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

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

Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson\u27s Disease Patients

Parkinson\u27s Disease (PD) affects over 1% of the population over 60 years of age and is expected to reach 1 million in the USA by the year 2020, growing by 60 thousand each year. It is well understood that PD is characterized by dopaminergic loss, leading to decreased executive function causing motor symptoms such as tremors, bradykinesia, dyskinesia, and freezing of gait (FoG) as well as non-motor symptoms such as loss of smell, depression, and sleep abnormalities. A PD diagnosis is difficult to make since there is no worldwide approved test and difficult to manage since its manifestations are widely heterogeneous among subjects. Thus, understanding the patient subsets and the neural biomarkers that set them apart will lead to improved personalized care. To explore the physiological alternations caused by PD on neurological pathways and their effect on motor control, it is necessary to detect the neural activity and its dissociation with healthy physiological function. To this effect, this study presents a custom ultra-wearable sensor solution, consisting of electroencephalograph, electromyograph, ground reaction force, and symptom measurement sensors for the exploration of neural biomarkers during active gait paradigms. Additionally, this study employed novel de-noising techniques for dealing with the motion artifacts associated with active gait EEG recordings and compared time-frequency features between a group of PD with FoG and a group of age-matched controls and found significant differences between several EEG frequency bands during start and end of normal walking (with a p\u3c0.05)

Detection and Prediction of Freezing of Gait in Parkinson’s Disease using Wearable Sensors and Machine Learning

Freezing of gait (FOG), is a brief episodic absence of forward body progression despite the intention to walk. Appearing mostly in mid-late stage Parkinson’s disease (PD), freezing manifests as a sudden loss of lower-limb function, and is closely linked to falling, decreased functional mobility, and loss of independence. Wearable-sensor based devices can detect freezes already in progress, and intervene by delivering auditory, visual, or tactile stimuli called cues. Cueing has been shown to reduce FOG duration and allow walking to continue. However, FOG detection and cueing systems require data from the freeze episode itself and are thus unable to prevent freezing. Anticipating the FOG episode before onset and supplying a timely cue could prevent the freeze from occurring altogether. FOG has been predicted in offline analyses by training machine learning models to identify wearable-sensor signal patterns known to precede FOG. The most commonly used sensors for FOG detection and prediction are inertial measurement units (IMU) that include an accelerometer, gyroscope and sometimes magnetometer. Currently, the best FOG prediction systems use data collected from multiple sensors on various body locations to develop person-specific models. Multi-sensor systems are more complex and may be challenging to integrate into real-life assistive devices. The ultimate goal of FOG prediction systems is a user-friendly assistive device that can be used by anyone experiencing FOG. To achieve this goal, person-independent models with high FOG prediction performance and a minimal number of conveniently located sensors are needed. The objectives of this thesis were: to develop and evaluate FOG detection and prediction models using IMU and plantar pressure data; determine if event-based or period of gait disruption FOG definitions have better classification performance for FOG detection and prediction; and evaluate FOG prediction models that use a single unilateral plantar pressure insole sensor or bilateral sensors. In this thesis, IMU (accelerometer and gyroscope) and plantar pressure insole sensors were used to collect data from 11 people with FOG while they walked a freeze provoking path. A custom-made synchronization and labeling program was used synchronize the IMU and plantar pressure data and annotate FOG episodes. Data were divided into overlapping 1 s windows with 0.2 s shift between consecutive windows. Time domain, Fourier transform based, and wavelet transform based features were extracted from the data. A total of 861 features were extracted from each of the 71,000 data windows. To evaluate the effectiveness of FOG detection and prediction models using plantar pressure and IMU data features, three feature sets were compared: plantar pressure, IMU, and both plantar pressure and IMU features. Minimum-redundancy maximum-relevance (mRMR) and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or Non-FOG states, wherein the Total-FOG class included windows with data from 2 s before the FOG onset until the end of the FOG episode. The plantar-pressure-only model had the greatest sensitivity, and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, freeze windows, and transition windows between Pre-FOG and FOG). The best model, which used plantar pressure and IMU features, detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Models using both plantar pressure and IMU features performed better than models that used either sensor type alone. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect FOG detection and prediction model performance, especially with respect to multiple FOG in rapid succession. This research examined the effects of defining FOG either as a period of gait disruption (merging successive FOG), or based on an event (no merging), on FOG detection and prediction. Plantar pressure and lower limb acceleration data were used to extract a set of features and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging had little effect on FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession. Despite the known asymmetry of PD motor symptom manifestation, the difference between the more severely affected side (MSS) and less severely affected side (LSS) is rarely considered in FOG detection and prediction studies. The additional information provided by the MSS or LSS, if any, may be beneficial to FOG prediction models, especially if using a single sensor. To examine the effect of using data from the MSS, LSS, or both limbs, multiple FOG prediction models were trained and compared. Three datasets were created using plantar pressure data from the MSS, LSS, and both sides together. Feature selection was performed, and FOG prediction models were trained using the top 5, 10, 15, 20, 25 or 30 features for each dataset. The best models were the MSS model with 15 features, and the LSS and bilateral features with 5 features. The LSS model reached the highest sensitivity (79.5%) and identified the highest percentage of FOG episodes (94.9%). The MSS model achieved the highest specificity (84.9%) and the lowest false positive (FP) rate (2 FP/walking trial). Overall, the bilateral model was best. The bilateral model had 77.3% sensitivity, 82.9% specificity, and identified 94.3% of FOG episodes an average of 1.1 s before FOG onset. Compared to the bilateral model, the LSS model had a higher false positive rate; however, the bilateral and LSS models were similar in all other evaluation metrics. Therefore, using the LSS model instead of the bilateral model would produce similar FOG prediction performance at the cost of slightly more false positives. Given the advantages of single sensor systems, the increased FP rate may be acceptable. Therefore, a single plantar pressure sensor placed on the LSS could be used to develop a FOG prediction system and produce performance similar to a bilateral system

Can deep brain stimulation of the nucleus basalis of Meynert improve thinking and memory problems in Lewy body dementias?

The Lewy body dementias, Parkinson’s disease dementia and dementia with Lewy bodies, are two of the most common causes of dementia worldwide, and share both a common clinical phenotype and underlying pathology. Despite their growing economic and societal disease burden, there are currently only a small number of limited symptomatic therapies available, while modern approaches to develop disease modifying biologic agents have so far produced little tangible effect. There is growing recognition of the need to explore alternative treatment avenues, and the success of deep brain stimulation (DBS) in modulating aberrant neural network processing to relieve symptoms in other neuropsychiatric diseases raises the possibility that this might be achievable in Lewy body dementias. The nucleus basalis of Meynert (NBM) provides the major source of ascending cholinergic innervation to the cortex, and is proposed to be a key node in multiple distributed cognitive networks. The nucleus degenerates significantly in Lewy body dementias, which correlates closely with the severity of cognitive decline. It is therefore proposed that deep brain stimulation to the NBM may be able to modulate cholinergic transmission to cortex, and thereby impact directly upon dementia symptoms. In this thesis I will present preliminary evidence from two experimental clinical trials of deep brain stimulation to the NBM in Lewy body dementias. I will present data showing that this invasive neurosurgical procedure is both safe and well tolerated in patients with advanced dementia, and that low frequency stimulation may be associated with improvements in both memory functions and neuropsychiatric symptomatology. Furthermore, I will present results from the first direct electrophysiological recordings from human NBM in vivo, showing that activity in the nucleus may reflect levels of sustained attention. Finally, I evaluate the overall clinical impact of this novel therapeutic approach in Lewy body dementias, and discuss how our electrophysiological findings may relate to this, and how they contribute to our existing understanding of the physiological function of NBM

Immunohistochemical and electrophysiological investigation of E/I balance alterations in animal models of frontotemporal dementia

Behavioural variant frontotemporal dementia (bvFTD) is a neurodegenerative disease characterised by changes in behaviour. Apathy, behavioural disinhibition and stereotyped behaviours are the first symptoms to appear and all have a basis in reward and pleasure deficits. The ventral striatum and ventral regions of the globus pallidus are involved in reward and pleasure. It is therefore reasonable to suggest alterations in these regions may underpin bvFTD. One postulated contributory factor is alteration in E/I balance in striatal regions. GABAergic interneurons play a role in E/I balance, acting as local inhibitory brakes, they are therefore a rational target for research investigating early biological predictors of bvFTD. To investigate this, we will carry out immunohistochemical staining for GABAergic interneurons (parvalbumin and neuronal nitric oxide synthase) in striatal regions of brains taken from CHMP2B mice, a validated animal model of bvFTD. We hypothesise that there will be fewer GABAergic interneurons in the striatum which may lead to ‘reward-seeking’ behaviour in bvFTD. This will also enable us to investigate any preclinical alterations in interneuron expression within this region. Results will be analysed using a mixed ANOVA and if significant, post hoc t-tests will be used. The second part of our study will involve extracellular recordings from CHMP2B mouse brains using a multi-electrode array (MEA). This will enable us to determine if there are alterations in local field potentials (LFP) in preclinical and symptomatic animals. We will also be able to see if neuromodulators such as serotonin and dopamine effect LFPs after bath application. We will develop slice preparations to preserve pathways between the ventral tegmental area and the ventral pallidum, an output structure of the striatum, and the dorsal raphe nucleus and the VP. Using the MEA we will stimulate an endogenous release of dopamine and serotonin using the slice preparations as described above. This will enable us to see if there are any changes in LFPs after endogenous release of neuromodulators. We hypothesise there will be an increase in LFPs due to loss of GABAergic interneurons

Identifying montages that best detect the electroencephalogram power spectrum alteration during freezing of gait in Parkinson's disease patients

© 2016 IEEE. Our research team has previously used four Electroencephalography (EEG) leads to successfully detect and predict Freezing of Gait (FOG) in Parkinson's disease (PD). However, it remained to be determined whether these four sensor locations that were arbitrarily chosen based on their role in motor control are indeed the most optimal for FOG detection. The aim of this study was therefore to determine the most optimal location and combination of sensors to detect FOG amongst a 32-channel EEG montage using our EEG classification system. EEG measures, including power spectral density, centroid frequency and power spectral entropy, were extracted from 7 patients with PD and FOG during a series of Timed up and Go tasks. By applying a feed-forward neural networks to classify EEG data, the obtained results showed that even a small number of electrodes suffice to construct a FOG detector with expected performance, which is comparable to the use of a full 32 channels montage. This finding therefore progresses the realization of a FOG detection system that can be effectively implemented on a daily basis for FOG prevention, improving the quality of life for many patients with PD

Serotonergic modulation of the ventral pallidum by 5HT1A, 5HT5A, 5HT7 AND 5HT2C receptors

Introduction: Serotonin's involvement in reward processing is controversial. The large number of serotonin receptor sub-types and their individual and unique contributions have been difficult to dissect out, yet understanding how specific serotonin receptor sub-types contribute to its effects on areas associated with reward processing is an essential step. Methods: The current study used multi-electrode arrays and acute slice preparations to examine the effects of serotonin on ventral pallidum (VP) neurons. Approach for statistical analysis: extracellular recordings were spike sorted using template matching and principal components analysis, Consecutive inter-spike intervals were then compared over periods of 1200 seconds for each treatment condition using a student’s t test. Results and conclusions: Our data suggests that excitatory responses to serotonin application are pre-synaptic in origin as blocking synaptic transmission with low-calcium aCSF abolished these responses. Our data also suggests that 5HT1a, 5HT5a and 5HT7 receptors contribute to this effect, potentially forming an oligomeric complex, as 5HT1a antagonists completely abolished excitatory responses to serotonin application, while 5HT5a and 5HT7 only reduced the magnitude of excitatory responses to serotonin. 5HT2c receptors were the only serotonin receptor sub-type tested that elicited inhibitory responses to serotonin application in the VP. These findings, combined with our previous data outlining the mechanisms underpinning dopamine's effects in the VP, provide key information, which will allow future research to fully examine the interplay between serotonin and dopamine in the VP. Investigation of dopamine and serotonins interaction may provide vital insights into our understanding of the VP's involvement in reward processing. It may also contribute to our understanding of how drugs of abuse, such as cocaine, may hijack these mechanisms in the VP resulting in sensitization to drugs of abuse