Temporal accuracy of human cortico-cortical interactions.

The precision in space and time of interactions among multiple cortical sites was evaluated by examining repeating precise spatiotemporal patterns of instances in which cortical currents showed brief amplitude undulations. The amplitudes of the cortical current dipoles were estimated by applying a variant of synthetic aperture magnetometry to magnetoencephalographic (MEG) recordings of subjects tapping to metric auditory rhythms of drum beats. Brief amplitude undulations were detected in the currents by template matching at a rate of 2-3 per second. Their timing was treated as point processes, and precise spatiotemporal patterns were searched for. By randomly teetering these point processes within a time window W, we estimated the accuracy of the timing of these brief amplitude undulations and compared the results with those obtained by applying the same analysis to traces composed of random numbers. The results demonstrated that the timing accuracy of patterns was better than 3 ms. Successful classification of two different cognitive processes based on these patterns suggests that at least some of the repeating patterns are specific to a cognitive process.

Precise spatiotemporal patterns among visual cortical areas and their relation to visual stimulus processing.

Visual processing shows a highly distributed organization in which the presentation of a visual stimulus simultaneously activates neurons in multiple columns across several cortical areas. It has been suggested that precise spatiotemporal activity patterns within and across cortical areas play a key role in higher cognitive, motor, and visual functions. In the visual system, these patterns have been proposed to take part in binding stimulus features into a coherent object, i.e., to be involved in perceptual grouping. Using voltage-sensitive dye imaging (VSDI) in behaving monkeys (Macaca fascicularis, males), we simultaneously measured neural population activity in the primary visual cortex (V1) and extrastriate cortex (V2, V4) at high spatial and temporal resolution. We detected time point population events (PEs) in the VSDI signal of each pixel and found that they reflect transient increased neural activation within local populations by establishing their relation to spiking and local field potential activity. Then, we searched for repeating space and time relations between the detected PEs. We demonstrate the following: (1) spatiotemporal patterns occurring within (horizontal) and across (vertical) early visual areas repeat significantly above chance level; (2) information carried in only a few patterns can be used to reliably discriminate between stimulus categories on a single-trial level; (3) the spatiotemporal patterns yielding high classification performance are characterized by late temporal occurrence and top-down propagation, which are consistent with cortical mechanisms involving perceptual grouping. The pattern characteristics and the robust relation between the patterns and the stimulus categories suggest that spatiotemporal activity patterns play an important role in cortical mechanisms of higher visual processing.

A compact representation of drawing movements with sequences of parabolic primitives.

Some studies suggest that complex arm movements in humans and monkeys may optimize several objective functions, while others claim that arm movements satisfy geometric constraints and are composed of elementary components. However, the ability to unify different constraints has remained an open question. The criterion for a maximally smooth (minimizing jerk) motion is satisfied for parabolic trajectories having constant equi-affine speed, which thus comply with the geometric constraint known as the two-thirds power law. Here we empirically test the hypothesis that parabolic segments provide a compact representation of spontaneous drawing movements. Monkey scribblings performed during a period of practice were recorded. Practiced hand paths could be approximated well by relatively long parabolic segments. Following practice, the orientations and spatial locations of the fitted parabolic segments could be drawn from only 2-4 clusters, and there was less discrepancy between the fitted parabolic segments and the executed paths. This enabled us to show that well-practiced spontaneous scribbling movements can be represented as sequences ("words") of a small number of elementary parabolic primitives ("letters"). A movement primitive can be defined as a movement entity that cannot be intentionally stopped before its completion. We found that in a well-trained monkey a movement was usually decelerated after receiving a reward, but it stopped only after the completion of a sequence composed of several parabolic segments. Piece-wise parabolic segments can be generated by applying affine geometric transformations to a single parabolic template. Thus, complex movements might be constructed by applying sequences of suitable geometric transformations to a few templates. Our findings therefore suggest that the motor system aims at achieving more parsimonious internal representations through practice, that parabolas serve as geometric primitives and that non-Euclidean variables are employed in internal movement representations (due to the special role of parabolas in equi-affine geometry).

Correlations between groups of premotor neurons carry information about prehension.

How distinct parameters are bound together in brain activity is unknown. Combination coding by interneuronal interactions is one possibility, but, to coordinate parameters, interactions between neuronal pairs must carry information about them. To address this issue, we recorded neural activity from multiple sites in the premotor cortices of monkeys that memorized reach direction and grasp type followed by actual prehension. We found that correlations between individual spiking neurons are generally weak and carry little information about prehension. In contrast, correlations and synchronous interactions between small groups of neurons, quantified by multiunit activity (MUA), are an order of magnitude stronger. A substantial fraction of the information carried by pairwise interactions between MUAs is about combinations of reach and grasp. This contrasts with the information carried by individual neurons and individual MUAs, which is mainly about reach and/or grasp but much less about their combinations. The main contribution of pairwise interactions to the coding of reach-grasp combinations is when animals memorize prehension parameters, consistent with an internal composite representation. The informative interactions between neuronal groups may facilitate the coordination of reach and grasp into coherent prehension.

EEG generator--a model of potentials in a volume conductor.

EEG generator-a model of potentials in a volume conductor. The potential recorded over the cortex electro-corticogram (ECoG) or over the scalp [electroencephalograph (EEG)] derives from the activity of many sources known as "EEG generators." The recorded amplitude is basically a function of the unitary potential of a generator and the statistical relationship between different EEG generators in the recorded population. In this study, we first suggest a new definition of the EEG generator. We use the theory of potentials in a volume conductor and model the contribution of a single synapse activated to the surface potential. We then model the contribution of the generator to the surface potential. Once the generator and its contribution are well defined, we can quantitatively assess the degree of synchronization among generators. The measures obtained by the model for a real life scenario of a group of generators organized in a specific statistical way were consistent with the expected values that were reported experimentally. The study sheds new light on macroscopic modeling approaches which make use of mean soma membrane potential. We showed major contribution of activity of superficial apical synapses to the ECoG signal recorded relative to lower somatic or basal synapses activity.

Analyzing movement trajectories using a Markov bi-clustering method.

In this study we treat scribbling motion as a compositional system in which a limited set of elementary strokes are capable of concatenating amongst themselves in an endless number of combinations, thus producing an unlimited repertoire of complex constructs. We broke the continuous scribblings into small units and then calculated the Markovian transition matrix between the trajectory clusters. The Markov states are grouped in a way that minimizes the loss of mutual information between adjacent strokes. The grouping algorithm is based on a novel markov-state bi-clustering algorithm derived from the Information-Bottleneck principle. This approach hierarchically decomposes scribblings into increasingly finer elements. We illustrate the usefulness of this approach by applying it to human scribbling.

Motor cortical activity related to movement kinematics exhibits local spatial organization.

While it is generally accepted that multiple neurons cooperate to generate movement, the precise mechanisms are largely unknown. One way to generate a robust local control signal is for nearby neurons to share similar properties. To study this possibility, we recorded neural activity from the macaque motor cortex during two drawing tasks: free scribbling, and tracing given paths. We analyzed neural activity in relation to three kinematic parameters - position, velocity, and acceleration - while explicitly considering temporal correlations between them. Single-unit (SU) activity was typically related to one parameter, most often velocity, and tended to precede movement. Different SUs encoded different parameters, but nearby units tended to prefer the same parameter. Moreover, while SUs covered a wide range of positions, velocity directions, and acceleration directions, SUs recorded by the same electrode tended to prefer similar values of the same parameter. Nevertheless, some nearby units exhibited marked differences. Multi-unit activity (MUA), estimating the spiking activity of many neurons around the recording electrode, also tended to be related to one parameter and precede movement. However, overall correlations between MUA and movement were more than twice as strong as SU correlations. Finally, SUs and MUAs recorded by the same electrode tended to share similar properties. These two lines of evidence converge to suggest that activity of motor cortex neurons within approximately 200 micrometers is accumulated in a manner useful for representing a single parameter. However, even within a small region there are also neurons related to other parameters, potentially facilitating coordination between distinct parameters.

Parabolic movement primitives and cortical states: merging optimality with geometric invariance.

Previous studies have suggested that several types of rules govern the generation of complex arm movements. One class of rules consists of optimizing an objective function (e.g., maximizing motion smoothness). Another class consists of geometric and kinematic constraints, for instance the coupling between speed and curvature during drawing movements as expressed by the two-thirds power law. It has also been suggested that complex movements are composed of simpler elements or primitives. However, the ability to unify the different rules has remained an open problem. We address this issue by identifying movement paths whose generation according to the two-thirds power law yields maximally smooth trajectories. Using equi-affine differential geometry we derive a mathematical condition which these paths must obey. Among all possible solutions only parabolic paths minimize hand jerk, obey the two-thirds power law and are invariant under equi-affine transformations (which preserve the fit to the two-thirds power law). Affine transformations can be used to generate any parabolic stroke from an arbitrary parabolic template, and a few parabolic strokes may be concatenated to compactly form a complex path. To test the possibility that parabolic elements are used to generate planar movements, we analyze monkeys' scribbling trajectories. Practiced scribbles are well approximated by long parabolic strokes. Of the motor cortical neurons recorded during scribbling more were related to equi-affine than to Euclidean speed. Unsupervised segmentation of simulta- neously recorded multiple neuron activity yields states related to distinct parabolic elements. We thus suggest that the cortical representation of movements is state-dependent and that parabolic elements are building blocks used by the motor system to generate complex movements.

Predicting movement from multiunit activity.

Previous studies have shown that intracortical activity can be used to operate prosthetic devices such as an artificial limb. Previously used neuronal signals were either the activity of tens to hundreds of spiking neurons, which are difficult to record for long periods of time, or local field potentials, which are highly correlated with each other. Here, we show that by estimating multiunit activity (MUA), the superimposed activity of many neurons around a microelectrode, and using a small number of electrodes, an accurate prediction of the upcoming movement is obtained. Compared with single-unit spikes, single MUA recordings are obtained more easily and the recordings are more stable over time. Compared with local field potentials, pairs of MUA recordings are considerably less redundant. Compared with any other intracortical signal, single MUA recordings are more informative. MUA is informative even in the absence of spikes. By combining information from multielectrode recordings from the motor cortices of monkeys that performed either discrete prehension or continuous tracing movements, we demonstrate that predictions based on multichannel MUA are superior to those based on either spikes or local field potentials. These results demonstrate that considerable information is retained in the superimposed activity of multiple neurons, and therefore suggest that neurons within the same locality process similar information. They also illustrate that complex movements can be predicted using relatively simple signal processing without the detection of spikes and, thus, hold the potential to greatly expedite the development of motor-cortical prosthetic devices.

Resolving the dynamics of EEG generators by multichannel recordings.

The voltage recorded over the cortex (ECoG) or over the scalp (EEG) is generated by currents derived from many sources called "generators". Different patterns and amplitudes are observed in aroused, sleepy, epileptic or other brain states. Differences in amplitude are generally attributed to differences in synchrony among generators. The degree of EEG synchrony is measured by the correlation between electrodes placed over different cortical regions. We present a new way to quantitatively assess the degree of synchronization of these generators via multichannel recordings. We illustrate how situations where there are several groups of generators with different inter-group and intra-group synchronies can be analyzed. Finally, we present a way to identify the organization of groups exhibiting topographic organization. Although the model presented here is highly simplified, several methods are based on averaging activity over increasingly larger areas. These types of measurements may be applied as well to EEG and ECoG recordings.

Source localization scale correction for Beamformer analysis.

BACKGROUND: Magnetoencephalography measurements are often processed by using imaging algorithms such as beamforming. The estimated source magnitude tends to suffer from unbalanced scaling across different brain locations. Hence, when examining current estimates for source activity it is vital to rescale the estimated source magnitude, in order to obtain a uniformly scaled image. NEW METHOD: We present a generalized scale correction method (Nempty) that uses empty room MEG measurements to evaluate the noise level. RESULTS: The location bias and spatial resolution of the estimated signal indicated that some scaling correction needs to be applied. Of all the scale correction methods that were tested, the best correction was achieved when using Nempty. COMPARISON WITH EXISTING METHODS: We show that a diagonal matrix does not reflect the true nature of the noise covariance matrix. Hence, diagonal matrix based methods are sub-optimal. CONCLUSION: We recommend adding empty room MEG measurements to each experimental recording session, for purposes of both scale correction and beamformer performance verification.

On the transmission of rate code in long feedforward networks with excitatory-inhibitory balance.

The capability of feedforward networks composed of multiple layers of integrate-and-fire neurons to transmit rate code was examined. Synaptic connections were made only from one layer to the next, and excitation was balanced by inhibition. When time is discrete and the synaptic potentials rise instantaneously, we show that, for random uncorrelated input to layer one, the mean rate of activity in deep layers is essentially independent of input firing rate. This implies that the input rate cannot be transmitted reliably in such feedforward networks because neurons in a given layer tend to synchronize partially with each other because of shared inputs. As a result of this synchronization, the average firing rate in deep layers will either decay to zero or reach a stable fixed point, depending on model parameters. When time is treated continuously and the synaptic potentials rise instantaneously, these effects develop slowly, and rate transmission over a limited number of layers is possible. However, the correlations among neurons at the same layer hamper reliable assessment of firing rate by averaging over 100 msec (or less). When the synaptic potentials develop gradually, as is the realistic case, transmission of rate code fails. In a network in which inhibition only balances the mean excitation but is not timed precisely with it, neurons in each layer fire together, and this volley successively propagates from layer to layer. We conclude that the transmission of rate code in feedforward networks is highly unlikely.

Applying resampling methods to neurophysiological data.

Standard statistical techniques do not always provide answers to complex physiological questions because often there are no parametric or non-parametric distributions on which significance can be estimated. Resampling methods provide a battery of tests that can be used in such circumstances. In the past few years these methods have been explored theoretically and are now employed frequently. In this paper we describe a unified framework for the use of such methods in the context of neurophysiological data analysis. We construct specific tests for placing confidence limits on estimates of mutual information and on parameters of circular data, and we present procedures for testing hypotheses on circular and on partitioned data. These tests are explained in detail and illustrated with real data from experiments with behaving monkeys.

Synchronization among neuronal pools without common inputs: in vivo study.

Periodic synchronization of activity among neuronal pools has been related to substantial neural processes and information throughput in the neocortical network. However, the mechanisms of generating such periodic synchronization among distributed pools of neurons remain unclear. We hypothesize that to a large extent there is interplay between the topology of the neocortical networks and their reverberating modes of activity. The firing synchronization is governed by a nonlocal mechanism, the network delay loops, such that distant neuronal pools without common drives can be synchronized. This theoretical interplay between network topology and the synchronized mode is verified using an iterative procedure of a single intracellularly recorded neuron in vivo, imitating the dynamics of the entire network. The input is injected to the neuron via the recording electrode as current and computed from the filtered, evoked spikes of its pre-synaptic sources, previously emulated by the same neuron. In this manner we approximate subthreshold synaptic inputs from afferent neuronal pools to the neuron. Embedding the activity of these recurrent motifs in the intact brain allows us to measure the effects of connection probability, synaptic strength, and ongoing activity on the neuronal synchrony. Our in vivo experiments indicate that an initial stimulus given to a single pool is dynamically evolving into the formations of zero-lag and cluster synchronization. By applying results from theoretical models and in vitro experiments to in vivo activity in the intact brain, we support the notion that this mechanism may account for the binding activity across distributed brain areas.

Synchronization by elastic neuronal latencies.

Psychological and physiological considerations entail that formation and functionality of neuronal cell assemblies depend upon synchronized repeated activation such as zero-lag synchronization. Several mechanisms for the emergence of this phenomenon have been suggested, including the global network quantity, the greatest common divisor of neuronal circuit delay loops. However, they require strict biological prerequisites such as precisely matched delays and connectivity, and synchronization is represented as a stationary mode of activity instead of a transient phenomenon. Here we show that the unavoidable increase in neuronal response latency to ongoing stimulation serves as a nonuniform gradual stretching of neuronal circuit delay loops. This apparent nuisance is revealed to be an essential mechanism in various types of neuronal time controllers, where synchronization emerges as a transient phenomenon and without predefined precisely matched synaptic delays. These findings are described in an experimental procedure where conditioned stimulations were enforced on a circuit of neurons embedded within a large-scale network of cortical cells in vitro, and are corroborated and extended by simulations of circuits composed of Hodgkin-Huxley neurons with time-dependent latencies. These findings announce a cortical time scale for time controllers based on tens of microseconds stretching of neuronal circuit delay loops per spike. They call for a reexamination of the role of the temporal periodic mode in brain functionality using advanced in vitro and in vivo experiments.

Compositionality in neural control: an interdisciplinary study of scribbling movements in primates.

This article discusses the compositional structure of hand movements by analyzing and modeling neural and behavioral data obtained from experiments where a monkey (Macaca fascicularis) performed scribbling movements induced by a search task. Using geometrically based approaches to movement segmentation, it is shown that the hand trajectories are composed of elementary segments that are primarily parabolic in shape. The segments could be categorized into a small number of classes on the basis of decreasing intra-class variance over the course of training. A separate classification of the neural data employing a hidden Markov model showed a coincidence of the neural states with the behavioral categories. An additional analysis of both types of data by a data mining method provided evidence that the neural activity patterns underlying the behavioral primitives were formed by sets of specific and precise spike patterns. A geometric description of the movement trajectories, together with precise neural timing data indicates a compositional variant of a realistic synfire chain model. This model reproduces the typical shapes and temporal properties of the trajectories; hence the structure and composition of the primitives may reflect meaningful behavior.

Unbiased estimation of precise temporal correlations between spike trains.

A key issue in systems neuroscience is the contribution of precise temporal inter-neuronal interactions to information processing in the brain, and the main analytical tool used for studying pair-wise interactions is the cross-correlation histogram (CCH). Although simple to generate, a CCH is influenced by multiple factors in addition to precise temporal correlations between two spike trains, thus complicating its interpretation. A Monte-Carlo-based technique, the jittering method, has been suggested to isolate the contribution of precise temporal interactions to neural information processing. Here, we show that jittering spike trains is equivalent to convolving the CCH derived from the original trains with a finite window and using a Poisson distribution to estimate probabilities. Both procedures over-fit the original spike trains and therefore the resulting statistical tests are biased and have low power. We devise an alternative method, based on convolving the CCH with a partially hollowed window, and illustrate its utility using artificial and real spike trains. The modified convolution method is unbiased, has high power, and is computationally fast. We recommend caution in the use of the jittering method and in the interpretation of results based on it, and suggest using the modified convolution method for detecting precise temporal correlations between spike trains.

Programmed acute electrical stimulation of ventral tegmental area alleviates depressive-like behavior.

Depressive disorders affect approximately 5% of the population in any given year. Antidepressants may require several weeks to produce their clinical effects. Despite progress being made in this area there is still room and a need to explore additional therapeutic modes to increase treatment effectiveness and responsiveness. Herein, we examined a new method for intervention in depressive states based on deep brain stimulation of the ventral tegmental area (VTA) as a source of incentive motivation and hedonia, in comparison to chemical antidepressants. The pattern of stimulation was fashioned to mimic the firing pattern of VTA neurons in the normal rat. Behavioral manifestations of depression were then monitored weekly using a battery of behavioral tests. The results suggest that treatment with programmed acute electrical stimulation of the VTA substantially alleviates depressive behavior, as compared to chemical antidepressants or electroconvulsive therapy, both in onset time and longitudinal effect. These results were also highly correlated with increases in brain-derived neurotrophic factor mRNA levels in the prefrontal cortex.

Dependence of neuronal correlations on filter characteristics and marginal spike train statistics.

Correlated neural activity has been observed at various signal levels (e.g., spike count, membrane potential, local field potential, EEG, fMRI BOLD). Most of these signals can be considered as superpositions of spike trains filtered by components of the neural system (synapses, membranes) and the measurement process. It is largely unknown how the spike train correlation structure is altered by this filtering and what the consequences for the dynamics of the system and for the interpretation of measured correlations are. In this study, we focus on linearly filtered spike trains and particularly consider correlations caused by overlapping presynaptic neuron populations. We demonstrate that correlation functions and statistical second-order measures like the variance, the covariance, and the correlation coefficient generally exhibit a complex dependence on the filter properties and the statistics of the presynaptic spike trains. We point out that both contributions can play a significant role in modulating the interaction strength between neurons or neuron populations. In many applications, the coherence allows a filter-independent quantification of correlated activity. In different network models, we discuss the estimation of network connectivity from the high-frequency coherence of simultaneous intracellular recordings of pairs of neurons.

The point of no return in planar hand movements: an indication of the existence of high level motion primitives.

Previous psychophysical studies have sought to determine whether the processes of movement engagement and termination are dissociable, whether stopping an action is a generic process, and whether there is a point in time in which the generation of a planned action is inevitable ("point of no return"). It is not clear yet, however, whether the action of stopping is merely a manifestation of low level, dynamic constraints, or whether it is also subject to a high level, kinematic plan. In the present study, stopping performance was studied while nine subjects, who generated free scribbling movements looking for the location of an invisible circular target, were requested unexpectedly to impede movement. Temporal analysis of the data shows that in 87% of the movements subsequent to the 'stop' cue, the tangential motion velocity profile was not a decelerating function of the time but rather exhibited a complex pattern comprised of one or more velocity peaks, implying an unstoppable motion element. Furthermore, geometrical analysis shows that the figural properties of the path generated after the 'stop' cue were part of a repetitive geometrical pattern and that the probability of completing a pattern after the 'stop' cue was correlated with the relative advance in the geometrical plan rather than the amount of time that had elapsed from the pattern initiation. Altogether, these findings suggest that the "point of no return" phenomenon in humans may also reflect a high level kinematic plan and could serve as a new operative definition of motion primitives.