2021 NCPD Shenghong He

Dr Shenghong He

2021 Post-doctoral Non-clinical Fellowship

Investigating gait-phase dependent adaptive deep brain stimulation and neurofeedback training for patients with gait disturbances

Apart from symptoms such as slow and/or stiff movements, many patients with Parkinson’s disease can also have difficulties with gait. Currently, continuous deep brain stimulation is an effective treatment for many symptoms of Parkinson’s disease, but for gait, the effect is less consistent and could even make it worse. Previous work reported that electrical waves deep in the brain varied in time with stepping, alternating between left and right sides, and this alternating effect was stronger with more regular stepping in patients with less gait difficulties. Furthermore, a type of brain stimulation that mimics this natural alternating activity could modify stepping in patients with Parkinson’s disease. These together lead to the hypothesis of this project: facilitating this alternating pattern could improve gait. I will be testing this hypothesis through two interventions, including Gait-phase dependent adaptive deep brain stimulation, and neurofeedback training. The overall goal of this project is to better understand the relationship between the observed brain patterns and gait, as well as to improve therapy for gait difficulties.

2021 NCPD Shenghong He Figure

Gait-phase dependent adaptive deep brain stimulation (A) and neurofeedback training (B) for facilitating gait.


Gait-phase modulates alpha and beta oscillations in the pedunculopontine nucleus

He S, Deli A, Fischer P, Wiest C, Huang Y, Martin S, Khawaldeh S, Aziz TZ, Green AL, Brown P, Tan H.

Journal of Neuroscience. 2021 Oct 6;41(40):8390-402

Oct 2021

Entraining stepping movements of Parkinson's patients to alternating subthalamic nucleus deep brain stimulation

Fischer P, He S (co-first author), de Roquemaurel A, Akram H, Foltynie T, Limousin P, Zrinzo L, Hyam J, Cagnan H, Brown P, Tan H

Journal of Neuroscience. 2020 Nov 11;40(46):8964-72

Nov 2020