD. NFB enables individuals to regulate brain activity in near real-time and has shown the potential to increase SMA activation (Subramanian et al., 2011, 2016). This study is the first to use functional near-infrared spectroscopy (fNIRS) to guide NFB based on changes in deoxygenated hemoglobin ([HbR]) concentration during MI tasks in PD. After validating using fMRI that fNIRS can reliably measure SMA (Klein et al., 2022), the present study developed and tested an NFB system with healthy older adults and PD patients to explore its potential as a tool for motor neurorehabilitation (Klein et al., 2024, preprint).
Neurofeedback (NFB) combined with motor imagery (MI) offers a promising complementary for PD. NFB enables individuals to regulate brain activity in near real-time and has shown the potential to increase SMA activation (Subramanian et al., 2011, 2016). This study is the first to use functional near-infrared spectroscopy (fNIRS) to guide NFB based on changes in deoxygenated hemoglobin ([HbR]) concentration during MI tasks in PD. After validating using fMRI that fNIRS can reliably measure SMA (Klein et al., 2022), the present study developed and tested an NFB system with healthy older adults and PD patients to explore its potential as a tool for motor neurorehabilitation (Klein et al., 2024, preprint).
Methods:
19 early-stage PD patients (PD-NFB group, 63.95 ± 8.41 years, 7F/12M) and 38 healthy older adults participated in the study. Healthy older adults were assigned to a NFB group (63.63 ± 9.04 years) or noNFB control group (63.68 ± 7.75 years). NFB and noNFB groups were age- and gender-matched to the PD-NFB group. The NFB groups performed MI of whole-body movements with real-time NFB based on SMA activity measured by fNIRS using [HbR], while the noNFB group performed MI without NFB. Each participant completed four training sessions (S1–S4), with SMA activation assessed before and after training by performing MI without NFB (PRE & POST). After each session, the NFB groups reported their perceived controllability of the NFB. SMA activation was captured with fNIRS and quantified using GLM-based analyses, including nuisance regressors derived from short-distance channels and electromyography (EMG) of all limbs to control for possible voluntary movements during MI.
Results:
As illustrated in Figure 1, the NFB group showed significantly higher SMA activation than the noNFB group during training sessions (S1–S4), particularly for [HbR]. Within the NFB group, SMA activation increased significantly from PRE to the first training session (S1, p < 0.05), while the noNFB group showed minimal changes between sessions. Between-group comparisons (NFB vs. noNFB) showed significantly higher activation for the NFB group during sessions S1 and S3 (p < 0.05). The PD-NFB group showed moderate but non-significant increases in SMA activation during training. Activation levels remained lower than those of the NFB group. Both the NFB and PD-NFB groups reported positive perceptions of NFB controllability during the sessions.
·Figure 1: Mean beta values across all runs (PRE, S1–S4, POST) for [HbO] (a, b) and [HbR] (c, d). Panels (a) and (c) compare noNFB and NFB groups, while panels (b) and (d) compare PD-NFB and NFB.
Conclusions:
This study demonstrated the feasibility of an fNIRS-guided NFB system targeting the SMA during MI in both healthy older adults and PD patients. Results showed that combining MI with NFB significantly improved SMA activation compared to MI alone in healthy adults, with both NFB groups reporting good perceived controllability across sessions. However, the PD group showed lower and more variable SMA activation, highlighting potential challenges related to disease pathology and individual variability. These results underscore the potential of fNIRS-based NFB for motor rehabilitation while emphasizing the need for further refinement of the NFB protocol, optimized channel selection, inclusion of functional assessments of motor improvements, as well as further evaluation with a PD control group.
Motor Behavior:
Brain Machine Interface 1
Motor Behavior Other
Novel Imaging Acquisition Methods:
NIRS 2
Keywords:
Computational Neuroscience
Degenerative Disease
Motor
Movement Disorder
Near Infra-Red Spectroscopy (NIRS)
Open Data
Other - Neurofeedback
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Provide references using APA citation style.
Kouli, A., Torsney, K. M., & Kuan, W. L. (2018). Parkinson’s Disease: Etiology, Neuropathology, and Pathogenesis. In T. B. Stoker (Eds.) et. al., Parkinson’s Disease: Pathogenesis and Clinical Aspects. Codon Publications.
Rahimpour, S., Rajkumar, S., & Hallett, M. (2022). The Supplementary Motor Complex in Parkinson's Disease. Journal of movement disorders, 15(1), 21–32.
Mehler, DMA. (2022). Turning markers into targets – scoping neural circuits for motor neurofeedback training in Parkinson’s disease. Brain-Apparatus Communication: A Journal of Bacomics, 1(1), 1–27.
Subramanian, L., Hindle, J. V., Johnston, S., Roberts, M. V., Husain, M., Goebel, R., & Linden, D. (2011). Real-time fMRI neurofeedback for treatment of Parkinson's disease. The Journal of neuroscience : the official journal of the Society for Neuroscience, 31(45), 16309–16317.
Subramanian, L., Morris, M. B., Brosnan, M., Turner, D. L., Morris, H. R., & Linden, D. E. (2016). FMRI Neurofeedback-guided Motor Imagery Training and Motor Training for Parkinson's Disease: Randomized Trial. Frontiers in behavioral neuroscience, 10, 111.
Kohl, S. H., Mehler, D. M. A., Lührs, M., Thibault, R. T., Konrad, K., & Sorger, B. (2020). The Potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback-A Systematic Review and Recommendations for Best Practice. Frontiers in neuroscience, 14, 594.
Klein, F., Debener, S., Witt, K., & Kranczioch, C. (2022). fMRI-based validation of continuous-wave fNIRS of supplementary motor area activation during motor execution and motor imagery. Scientific reports, 12(1), 3570.
Klein, F., Kohl, S. H., Lührs, M., Mehler, D. M. A., & Sorger, B. (2024). From lab to life: challenges and perspectives of fNIRS for haemodynamic-based neurofeedback in real-world environments. Philos Trans R Soc Lond B Biol Sci, 379(1915), 20230087.
Klein, F., Lührs, M., Topp, S., Debener, S., Witt, K., & Kranczioch, C. (2024, December 9). Exploring fNIRS-guided neurofeedback to alleviate motor symptoms: A proof-of-concept study in Parkinson‘s disease and healthy older adults.