Dopaminergic contributions to distance estimation in Parkinson’s disease: A sensory-perceptual deficit?

Recent research has found that perceptual deficits exist in Parkinson’s disease (PD), yet the link between perception and movement impairments is not well understood. Inaccurate estimation of distance has the potential to be an underlying cause of movement impairments. Alternatively, those with PD may not be able to perceive their own movements accurately. The main objective of this thesis was to evaluate (1) whether distance estimation is influenced by static perception compared to perception during movement in PD, (2) how visual motion processing contributes to distance estimation during movement, and (3) how dopaminergic medication contributes to these distance estimation deficits. Thirty-seven participants (19 individuals with PD, 18 age-matched healthy control participants (HC) estimated distance to a remembered target in a total of 48 trials, in 4 randomized blocks. Estimation conditions included: (i) no motion: participants pointed with a laser, (ii) motion: participants walked to the estimated position, (iii) visual motion (wheelchair): participants were pushed in a wheelchair while they gave their estimate, (iv) visual motion (VR): participants completed their distance estimate while seated and viewed themselves (as if they were walking) in VR. PD patients completed this protocol twice; once OFF and once ON dopaminergic medication. Participants were matched for age, distance acuity, Modified Mini Mental State Exam (3MS), spatial working memory and motor planning ability. In Study 1 (no motion vs. motion), individuals with PD and healthy control participants did not differ in judgment accuracy during the no motion condition. However, those with PD did have greater amounts of error compared to healthy control participants while estimating distance during the motion condition. Similarly, those with PD significantly underestimated the target position compared to healthy control participants during the motion condition only. Individuals with PD demonstrated greater variability overall. In Study 2, error did not differ between PD and HC groups during visual motion perception (wheelchair). Interestingly, the HC group tended to perform significantly worse than those with PD in the VR condition. Overall, across both studies there was no significant influence of dopaminergic medication in any of the conditions. Individuals with PD demonstrated distance estimation deficits only when required to move through their environment. In contrast to estimations made with movement, neither static estimation nor estimations made with visual motion revealed significant differences between the two groups. Thus perceptual estimation deficits appear to occur only during movement, which may be suggestive of an underlying sensory processing deficit which leads to a problem integrating vision and self-motion information

Understanding the influence of anxiety on gait in Parkinson's disease

Anxiety is a prevalent non-motor symptom of Parkinson’s disease (PD) and has been linked to motor impairments in PD, yet there is a huge gap in the understanding of whether anxiety affects movement, namely gait, in those with PD. Thus, the main objective of the current thesis was to understand if and how anxiety influences gait in PD and whether dopaminergic replacement therapy mediates this relationship. Three studies were conducted to achieve this objective by using a virtual reality setup where participants were asked to walk in virtual environments with and without threat (i.e. across an ELEVATED plank versus across a plank located on the GROUND). Throughout all of the studies all participants (PD and healthy age-matched controls) had greater levels of anxiety in the ELEVATED condition and walked with a slower velocity, smaller steps and greater step-to-step variability compared to the GROUND condition. These results confirmed that the experimental manipulation was effective in every study. The most interesting results in this thesis found that the ELEVATED condition provoked a greater number of freezing of gait (FOG) episodes in PD Freezers (study 1) and significantly more variable gait specifically in Freezers compared to Non-freezers (study 1) and in those with PD who had high trait anxiety compared to those with PD who had low trait anxiety and healthy control participants (study 2). Highly trait anxious PD also appeared to be less able to use visual feedback about their lower limbs when it was provided (study 3) to improve gait especially in the ELEVATED condition. Notably, the frequency of FOG in Freezers (study 1) and step-to-step variability (among other gait parameters) in highly trait anxious PD (study 2 and 3) were improved with dopaminergic replacement therapy. Furthermore, dopaminergic medication also improved step time variability in highly trait anxious PD when visual feedback about their lower limbs was available (study 3). Taken together, this thesis provides strong evidence to suggest that anxiety influences gait in PD, possibly by demanding shared processing resources at the level of the basal ganglia, which may interfere with other processes (such as processing sensory information) necessary to control gait. Dopaminergic replacement therapy might improve information processing within the basal ganglia and thus alleviate some of the interference due to the competition for shared resources. In conclusion, this thesis has (i) provided evidence that suggests anxiety does have an important impact on gait in PD; (ii) provided a mechanistic explanation for how anxiety exacerbates gait impairments in PD; and (iii) elucidated the role of dopamine in mediating anxiety’s influence on gait in PD. Therefore, this thesis has extended the current understanding of anxiety’s influence on movement in PD which has important implications for better management of anxiety and gait impairments in PD

Measuring Anxiety in Lewy Body Disease – Which Scale to Choose?

Background: Anxiety is among the most prevalent mood disorders in Lewy Body Disease (LBD) (i.e., Parkinson’s disease (PD), Dementia with Lewy bodies DLB), and those at-risk for developing LBD (e.g. isolated REM Sleep Behaviour Disorder (iRBD)). Yet, there is little consensus on which clinical scale best evaluates anxiety across synuclein-based diseases. Objective: This study compared the convergent validity of commonly used anxiety scales across PD, DLB and iRBD patients. Methods: Anxiety was assessed using the Hospital Anxiety and Depression Scale (HADS-A), State-Trait Anxiety Inventory (STAI), MDS-UPDRS Anxiety item, and the Parkinson Anxiety Scale (PAS) in 57 participants (17 PD, 16 DLB, and 23 iRBD). Results: Across all groups, PAS total score was significantly associated with trait anxiety (STAI-Y2), whilst HADS-A was associated with PAS total score in the PD and iRBD group. In DLB patients, HADS-A was weakly associated with PAS total score, and significantly correlated with PAS episodic anxiety. Notably, the anxiety item from the MDS-UPDRS did not correlate with any of the other anxiety outcome measures in any group. Conclusions: PAS and STAI-Y2 are the most suitable scales to assess anxiety in synuclein-based diseases. HADS-A showed strong convergent validity in PD and iRBD, it had weaker convergent validity in DLB. The UPDRS anxiety item did not correlate with any of the other anxiety measures, and thus may not be sensitive at detecting anxiety symptoms. Future work should validate anxiety scales in all Lewy Body Disease groups if they are to be implemented in prospective longitudinal cohorts

Changes in structural network topology correlate with severity of hallucinatory behavior in Parkinson's disease

Inefficient integration between bottom-up visual input and higher order visual processing regions is implicated in visual hallucinations in Parkinson's disease (PD). Here, we investigated white matter contributions to this perceptual imbalance hypothesis. Twenty-nine PD patients were assessed for hallucinatory behavior. Hallucination severity was correlated to connectivity strength of the network using the network-based statistic approach. The results showed that hallucination severity was associated with reduced connectivity within a subnetwork that included the majority of the diverse club. This network showed overall greater between-module scores compared with nodes not associated with hallucination severity. Reduced between-module connectivity in the lateral occipital cortex, insula, and pars orbitalis and decreased within-module connectivity in the prefrontal, somatosensory, and primary visual cortices were associated with hallucination severity. Conversely, hallucination severity was associated with increased between- and within-module connectivity in the orbitofrontal and temporal cortex, as well as regions comprising the dorsal attentional and default mode network. These results suggest that hallucination severity is associated with marked alterations in structural network topology with changes in participation along the perceptual hierarchy. This may result in the inefficient transfer of information that gives rise to hallucinations in PD. Author SummaryInefficient integration of information between external stimuli and internal perceptual predictions may lead to misperceptions or visual hallucinations in Parkinson's disease (PD). In this study, we show that hallucinatory behavior in PD patients is associated with marked alterations in structural network topology. Severity of hallucinatory behavior was associated with decreased connectivity in a large subnetwork that included the majority of the diverse club, nodes with a high number of between-module connections. Furthermore, changes in between-module connectivity were found across brain regions involved in visual processing, top-down prediction centers, and endogenous attention, including the occipital, orbitofrontal, and posterior cingulate cortex. Together, these findings suggest that impaired integration across different sides across different perceptual processing regions may result in inefficient transfer of information

An adaptive measure of visuospatial impairment in dementia with Lewy bodies

Background: Background Dementia with Lewy bodies (DLB) is a common cause of dementia with poor prognosis and high hospitalization rates. DLB is frequently misdiagnosed, with clinical features that overlap significantly with other diseases including Parkinson’s disease (PD). Clinical instruments that discriminate and track the progression of cognitive impairment in DLB are needed. Objectives: Objectives The current study was designed to assess the utility of a mental rotation (MR) task for assessing visuospatial impairments in early DLB. Methods: Methods Accuracy of 22 DLB patients, 22 PD patients and 22 age-matched healthy controls in the MR task were compared at comparing shapes with 0°, 45° and 90° rotations. Results: Results Healthy controls and PD patients performed at similar levels while the DLB group were significantly impaired. Further, impairment in the visuospatial and executive function measures correlated with MR poor outcomes. Conclusion: Conclusion These findings support the MR task as an objective measure of visuospatial impairment with the ability to adjust difficulty to suit impairments in a DLB population. This would be a useful tool within clinical trials

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. A novel adversarial training scheme is introduced 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. Experimental results on 393 trials collected from 21 subjects demonstrate encouraging performance of the proposed ASTN for FoG detection with an AUC 0.85

Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia

Cognitive fluctuations are a characteristic and distressing disturbance of attention and consciousness seen in patients with Dementia with Lewy bodies and Parkinson’s disease dementia. It has been proposed that fluctuations result from disruption of key neuromodulatory systems supporting states of attention and wakefulness which are normally characterised by temporally variable and highly integrated functional network architectures. In this study, patients with DLB (n = 25) and age-matched controls (n = 49) were assessed using dynamic resting state fMRI. A dynamic network signature of reduced temporal variability and integration was identified in DLB patients compared to controls. Reduced temporal variability correlated significantly with fluctuation-related measures using a sustained attention task. A less integrated (more segregated) functional network architecture was seen in DLB patients compared to the control group, with regions of reduced integration observed across dorsal and ventral attention, sensorimotor, visual, cingulo-opercular and cingulo-parietal networks. Reduced network integration correlated positively with subjective and objective measures of fluctuations. Regions of reduced integration and unstable regional assignments significantly matched areas of expression of specific classes of noradrenergic and cholinergic receptors across the cerebral cortex. Correlating topological measures with maps of neurotransmitter/neuromodulator receptor gene expression, we found that regions of reduced integration and unstable modular assignments correlated significantly with the pattern of expression of subclasses of noradrenergic and cholinergic receptors across the cerebral cortex. Altogether, these findings demonstrate that cognitive fluctuations are associated with an imaging signature of dynamic network impairment linked to specific neurotransmitters/neuromodulators within the ascending arousal system, highlighting novel potential diagnostic and therapeutic approaches for this troubling symptom

Necessary Skills and Knowledge for Staff Providing Telehealth Services

Background Although motor abnormalities have been flagged as potentially the most sensitive and specific clinical features for predicting the future progression to Parkinson's disease, little work has been done to characterize gait and balance impairments in idiopathic rapid eye movement sleep behavior disorder (iRBD). Objective The objective of this study was to quantitatively determine any static balance as well as gait impairments across the 5 independent domains of gait in polysomnography-confirmed iRBD patients using normal, fast-paced, and dual-task walking conditions. Methods A total of 38 participants (24 iRBD, 14 healthy controls) completed the following 5 different walking trials across a pressure sensor carpet: (1) normal pace, (2) fast pace, (3) while counting backward from 100 by 1s, (4) while naming as many animals as possible, (5) while subtracting 7s from 100. Results Although no gait differences were found between the groups during normal walking, there were significant differences between groups under the fast-paced and dual-task gait conditions. Specifically, in response to the dual tasking, healthy controls widened their step width without changing step width variability, whereas iRBD patients did not widen their step width but, rather, significantly increased their step width variability. Similarly, changes between the groups were observed during fast-paced walking wherein the iRBD patients demonstrated greater step length asymmetry when compared with controls. Conclusions This study demonstrates that iRBD patients have subtle gait impairments, which likely reflect early progressive degeneration in brainstem regions that regulate both REM sleep and gait coordination. Such gait assessments may be useful as a diagnostic preclinical screening tool for future fulminant gait abnormalities for trials of disease-preventive agents. (c) 2019 International Parkinson and Movement Disorder Societ

Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals

© 2017 IEEE. Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation