Bicycling suppresses abnormal beta synchrony in the Parkinsonian basal ganglia.

OBJECTIVE: Freezing of gait is a poorly understood symptom of Parkinson disease, and can severely disrupt the locomotion of affected patients. However, bicycling ability remains surprisingly unaffected in most patients suffering from freezing, suggesting functional differences in the motor network. The purpose of this study was to characterize and contrast the oscillatory dynamics underlying bicycling and walking in the basal ganglia. METHODS: We present the first local field potential recordings directly comparing bicycling and walking in Parkinson disease patients with electrodes implanted in the subthalamic nuclei for deep brain stimulation. Low (13-22Hz) and high (23-35Hz) beta power changes were analyzed in 22 subthalamic nuclei from 13 Parkinson disease patients (57.5 ± 5.9 years old, 4 female). The study group consisted of 5 patients with and 8 patients without freezing of gait. RESULTS: In patients without freezing of gait, both bicycling and walking led to a suppression of subthalamic beta power (13-35Hz), and this suppression was stronger for bicycling. Freezers showed a similar pattern in general. Superimposed on this pattern, however, we observed a movement-induced, narrowband power increase around 18Hz, which was evident even in the absence of freezing. INTERPRETATION: These results indicate that bicycling facilitates overall suppression of beta power. Furthermore, movement leads to exaggerated synchronization in the low beta band specifically within the basal ganglia of patients susceptible to freezing. Abnormal ∼18Hz oscillations are implicated in the pathophysiology of freezing of gait, and suppressing them may form a key strategy in developing potential therapies. Ann Neurol 2017;82:592-601.

Power spectral density changes and language lateralization during covert object naming tasks measured with high-density EEG recordings.

Our objective was to study changes in EEG time-domain power spectral density (PSDt) and localization of language areas during covert object naming tasks in human subjects with epilepsy. EEG data for subjects with epilepsy were acquired during the covert object naming tasks using a net of 256 electrodes. The trials required each subject to provide the names of common objects presented every 4 seconds on slides. Each trial comprised the 1.0 second before and 3.0 seconds after initial object presentation. PSDt values at baseline and during tasks were calculated in the theta, alpha, beta, low gamma, and high gamma bands. The spatial contour plots reveal that PSDt values during object naming were 10-20% higher than the baseline values for different bands. Language was lateralized to left frontal or temporal areas. In all cases, the Wada test disclosed language lateralization to the left hemisphere as well.

Is the pain in chronic pancreatitis of neuropathic origin? Support from EEG studies during experimental pain.

AIM: To prove the hypothesis that patients with chronic pancreatitis would show increased theta activity during painful visceral stimulation. METHODS: Eight patients and 12 healthy controls underwent an experiment where the esophagus was electrically stimulated at the pain threshold using a nasal endoscope. The electroencephalogram (EEG) was recorded from 64 surface electrodes and "topographic matching pursuit" was used to extract the EEG information in the early brain activation after stimulation. RESULTS: A major difference between controls and patients were seen in delta and theta bands, whereas there were only minor differences in other frequency bands. In the theta band, the patients showed higher activity than controls persisting throughout the 450 ms of analysis with synchronous brain activation between the channels. The main theta components oscillated with 4.4 Hz in the patients and 5.5 Hz in the controls. The energy in the delta (0.5-3.5 Hz) band was higher in the controls, whereas the patients only showed scattered activity in this band. CONCLUSION: The differences in the theta band indicate that neuropathic pain mechanisms are involved in chronic pancreatitis. This has important implications for the understanding and treatment of pain in these patients, which should be directed against drugs with effects on neuropathic pain disorders.

Time-frequency filtering of MEG signals with matching pursuit.

Time-frequency signal analysis based on various decomposition techniques is widely used in biomedical applications. Matching Pursuit is a new adaptive approach for time-frequency decomposition of such biomedical signals. Its advantage is that it creates a concise signal approximation with the help of a small set of Gabor atoms chosen iteratively from a large and redundant set. In this paper, the usage of Matching Pursuit for time-frequency filtering of biomagnetic signals is proposed. The technique was validated on artificial signals and its performance was tested for varying signal-to-noise ratios using both simulated and real MEG somatic evoked magnetic field data.

BrainCycles: Experimental Setup for the Combined Measurement of Cortical and Subcortical Activity in Parkinson's Disease Patients during Cycling.

Recently, it has been demonstrated that bicycling ability remains surprisingly preserved in Parkinson's disease (PD) patients who suffer from freezing of gait. Cycling has been also proposed as a therapeutic means of treating PD symptoms, with some preliminary success. The neural mechanisms behind these phenomena are however not yet understood. One of the reasons is that the investigations of neuronal activity during pedaling have been up to now limited to PET and fMRI studies, which restrict the temporal resolution of analysis, and to scalp EEG focused on cortical activation. However, deeper brain structures like the basal ganglia are also associated with control of voluntary motor movements like cycling and are affected by PD. Deep brain stimulation (DBS) electrodes implanted for therapy in PD patients provide rare and unique access to directly record basal ganglia activity with a very high temporal resolution. In this paper we present an experimental setup allowing combined investigation of basal ganglia local field potentials (LFPs) and scalp EEG underlying bicycling in PD patients. The main part of the setup is a bike simulator consisting of a classic Dutch-style bicycle frame mounted on a commercially available ergometer. The pedal resistance is controllable in real-time by custom software and the pedal position is continuously tracked by custom Arduino-based electronics using optical and magnetic sensors. A portable bioamplifier records the pedal position signal, the angle of the knee, and the foot pressure together with EEG, EMG, and basal ganglia LFPs. A handlebar-mounted display provides additional information for patients riding the bike simulator, including the current and target pedaling rate. In order to demonstrate the utility of the setup, example data from pilot recordings are shown. The presented experimental setup provides means to directly record basal ganglia activity not only during cycling but also during other movement tasks in patients who have undergone DBS treatment. Thus, it can facilitate studies comparing bicycling and walking, to elucidate why PD patients often retain the ability to bicycle despite severe freezing of gait. Moreover it can help clarifying the mechanism through which cycling may have therapeutic benefits.

Bicycling and Walking are Associated with Different Cortical Oscillatory Dynamics.

Although bicycling and walking involve similar complex coordinated movements, surprisingly Parkinson's patients with freezing of gait typically remain able to bicycle despite severe difficulties in walking. This observation suggests functional differences in the motor networks subserving bicycling and walking. However, a direct comparison of brain activity related to bicycling and walking has never been performed, neither in healthy participants nor in patients. Such a comparison could potentially help elucidating the cortical involvement in motor control and the mechanisms through which bicycling ability may be preserved in patients with freezing of gait. The aim of this study was to contrast the cortical oscillatory dynamics involved in bicycling and walking in healthy participants. To this end, EEG and EMG data of 14 healthy participants were analyzed, who cycled on a stationary bicycle at a slow cadence of 40 revolutions per minute (rpm) and walked at 40 strides per minute (spm), respectively. Relative to walking, bicycling was associated with a stronger power decrease in the high beta band (23-35 Hz) during movement initiation and execution, followed by a stronger beta power increase after movement termination. Walking, on the other hand, was characterized by a stronger and persisting alpha power (8-12 Hz) decrease. Both bicycling and walking exhibited movement cycle-dependent power modulation in the 24-40 Hz range that was correlated with EMG activity. This modulation was significantly stronger in walking. The present findings reveal differential cortical oscillatory dynamics in motor control for two types of complex coordinated motor behavior, i.e., bicycling and walking. Bicycling was associated with a stronger sustained cortical activation as indicated by the stronger high beta power decrease during movement execution and less cortical motor control within the movement cycle. We speculate this to be due to the more continuous nature of bicycling demanding less phase-dependent sensory processing and motor planning, as opposed to walking.

Topographic analysis of engagement and disengagement of neural oscillators in photic driving: a combined electroencephalogram/magnetoencephalogram study.

OBJECTIVE: A coupled system of nonlinear neural oscillators with an individual resonance frequency is assumed to form the neuronal substrate for the photic driving phenomenon. The aim was to investigate the spatiotemporal stability of these oscillators and quantify the spatiotemporal process of engagement and disengagement of the neuronal oscillators in both multitrial and single-trial data. METHODS: White light-emitting diode flicker stimulation was used at 15 frequencies, which were set relative to the individual α frequency of each of the 10 healthy participants. Simultaneously, the electroencephalogram (EEG) and the magnetoencephalogram (MEG) were recorded. Subsequently, spatiotemporal matching pursuit (MP) algorithms were used to analyze the EEG and MEG topographies. RESULTS: Intraindividually similar topographies were found at stimulation frequencies close to (1) the individual α frequency and (2) half the individual α frequency in the multitrial and the single-trial cases. In both stimulation frequency ranges, the authors observed stable topographies 5 to 10 stimuli after the beginning of the stimulation and lasting three nonexisting periods after the end of the stimulation. This was interpreted as the engaging/disengaging effect of the observed oscillations, because especially the frequency parameter adopted before and after stable topographies were observed. Topographic entrainment was slightly more pronounced in MEG as compared with that in EEG. CONCLUSIONS: The results support the hypothesis of nonlinear information processing in human visual system, which can be described by nonlinear neural oscillators.

Multichannel matching pursuit validation and clustering--a simulation and empirical study.

INTRODUCTION: Multichannel matching pursuit (MMP) is a relatively new method that can be applied to electroencephalogram (EEG) signals in combination with inverse modelling. However, limitations of MMP have not been adequately tested. The aims of this study were to investigate how the accuracy of MMP algorithm is altered due to increased number of brain sources and increased noise level, and to implement and test a modified K-means clustering algorithm in order to group similar MMP atoms in time-frequency and space between subjects together. METHODS: Four groups of 20 EEG signals were simulated. The groups consisted of simulations with 5, 10, 15, and 20 brain sources. The accuracy of MMP algorithm was first tested on increasing number of sources. Then, different levels of noise were added to the simulations and accuracy of the algorithm was tested on increasing noise level. K-means clustering algorithm was tested on 4 datasets (5, 10, 15, and 20 sources) of 10 similar phantom subjects. Finally, the clustering algorithm was tested on empirical somatosensory evoked potential and brainstem evoked potential data. RESULTS: The MMP accuracy decreased as the number of sources increased and MMP accuracy was robust to noise. Furthermore, we found that when applying the clustering method to a subject group's MMP data, the clustering method grouped the similar atoms between subjects correctly. CONCLUSION: The MMP and clustering method proved to be an efficient way to group similar brain activity and thus study differences in brain activation sequence to sensory stimulation between groups of subjects.

Inverse modeling on decomposed electroencephalographic data: a way forward?

Inverse modeling is typically applied to instantaneous electroencephalogram signals. However, this approach has several shortcomings including its instability to model multiple and deep located dipole sources and the interference of background noise may hamper the sensitivity, stability, and precision of the estimated dipoles. This article validates different dipole estimation techniques to find the most optimal combination of different analysis principles using both simulations and recordings. Electroencephalogram data were simulated with six known source locations. First, a dataset was simulated with sources chosen to mimic somatosensory-evoked potentials to electrical stimuli. Additionally, 20 further datasets were simulated each containing six randomly located and oriented sources. The simulated sources included superficial, deep, and simultaneously active sources. Furthermore, somatosensory-evoked potentials to median nerve stimuli were recorded from one subject. On both simulated and recorded evoked potential data, three different methods of signal decomposition were compared: independent component analysis (ICA), second-order blind identification (SOBI), and multichannel matching pursuit (MMP). For inverse modeling of the brain sources, the DIPFIT function of the EEGLAB software was used on raw and decomposed data. MMP was able to separate all simulated components that corresponded to superficial, deep, and simultaneously active sources. ICA and SOBI were only able to find components that corresponded to superficial sources. For the 20 randomized simulations, the results from the evoked potential simulation were reproduced. Inverse modeling on MMP components (atoms) was better than on ICA or SOBI components (P < 0.001). DIPFIT on MMP atoms localized 99.2% of the simulated dipoles in correct areas with their correct time/frequency distribution. DIPFIT on ICA and SOBI components localized 35% and 39.6%, respectively of the simulated dipoles in correct areas. As for the real-evoked potentials recorded in one subject, DIPFIT on MMP atoms allowed us to build a dipole model closer to the current physiological knowledge than dipole modeling of ICA and SOBI components. The results show that using MMP before inverse modeling is a reliable way to noninvasively estimate cortical activation.