Plasticity Resembling Spike-Timing Dependent Synaptic Plasticity: The Evidence in Human Cortex

Spike-timing dependent plasticity (STDP) has been studied extensively in a variety of animal models during the past decade but whether it can be studied at the systems level of the human cortex has been a matter of debate. Only recently newly developed non-invasive brain stimulation techniques such as transcranial magnetic stimulation (TMS) have made it possible to induce and assess timing dependent plasticity in conscious human subjects. This review will present a critical synopsis of these experiments, which suggest that several of the principal characteristics and molecular mechanisms of TMS-induced plasticity correspond to those of STDP as studied at a cellular level. TMS combined with a second phasic stimulation modality can induce bidirectional long-lasting changes in the excitability of the stimulated cortex, whose polarity depends on the order of the associated stimulus-evoked events within a critical time window of tens of milliseconds. Pharmacological evidence suggests an NMDA receptor mediated form of synaptic plasticity. Studies in human motor cortex demonstrated that motor learning significantly modulates TMS-induced timing dependent plasticity, and, conversely, may be modulated bidirectionally by prior TMS-induced plasticity, providing circumstantial evidence that long-term potentiation-like mechanisms may be involved in motor learning. In summary, convergent evidence is being accumulated for the contention that it is now possible to induce STDP-like changes in the intact human central nervous system by means of TMS to study and interfere with synaptic plasticity in neural circuits in the context of behavior such as learning and memory

Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications

Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation

Lying obliquely—a clinical sign of cognitive impairment: cross sectional observational study

Objective To determine if failure to spontaneously orient the body along the longitudinal axis of a hospital bed when asked to lie down is associated with cognitive impairment in older patients

Motor Sequence Learning Deficits in Idiopathic Parkinson’s Disease Are Associated With Increased Substantia Nigra Activity

Previous studies have shown that persons with Parkinson’s disease (pwPD) share specific deficits in learning new sequential movements, but the neural substrates of this impairment remain unclear. In addition, the degree to which striatal dopaminergic denervation in PD affects the cortico-striato-thalamo-cerebellar motor learning network remains unknown. We aimed to answer these questions using fMRI in 16 pwPD and 16 healthy age-matched control subjects while they performed an implicit motor sequence learning task. While learning was absent in both pwPD and controls assessed with reaction time differences between sequential and random trials, larger error-rates during the latter suggest that at least some of the complex sequence was encoded. Moreover, we found that while healthy controls could improve general task performance indexed by decreased reaction times across both sequence and random blocks, pwPD could not, suggesting disease-specific deficits in learning of stimulus-response associations. Using fMRI, we found that this effect in pwPD was correlated with decreased activity in the hippocampus over time. Importantly, activity in the substantia nigra (SN) and adjacent bilateral midbrain was specifically increased during sequence learning in pwPD compared to healthy controls, and significantly correlated with sequence-specific learning deficits. As increased SN activity was also associated (on trend) with higher doses of dopaminergic medication as well as disease duration, the results suggest that learning deficits in PD are associated with disease progression, indexing an increased drive to recruit dopaminergic neurons in the SN, however, unsuccessfully. Finally, there were no differences between pwPD and controls in task modulation of the cortico-striato-thalamo-cerebellar network. However, a restricted nigral-striatal model showed that negative modulation of SN to putamen connection was larger in pwPD compared to controls during random trials, while no differences between the groups were found during sequence learning. We speculate that learning-specific SN recruitment leads to a relative increase in SN- > putamen connectivity, which returns to a pathological reduced state when no learning takes plac

Consensus Paper: Probing Homeostatic Plasticity of Human Cortex With Non-invasive Transcranial Brain Stimulation

AbstractHomeostatic plasticity is thought to stabilize neural activity around a set point within a physiologically reasonable dynamic range. Over the last ten years, a wide range of non-invasive transcranial brain stimulation (NTBS) techniques have been used to probe homeostatic control of cortical plasticity in the intact human brain. Here, we review different NTBS approaches to study homeostatic plasticity on a systems level and relate the findings to both, physiological evidence from in vitro studies and to a theoretical framework of homeostatic function. We highlight differences between homeostatic and other non-homeostatic forms of plasticity and we examine the contribution of sleep in restoring synaptic homeostasis. Finally, we discuss the growing number of studies showing that abnormal homeostatic plasticity may be associated to a range of neuropsychiatric diseases

Axonal Degeneration of the Vagus Nerve in Parkinson's Disease—A High-Resolution Ultrasound Study

Background: Recent histopathological studies revealed degeneration of the dorsal motor nucleus early in the course of Parkinson's disease (PD). Degeneration of the vagus nerve (VN) axons following neurodegeneration of brainstem vagal nuclei should be detectable by high-resolution ultrasound (HRUS) as a thinning of the VNs.Methods: We measured both VNs cross-sectional area (VN-CSA) of 35 patients with PD and 35 age- and sex-matched healthy controls at the level of the thyroid gland using HRUS.Results: On both sides, the VN-CSA was significantly smaller in PD patients than in controls (right: 2.1 ± 0.4 vs. 2.3 ± 0.5 mm2, left 1.5 ± 0.4 vs. 1.8 ± 0.4 mm2; both p < 0.05). There was no correlation between the right or left VN-CSA and age, the Hoehn & Yahr stage, disease duration, the motor part of the Unified Parkinson's Disease Rating Scale score, the Montreal Cognitive Assessment score, or the Non-motor Symptoms Questionnaire, and Scale for Parkinson's disease score including its gastrointestinal domain.Conclusions: These findings provide evidencethat atrophy of the VNs in PD patients can be detected in-vivo by HRUS

Плазменное получение тепловой энергии из сульфатного лигнина

This article shows an overview and analysis of the literature on methods of using sludge lignin. This product obtained after treatment of pulp. As a result of calculating the optimum composition of water, organic materials with mechanical impurities from the adiabatic combustion temperature of about 1200 K were determined. Using the obtained results of experimental studies have been carried out in a plasma reactor of the catalytic reactor and has been optimized. The obtained results can be used to create industrial enterprises based on plasma catalytic reactors for waste sludge lignin for the purpose of obtaining heat

Dual-Site Transcranial Magnetic Stimulation for the Treatment of Parkinson's Disease

Abnormal oscillatory activity in the subthalamic nucleus (STN) may be relevant for motor symptoms in Parkinson's disease (PD). Apart from deep brain stimulation, transcranial magnetic stimulation (TMS) may be suitable for altering these oscillations. We speculated that TMS to different cortical areas (primary motor cortex, M1, and dorsal premotor cortex, PMd) may activate neuronal subpopulations within the STN via corticofugal neurons projecting directly to the nucleus. We hypothesized that PD symptoms can be ameliorated by a lasting decoupling of STN neurons by associative dual-site repetitive TMS (rTMS). Associative dual-site rTMS (1 Hz) directed to PMd and M1 (“ADS-rTMS”) was employed in 20 PD patients treated in a blinded, placebo-controlled cross-over design. Results: No adverse events were noted. We found no significant improvement in clinical outcome parameters (videography of MDS-UPDRS-III, finger tapping, spectral tremor power). Variation of the premotor stimulation site did not induce beneficial effects either. A single session of ADS-rTMS was tolerated well, but did not produce a clinically meaningful benefit on Parkinsonian motor symptoms. Successful treatment using TMS targeting subcortical nuclei may require an intervention over several days or more detailed physiological information about the individual brain state and stimulation-induced subcortical effects