Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemani

Neuromagnetic investigations of mechanisms and effects of STN-DBS and medication in Parkinson's disease

Parkinson’s disease (PD) is a neurodegenerative disorder cardinally marked by motor symptoms, but also sensory symptoms and several other non-motor symptoms. PD patients are typically treated with dopaminergic medication for several years. Many patients eventually experience bouts of periods where medication might not be able to effectively control symptoms as well as experience side-effects of long-term dopaminergic treatments. Deep brain stimulation (DBS) is an option as the next therapeutic recourse for such patients. DBS treatment essentially involves placement of stimulating electrodes in the subthalamic nucleus (STN) or the globus pallidus internum (GPi) along with an implanted pulse generator (IPG) in the sub-clavicular space. STN-DBS alleviates motor symptoms and leads to substantial improvements in quality of life for PD patients. Although DBS is known to improve several classes of symptoms, the effect mechanism of DBS is still not clear. While there is a lack of electrophysiological investigation of sensory processing and the effects of treatments in PD altogether, the electrophysiological studies of the cortical dynamics during motor tasks and at rest lack consensus.We recorded magnetoencephalography (MEG) and electromyography (EMG) from PD patients in three studies: (i) at rest, (ii) during median nerve stimulation, and (iii) while performing phasic contractions (hand gripping). The three studies focused on cortical oscillatory dynamics at rest, during somatosensory processing and during movement, respectively. The measurements were conducted in DBS-treated, untreated (DBS washout) and dopaminergic-medicated states. While both treatments (DBS and dopaminergic medication) ameliorated motor symptoms similarly in all studies, they showed differentiated effects on: (i) increased sensorimotor cortical low-gamma spectral power (31-45 Hz) (but no changes in beta power (13-30 Hz)) at rest only during DBS, (ii) somatosensory processing with higher gamma augmentation (31-45 Hz, 20-60 ms) in the dopaminergic-medicated state compared to DBS-treated and untreated states, and (iii) hand gripping with increased motor-related beta corticomuscular coherence (CMC, 13-30 Hz) during dopaminergic medication in contrast to increased gamma power (31-45 Hz) during DBS.Firstly, we infer from the three studies that DBS and dopaminergic medication employ partially different anatomo-functional pathways and functional strategies when improving PD symptoms. Secondly, we suggest that treatments act on pathological oscillatory dynamics differently at cortical and sub-cortical levels and may do so through more sophisticated mechanisms than mere suppression of the pathological spectral power in a particular band. And thirdly, we urge exploring effect mechanisms of PD treatments beyond the motor system. The effects of dopaminergic medication on early somatosensory processing has opened the door for exploring the effects of treatments and studying their mechanisms using electrophysiology, especially in higher order sensory deficits. Integration of such research findings into a holistic view on mechanisms of treatments could pave way for better disease management paradigms. 

Spectral analysis of signals by time-domain statistical characterization and neural network processing: Application to correction of spectral amplitude alterations in pulse-like waveforms

We present a time-domain method to detect and correct spectral alterations of signals by employing statistical characterization of waveforms and a pattern-recognition procedure using simple Artificial Neural Networks. The proposed strategy implements very-fast routines with a computational cost proportional to the number of signal samples, being convenient for applications in embedded environments with limited computational capabilities or fast real-time control tasks. We use the proposed algorithms to correct spectral amplitude attenuations in a pulse-like waveform with a sinc profile as an application example

DIMETER: a haptic master device for tremor diagnosis in neurodegenerative diseases

In this study, a device based on patient motion capture is developed for the reliable and non-invasive diagnosis of neurodegenerative diseases. The primary objective of this study is the classification of differential diagnosis between Parkinson's disease (PD) and essential tremor (ET). The DIMETER system has been used in the diagnoses of a significant number of patients at two medical centers in Spain. Research studies on classification have primarily focused on the use of well-known and reliable diagnosis criteria developed by qualified personnel. Here, we first present a literature review of the methods used to detect and evaluate tremor; then, we describe the DIMETER device in terms of the software and hardware used and the battery of tests developed to obtain the best diagnoses. All of the tests are classified and described in terms of the characteristics of the data obtained. A list of parameters obtained from the tests is provided, and the results obtained using multilayer perceptron (MLP) neural networks are presented and analyzed

Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review

Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and nonmotor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods, and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment

The role of oscillatory synchrony in motor control.

Synchronized oscillations are manifest in various regions in the motor system. Their variable nature has increased the interest in the functional significance. Subcortical and cortical activity in the beta band is pathologically increased in Parkinson's disease (PD) - a state dominated by bradykinesia and rigidity. After the administration of the drug levodopa, beta activity and motor impairment are substantially decreased, while activity in the gamma band is increased. The function of beta bursts within the healthy motor system remains unknown. Recent evidence suggests that beta activity may promote the existing motor set and posture. In this thesis, with the use of positional hold tasks the role of beta activity on performance will be examined. It will be demonstrated that during bursts of beta synchrony in the corticomuscular system of healthy subjects there is an improvement, in the performance of these tasks. The findings will argue that physiological fluctuations in the beta band in the motor system may be of behavioural advantage during fine postural tasks involving the hand. The present work will also examine the role of population oscillations in the parkinsonian basal ganglia. It will demonstrate that under levodopa treatment the pattern of movement-related reactivity in the subthalamic nucleus (STN) and the pedunculopontine nucleus (PPN) as well as the background activity in the PPN change significantly. It will be shown that levodopa suppresses movement-related beta activity around the time of self-paced movements and promotes the increase of movement-related gamma activity contralateral to the movement side, following the same pattern as in the non dopamine-depleted brain. This suggests that dopaminergic therapy restores a more physiological pattern of reactivity in the STN. In the untreated state, beta activity in the STN will be shown to be modulated during repetitive self-paced movements, reflecting a role in ongoing performance, but only when motor performance is maximal and not when bradykinesia occurs. Finally, it will be demonstrated that levodopa promotes alpha band activity in the PPN at rest and before movement suggesting a possible physiological role of this activity in this nucleus. These observations provide further insight in the function of neuronal synchronization in the motor system in health and disease

Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Project RTI-2018-101674-B-I00 and the projects from Junta de Andalucia B-TIC-414, A-TIC-530-UGR20 and P20-00163.In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.Spanish Government RTI-2018-101674-B-I00Junta de Andalucia B-TIC-414 A-TIC-530-UGR20 P20-0016

Action selection in the rhythmic brain: The role of the basal ganglia and tremor.

Low-frequency oscillatory activity has been the target of extensive research both in cortical structures and in the basal ganglia (BG), due to numerous reports of associations with brain disorders and the normal functioning of the brain. Additionally, a plethora of evidence and theoretical work indicates that the BG might be the locus where conflicts between prospective actions are being resolved. Whereas a number of computational models of the BG investigate these phenomena, these models tend to focus on intrinsic oscillatory mechanisms, neglecting evidence that points to the cortex as the origin of this oscillatory behaviour. In this thesis, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. To do so, we build a complete suite of computational tools for the design, optimization and simulation of spiking neural networks. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG, and it was used to make a number of biologically-plausible predictions. First, we investigate the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. Our results indicate the existence of a multimodal gating mechanism at the level of the BG that can be entirely controlled by cortical oscillations, and provide evidence for the hypothesis of cortically-entrained but locally-generated subthalamic beta activity. Next, we explore the relationship of wave properties of entrained cortical inputs, dopamine and the transient effectiveness of the BG, when viewed as an action selection device. We found that cortical frequency, phase, dopamine and the examined time scale, all have a very important impact on the ability of our model to select. Our simulations resulted in a canonical profile of selectivity, which we termed selectivity portraits. Taking together, our results suggest that the cortex is the structure that determines whether action selection will be performed and what strategy will be utilized while the role of the BG is to perform this selection. Some frequency ranges promote the exploitation of actions of whom the outcome is known, others promote the exploration of new actions with high uncertainty while the remaining frequencies simply deactivate selection. Based on this behaviour, we propose a metaphor according to which, the basal ganglia can be viewed as the ''gearbox" of the cortex. Coalitions of rhythmic cortical areas are able to switch between a repertoire of available BG modes which, in turn, change the course of information flow back to and within the cortex. In the same context, dopamine can be likened to the ''control pedals" of action selection that either stop or initiate a decision. Finally, the frequency of active cortical areas that project to the BG acts as a gear lever, that instead of controlling the type and direction of thrust that the throttle provides to an automobile, it dictates the extent to which dopamine can trigger a decision, as well as what type of decision this will be. Finally, we identify a selection cycle with a period of around 200 ms, which was used to assess the biological plausibility of the most popular architectures in cognitive science. Using extensions of the BG model, we further propose novel mechanisms that provide explanations for (1) the two distinctive dynamical behaviours of neurons in globus pallidus external, and (2) the generation of resting tremor in Parkinson's disease. Our findings agree well with experimental observations, suggest new insights into the pathophysiology of specific BG disorders, provide new justifications for oscillatory phenomena related to decision making and reaffirm the role of the BG as the selection centre of the brain.Open Acces