Citation

D'cruz N, Nieuwboer A. Front. Hum. Neurosci. 2021; 15: e808734.

Copyright

(Copyright © 2021, Frontiers Research Foundation)

DOI

10.3389/fnhum.2021.808734

PMID

34975441

PMCID

PMC8716925

Abstract

People with Parkinson's disease (PD) have an increased risk of falling, which is often associated with the manifestation of freezing of gait (FOG) (Pelicioni et al., 2019). Not surprisingly, turning and gait initiation are frequent triggers of FOG as these complex maneuvers require precise control of the center of mass as well as adaptation of the locomotion pattern (Bekkers et al., 2018). Key to the motor deficits of PD is the loss of motor automaticity, defined as the ability to perform movements without attention directed toward the details of movement (Wu et al., 2015). As such, fine-tuning of gait control becomes especially compromised in daily life when locomotion is less regulated by conscious processing in PD. FOG is more imminent when people with PD are multi-tasking and coping with doorways and obstacles (Beck et al., 2015; Mancini et al., 2018). Equally, FOG is more likely when under stress of FOG-anticipation at "freezing hotspots" or when experiencing fear of falling (Economou et al., 2021). While recognizing that there may be common-end mechanisms between FOG, dynamic balance disturbances, attention and anxiety, in this view point we want to focus on the relevance of studying freezing of repetitive movements of the extremities as a handle on understanding FOG.

The main bottleneck to better understand when and why FOG emerges and how to manage it is the lack of valid markers of FOG, justifying the search for models of freezing in other effectors than in gait. Several instrumented methods for measuring FOG episodes in daily life as well as during standardized lab tests are currently in the validation pipeline (Mancini et al., 2021; Pardoel et al., 2021). However, as yet, they have not demonstrated robust construct and predictive validity, particularly for short and more subtle episodes that are likely to occur in early disease and when ON-medication (Mancini et al., 2019). Digitized outcome measures of FOG vary from fairly simple detection algorithms, as derived from wearable sensor signals, to artificial intelligence-based methodologies (Pardoel et al., 2021). Most of these algorithms are apt in capturing the high frequency movement phenomena associated with FOG, including leg trembling or small shuffling steps (Mancini et al., 2021; Pardoel et al., 2021). Yet, "akinetic FOG," displaying no discernable movement during the episode is more difficult to distinguish from voluntary stops (Cockx et al., 2021). Also, the variable and often interrupted gait bouts observed in daily life provide a noisy background from which to pick up FOG-signals, creating high rates of false positives (Mazilu et al., 2015). The heterogeneous clinical manifestation of FOG by itself also complicates validation work as it affects the robustness of the gold standard measure of FOG. At present, the percentage time frozen (%timeFR) determined during expert video annotation of standardized gait tests constitutes the best reference test, most notably when performing turning tasks (Morris et al., 2012). However, turning is also a hazardous test when no supervision is available to prevent falling, especially in a home setting. As such, markers of freezing which are safe, reliable, responsive and predictive of FOG along the disease progression axis are much needed...

Language: en

Keywords

Parkinson's disease; freezing of gait; biomarkers; conversion; motor blocks