Background: Turning in place is particularly bothersome for patients with Parkinson's disease (PD) experiencing freezing of gait (FOG). Cues designed to enforce goal-directed turning are not yet available. Objectives: Assess whether augmented reality (AR) visual cues improve FOG and turning in place in PD patients with FOG. Methods: Sixteen PD patients with FOG performed a series of 180 • turns under an experimental condition with AR visual cues displayed through a HoloLens and two control conditions (one consisting of auditory cues and one without any cues). FOG episodes were annotated by two independent raters from video recordings. Motion data were measured with 17 inertial measurement units for calculating axial kinematics, scaling, and timing of turning. Results: AR visual cues did not reduce the percent time frozen (p = 0.73) or the number (p = 0.73) and duration (p = 0.78) of FOG episodes compared to the control condition without cues. All FOG parameters were higher with AR visual cues than with auditory cues [percent time frozen (p = 0.01), number (p = 0.02), and duration (p = 0.007) of FOG episodes]. The AR visual cues did reduce the peak angular velocity (visual vs. uncued p = 0.03; visual vs. auditory p = 0.02) and step height (visual vs. uncued p = 0.02; visual vs. auditory p = 0.007), and increased the step height coefficient of variation (visual vs. uncued p = 0.04; visual vs. auditory p = 0.01) and time to maximum head-pelvis separation (visual vs. uncued p = 0.02; visual vs. auditory p = 0.005), compared to both control conditions. Conclusions: The AR visual cues in this study did not reduce FOG, and worsened some measures of axial kinematics, and turn scaling and timing. Stimulating goal-directed turning might, by itself, be insufficient to reduce FOG and improve turning performance. Trial Registration: This study was registered in the Dutch trial registry (NTR6409; 2017-02-16).

Significance: The expansion of functional near-infrared spectroscopy (fNIRS) methodology and analysis tools gives rise to various design and analytical decisions that researchers have to make. Several recent efforts have developed guidelines for preprocessing, analyzing, and reporting practices. For the planning stage of fNIRS studies, similar guidance is desirable. Study preregistration helps researchers to transparently document study protocols before conducting the study, including materials, methods, and analyses, and thus, others to verify, understand, and reproduce a study. Preregistration can thus serve as a useful tool for transparent, careful, and comprehensive fNIRS study design.Aim: We aim to create a guide on the design and analysis steps involved in fNIRS studies and to provide a preregistration template specified for fNIRS studies.Approach: The presented preregistration guide has a strong focus on fNIRS specific requirements, and the associated template provides examples based on continuous-wave (CW) fNIRS studies conducted in humans. These can, however, be extended to other types of fNIRS studies.Results: On a step-by-step basis, we walk the fNIRS user through key methodological and analysis-related aspects central to a comprehensive fNIRS study design. These include items specific to the design of CW, task-based fNIRS studies, but also sections that are of general importance, including an in-depth elaboration on sample size planning.Conclusions: Our guide introduces these open science tools to the fNIRS community, providing researchers with an overview of key design aspects and specification recommendations for comprehensive study planning. As such it can be used as a template to preregister fNIRS studies or merely as a tool for transparent fNIRS study design.

Background: Freezing of gait (FOG) is an unpredictable gait arrest that hampers the lives of 40% of people with Parkinson’s disease. Because the symptom is heterogeneous in phenotypical presentation (it can present as trembling, shuffling, or akinesia) and manifests during various circumstances (it can be triggered by e.g. turning, passing doors, and dual-tasking), it is particularly difficult to detect with motion sensors. The freezing index (FI) is one of the most frequently used accelerometer-based methods for FOG detection. However, it might not adequately distinguish FOG from voluntary stops, certainly for the akinetic type of FOG. Interestingly, a previous study showed that heart rate signals could distinguish FOG from stopping and turning movements. This study aimed to investigate for which phenotypes and evoking circumstances the FI and heart rate might provide reliable signals for FOG detection.Methods: Sixteen people with Parkinson’s disease and daily freezing completed a gait trajectory designed to provoke FOG including turns, narrow passages, starting, and stopping, with and without a cognitive or motor dual-task. We compared the FI and heart rate of 406 FOG events to baseline levels, and to stopping and normal gait events (i.e. turns and narrow passages without FOG) using mixed-effects models. We specifically evaluated the influence of different types of FOG (trembling vs akinesia) and triggering situations (turning vs narrow passages; no dual-task vs cognitive dual-task vs motor dual-task) on both outcome measures. Results: The FI increased significantly for trembling FOG, but not for akinetic FOG. Furthermore, the index increased similarly during stopping and was therefore not significantly different from FOG. In contrast, heart rate change during FOG was for all types and during all triggering situations statistically different from stopping, but not from normal gait events. Conclusion: The FI has issues to distinguish FOG from voluntary stopping, especially of the akinetic type. In contrast, the clear distinction in heart rate change between FOG and voluntary stops, which was independent of the heterogeneous presentation of FOG, might provide a solution for this issue. Therefore, we suggest that combining a heart rate monitor with a motion sensor may be promising for future FOG detection.

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Background: functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool to study cortical activity during movement and gait. However, this comes with a number of challenges, like the correction for movement-induced systemic changes (i.e., changes in blood pressure, heart rate, breathing). We investigated gait-related tasks in a controlled and seated environment, to validate whether fNIRS can yield comparable results to functional magnetic resonance imaging. More specifically, we studied the fNIRS sensitivity to leg movements and to movement automaticity, and compared this to finger movements.Methods: Twenty-seven healthy participants performed sequential automatic and non-automatic finger tapping and foot stepping tasks. A multichannel fNIRS device including 12 short channels covered the primary motor cortex (M1), the premotor cortex, the prefrontal cortex, and the posterior parietal cortex. Changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) were assessed by a general linear model (GLM). However, we noticed a correlation between the short channels and the expected functional hemodynamic responses, necessitating performing a separate short channel regression instead of adding them as nuisance regressors to the GLM.Results: Consistent with the M1 somatotopy, we found significant HbO increases of very large effect size in the lateral M1 channels during finger tapping (Cohen’s d = 1.35, p<0.001) and significant HbO increases of moderate effect size in the medial M1 channels during foot stepping (Cohen’s d = 0.8, p<0.05). The cortical activity differences between automatic and non-automatic tasks were in line with expectations, though the effect sizes did not yield significance. Importantly, leg movements produced large systemic fluctuations, which were adequately removed by the use of all available short channels.Discussion: Our results indicate that fNIRS is sensitive to leg activity in M1, though the sensitivity is lower than for finger activity and requires rigorous correction for systemic fluctuations. We furthermore highlight that task-evoked systemic effects may cause destabilization of GLM estimates when short channels are correlated with the task regressor.