Multimodal Detection/Prediction and Management of Freezing of Gait

Background

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders. Reported standardized incidence rates of PD are 8-18 per 100,000 person-years with a prevalence as high as 3-4 % in individuals 70 years of age and older [1]. It manifests a wide array of motor symptoms, including tremor, bradykinesia, rigidity, and impaired postural balance, significantly affecting individuals’ quality of life. One of the most disabling motor symptoms of PD is Freezing of Gait (FoG). FoG is quite prevalent in people with PD  (pwPD) and affects about 80% of pwPD with late-stage disease [2,3]. This distinct and incapacitating clinical syndrome is defined by brief episodes of being unable to walk or taking very few steps [4]. Freezing episodes are often described as “the feet are glued to the floor” while the body’s center of mass continues to move forward, sometimes resulting in a forward fall. FoG not only increases the risk of falls, leading to further physical injury and psychological distress but also contributes to a profound sense of loss of independence among pwPD [5].

Researchers have used a variety of methods and approaches to understand the pathophysiological mechanisms behind FoG [6,7].  Previous studies show that the presence of FoG affects footstrike variability8,9 and has mainly an impact on ankle and knee joints [9]. Moreover, during freezing episodes, pwPD exhibits a lower overall muscle activity of distal lower limb muscles, a decrease in muscle variability, and an alteration in inter-muscle coordination [10,11]. Going up in the hierarchical organization of the neuromusculoskeletal system, abnormalities in the evoked reflex responses have been highlighted in patients with FoG [12], and abnormal cortical and subcortical oscillations were found during freezing episodes [13–15].

The intricate nature of FoG, influenced by complex interactions between neurological dysfunction, medication effects, and individual patient factors, challenges the development of universally effective interventions (one-fits-all solution). There remains a critical need for innovative, personalized solutions that address the underlying mechanisms of FoG, adapt to individual patient needs over time, and integrate seamlessly into daily life, enhancing autonomy and quality of life for pwPD. Thus, this project aims to bridge these gaps by i) identifying unique biomarkers associated with FoG in pwPD by collecting and analyzing comprehensive data encompassing electroencephalography (EEG) recordings, biomechanical measurements using wearable technology, evoked reflex responses, and electromyographic (EMG) data; ii) developing an innovative, non-invasive, closed-loop wearable technology to detect or predict biomarkers of FoG episodes and iii) use this information to apply non-invasive treatment strategies in a closed-loop fashion to reduce FoG events.

Goals

Our research aims to establish a comprehensive framework for the objective detection, prediction, and management of freezing episodes by integrating advanced sensing with real-time intervention strategies. We focus on characterizing robust multimodal biomarkers across kinematic, physiological, and neural domains to identify the specific physiological signatures that define the transition from fluid locomotion into a frozen state. By leveraging these markers, we seek to move beyond reactive detection toward prediction, establishing a reliable pre-FoG window that enables preventive action before a complete motor block occurs. Central to this work is the design and implementation of closed-loop wearable solutions capable of delivering non-invasive, real-time stimuli to facilitate gait recovery and mitigate the impact of freezing events.

References

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4. Nutt, J. G. et al. Freezing of gait: moving forward on a mysterious clinical phenomenon. Lancet Neurol. 10, 734–744 (2011).
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8. Pillai, L., Shah, K., Glover, A. & Virmani, T. Increased foot strike variability during turning in Parkinson’s disease patients with Freezing of Gait. Gait Posture 92, 321–327 (2022).
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11. Nieuwboer, A. et al. Electromyographic profiles of gait prior to onset of freezing episodes in patients with Parkinson’s disease. Brain J. Neurol. 127, 1650–1660 (2004).
12. Lira, J. L. O. et al. Loss of presynaptic inhibition for step initiation in parkinsonian individuals with freezing of gait. J. Physiol. 598, 1611–1624 (2020).
13. Thenaisie, Y. et al. Principles of gait encoding in the subthalamic nucleus of people with Parkinson’s disease. Sci. Transl. Med. 14, eabo1800 (2022).
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