Noninvasive neuromagnetic single-trial analysis of human neocortical population spikes.

Neuronal spiking is commonly recorded by invasive sharp microelectrodes, whereas standard noninvasive macroapproaches (e.g., electroencephalography [EEG] and magnetoencephalography [MEG]) predominantly represent mass postsynaptic potentials. A notable exception are low-amplitude high-frequency (∼600 Hz) somatosensory EEG/MEG responses that can represent population spikes when averaged over hundreds of trials to raise the signal-to-noise ratio. Here, a recent leap in MEG technology-featuring a factor 10 reduction in white noise level compared with standard systems-is leveraged to establish an effective single-trial portrayal of evoked cortical population spike bursts in healthy human subjects. This time-resolved approach proved instrumental in revealing a significant trial-to-trial variability of burst amplitudes as well as time-correlated (∼10 s) fluctuations of burst response latencies. Thus, ultralow-noise MEG enables noninvasive single-trial analyses of human cortical population spikes concurrent with low-frequency mass postsynaptic activity and thereby could comprehensively characterize cortical processing, potentially also in diseases not amenable to invasive microelectrode recordings.

Optimization of data pre-processing methods for time-series classification of electroencephalography data.

The performance of time-series classification of electroencephalographic data varies strongly across experimental paradigms and study participants. Reasons are task-dependent differences in neuronal processing and seemingly random variations between subjects, amongst others. The effect of data pre-processing techniques to ameliorate these challenges is relatively little studied. Here, the influence of spatial filter optimization methods and non-linear data transformation on time-series classification performance is analyzed by the example of high-frequency somatosensory evoked responses. This is a model paradigm for the analysis of high-frequency electroencephalography data at a very low signal-to-noise ratio, which emphasizes the differences of the explored methods. For the utilized data, it was found that the individual signal-to-noise ratio explained up to 74% of the performance differences between subjects. While data pre-processing was shown to increase average time-series classification performance, it could not fully compensate the signal-to-noise ratio differences between the subjects. This study proposes an algorithm to prototype and benchmark pre-processing pipelines for a paradigm and data set at hand. Extreme learning machines, Random Forest, and Logistic Regression can be used quickly to compare a set of potentially suitable pipelines. For subsequent classification, however, machine learning models were shown to provide better accuracy.

Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses.

Brain responses vary considerably from moment to moment, even to identical sensory stimuli. This has been attributed to changes in instantaneous neuronal states determining the system's excitability. Yet the spatiotemporal organization of these dynamics remains poorly understood. Here we test whether variability in stimulus-evoked activity can be interpreted within the framework of criticality, which postulates dynamics of neural systems to be tuned toward the phase transition between stability and instability as is reflected in scale-free fluctuations in spontaneous neural activity. Using a novel noninvasive approach in 33 male human participants, we tracked instantaneous cortical excitability by inferring the magnitude of excitatory postsynaptic currents from the N20 component of the somatosensory evoked potential. Fluctuations of cortical excitability demonstrated long-range temporal dependencies decaying according to a power law across trials, a hallmark of systems at critical states. As these dynamics covaried with changes in prestimulus oscillatory activity in the alpha band (8-13 Hz), we establish a mechanistic link between ongoing and evoked activity through cortical excitability and argue that the co-emergence of common temporal power laws may indeed originate from neural networks poised close to a critical state. In contrast, no signatures of criticality were found in subcortical or peripheral nerve activity. Thus, criticality may represent a parsimonious organizing principle of variability in stimulus-related brain processes on a cortical level, possibly reflecting a delicate equilibrium between robustness and flexibility of neural responses to external stimuli.SIGNIFICANCE STATEMENT Variability of neural responses in primary sensory areas is puzzling, as it is detrimental to the exact mapping between stimulus features and neural activity. However, such variability can be beneficial for information processing in neural networks if it is of a specific nature, namely, if dynamics are poised at a so-called critical state characterized by a scale-free spatiotemporal structure. Here, we demonstrate the existence of a link between signatures of criticality in ongoing and evoked activity through cortical excitability, which fills the long-standing gap between two major directions of research on neural variability: the impact of instantaneous brain states on stimulus processing on the one hand and the scale-free organization of spatiotemporal network dynamics of spontaneous activity on the other.

Cingulate and cerebellar beta oscillations are engaged in the acquisition of auditory-motor sequences.

Singing, music performance, and speech rely on the retrieval of complex sounds, which are generated by the corresponding actions and are organized into sequences. It is crucial in these forms of behavior that the serial organization (i.e., order) of both the actions and associated sounds be monitored and learned. To investigate the neural processes involved in the monitoring of serial order during the initial learning of sensorimotor sequences, we performed magnetoencephalographic recordings while participants explicitly learned short piano sequences under the effect of occasional alterations of auditory feedback (AAF). The main result was a prominent and selective modulation of beta (13-30 Hz) oscillations in cingulate and cerebellar regions during the processing of AAF that simulated serial order errors. Furthermore, the AAF-induced modulation of beta oscillations was associated with higher error rates, reflecting compensatory changes in sequence planning. This suggests that cingulate and cerebellar beta oscillations play a role in tracking serial order during initial sensorimotor learning and in updating the mapping of the sensorimotor representations. The findings support the notion that the modulation of beta oscillations is a candidate mechanism for the integration of sequential motor and auditory information during an early stage of skill acquisition in music performance. This has potential implications for singing and speech. Hum Brain Mapp 38:5161-5179, 2017. © 2017 Wiley Periodicals, Inc.

Multiscale temporal neural dynamics predict performance in a complex sensorimotor task.

Ongoing neuronal oscillations are pivotal in brain functioning and are known to influence subjects' performance. This modulation is usually studied on short time scales whilst multiple time scales are rarely considered. In our study we show that Long-Range Temporal Correlations (LRTCs) estimated from the amplitude of EEG oscillations over a range of time-scales predict performance in a complex sensorimotor task, based on Brain-Computer Interfacing (BCI). Our paradigm involved eighty subjects generating covert motor responses to dynamically changing visual cues and thus controlling a computer program through the modulation of neuronal oscillations. The neuronal dynamics were estimated with multichannel EEG. Our results show that: (a) BCI task accuracy may be predicted on the basis of LRTCs measured during the preceding training session, and (b) this result was not due to signal-to-noise ratio of the ongoing neuronal oscillations. Our results provide direct empirical evidence in addition to previous theoretical work suggesting that scale-free neuronal dynamics are important for optimal brain functioning.

Correlates of a single cortical action potential in the epidural EEG.

To identify the correlates of a single cortical action potential in surface EEG, we recorded simultaneously epidural EEG and single-unit activity in the primary somatosensory cortex of awake macaque monkeys. By averaging over EEG segments coincident with more than hundred thousand single spikes, we found short-lived (≈ 0.5 ms) triphasic EEG deflections dominated by high-frequency components >800 Hz. The peak-to-peak amplitude of the grand-averaged spike correlate was 80 nV, which matched theoretical predictions, while single-neuron amplitudes ranged from 12 to 966 nV. Combining these estimates with post-stimulus-time histograms of single-unit responses to median-nerve stimulation allowed us to predict the shape of the evoked epidural EEG response and to estimate the number of contributing neurons. These findings establish spiking activity of cortical neurons as a primary building block of high-frequency epidural EEG, which thus can serve as a quantitative macroscopic marker of neuronal spikes.

Non-invasive single-trial EEG detection of evoked human neocortical population spikes.

QUESTION: Human high-frequency (>400 Hz) components of somatosensory evoked potentials (hf-SEPs), which can be recorded non-invasively at the scalp, are generated by cortical population spikes, as inferred from microelectrode recordings in non-human primates. It is a critical limitation to broader neurophysiological study of hf-SEPs in that hundreds of responses have to be averaged to detect hf-SEPs reliably. Here, we establish a framework for detecting human hf-SEPs non-invasively in single trials. METHODS: Spatio-temporal features were extracted from band-pass filtered (400-900 Hz) hf-SEPs by bilinear Common Spatio-Temporal Patterns (bCSTP) and then classified by a weighted Extreme Learning Machine (w-ELM). The effect of varying signal-to-noise ratio (SNR), number of trials, and degree of w-ELM re-weighting was characterized using surrogate data. For practical demonstration of the algorithm, median nerve hf-SEPs were recorded inside a shielded room in four subjects, spanning the hf-SEP signal-to-noise ratio characteristic for a larger population, utilizing a custom-built 29-channel low-noise EEG amplifier. RESULTS: Using surrogate data, the SNR proved to be pivotal to detect hf-SEPs in single trials efficiently, with the trade-off between sensitivity and specificity of the algorithm being obtained by the w-ELM re-weighting parameter. In practice, human hf-SEPs were detected non-invasively in single trials with a sensitivity of up to 99% and a specificity of up to 97% in two subjects, even without any recourse to knowledge of stimulus timing. Matching with the results of the surrogate data analysis, these rates dropped to 62-79% sensitivity and 18-31% specificity in two subjects with lower SNR. CONCLUSIONS: Otherwise buried in background noise, human high-frequency EEG components can be extracted from low-noise recordings. Specifically, refined supervised filter optimization and classification enables the reliable detection of single-trial hf-SEPs, representing non-invasive correlates of cortical population spikes. SIGNIFICANCE: While low-frequency EEG reflects summed postsynaptic potentials, and thereby neuronal input, we suggest that high-frequency EEG (>400 Hz) can provide non-invasive access to the unaveraged output of neuronal computation, i.e., single-trial population spike activity evoked in the responsive neuronal ensemble.

Power-law dynamics in neuronal and behavioral data introduce spurious correlations.

Relating behavioral and neuroimaging measures is essential to understanding human brain function. Often, this is achieved by computing a correlation between behavioral measures, e.g., reaction times, and neurophysiological recordings, e.g., prestimulus EEG alpha-power, on a single-trial-basis. This approach treats individual trials as independent measurements and ignores the fact that data are acquired in a temporal order. It has already been shown that behavioral measures as well as neurophysiological recordings display power-law dynamics, which implies that trials are not in fact independent. Critically, computing the correlation coefficient between two measures exhibiting long-range temporal dependencies may introduce spurious correlations, thus leading to erroneous conclusions about the relationship between brain activity and behavioral measures. Here, we address data-analytic pitfalls which may arise when long-range temporal dependencies in neural as well as behavioral measures are ignored. We quantify the influence of temporal dependencies of neural and behavioral measures on the observed correlations through simulations. Results are further supported in analysis of real EEG data recorded in a simple reaction time task, where the aim is to predict the latency of responses on the basis of prestimulus alpha oscillations. We show that it is possible to "predict" reaction times from one subject on the basis of EEG activity recorded in another subject simply owing to the fact that both measures display power-law dynamics. The same is true when correlating EEG activity obtained from different subjects. A surrogate-data procedure is described which correctly tests for the presence of correlation while controlling for the effect of power-law dynamics.

Monochromatic ultra-slow (~0.1 Hz) oscillations in the human electroencephalogram and their relation to hemodynamics.

Previous studies demonstrated the presence of Monochromatic Ultra-Slow Oscillations (MUSO) in human EEG. In the present study we explored the biological origin of MUSO by simultaneous recordings of EEG, Near-Infrared Spectroscopy (NIRS), arterial blood pressure, respiration and Laser Doppler flowmetry. We used a head-up tilt test in order to check whether MUSO might relate to Mayer waves in arterial blood pressure, known to be enhanced by the tilting procedure. MUSO were detected in 8 out of 10 subjects during rest and showed a striking monochromatic spectrum (0.07-0.14 Hz). The spatial topography of MUSO was complex, showing multiple foci variable across subjects. While the head-up tilt test increased the relative power of Mayer waves, it had no effect on MUSO. On the other hand, the relative spectral power of 0.1 Hz oscillations in EEG, NIRS and blood pressure signals were positively correlated across subjects in the tilted condition. Eight subjects showed a coherence between MUSO and NIRS/arterial blood pressure. Moreover, MUSO at different electrode sites demonstrated coherence not reducible to volume conduction, thus indicating that MUSO are unlikely to be generated by one source. We related our experimental findings to known biological phenomena being generated at about 0.1 Hz, i.e.: arterial blood pressure, cerebral and skin vasomotion, respiration and neuronal activity. While no definite conclusion can yet be drawn as to an exact physiological mechanism of MUSO, we suggest that these oscillations might be of a rather extraneuronal origin reflecting cerebral vasomotion.

It is not all about phase: amplitude dynamics in corticomuscular interactions.

Corticomuscular interactions are studied mostly with EEG/EMG coherence, which, however, does not allow quantification of amplitude dynamics of sensorimotor oscillations. Here, we investigated the amplitude dynamics of sensorimotor EEG beta oscillations during an isometric task and their relation to corticomuscular coherence (CMC). We used amplitude envelopes of beta oscillations, derived from multichannel EEG and EMG recordings, as a measure of local cortical and spinal-cord synchronization. In general, we showed that the amplitude of cortical beta oscillations can influence CMC in two ways. First, we showed that the signal-to-noise ratio of pre-stimulus beta oscillations affects CMC. Second, we demonstrated that the attenuation of beta oscillations upon imperative stimulus correlated with the CMC strength. Attenuation of cortical beta oscillations was previously hypothesized to reflect increased motor cortex excitability. Consequently, this correlation might indicate that high cortical excitability, produced by imperative stimulus, facilitates the recruitment of neuronal networks responsible for establishing reliable corticospinal control manifested in larger CMC. Critically, we demonstrated that the amplitude envelopes of beta oscillations in EEG and EMG are positively correlated on time scales ranging from 50 to 1000 ms. Such correlations indicate that the amplitude of cortical beta oscillations might relate to the rhythmic spiking output of both corticospinal neurons and their spinal targets. Compared to CMC, however, amplitude-envelope correlations were detected in fewer cases, which might relate to a higher susceptibility of these correlations to signal-to-noise ratio. We conclude that EEG beta oscillations, originating from the sensorimotor cortex, can transmit not only their phase but also amplitude dynamics through the spinal motoneurons down to peripheral effectors.

No somatotopy of sensorimotor alpha-oscillation responses to differential finger stimulation.

The somatotopic layout of the primary somatosensory cortex is known for its fine spatial structure as delineated in single cell recordings and macroscopic EEG evoked responses. While a gross somatotopic layout has been revealed also for neuronal oscillations responding to sensorimotor stimulation of distant body parts (e.g. hand vs. foot), it is still unclear whether these oscillatory dynamics exhibit fine spatial layout comparable to those found in evoked responses. In twelve healthy subjects we applied electric stimuli to the first (D1) and fifth finger (D5) of the same hand while performing high-density electroencephalography. We used Common Spatial Pattern analysis to optimally extract components showing the strongest Event-Related Desynchronization (ERD) in neuronal alpha oscillations. In agreement with the previous studies, dipole locations of Somatosensory Evoked Potentials (SEPs) confirmed the existence of spatially distinct representations of each finger. In contrast, dipole locations of alpha-ERD patterns did not yield spatially different source locations, indicating that the stimulation of different fingers most likely resulted in oscillatory activity of overlapping neuronal populations. When both fingers were stimulated simultaneously the SEP dipole strength was found increased in comparison to a stimulation of either finger alone, in agreement with spatially distinct SEP to finger stimulation. The strength of ERD, on the other hand, was the same regardless of whether either one or both fingers were stimulated. Our findings might reflect anatomical constraints on the sequential temporal activation of fingers' skin where almost simultaneous activation of many fingers usually occurs in everyday activities, such as grasping or holding objects. Such simultaneity is unlikely to benefit from slow amplitude modulation of alpha oscillations, which would rather be beneficial for contrasting somatosensory processing of distinct body parts.

Enhanced performance by a hybrid NIRS-EEG brain computer interface.

Noninvasive Brain Computer Interfaces (BCI) have been promoted to be used for neuroprosthetics. However, reports on applications with electroencephalography (EEG) show a demand for a better accuracy and stability. Here we investigate whether near-infrared spectroscopy (NIRS) can be used to enhance the EEG approach. In our study both methods were applied simultaneously in a real-time Sensory Motor Rhythm (SMR)-based BCI paradigm, involving executed movements as well as motor imagery. We tested how the classification of NIRS data can complement ongoing real-time EEG classification. Our results show that simultaneous measurements of NIRS and EEG can significantly improve the classification accuracy of motor imagery in over 90% of considered subjects and increases performance by 5% on average (p<0:01). However, the long time delay of the hemodynamic response may hinder an overall increase of bit-rates. Furthermore we find that EEG and NIRS complement each other in terms of information content and are thus a viable multimodal imaging technique, suitable for BCI.

Electrophysiological characterization of the hyperdirect pathway and its functional relevance for subthalamic deep brain stimulation.

The subthalamic nucleus (STN) receives input from various cortical areas via hyperdirect pathway (HDP) which bypasses the basal-ganglia loop. Recently, the HDP has gained increasing interest, because of its relevance for STN deep brain stimulation (DBS). To understand the HDP's role cortical responses evoked by STN-DBS have been investigated. These responses have short (<2 ms), medium (2-15 ms), and long (20-70 ms) latencies. Medium-latency responses are supposed to represent antidromic cortical activations via HDP. Together with long-latency responses the medium responses can potentially be used as biomarker of DBS efficacy as well as side effects. We here propose that the activation sequence of the cortical evoked responses can be conceptualized as high frequency oscillations (HFO) for signal analysis. HFO might therefore serve as marker for antidromic activation. Using existing knowledge on HFO recordings, this approach allows data analyses and physiological modeling to advance the pathophysiological understanding of cortical DBS-evoked high-frequency activity.

Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations.

Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.

A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition.

Neuronal oscillations have been shown to underlie various cognitive, perceptual and motor functions in the brain. However, studying these oscillations is notoriously difficult with EEG/MEG recordings due to a massive overlap of activity from multiple sources and also due to the strong background noise. Here we present a novel method for the reliable and fast extraction of neuronal oscillations from multi-channel EEG/MEG/LFP recordings. The method is based on a linear decomposition of recordings: it maximizes the signal power at a peak frequency while simultaneously minimizing it at the neighboring, surrounding frequency bins. Such procedure leads to the optimization of signal-to-noise ratio and allows extraction of components with a characteristic "peaky" spectral profile, which is typical for oscillatory processes. We refer to this method as spatio-spectral decomposition (SSD). Our simulations demonstrate that the method allows extraction of oscillatory signals even with a signal-to-noise ratio as low as 1:10. The SSD also outperformed conventional approaches based on independent component analysis. Using real EEG data we also show that SSD allows extraction of neuronal oscillations (e.g., in alpha frequency range) with high signal-to-noise ratio and with the spatial patterns corresponding to central and occipito-parietal sources. Importantly, running time for SSD is only a few milliseconds, which clearly distinguishes it from other extraction techniques usually requiring minutes or even hours of computational time. Due to the high accuracy and speed, we suggest that SSD can be used as a reliable method for the extraction of neuronal oscillations from multi-channel electrophysiological recordings.

Optimal imaging of cortico-muscular coherence through a novel regression technique based on multi-channel EEG and un-rectified EMG.

Cortico-muscular coherence (CMC) reflects interactions between muscular and cortical activities as detected with EMG and EEG recordings, respectively. Most previous studies utilized EMG rectification for CMC calculation. Yet, recent modeling studies predicted that EMG rectification might have disadvantages for CMC evaluation. In addition, previously the effect of rectification on CMC was estimated with single-channel EEG which might be suboptimal for detection of CMC. In order to optimally detect CMC with un-rectified EMG and resolve the issue of EMG rectification for CMC estimation, we introduce a novel method, Regression CMC (R-CMC), which maximizes the coherence between EEG and EMG. The core idea is to use multiple regression where narrowly filtered EEG signals serve as predictors and EMG is the dependent variable. We investigated CMC during isometric contraction of the abductor pollicis brevis muscle. In order to facilitate the comparison with previous studies, we estimated the effect of rectification with frequently used Laplacian filtering and C3/C4 vs. linked earlobes. For all three types of analysis, we detected CMC in the beta frequency range above the contralateral sensorimotor areas. The R-CMC approach was validated with simulations and real data and was found capable of recovering CMC even in case of high levels of background noise. When using single channel data, there were no changes in the strength of CMC estimated with rectified or un-rectified EMG--in agreement with the previous findings. Critically, for both Laplacian and R-CMC analyses EMG rectification resulted in significantly smaller CMC values compared to un-rectified EMG. Thus, the present results provide empirical evidence for the predictions from the earlier modeling studies that rectification of EMG can reduce CMC.

High-frequency EEG covaries with spike burst patterns detected in cortical neurons.

Invasive microelectrode recordings measure neuronal spikes, which are commonly considered inaccessible through standard surface electroencephalogram (EEG). Yet high-frequency EEG potentials (hf-EEG, f > 400 Hz) found in somatosensory evoked potentials of primates may reflect the mean population spike responses of coactivated cortical neurons. Since cortical responses to electrical nerve stimulation vary strongly from trial to trial, we investigated whether the hf-EEG signal can also echo single-trial variability observed at the single-unit level. We recorded extracellular single-unit activity in the primary somatosensory cortex of behaving macaque monkeys and identified variable spike burst responses following peripheral stimulation. Each of these responses was classified according to the timing of its spike constituents, conforming to one of a discrete set of spike patterns. We here show that these spike patterns are accompanied by variations in the concomitant epidural hf-EEG. These variations cannot be explained by fluctuating stimulus efficacy, suggesting that they were generated within the thalamocortical network. As high-frequency EEG signals can also be reliably recorded from the scalp of human subjects, they may provide a noninvasive window on fluctuating cortical spike activity in humans.

Neurophysiological predictor of SMR-based BCI performance.

Brain-computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serve two purposes: a better understanding of the 'BCI-illiteracy phenomenon', and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minute recording of a 'relax with eyes open' condition using two Laplacian EEG channels. A correlation of r=0.53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N=80 BCI-naive participants in their first session with the Berlin brain-computer interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).

Role of neuronal synchrony in the generation of evoked EEG/MEG responses.

Evoked EEG/MEG responses are a primary real-time measure of perceptual and cognitive activity in the human brain, but their neuronal generator mechanisms are not yet fully understood. Arguments have been put forward in favor of either "phase-reset" of ongoing oscillations or "added-energy" models. Instead of advocating for one or the other model, here we show theoretically that the differentiation between these two generation mechanisms might not be possible if based solely on macroscopic EEG/MEG recordings. Using mathematical modeling, we show that a simultaneous phase reset of multiple oscillating neuronal (microscopic) sources contributing to EEG/MEG can produce evoked responses in agreement with both, the "added-energy" and the "phase-reset" model. We observe a smooth transition between the two models by just varying the strength of synchronization between the multiple microscopic sources. Consequently, because precise knowledge about the strength of microscopic ensemble synchronization is commonly not available in noninvasive EEG/MEG studies, they cannot, in principle, differentiate between the two mechanisms for macroscopic-evoked responses.

Speed effects of deep brain stimulation for Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) accelerates reaction time (RT) in patients with Parkinson's disease (PD), particularly in tasks in which decisions on the response side have to be made. This might indicate that DBS speeds up both motor and nonmotor operations. Therefore, we studied the extent to which modifications of different processing streams could explain changes of RT under subthalamic DBS. Ten PD patients on-DBS and off-DBS and 10 healthy subjects performed a choice-response task (CRT), requiring either right or left finger button presses. At the same time, EEG recordings were performed, so that RTs could be assessed together with lateralized readiness potentials (LRP), indicative of movement preparation. Additionally, an oddball task (OT) was run, in which right finger responses to target stimuli were recorded along with cognitive P300 responses. Generally, PD patients off-DBS had longer RTs than controls. Subthalamic DBS accelerated RT only in CRT. This could largely be explained by analog shortenings of LRP. No DBS-dependent changes were identified in OT, neither on the level of RT nor on the level of P300 latencies. It follows that RT accelerations under DBS of the STN are predominantly due to effects on the timing of motor instead of nonmotor processes. This starting point explains why DBS gains of response speed are low in tasks in which reactions are initiated from an advanced level of movement preparation (as in OT), and high whenever motor responses have to be raised from scratch (as in CRT).