Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients

Parkinson's Disease (PD) patients with Freezing of Gait (FOG) often experience sudden and unpredictable failure in their ability to start or continue walking, making it potentially a dangerous symptom. Emerging knowledge about brain connectivity is leading to new insights into the pathophysiology of FOG and has suggested that electroencephalogram (EEG) may offer a novel technique for understanding and predicting FOG. In this study we have integrated spatial, spectral, and temporal features of the EEG signals utilizing wavelet coefficients as our input for the Multilayer Perceptron Neural Network and k-Nearest Neighbor classifier. This approach allowed us to predict transition from walking to freezing with 87 % sensitivity and 73 % accuracy. This preliminary data affirms the functional breakdown between areas in the brain during FOG and suggests that EEG offers potential as a therapeutic strategy in advanced PD. © 2013 IEEE

Prediction of freezing of gait using analysis of brain effective connectivity

© 2014 IEEE. Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), in which patients experience sudden difficulties in starting or continuing locomotion. It is described by patients as the sensation that their feet are suddenly glued to the ground. This, disturbs their balance, and hence often leads to falls. In this study, directed transfer function (DTF) and partial directed coherence (PDC) were used to calculate the effective connectivity of neural networks, as the input features for systems that can detect FOG based on a Multilayer Perceptron Neural Network, as well as means for assessing the causal relationships in neurophysiological neural networks during FOG episodes. The sensitivity, specificity and accuracy obtained in subject dependent analysis were 82%, 77%, and 78%, respectively. This is a significant improvement compared to previously used methods for detecting FOG, bringing this detection system one step closer to a final version that can be used by the patients to improve their symptoms

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores

© 2012 IEEE. Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of 96% (79%). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of 94% (84%) for ankle and 89% (94%) for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., 3 s versus 7.5 s) and/or lower tolerance (e.g., 0.4 s versus 2 s)

Subcellular compartmentalisation of copper, iron, manganese, and zinc in the Parkinson's disease brain

© 2017 The Royal Society of Chemistry. Elevated iron and decreased copper levels are cardinal features of the degenerating substantia nigra pars compacta in the Parkinson's disease brain. Both of these redox-active metals, and fellow transition metals manganese and zinc, are found at high concentrations within the midbrain and participate in a range of unique biological reactions. We examined the total metal content and cellular compartmentalisation of manganese, iron, copper and zinc in the degenerating substantia nigra, disease-affected but non-degenerating fusiform gyrus, and unaffected occipital cortex in the post mortem Parkinson's disease brain compared with age-matched controls. An expected increase in iron and a decrease in copper concentration was isolated to the soluble cellular fraction, encompassing both interstitial and cytosolic metals and metal-binding proteins, rather than the membrane-associated or insoluble fractions. Manganese and zinc levels did not differ between experimental groups. Altered Fe and Cu levels were unrelated to Braak pathological staging in our cases of late-stage (Braak stage V and VI) disease. The data supports our hypothesis that regional alterations in Fe and Cu, and in proteins that utilise these metals, contribute to the regional selectively of neuronal vulnerability in this disorder

Progression of Clinical Features in Lewy Body Dementia Can Be Detected Over 6 Months

Copyright \ua9 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.OBJECTIVE: This study aimed to quantify the trajectory and magnitude of change of the key clinical features and corresponding symptom domains of dementia with Lewy bodies (DLB) and Parkinson disease dementia (PDD), including global cognition, parkinsonism, recurrent visual hallucinations, cognitive fluctuations, and sleep disturbance. METHODS: One hundred sixteen patients with Lewy body dementia (DLB = 72, PDD = 44) underwent assessment at baseline and 3 and 6 months as part of a prospective multicenter randomized controlled trial. Linear mixed models were constructed for core outcome measures using the Mini-Mental State Examination (MMSE), motor section of the Unified Parkinson\u27s Disease Rating Scale (UPDRS-III), Dementia Cognitive Fluctuations Scale (DCFS), and Neuropsychiatric Inventory (NPI). RESULTS: Within the time frame of our study (6 months), we were able to identify a significant cognitive decline of 1.3 points on the MMSE (p = 0.002) and significant worsening of motor parkinsonism with an increase in UPDRS-III score of 3.2 points (p = 0.018). Fluctuation severity also increased using the DCFS with a 6-month change in score of 1.3 points (p = 0.001). Uniquely, a signal for increased severity of sleep symptoms of 1.2 points (NPI-sleep) was also detectable (p = 0.04). Significant changes in neuropsychiatric symptoms were not detected. There was no difference in rates of change of scores between DLB and PDD. DISCUSSION: Clinically significant rates of change in core clinical features can be detected and quantified in Lewy body dementia over a relatively short period (6 months) using common clinical instruments and thus may be useful as clinical endpoints for therapeutic trials of disease-modifying and symptomatic agents

Identification of EEG Dynamics during Freezing of Gait and Voluntary Stopping in Patients with Parkinson’s Disease

Mobility is severely impacted in patients with Parkinson's disease (PD), who often experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between “voluntary stopping” and “involuntary stopping” caused by FOG is vital for the detection of and potential intervention for FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal “stop” and “walk” instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was performed to study the dynamics of the EEG spectra induced by different walking phases, including normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that during the transition to involuntary stopping caused by FOG. The EEG signature of voluntary stopping exhibits a significantly decreased power spectrum compared with that of FOG episodes, with distinctly different patterns in the delta and low-beta power in the central area. These findings suggest the possibility of a practical EEG-based tool that can accurately predict FOG episodes, excluding the potential confounding of voluntary stopping

Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine

© 2017 IEEE. Gait initiation Failure (GIF) is the situation in which patients with Parkinson's disease (PD) feel as if their feet get 'stuck' to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG

Isolated rapid eye movement sleep behaviour disorder (iRBD) in the Island Study Linking Ageing and Neurodegenerative Disease (ISLAND) Sleep Study: protocol and baseline characteristics

Isolated rapid eye movement (REM) sleep behaviour disorder (iRBD) is a sleep disorder that is characterised by dream enactment episodes during REM sleep. It is the strongest known predictor of α-synuclein-related neurodegenerative disease (αNDD), such that >80% of people with iRBD will eventually develop Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy in later life. More research is needed to understand the trajectory of phenoconversion to each αNDD. Only five 'gold standard' prevalence studies of iRBD in older adults have been undertaken previously, with estimates ranging from 0.74% to 2.01%. The diagnostic recommendations for video-polysomnography (vPSG) to confirm iRBD makes prevalence studies challenging, as vPSG is often unavailable to large cohorts. In Australia, there have been no iRBD prevalence studies, and little is known about the cognitive and motor profiles of Australian people with iRBD. The Island Study Linking Ageing and Neurodegenerative Disease (ISLAND) Sleep Study will investigate the prevalence of iRBD in Tasmania, an island state of Australia, using validated questionnaires and home-based vPSG. It will also explore several cognitive, motor, olfactory, autonomic, visual, tactile, and sleep profiles in people with iRBD to better understand which characteristics influence the progression of iRBD to αNDD. This paper details the ISLAND Sleep Study protocol and presents preliminary baseline results

Activities of Daily Living, Depression, and Quality of Life in Parkinson’s Disease

This study examined whether activities of daily living (ADL) mediate the relationship between depression and health-related quality of life (HR-QOL) in people with Parkinson’s disease (PD). A cross-sectional, correlational research design examined data from 174 participants who completed the Geriatric Depression Scale (GDS-15), Parkinson’s Disease Questionnaire-39 (PDQ-39), and Unified Parkinson’s Disease Rating Scale-section 2 (UPDRS-section 2 [ADL]). Multiple Regression Analysis (MRA) was used to examine the mediator model. Depression and ADL significantly (p<.001) predicted HR-QOL, and depression significantly (p<.001) predicted ADL. Whilst ADL did not impact on the relationship between depression and HRQOL, there was a significant (p<.001) indirect effect of depression on HR-QOL via ADL, suggesting both direct and indirect (via ADL) effects of depression on HR-QOL. The magnitude of this effect was moderate (R2 = .13). People with PD who report depression also experience greater difficulty completing ADL, which impacts upon their HR-QOL. It is recommended that clinicians adopt a multidisciplinary approach to care by combining pharmacological treatments with psycho/occupational therapy, thereby alleviating the heterogeneous impact of motor and non-motor symptoms on HR-QOL in people with PD