Migraine is a highly prevalent brain condition with paroxysmal changes in brain excitability believed to contribute to the initiation of an attack. The attacks and their unpredictability have a major impact on the lives of patients. Clinical management is hampered by a lack of reliable predictors for upcoming attacks, which may help in understanding pathophysiological mechanisms to identify new treatment targets that may be positioned between the acute and preventive possibilities that are currently available. So far, a large range of studies using conventional hospital-based EEG recordings have provided contradictory results, with indications of both cortical hyper- as well as hypo-excitability. These heterogeneous findings may largely be because most studies were cross-sectional in design, providing only a snapshot in time of a patient’s brain state without capturing day-to-day fluctuations. The scope of this narrative review is to (i) reflect on current knowledge on EEG changes in the context of migraine, the attack cycle, and underlying pathophysiology; (ii) consider the effects of migraine treatment on EEG features; (iii) outline challenges and opportunities in using EEG for monitoring attack susceptibility; and (iv) discuss future applications of EEG in home-based settings.@en

Introduction: Migraine is associated with enhanced visual sensitivity during and outside attacks. Processing of visual information is a highly non-linear process involving complex interactions across (sub)cortical networks. In this exploratory study, we combined electroencephalography with bi-sinusoidal light stimulation to assess non-linear features of visual processing in participants with migraine. Methods: Twenty participants with migraine (10 with aura, 10 without aura) and ten non-headache controls were measured (outside attacks). Participants received bi-sinusoidal 13 + 23 Hz red light visual stimulation. Electroencephalography spectral power and multi-spectral phase coherence were compared between groups at the driving stimulation frequencies together with multiples and combinations of these frequencies (harmonic and intermodulation frequencies) caused by non-linearities. Results: Only at the driving frequency of 13 Hz higher spectral power was found in migraine with aura participants compared with those with migraine without aura and controls. Differences in phase coherence were present for 2nd, 4th, and 5th-order non-linearities in those with migraine (migraine with and without aura) compared with controls. Bi-sinusoidal light stimulation revealed evident non-linearities in the brain’s electroencephalography response up to the 5th order with reduced phase coherence for higher order interactions in interictal participants with migraine. Discussion: Insight into interictal non-linear visual processing may help understand brain dynamics underlying migraine attack susceptibility. Future research is needed to determine the clinical value of the results.@en

Humans vary the stiffness in their joints depending on tasks and circumstances. For posture control a high joint stiffness is required to withstand perturbations, whereas for force control a low joint stiffness is required. To investigate how humans vary their joint stiffness precisely for moving an arm, a wearable device is needed that can generate small force perturbations at the wrist while measuring the resulting muscular reactions. The majority of the state-of-the-art devices either offer too little versatility or impede the free movement of the arm. Based on a 3-DoF spatial redundant 4-RUU parallel manipulator applied in an inverted way where the original base with actuators has become the moving platform and the original moving platform is attached to the wrist as a bracelet, a versatile, 0.175 kg lightweight, low impedance, and compact wearable device was developed that can generate perturbation forces in X-, Y-, and Z-direction. The design and a prototype of the device are presented with experimental tests showing controlled perturbations in the order of 4 N with frequencies up to 12 Hz.

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Background: Upper limb impairments in a hemiparetic arm are clinically quantified by well-established clinical scales, known to suffer poor validity, reliability, and sensitivity. Alternatively, robotics can assess motor impairments by characterizing joint dynamics through system identification. In this study, we establish the merits of quantifying abnormal synergy, spasticity, and changes in joint viscoelasticity using system identification, evaluating (1) feasibility and quality of parametric estimates, (2) test–retest reliability, (3) differences between healthy controls and patients with upper limb impairments, and (4) construct validity. Methods: Forty-five healthy controls, twenty-nine stroke patients, and twenty cerebral palsy patients participated. Participants were seated with the affected arm immobilized in the Shoulder-Elbow-Perturbator (SEP). The SEP is a one-degree-of-freedom perturbator that enables applying torque perturbations to the elbow while providing varying amounts of weight support to the human arm. Participants performed either a ‘do not intervene’ or a resist task. Elbow joint admittance was quantified and used to extract elbow viscosity and stiffness. Fifty-four of the participants performed two sessions to establish the test–retest reliability of the parameters. Construct validity was assessed by correlating system identification parameters to parameters extracted using a SEP protocol that objectifies current clinical scales (Re-Arm protocol). Results: Feasibility was confirmed by all participants successfully completing the study protocol within ~ 25 min without reporting pain or burden. The parametric estimates were good with a variance-accounted-for of ~ 80%. A fair to excellent test–retest reliability was found (ICC= 0.46 - 0.98) for patients, except for elbow stiffness with full weight support (ICC= 0.35). Compared to healthy controls, patients had a higher elbow viscosity and stiffness during the ‘do not intervene’ task and lower viscosity and stiffness during the resist task. Construct validity was confirmed by a significant (all p< 0.03) but weak to moderate (r= 0.36 - 0.50) correlation with parameters from the Re-Arm protocol. Conclusions: This work demonstrates that system identification is feasible and reliable for quantifying upper limb motor impairments. Validity was confirmed by differences between patients and controls and correlations with other measurements, but further work is required to optimize the experimental protocol and establish clinical value.

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Careful control of joint impedance, or dynamic joint stiffness, is crucial for successful performance of movement. Time-varying system identification (TV-SysID) enables quantification of joint impedance during movement. Several TV-SysID methods exist, but have never been systematically compared. Here, we simulate time-varying joint behavior and propose three performance metrics that enable to quantify and compare TV-SysID methods. Time-varying joint stiffness is simulated using a square wave and subsequently estimated with three TV-SysID methods: the ensemble, short data segment, and basis impulse response function method. These methods were compared based on (1) bias with respect to the simulated joint stiffness, (2) random error across 100 simulation trials, and (3) maximum adaptation speed in joint stiffness that can be captured. This approach revealed that each TV-SysID method has its own unique properties. The simulation method and performance metrics pave the way for developing a framework to quantify the strengths and weaknesses of TV-SysID algorithms for estimating joint impedance.

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Accurate and swift tuning of joint impedance is crucial to perform movement and interaction with our environment. Time-varying system identification enables quantification of joint impedance during movement. Many methods have been developed over the years, each with their own mathematical approach and underlying assumptions. Yet, for the identification of joint impedance, a systematic comparison revealing each method's unique strengths and weaknesses, is lacking. Here, we propose a quantitative framework to compare these methods. The framework is used to review five time-varying system identification methods using both simulated data and experimental data. These methods included three time-domain methods: ensemble, short data segment, and basis impulse response function; and two frequency-domain methods: ensemble spectral, and kernel-based regression. In the simulation study, joint stiffness – the static component of impedance – was simulated as a square wave to mimic the most extreme case of time-varying behavior. The identification results were compared based on the (1) variance accounted for (VAF), (2) bias, (3) random, and (4) total estimation error with respect to the simulated joint stiffness; and (5) rise time between two stiffness levels. In the experimental study, human ankle joint impedance was identified. Identification performance was compared using the variability in estimating joint stiffness – representative of the random error – and VAF. The performance metrics revealed distinct identification properties for each method. Therefore, researchers must make a well-justified decision which method is most appropriate for their application. The combination of simulation and experimental work with extensive performance quantification creates a framework for quantitative assessment of newly developed time-varying system identification methods.

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Background: Proprioception is important for regaining motor function in the paretic upper extremity after stroke. However, clinical assessments of proprioception are subjective and require verbal responses from the patient to applied proprioceptive stimuli. Cortical responses evoked by robotic wrist perturbations and measured by electroencephalography (EEG) may be an objective method to support current clinical assessments of proprioception. Objective: To establish whether evoked cortical responses reflect proprioceptive deficits as assessed by clinical scales and whether they predict upper extremity motor function at 26 weeks after stroke. Methods: Thirty-one patients with stroke were included. In week 1, 3, 5, 12, and 26 after stroke, the upper extremity sections of the Erasmus modified Nottingham Sensory Assessment (EmNSA-UE) and the Fugl-Meyer Motor Assessment (FM-UE) and the EEG responses (64 channels) to robotic wrist perturbations were measured. The extent to which proprioceptive input was conveyed to the affected hemisphere was estimated by the signal-to-noise ratio (SNR) of the evoked response. The relationships between SNR and EmNSA-UE as well as SNR and time after stroke were investigated using linear regression. Receiver-operating-characteristic curves were used to compare the predictive values of SNR and EmNSA-UE for predicting whether patients regained some selective motor control (FM-UE > 22) or whether they could only move their paretic upper extremity within basic limb synergies (FM-UE ≤ 22) at 26 weeks after stroke. Results: Patients (N = 7) with impaired proprioception (EmNSA-UE proprioception score < 8) had significantly smaller SNR than patients with unimpaired proprioception (N = 24) [EmNSA-UE proprioception score = 8, t(29) = 2.36, p = 0.03]. No significant effect of time after stroke on SNR was observed. Furthermore, there was no significant difference in the predictive value between EmNSA-UE and SNR for predicting motor function at 26 weeks after stroke. Conclusion: The SNR of the evoked cortical response does not significantly change as a function of time after stroke and differs between patients with clinically assessed impaired and unimpaired proprioception, suggesting that SNR reflects persistent damage to proprioceptive pathways. A similar predictive value with respect to EmNSA-UE suggests that SNR may be used as an objective predictor next to clinical sensory assessments for predicting motor function at 26 weeks after stroke.

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Background: A trend in the non-invasive brain stimulation literature is to assess the outcome of an intervention using a responder analysis whereby participants are di- or trichotomised in order that they may be classified as either responders or non-responders. Objective: Examine the extent of the Type I error in motor evoked potential (MEP) data subjected to responder analyses. Methods: Seven sets of 30 MEPs were recorded from the first dorsal interosseous muscle in 52 healthy volunteers. Four classification techniques were used to classify the participants as responders or non-responders: (1) the two-step cluster analysis, (2) dichotomised thresholding, (3) relative method and (4) baseline variance method. Results: Despite the lack of any intervention, a significant number of participants were classified as responders (21–71%). Conclusion: This study highlights the very large Type I error associated with dichotomising continuous variables such as the TMS MEP.

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Motor learning has been linked with increases in corticospinal excitability (CSE). However, the robustness of this link is unclear. In this study, changes in CSE associated with learning a visuomotor tracking task were mapped using transcranial magnetic stimulation (TMS). TMS maps were obtained before and after training with the first dorsal interosseous (FDI) of the dominant and nondominant hand, and for a distal (FDI) and proximal (biceps brachii) muscle. Tracking performance improved following 20 min of visuomotor training, while map area was unaffected. Large individual differences were observed with 18%–36% of the participants revealing an increase in TMS map area. This result highlights the complex relationship between motor learning and use-dependent plasticity of the motor cortex.

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In this study, a nonparametric method, developed in Lataire et al. (2012), is applied to the identification of linear time-varying human joint admittance. The aim of the method, denoted Skirt Decomposition method, is to reconstruct the time-varying system function. The main contribution of the paper is to evaluate the possibilities and limitations of the method for the identification of linear time-varying human joint admittance in simulation. The proposed method delivers an estimate of linear time-varying joint admittance from a single experimental trial, provided that a multisine is used as excitation signal. The trade-off between i) the frequency resolution of the dynamics, and ii) the allowable complexity of the time variation is explored.@en

Background During Transcranial Magnetic Stimulation (TMS) experiments researchers often use a neuronavigation system to precisely and accurately maintain coil position and orientation. New method This study aimed to develop and validate an open-source software for TMS coil navigation. StimTrack uses an optical tracker and an intuitive user interface to facilitate the maintenance of position and orientation of any type of coil within and between sessions. Additionally, online access to navigation data is provided, hereby adding e.g. the ability to start or stop the magnetic stimulator depending on the distance to target or the variation of the orientation angles. Results StimTrack allows repeatable repositioning of the coil within 0.7 mm for translation and <1° for rotation. Stimulus-response (SR) curves obtained from 19 healthy volunteers were used to demonstrate that StimTrack can be effectively used in a typical experiment. An excellent intra and inter-session reliability (ICC > 0.9) was obtained on all parameters computed on SR curves acquired using StimTrack. Comparison with existing method StimTrack showed a target accuracy similar to that of a commercial neuronavigation system (BrainSight, Rogue Research Inc.). Indeed, small differences both in position (∼0.2 mm) and orientation (<1°) were found between the systems. These differences are negligible given the human error involved in landmarks registration. Conclusions StimTrack, available as supplementary material, is found to be a good alternative for commercial neuronavigation systems facilitating assessment changes in corticospinal excitability using TMS. StimTrack allows researchers to tailor its functionality to their specific needs, providing added value that benefits experimental procedures and improves data quality.

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During movement, our central nervous system (CNS) takes into account the dynamics of our environment to optimally adapt our joint dynamics. In this study we explored the adaptation of shoulder joint dynamics when a participant interacted with a time-varying virtual environment created by a haptic manipulator. Participants performed a position task, i.e., minimizing position deviations, in face of continuous mechanical force perturbations. During a trial the environmental damping, mimicked by the manipulator, was either increased (0 to 200 N s/m) or decreased (200 to 0 N s/m) in 1 s or 8 s. A system identification technique, kernel-based regression, was used to reveal time-varying shoulder joint dynamics using the frequency response function (FRF). The FRFs revealed that the rate at which shoulder joint dynamics is adapted depends on the rate and direction of change in environmental damping. Adaptation is slow, but starts immediately, after the environmental damping increases, whereas adaptation is fast but delayed when environmental damping decreases. The results obtained in our participants comply with the framework of optimal feedback control, i.e., adaptation of joint dynamics only takes place when motor performance is at risk or when this is energetically advantageous.

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During movements, humans continuously regulate their joint impedance to minimize control effort and optimize performance. Joint impedance describes the relationship between a joint's position and torque acting around the joint. Joint impedance varies with joint angle and muscle activation and differs from trial-to-trial due to inherent variability in the human control system. In this paper, a dedicated time-varying system identification (SI) framework is developed involving a parametric, kernel-based regression, and nonparametric, “skirt decomposition,” SI method to monitor the time-varying joint impedance during a force task. Identification was performed on single trials and the estimators included little a priori assumptions regarding the underlying time-varying joint mechanics. During the experiments, six (human) participants used flexion of the wrist to apply a slow sinusoidal torque to the handle of a robotic manipulator, while receiving small position perturbations. Both methods revealed that the sinusoidal change in joint torque by activation of the wrist flexor muscles resulted in a sinusoidal time-varying joint stiffness and resonance frequency. A third-order differential equation allowed the parametric kernel-based estimator to explain on average 76% of the variance (range 52%-90%). The nonparametric skirt decomposition method could explain on average 84% of the variance (range 66%-91%). This paper presents a novel framework for identification of time-varying joint impedance by making use of linear time-varying models based on a single trial of data.

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Objective: The clinical use of Transcranial Magnetic Stimulation (TMS) as a technique to assess corticospinal excitability is limited by the time for data acquisition and the measurement variability. This study aimed at evaluating the reliability of Stimulus-Response (SR) curves acquired with a recently proposed rapid protocol on tibialis anterior muscle of healthy older adults. Methods: Twenty-four neurologically-intact adults (age:55–75 years) were recruited for this test-retest study. During each session, six SR curves, 3 at rest and 3 during isometric muscle contractions at 5% of maximum voluntary contraction (MVC), were acquired. Motor Evoked Potentials (MEPs) were normalized to the maximum peripherally evoked response; the coil position and orientation were monitored with an optical tracking system. Intra- and inter-session reliability of motor threshold (MT), area under the curve (AURC), MEP_max, stimulation intensity at which the MEP is mid-way between MEP_max and MEP_min (I50), slope in I50, MEP latency, and silent period (SP) were assessed in terms of Standard Error of Measurement (SEM), relative SEM, Minimum Detectable Change (MDC), and Intraclass Correlation Coefficient (ICC). Results: The relative SEM was ≤10% for MT, I50, latency and SP both at rest and 5%MVC, while it ranged between 11% and 37% for AURC, MEP_max, and slope. MDC values were overall quite large; e.g., MT required a change of 12%MSO at rest and 10%MSO at 5%MVC to be considered a real change. Inter-sessions ICC were >0.6 for all measures but slope at rest and MEP_max and latency at 5%MVC. Conclusions: Measures derived from SR curves acquired in <4 minutes are affected by similar measurement errors to those found with long-lasting protocols, suggesting that the rapid method is at least as reliable as the traditional methods. As specifically designed to include older adults, this study provides normative data for future studies involving older neurological patients (e.g. stroke survivors).

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Both motor imagery and mirror training have been used in motor rehabilitation settings to promote skill learning and plasticity. As motor imagery and mirror training are suggested to be closely linked, it was hypothesized that mirror training augmented by motor imagery would increase corticospinal excitability (CSE) significantly compared to mirror training alone. Forty-four participants were split over two experimental groups. Each participant visited the laboratory once to receive either mirror training alone or mirror training augmented with layered stimulus response training (LSRT), a type of motor imagery training. Participants performed 16 min of mirror training, making repetitive grasping movements paced by a metronome. Transcranial magnetic stimulation (TMS) mapping was performed before and after the mirror training to test for changes in CSE of the untrained hand. Self-reports suggested that the imagery training was effective in helping the participant to perform the mirror training task as instructed. Nonetheless, neither training type resulted in a significant change of TMS map area, nor was there an interaction between the groups. The results from the study revealed no effect of a single session of 16 min of either mirror training or mirror training enhanced by imagery on TMS map area. Despite the negative result of the present experiment, this does not suggest that either motor imagery or mirror training might be ineffective as a rehabilitation therapy. Further study is required to allow disentangling the role of imagery and action observation in mirror training so that mirror training can be further tailored to the individual according to their abilities.@en

Coupling between cortical oscillations and muscle activity facilitates neuronal communication during motor control. The linear part of this coupling, known as corticomuscular coherence, has received substantial attention, even though neuronal communication underlying motor control has been demonstrated to be highly nonlinear. A full assessment of corticomuscular coupling, including the nonlinear part, is essential to understand the neuronal communication within the sensorimotor system. In this study, we applied the recently developed n:m coherence method to assess nonlinear corticomuscular coupling during isotonic wrist flexion. The n:m coherence is a generalized metric for quantifying nonlinear cross-frequency coupling as well as linear iso-frequency coupling. By using independent component analysis (ICA) and equivalent current dipole source localization, we identify four sensorimotor related brain areas based on the locations of the dipoles, i.e., the contralateral primary sensorimotor areas, supplementary motor area (SMA), prefrontal area (PFA) and posterior parietal cortex (PPC). For all these areas, linear coupling between electroencephalogram (EEG) and electromyogram (EMG) is present with peaks in the beta band (15–35 Hz), while nonlinear coupling is detected with both integer (1:2, 1:3, 1:4) and non-integer (2:3) harmonics. Significant differences between brain areas is shown in linear coupling with stronger coherence for the primary sensorimotor areas and motor association cortices (SMA, PFA) compared to the sensory association area (PPC); but not for the nonlinear coupling. Moreover, the detected nonlinear coupling is similar to previously reported nonlinear coupling of cortical activity to somatosensory stimuli. We suggest that the descending motor pathways mainly contribute to linear corticomuscular coupling, while nonlinear coupling likely originates from sensory feedback.@en

Transcranial Magnetic Stimulation (TMS) is a widespread technique to study corticospinal excitability. Its use in longitudinal studies is crucial to analyze the plasticity induced by rehabilitation treatments on neuromotor-impaired subjects. As TMS-related measures fluctuate with time and attention, it is crucial to collect them rapidly. Recently, it has been shown that stimulus-response (SR) curves can be acquired in less than two minutes (Mathias et al.). This technique favors the clinical use of TMS-related measures, but its intersession reliability has not been investigated yet. In this study we propose a test-retest reliability analysis of five parameters extracted from SR curves acquired on the preactivated Tibialis Anterior (TA) using the fast acquisition method. A secondary aim was to compare the reliability of these parameters for two different calculation methods of the Motor Evoked Potential (MEP) amplitude: the peak-to-peak value, referred as MEPpp, and the root mean square value, referred as MEParea. The intraclass correlation coefficients (ICC), the standard error of measurement (SEM) and the minimum detectable changes (MDC) were evaluated. Thirteen neurologically-intact elders (age 62.2±5.1) were recruited. The acquisition protocol was set up to minimize the variability of the measurement between the two sessions, carried out on separate days: an optical tracking system was used to maintain the coil placement, the EMG data were normalized over the maximum peripherally evoked action potential and a visual feedback was displayed to the testing subject to maintain the TA activation level. All the parameters, regardless the method used to compute the MEP amplitude , showed good to excellent reliability (0.75

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