The effects of trait and state anxiety on gait in healthy young adults.

Stable, personality-based (trait), and fluctuating, situational (state) anxiety have both been shown to consume attentional resources and reduce functional cognitive capacity, which may play a role in gait control. However, the role of attention in the relationship between trait and state anxiety has not yet been investigated formally. This study used a virtual reality-threat environment to evaluate whether changes in attention mediate the effects of state and trait anxiety on gait. Thirty adults aged 19-28 completed five walking trials in four conditions: (i) low threat-walking across a virtual plank (0.5 m wide) on flat ground; (ii) low threat + dual task (auditory digit monitoring); (iii) high threat-walking across a virtual plank elevated above a deep pit; and (iv) high threat + dual task. Trait anxiety levels were determined by the State-Trait Anxiety Inventory, while state anxiety was captured using self-assessment manikins. Higher trait anxiety predicted slower gait velocity and longer time in double support in the high-threat condition compared to low-threat condition (i vs iii), but not when dual tasking, compared to single-task walking, in the absence of threat (ii vs i). Additionally, higher trait anxiety predicted increased step length variability in the high compared to low-threat dual-task condition. Overall, trait anxiety predicts a slower, more cautious gait pattern during threatening conditions while dual tasking during the threat.

Evaluating the Sustained Attention Response Task to Quantify Cognitive Fluctuations in Dementia With Lewy Bodies.

Cognitive fluctuations (CFs) are a core diagnostic feature of dementia with Lewy bodies (DLB). Detection of CF is still mostly based on subjective reports from the patient or informant; more quantitative measures are likely to improve the accuracy for the diagnosis of DLB. The purpose of the current study is to test whether performance on the Sustained Attention Response Task (SART) could distinguish those patients with DLB with and without CF. Twenty-four patients with DLB were tested on the SART and performance was related to scores on the Clinical Assessment of Fluctuations (CAFs) and One Day Fluctuation Assessment Scale (ODFAS). The number of "misses" made was a significant predictor of their fluctuation severity, attentional performance, disorganized thinking, and language production ratings on the ODFAS. However, measures on the SART did not correlate with measures on the CAF scale. In conclusion, these findings suggest that SART is a feasible measure of sustained attention in this population and has clinical and diagnostic relevance to the measurement of CF, particularly those aspects measured by the ODFAS.

Impaired Color Discrimination-A Specific Marker of Hallucinations in Lewy Body Disorders.

There is emerging evidence indicating that color discrimination impairments can predict the development of Lewy body dementia in patients with rapid eye movement sleep behavior disorder, Parkinson disease, and in patients with mild cognitive impairment. Despite this clear relationship, color vision deficits are not seen uniformly in patients with dementia with Lewy bodies (DLB), suggesting a more nuanced association with the underlying neuropathology. Visual hallucinations represent a discriminating feature of DLB, and recent evidence implicates visual pathway dysfunction as a significant contributor to this phenomenon. In this study, we examined the relationship between color vision impairment and visual hallucinations, along with other clinical and neuropsychological features in 24 well-characterized patients with DLB alongside 25 healthy controls. Color discrimination impairment was seen in 16 (67%) of 24 DLB participants with a higher error score relative to controls (P = .001). We demonstrate for the first time a strong association between color discrimination errors on the Farnsworth-Munsell 100 hue test and both the presence and severity of hallucinatory symptoms in DLB based on clinician-derived (P = .008) and questionnaire-derived (P = .03) measures. Correlation with clinical and neuropsychological variables revealed that color discrimination is significantly related to visuospatial difficulties measured by the clock-drawing task (P = .02) but not to global measures of cognition, motor severity, age, or disease duration in our cohort. Factor analysis confirmed a unique relationship between color discrimination, visual hallucinations, and visuospatial function. Our results suggest that color discrimination does not simply relate to dementia but rather indexes higher order perceptual deficits that may predict visual hallucinations in Lewy body disorders and share a common pathophysiological substrate.

Graph Fusion Network-Based Multimodal Learning for Freezing of Gait Detection.

Freezing of gait (FoG) is identified as a sudden and brief episode of movement cessation despite the intention to continue walking. It is one of the most disabling symptoms of Parkinson's disease (PD) and often leads to falls and injuries. Many computer-aided FoG detection methods have been proposed to use data collected from unimodal sources, such as motion sensors, pressure sensors, and video cameras. However, there are limited efforts of multimodal-based methods to maximize the value of all the information collected from different modalities in clinical assessments and improve the FoG detection performance. Therefore, in this study, a novel end-to-end deep architecture, namely graph fusion neural network (GFN), is proposed for multimodal learning-based FoG detection by combining footstep pressure maps and video recordings. GFN constructs multimodal graphs by treating the encoded features of each modality as vertex-level inputs and measures their adjacency patterns to construct complementary FoG representations, thus reducing the representation redundancy among different modalities. In addition, since GFN is devised to process multimodal graphs of arbitrary structures, it is expected to achieve superior performance with inputs containing missing modalities, compared to the alternative unimodal methods. A multimodal FoG dataset was collected, which included clinical assessment videos and footstep pressure sequences of 340 trials from 20 PD patients. Our proposed GFN demonstrates a great promise of multimodal FoG detection with an area under the curve (AUC) of 0.882. To the best of our knowledge, this is one of the first studies to utilize multimodal learning for automated FoG detection, which offers significant opportunities for better patient assessments and clinical trials in the future.

Multi-Level Adversarial Spatio-Temporal Learning for Footstep Pressure Based FoG Detection.

Freezing of gait (FoG) is one of the most common symptoms of Parkinson's disease, which is a neurodegenerative disorder of the central nervous system impacting millions of people around the world. To address the pressing need to improve the quality of treatment for FoG, devising a computer-aided detection and quantification tool for FoG has been increasingly important. As a non-invasive technique for collecting motion patterns, the footstep pressure sequences obtained from pressure sensitive gait mats provide a great opportunity for evaluating FoG in the clinic and potentially in the home environment. In this study, FoG detection is formulated as a sequential modelling task and a novel deep learning architecture, namely Adversarial Spatio-temporal Network (ASTN), is proposed to learn FoG patterns across multiple levels. ASTN introduces a novel adversarial training scheme with a multi-level subject discriminator to obtain subject-independent FoG representations, which helps to reduce the over-fitting risk due to the high inter-subject variance. As a result, robust FoG detection can be achieved for unseen subjects. The proposed scheme also sheds light on improving subject-level clinical studies from other scenarios as it can be integrated with many existing deep architectures. To the best of our knowledge, this is one of the first studies of footstep pressure-based FoG detection and the approach of utilizing ASTN is the first deep neural network architecture in pursuit of subject-independent representations. In our experiments on 393 trials collected from 21 subjects, the proposed ASTN achieved an AUC 0.85, clearly outperforming conventional learning methods.

Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait Detection.

Freezing of Gait (FoG) is a common symptom of Parkinson's disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, despite a patient's intention to move. Clinical assessment of FoG events from manual observations by experts is both time-consuming and highly subjective. Therefore, machine learning-based FoG identification methods would be desirable. In this article, we address this task as a fine-grained human action recognition problem based on vision inputs. A novel deep learning architecture, namely, higher order polynomial transformer (HP-Transformer), is proposed to incorporate pose and appearance feature sequences to formulate fine-grained FoG patterns. In particular, a higher order self-attention mechanism is proposed based on higher order polynomials. To this end, linear, bilinear, and trilinear transformers are formulated in pursuit of discriminative fine-grained representations. These representations are treated as multiple streams and further fused by a cross-order fusion strategy for FoG detection. Comprehensive experiments on a large in-house dataset collected during clinical assessments demonstrate the effectiveness of the proposed method, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 is achieved for detecting FoG.

Dissociating between sensory and perceptual deficits in PD: more than simply a motor deficit.

Although Parkinson's disease (PD) is traditionally considered a motor output disorder, recent evidence suggests that people with PD may have sensory and perceptual impairments that may underlie movement impairments. Yet there has not been any direct testing of perceptual judgments, especially when manipulating the sensory feedback on which these judgments are made. The present study investigated how perception might be influenced by sensory feedback to contribute to height estimations and obstacle stepping in PD relative to healthy age-matched control participants. Perceptual judgment accuracy was evaluated by judging 3 typically encountered obstacle heights in 2 sensory feedback conditions: (1) vision of foot available and (2) without vision of foot (reliance on proprioceptive feedback to estimate height). Then participants proceeded to walk and step over the obstacle. Fifteen individuals with PD and 15 healthy control participants completed the task. As seen with toe elevation, toe elevation variability, and toe error measures, individuals with PD overestimated the obstacle height and were significantly more variable when relying solely on proprioception (in contrast to when vision was available) compared with healthy controls, although no differences between groups in obstacle crossing were found. These results support the notion that sensory deficits may contribute to inaccuracy of perceptual judgment and has the potential to contribute to gait behaviors such as tripping and falling, especially when vision is not available. Future studies should carefully consider the impact of sensory and perceptual deficits that might contribute to movement planning problems and consequentially movement impairments.

Graph Sequence Recurrent Neural Network for Vision-based Freezing of Gait Detection.

Freezing of gait (FoG) is one of the most common symptoms of Parkinson's disease (PD), a neurodegenerative disorder which impacts millions of people around the world. Accurate assessment of FoG is critical for the management of PD and to evaluate the efficacy of treatments. Currently, the assessment of FoG requires well-trained experts to perform time-consuming annotations via vision-based observations. Thus, automatic FoG detection algorithms are needed. In this study, we formulate vision-based FoG detection, as a fine-grained graph sequence modelling task, by representing the anatomic joints in each temporal segment with a directed graph, since FoG events can be observed through the motion patterns of joints. A novel deep learning method is proposed, namely graph sequence recurrent neural network (GS-RNN), to characterize the FoG patterns by devising graph recurrent cells, which take graph sequences of dynamic structures as inputs. For the cases of which prior edge annotations are not available, a data-driven based adjacency estimation method is further proposed. To the best of our knowledge, this is one of the first studies on vision-based FoG detection using deep neural networks designed for graph sequences of dynamic structures. Experimental results on more than 150 videos collected from 45 patients demonstrated promising performance of the proposed GS-RNN for FoG detection with an AUC value of 0.90.

Restless mind, restless body.

In the present work, we investigate the hypothesis that failures of task-related executive control that occur during episodes of mind wandering are associated with an increase in extraneous movements (fidgeting). In 2 studies, we assessed mind wandering using thought probes while participants performed the metronome response task (MRT), which required them to synchronize button presses with tones. Participants performed this task while sitting on a Wii Balance Board providing us with an index of fidgeting. Results of Study 1 demonstrate that relative to on-task periods, mind wandering is indeed accompanied by increases in fidgeting, as well as increased response variability in the MRT. In Study 2, we observed that only deep mind wandering was associated with increases in fidgeting, whereas task-related response variability increased even during mild mind wandering. We interpret these findings in the context of current theories of mind wandering and suggest that (a) mind wandering is associated with costs not only to primary-task performance but also to secondary-task goals (e.g., controlling extraneous movements) and (b) these costs may depend on the degree to which task-related executive control processes are disengaged during mind wandering (i.e., depth of mind wandering).