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PubMed2006

Distinct electrical and chemical connectivity maps in the thalamic reticular nucleus: potential roles in synchronization and sensation.

GABAergic neurons of the thalamic reticular nucleus (nRt) provide thalamocortical relay neurons with feedback inhibition that influences sensory processing and thalamocortical rhythm generation. Mutual interactions between reticular neurons coordinate oscillatory activities developed within the network during normal sleep and in absence epilepsy, but the chemical versus electrical nature of these connections and their functional influence remain controversial. Here, we investigated the incidence and spatial extent of intra-nRt connectivity in vitro in horizontal and coronal thalamic slices from rat. Laser scanning photostimulation activated presynaptic nRt cells during patch-clamp recordings of postsynaptic neurons. Photolysis of caged glutamate evoked GABAergic IPSCs and/or depolarizing events (spikelets, mediated via electrical coupling) in a large proportion of neurons, thus indicating connectivity with presynaptic cell(s). Synaptic inputs were organized along the major axis of the nucleus in the same orientation as, but commonly exceeding the extent of, dendritic arborization of the postsynaptic neuron. In the anteroposterior (horizontal) plane, chemical connectivity had higher incidence (60% of recorded neurons vs 40% in vertical plane) and longer spatial extent, whereas in the dorsoventral (vertical) plane, electrical coupling dominated (47% incidence vs 37% in horizontal plane) and was more widely distributed. These data demonstrate that both electrical and chemical synapses are prominent within nRt and suggest different roles for the two types of connections. We thus propose that, along the vertical plane, electrical connectivity will promote coordinated rhythmic activity of sleep and/or thalamocortical epilepsy, whereas along the horizontal plane, chemical connectivity will oppose widespread thalamocortical synchronization and modulate sensory throughput.

Animals↗
PubMed2006

An integrate-and-fire model for synchronized bursting in a network of cultured cortical neurons.

It has been suggested that spontaneous synchronous neuronal activity is an essential step in the formation of functional networks in the central nervous system. The key features of this type of activity consist of bursts of action potentials with associated spikes of elevated cytoplasmic calcium. These features are also observed in networks of rat cortical neurons that have been formed in culture. Experimental studies of these cultured networks have led to several hypotheses for the mechanisms underlying the observed synchronized oscillations. In this paper, bursting integrate-and-fire type mathematical models for regular spiking (RS) and intrinsic bursting (IB) neurons are introduced and incorporated through a small-world connection scheme into a two-dimensional excitatory network similar to those in the cultured network. This computer model exhibits spontaneous synchronous activity through mechanisms similar to those hypothesized for the cultured experimental networks. Traces of the membrane potential and cytoplasmic calcium from the model closely match those obtained from experiments. We also consider the impact on network behavior of the IB neurons, the geometry and the small world connection scheme.

Action Potentials↗
PubMed2003

Synchronization in monkey motor cortex during a precision grip task. II. effect of oscillatory activity on corticospinal output.

Recordings from primary motor cortex (M1) during periods of steady contraction show oscillatory activity; these oscillations are coherent with the activity of contralateral muscles. We investigated synchronization of corticospinal output neurons with the oscillations, which could provide the pathway for their transmission to the spinal motoneurons. One hundred seventy-six antidromically identified pyramidal tract neurons (PTNs) were recorded from M1 in three macaque monkeys trained to perform a precision grip task. Local field potentials (LFP) were simultaneously recorded. All analysis was confined to the hold period of the task, where our previous work has shown that there is the strongest oscillatory activity. Coherence was calculated between LFP and PTN discharge. Significant coherence was seen in three bands, with frequencies of 10-14, 17-31, and 34-44 Hz. Coherence values were low, with the majority of PTN-LFP coherences having a peak lower than 0.05. The phase of coherence was approximately -pi/2 radians for each band (with LFP polarity defined as negative upward), although there was some dispersion of phase across the population of PTNs. Coherence was also calculated between pairs of PTNs that had been simultaneously recorded. Where there was significant coherence, it was also generally smaller than 0.05. The phase of PTN-PTN coherence clustered around zero radians. A computer model was constructed to assist the interpretation of the experimental results. It simulated an integrate-and-fire neuron responding to synaptic inputs. A fraction of the synaptic inputs was synchronized with a simulated LFP; the remainder were uncorrelated with it. The model showed that coherence between the LFP and the output spike train considerably underestimated the fraction of synchronized inputs. Additionally, for a given fraction of synchronized inputs, coherence was smaller for high- compared with low-frequency bins. Cell discharge rate also influenced the spike-LFP coherence: coherence was higher for simulations in which the cell discharged at a faster rate. Thus although levels of PTN-LFP coherence seen experimentally were low, a considerable proportion of the input to the PTN must be synchronized with the global oscillatory activity recorded by the LFP. The low LFP-PTN coherences do however indicate that cortical oscillations are transmitted with only low fidelity in the discharge of a single PTN. Using further computer simulations, it was demonstrated that a small population of PTNs could encode the cortical oscillatory signal effectively, since the action of averaging across the population improves the signal:noise ratio. The oscillations will therefore be effectively transmitted to spinal motoneurons, and this has important consequences for the possible role of oscillations in motor control of the hand.

Animals↗
PubMed2001

Phase and amplitude analysis in time-frequency space--application to voluntary finger movement.

Two methods operating in time-frequency space were applied to analysis of EEG activity accompanying voluntary finger movements. The first one, based on matching pursuit approach provided high-resolution distributions of power in time-frequency space. The phenomena of event related desynchronization (ERD) and synchronization (ERS) were investigated without the need of band-pass filtering. Time evolution of mu- and beta-components was observed in a detailed way. The second method was based on a multichannel autoregressive model (MVAR) adapted for investigation of short-time changes in EEG signal. The direction and spectral content of the EEG activity propagation was estimated by means of short-time directed transfer function (SDTF). The evidence of 'cross-talk' between different areas of motor and sensory cortex was found. The earlier known phenomena, connected with voluntary movements, were confirmed and a new evidence concerning focal ERD/surround ERS and beta activity post-movement synchronization was found.

Algorithms↗
PubMed2003

[Analysis of EEG coherence in examination of an automation deficit in reading-spelling disorder--a pilot study].

OBJECTIVES: Do dyslexic children exhibit a general automatizational deficit as well as a phonological deficit? METHODS: In 16 children aged 9-11 years the reaction time, the number of mistakes and EEG (19 scalp electrodes) were measured in three experiments (verbal and nonverbal). The EEG data was baseline-corrected and after a fast fourier transformation, analyzed with the coherence tool of the Brainvision Software. RESULTS: The dyslexic group made more mistakes than the control group on all tasks but their reaction times were significantly longer only on the verbal tasks. There were no coherence differences on the nonverbal task. On the language-dependent tasks the dyslexics showed higher total-frontal and lower left-frontal coherences only in the theta-frequency range, while in the alpha and beta frequency ranges coherences did not differ. CONCLUSIONS: A language-dependent cognitive automatizational deficit in the dyslexic group is assumed that is depicted by the higher synchronization of total-frontal coherences (involvement of the central executive) and is based on the less established functional coupling of cortical subsystems for language processing.

Brain Mapping↗
PubMed1994

Reduced synchronization in the visual cortex of cats with strabismic amblyopia.

Synchronous firing of spatially separate neurons was studied with multi-electrode recordings in area 17 of the visual cortex of strabismic cats which had developed behaviourally verified amblyopia of the deviated eye. Responses of neurons were evoked with moving light bars or gratings of different spatial frequency. Neurons driven by the normal eye displayed stronger synchronization of their responses than neurons dominated by the amblyopic eye. These interocular differences were highly significant and particularly pronounced for grating stimuli of high spatial frequency. No interocular differences were noted with respect to the amplitudes of responses to the light bars and gratings. These results suggest reduced synchronization of population responses as a neurophysiological correlate of strabismic amblyopia and underline the importance of correlated firing of spatially separate cortical neurons for normal processing of visual information.

Amblyopia↗
PubMed1999

Load-independent contributions from motor-unit synchronization to human physiological tremor.

This study describes two load-independent rhythmic contributions from motor-unit synchronization to normal physiological tremor, which occur in the frequency ranges 1-12 Hz and 15-30 Hz. In common with previous studies, we use increased inertial loading to identify load-independent components of physiological tremor. The data consist of simultaneous recordings of tremor acceleration from the third finger, a surface electromyogram (EMG), and the discharges of pairs of single motor units from the extensor digitorum communis (EDC) muscle, collected from 13 subjects, and divided into 2 data sets: 106 records with the finger unloaded and 84 records with added mass from 5 to 40 g. Frequency domain analysis of motor-unit data from individual subjects reveals the presence of two distinct frequency bands in motor-unit synchronization, 1-12 Hz and 15-30 Hz. A novel Fourier-based population analysis demonstrates that the same two rhythmic components are present in motor-unit synchronization across both data sets. These frequency components are not related to motor-unit firing rates. The same frequency bands are present in the correlation between motor-unit activity and tremor and between surface EMG activity and tremor, despite a significant alteration in the characteristics of the tremor with increased inertial loading. A multivariate analysis demonstrates conclusively that motor-unit synchronization is the source of these contributions to normal physiological tremor. The population analysis suggests that single motor-unit discharges can predict an average of 10% of the total tremor signal in these two frequency bands. Rectified surface EMG can predict an average of 20% of the tremor; therefore within our population of recordings, the two components of motor-unit synchronization account for an average of 20% of the total tremor signal, in the frequency ranges 1-12 Hz and 15-30 Hz. Our results demonstrate that normal physiological tremor is a complex signal containing information relating to motor-unit synchronization in different frequency bands, and lead to a revised definition of normal physiological tremor during low force postural contractions, which is based on using both the tremor spectra and the correlation between motor-unit activity and tremor to characterize the load-dependent and the load-independent components of tremor. In addition, both physiological tremor and rectified EMG emerge as powerful predictors of the frequency components of motor-unit synchronization.

Adult↗
PubMed2005

Dangerous phase.

"Use a quiet reference." How many times have we heard this mantra during training or practice, interpreting electroencephalogram (EEG) tracings, or implanting intracranial electrodes? How many of us have used common reference EEG for synchrony studies in recent years? Far too many.Perhaps one source of this problem is the number 104. This is the relatively small number of citations to the reference Fein et al. (1988), which should have put to rest any further use of referential EEG for coherence measurements. And in retrospect, a more careful reading by us of Nunez's (1981) text would have instructed us not to do this. How such warnings have managed to escape integration into common knowledge and practice is troublesome. Electrical potentials are all measured with respect to other potentials. Technically, a potential difference is calculated by integrating the electrical field over a given path from one place to another in EEG terms, we mea sure a potential with respect to another potential, measured at one or more electrodes. All EEG potential measurements reflect the paths used to measure those potentials, and do not directly reflect localized regions of the brain beneath one electrode. Worse, in scalp EEG, the layers of cerebrospinal fluid, dura, skull, and scalp serve to smooth, filter, spread out, and redirect currents generated within the brain so that the measured scalp potentials bear a rather tenuous relationship to the underlying (presumably dipole) current sources. In calculating coherence, it is easy to show that if the potential differences are all made with respect to a common reference, then the amplitude of the reference can dominate the coherence estimate (Fein et al., 1988). In recent years, phase synchronization has been increasingly applied to analyze the dynamics of nonlinear systems (Pikovsky et al., 2000). In Guevara et al. (in this issue), we see the extension of Fein's results for phase coherency. The geometry of Fig. 1 in Guevara et al. should be imprinted on all of us the amplitude of a common reference can dominate the calculated phase syn chronization. There is far too much literature within the past decade that calculated phase synchronization from common referenced EEG. The good news is that the fix to remove common reference artifacts is simple. The bad news is that the interpretation of reference- free synchronization results from brain signals requires considerable caution.

Algorithms↗
PubMedDate not supplied

Electrophysiological correlates of synchronous neural activity and attention: a short review.

Attentional selection implies preferential treatment of some sensory stimuli over others. This requires differential representation of attended and unattended stimuli. Most previous research has focused on pure rate codes for this representation but recent evidence indicates that a mixed code, involving both mean firing rate and temporal codes, may be employed. Of particular interest is a distinction of attended from unattended stimuli based on synchrony within neural populations. I review electrophysiological evidence at macroscopic, mesoscopic and microscopic spatial scales showing that the degree of synchronous activity varies with the attentional state of the perceiving organism.

Animals↗
PubMed2006

Adaptive synchronization of neural networks with or without time-varying delay.

In this paper, based on the invariant principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the synchronization of almost all kinds of coupled identical neural networks with time-varying delay, which can be chaotic, periodic, etc. We do not assume that the concrete values of the connection weight matrix and the delayed connection weight matrix are known. We show that two coupled identical neural networks with or without time-varying delay can achieve synchronization by enhancing the coupling strength dynamically. The update gain of coupling strength can be properly chosen to adjust the speed of achieving synchronization. Also, it is quite robust against the effect of noise and simple to implement in practice. In addition, numerical simulations are given to show the effectiveness of the proposed synchronization method.

Action Potentials↗
PubMed2003

A functional gamma-band defined by stimulus-dependent synchronization in area 18 of awake behaving cats.

During the past decade, numerous studies have demonstrated stimulus-specific synchronization of neuronal activity in the gamma-frequency range. However, it appears that the different analyses are based on widely different assumptions about which frequency range to investigate. Therefore, the term "gamma-synchronization" refers to an inhomogeneous spectrum of definitions and corresponding frequency bands. Moreover, most studies have been performed in anesthetized animals or in awake animals by use of fixation paradigms. Thus, it is difficult to relate these results to alert animals behaving under natural conditions. Here, we investigate stimulus specific synchronization in primary visual cortex of awake cats in a tracking paradigm. We record local field potentials and multiunit activity simultaneously from multiple electrodes. (1) We demonstrate that visual stimulation induces neuronal synchronization in a broad frequency range reaching well above 100 Hz. (2) We derive a functional gamma-band based on an objective criterion: We show that synchronization of neuronal activity is optimally orientation-tuned when a broad frequency band is considered. This band starts above 40 Hz, a frequency that is typically related to the term gamma-synchronization, and extends to very high frequencies. Interestingly, the frequency of maximum synchronization is different from the frequency at which synchronization is most stimulus specific. (3) We demonstrate synchronization of neuronal activity in a distinct low-frequency band with different properties suggesting separate functional roles of low- and high-frequency synchronization.

Animals↗