Estimation of time delay by coherence analysis

Using coherence analysis (which is an extensively used method to study the correlations in frequency domain, between two simultaneously measured signals) we estimate the time delay between two signals. This method is suitable for time delay estimation of narrow band coherence signals for which the conventional methods cannot be reliably applied. We show by analysing coupled R\"ossler attractors with a known delay, that the method yields satisfactory results. Then, we apply this method to human pathologic tremor. The delay between simultaneously measured traces of Electroencephalogram (EEG) and Electromyogram (EMG) data of subjects with essential hand tremor is calculated. We find that there is a delay of 11-27 milli-seconds ($ms$) between the tremor correlated parts (cortex) of the brain (EEG) and the trembling hand (EMG) which is in agreement with the experimentally observed delay value of 15 $ms$ for the cortico-muscular conduction time. By surrogate analysis we calculate error-bars of the estimated delay.Comment: 21 pages, 8 figures, elstart.cls file included. Accepted for publication in Physica

Identifying the origin of the source in multi-focal epilepsy patients [Poster]

The aim of this study, was firstly to identify the two neuronal sources in the brain which show the highest dipole strength; caused by the multi-focal epileptic activity using the Minimum-norm (MN) inverse solution technique on a realistic head model. The second aim was to study the dynamics of these two source signals so as to identify which part of the brain is activated first and which part follows, using time-frequency analysis. 1. The source analysis revealed the first two neuronal networks, which showed the highest dipolar strength, in each patient. 2. The power dynamics over the whole time duration and frequency gives valuable information in identifying the region of the brain which is activated first for each patient before the surgical procedure

Progressive ataxia with oculo-palatal tremor and optic atrophy

The final publication is available at Springer via doi: 10.​1007/​s00415-013-7136-

Estimation of Physiological Tremor from Accelerometers for Real-Time Applications

Accurate filtering of physiological tremor is extremely important in robotics assisted surgical instruments and procedures. This paper focuses on developing single stage robust algorithms for accurate tremor filtering with accelerometers for real-time applications. Existing methods rely on estimating the tremor under the assumption that it has a single dominant frequency. Our time-frequency analysis on physiological tremor data revealed that tremor contains multiple dominant frequencies over the entire duration rather than a single dominant frequency. In this paper, the existing methods for tremor filtering are reviewed and two improved algorithms are presented. A comparative study is conducted on all the estimation methods with tremor data from microsurgeons and novice subjects under different conditions. Our results showed that the new improved algorithms performed better than the existing algorithms for tremor estimation. A procedure to separate the intended motion/drift from the tremor component is formulated

The clinical and electrophysiological investigation of tremor

The various forms of tremor are now classified in two axes: clinical characteristics (axis 1) and etiology (axis 2). Electrophysiology is an extension of the clinical exam. Electrophysiologic tests are diagnostic of physiologic tremor, primary orthostatic tremor, and functional tremor, but they are valuable in the clinical characterization of all forms of tremor. Electrophysiology will likely play an increasing role in axis 1 tremor classification because many features of tremor are not reliably assessed by clinical examination alone. In particular, electrophysiology may be needed to distinguish tremor from tremor mimics, assess tremor frequency, assess tremor rhythmicity or regularity, distinguish mechanical-reflex oscillation from central neurogenic oscillation, determine if tremors in different body parts, muscles, or brain regions are strongly correlated, document tremor suppression or entrainment by voluntary movements of contralateral body parts, and document the effects of voluntary movement on rest tremor. In addition, electrophysiologic brain mapping has been crucial in our understanding of tremor pathophysiology. The electrophysiologic methods of tremor analysis are reviewed in the context of physiologic tremor and pathologic tremors, with a focus on clinical characterization and pathophysiology. Electrophysiology is instrumental in elucidating tremor mechanisms, and the pathophysiology of the different forms of tremor is summarized in this review

Primary Sensorimotor Cortex Drives the Common Cortical Network for Gamma Synchronization in Voluntary Hand Movements

Background: Gamma synchronization (GS) may promote the processing between functionally related cortico-subcortical neural populations. Our aim was to identify the sources of GS and to analyze the direction of information flow in cerebral networks at the beginning of phasic movements, and during medium-strength isometric contraction of the hand.Methods: We measured 64-channel electroencephalography in 11 healthy volunteers (age: 25 ± 8 years; four females); surface electromyography detected the movements of the dominant hand. In Task 1, subjects kept a constant medium-strength contraction of the first dorsal interosseus muscle, and performed a superimposed repetitive voluntary self-paced brisk squeeze of an object. In Task 2, brisk, and in Task 3, constant contractions were performed. Time-frequency analysis of the EEG signal was performed with the multitaper method. GS sources were identified in five frequency bands (30–49, 51–75, 76–99, 101–125, and 126–149 Hz) with beamformer inverse solution dynamic imaging of coherent sources. The direction of information flow was estimated by renormalized partial directed coherence for each frequency band. The data-driven surrogate test, and the time reversal technique were performed to identify significant connections.Results: In all tasks, we depicted the first three common sources for the studied frequency bands that were as follows: contralateral primary sensorimotor cortex (S1M1), dorsolateral prefrontal cortex (dPFC) and supplementary motor cortex (SMA). GS was detected in narrower low- (∼30–60 Hz) and high-frequency bands (>51–60 Hz) in the contralateral thalamus and ipsilateral cerebellum in all three tasks. The contralateral posterior parietal cortex was activated only in Task 1. In every task, S1M1 had efferent information flow to the SMA and the dPFC while dPFC had no detected afferent connections to the network in the gamma range. Cortical-subcortical information flow captured by the GS was dynamically variable in the narrower frequency bands for the studied movements.Conclusion: A distinct cortical network was identified for GS in voluntary hand movement tasks. Our study revealed that S1M1 modulated the activity of interconnected cortical areas through GS, while subcortical structures modulated the motor network dynamically, and specifically for the studied movement program

Update of the MDS research criteria for prodromal Parkinson's disease

The MDS Research Criteria for Prodromal PD allow the diagnosis of prodromal Parkinson's disease using an evidence‐based conceptual framework, which was designed to be updated as new evidence becomes available. New prospective evidence of predictive values of risk and prodromal markers published since 2015 was reviewed and integrated into the criteria. Many of the predictive values (likelihood ratios, LR) remain unchanged. The positive likelihood ratio notably increase for olfactory loss and decreased for substantia nigra hyperechogenicity. Negative likelihood ratio remained largely unchanged for all markers. New levels of diagnostic certainty for neurogenic and symptomatic orthostatic hypotension have been added, which substantially differ in positive likelihood ratio from the original publication. For intermediate strength genetic variants, their age‐related penetrance is now incorporated in the calculation of the positive likelihood ratio. Moreover, apart from prospective studies, evidence from cross‐sectional case‐control genome‐wide association studies is also considered (given their likely lack of confounding and reverse causation), and to account for the effect of multiple low‐penetrance genetic variants polygenic risk scores are added to the model. Diabetes, global cognitive deficit, physical inactivity, and low plasma urate levels in men enter the criteria as new markers. A web‐based prodromal PD risk calculator allows the calculation of probabilities of prodromal PD for individuals. Several promising candidate markers may improve the diagnostic accuracy of prodromal PD in the future