Internal models for motor control and trajectory planning

A number of internal model concepts are now widespread in neuroscience and cognitive science. These concepts are supported by behavioral, neurophysiological, and imaging data; furthermore, these models have had their structures and functions revealed by such data. In particular, a specific theory on inverse dynamics model learning is directly supported by unit recordings from cerebellar Purkinje cells. Multiple paired forward inverse models describing how diverse objects and environments can be controlled and learned separately have recently been proposed. The 'minimum variance model' is another major recent advance in the computational theory of motor control. This model integrates two furiously disputed approaches on trajectory planning, strongly suggesting that both kinematic and dynamic internal models are utilized in movement planning and control.     

Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex.

Cortical field potentials have been used for decades in neurophysiological studies to probe spatio-temporal activity patterns of local populations of neurons. Recently, however, interest in these signals was spurred as they were proposed as potential control signals for neuronal motor prostheses, i.e., for devices fit to record and decode brain activity to restore motor functions in paralyzed patients. Little is known, however, about the functional significance of these cortical field potentials. Here we compared information about arm movement direction in two types of movement related cortical field potentials, obtained during a four direction center-out arm reaching paradigm: local field potentials (LFPs) recorded with intracortical micro-electrodes from monkey motor cortex, and epicortical field potentials (EFPs) recorded with macro-electrode arrays subdurally implanted on the surface of the human cerebral cortex. While monkey LFPs showed a typical sequence of positive and negative potential peaks, an initial negative peak was the most salient feature of human EFPs. Individual contralateral LFPs from the monkey motor cortex carried approximately twice as much decoded information (DI) about arm movement direction (median 0.27 bit) as did individual EFPs from the contralateral hand/arm area of primary motor cortex in humans (median 0.12 bit). This relation was similar to the relation between median peak signal-to-noise ratios for directional modulation of movement related potentials (MRPs) of both types of signals. We discuss possible reasons for the observed differences, amongst them epi- vs. intracortical recording and the different electrode dimensions used to measure EFPs and LFPs.     

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Current neurophysiological research has the aim to develop methodologies to investigate the signal route from neuron to neuron, namely in the transitions from spikes to Local Field Potentials (LFPs) and from LFPs to spikes.LFPs have a complex dependence on spike activity and their relation is still poorly understood¹. The elucidation of these signal relations would be helpful both for clinical diagnostics (e.g. stimulation paradigms for Deep Brain Stimulation) and for a deeper comprehension of neural coding strategies in normal and pathological conditions (e.g. epilepsy, Parkinson disease, chronic pain). To this aim, one has to solve technical issues related to stimulation devices, stimulation paradigms and computational analyses. Therefore, a custom-made stimulation device was developed in order to deliver stimuli well regulated in space and time that does not incur in mechanical resonance. Subsequently, as an exemplification, a set of reliable LFP-spike relationships was extracted.The performance of the device was investigated by extracellular recordings, jointly spikes and LFP responses to the applied stimuli, from the rat Primary Somatosensory cortex. Then, by means of a multi-objective optimization strategy, a predictive model for spike occurrence based on LFPs was estimated.The application of this paradigm shows that the device is adequately suited to deliver high frequency tactile stimulation, outperforming common piezoelectric actuators. As a proof of the efficacy of the device, the following results were presented: 1) the timing and reliability of LFP responses well match the spike responses, 2) LFPs are sensitive to the stimulation history and capture not only the average response but also the trial-to-trial fluctuations in the spike activity and, finally, 3) by using the LFP signal it is possible to estimate a range of predictive models that capture different aspects of the spike activity.     

The relationship of post-stimulus time and interval histograms to the timming characteristics of spike trains.

PST (post-stimulus time) and interval histograms computed from recorded spike trains are related to an average timing characteristics of the spike train. The exact nature of this relationship varies with recording parameters, interfering signals, the histogram bin width, and the duration of the measurement interval. This work describes the conditions under which a PST histogram can serve as an unbiased estimate of the ensemble average of a spike train's intensity and an interval histogram can serve as an unbiased estimate of the probability density function of the interspike intervals. Simulation studies are used to confirm the validity of the theoretical results. As an example of an application, these results are used to analyze recordings of singleunit activity in the eight cranial nerve.     

A role for the cerebellum in the control of limb movement velocity

Accepting, rejecting or modifying the many different theories of the cerebellum's role in the control of movement requires an understanding of the signals encoded in the discharge of cerebellar neurons and how those signals are transformed by the cerebellar circuitry. Particularly challenging is understanding the sensory and motor signals carried by the two types of action potentials generated by cerebellar Purkinje cells, the simple spikes and complex spikes. Advances have been made in understanding this signal processing in the context of voluntary arm movements. Recent evidence suggests that mossy fiber afferents to the cerebellar cortex are a source of kinematic signals, providing information about movement direction and speed. In turn, the simple spike discharge of Purkinje cells integrates this mossy fiber information to generate a movement velocity signal. Complex spikes may signal errors in movement velocity. It is proposed that the cerebellum uses the signals carried by the simple and complex spike discharges to control movement velocity for both step and tracking arm movements.     

How local is the local field potential?

Local field potentials (LFPs) are of growing importance in neurophysiological investigations. LFPs supplement action potential recordings by indexing activity relevant to EEG, magnetoencephalographic, and hemodynamic (fMRI) signals. Recent reports suggest that LFPs reflect activity within very small domains of several hundred micrometers. We examined this conclusion by comparing LFP, current source density (CSD), and multiunit activity (MUA) signals in macaque auditory cortex. Estimated by frequency tuning bandwidths, these signals' "listening areas" differ systematically with an order of MUA?< CSD?< LFP. Computational analyses confirm that observed LFPs receive local contributions. Direct measurements indicate passive spread of LFPs to sites more than a centimeter from their origins. These findings appear to be independent of the frequency content of the LFP. Our results challenge the idea that LFP recordings typically integrate over extremely circumscribed local domains. Rather, LFPs appear as a mixture of local potentials with "volume conducted" potentials from distant sites.     

Subthalamic local field beta oscillations during ongoing deep brain stimulation in Parkinson's disease in hyperacute and chronic phases

In the past years, local field potential (LFP) signals recorded from the subthalamic nucleus (STN) in patients undergoing deep brain stimulation (DBS) for Parkinson's disease (PD) disclosed that DBS has a controversial effect on STN beta oscillations recorded 2-7 days after surgery for macroelectrode implantation. Nothing is known about these DBS-induced oscillatory changes 30 days after surgery. We recorded STN LFPs during ongoing DBS in 7 patients with PD, immediately (hyperacute phase) and 30 days (chronic phase) after surgery. STN LFP recordings showed stationary intranuclear STN beta LFP activity in hyperacute and chronic phases, confirming that beta peaks were also present in chronic recordings. Power spectra of nuclei with significant beta activity (54% of the sample) showed that it decreased significantly during DBS (p=0.021) under both recording conditions. The time course of beta activity showed more evident DBS-induced changes in the chronic than in the hyperacute phase (p=0.014). DBS-induced changes in STN beta LFPs in patients undergoing DBS in chronic phase provide useful information for developing a new neurosignal-controlled adaptive DBS system.     

Comparison of LFP-based and spike-based spectro-temporal receptive fields and cross-correlation in cat primary auditory cortex

Multi-electrode array recordings of spike and local field potential (LFP) activity were made from primary auditory cortex of 12 normal hearing, ketamine-anesthetized cats. We evaluated 259 spectro-temporal receptive fields (STRFs) and 492 frequency-tuning curves (FTCs) based on LFPs and spikes simultaneously recorded on the same electrode. We compared their characteristic frequency (CF) gradients and their cross-correlation distances. The CF gradient for spike-based FTCs was about twice that for 2-40 Hz-filtered LFP-based FTCs, indicating greatly reduced frequency selectivity for LFPs. We also present comparisons for LFPs band-pass filtered between 4-8 Hz, 8-16 Hz and 16-40 Hz, with spike-based STRFs, on the basis of their marginal frequency distributions. We find on average a significantly larger correlation between the spike based marginal frequency distributions and those based on the 16-40 Hz filtered LFP, compared to those based on the 4-8 Hz, 8-16 Hz and 2-40 Hz filtered LFP. This suggests greater frequency specificity for the 16-40 Hz LFPs compared to those of lower frequency content. For spontaneous LFP and spike activity we evaluated 1373 pair correlations for pairs with >200 spikes in 900 s per electrode. Peak correlation-coefficient space constants were similar for the 2-40 Hz filtered LFP (5.5 mm) and the 16-40 Hz LFP (7.4 mm), whereas for spike-pair correlations it was about half that, at 3.2 mm. Comparing spike-pairs with 2-40 Hz (and 16-40 Hz) LFP-pair correlations showed that about 16% (9%) of the variance in the spike-pair correlations could be explained from LFP-pair correlations recorded on the same electrodes within the same electrode array. This larger correlation distance combined with the reduced CF gradient and much broader frequency selectivity suggests that LFPs are not a substitute for spike activity in primary auditory cortex.     

Application of matched filtering to identify behavioral modulation of brain oscillations

Brain oscillations modulated by motor behaviors are coupled to steady-state and other potentially unrelated to movement oscillations, with energy in the same frequency bands as the signals of interest. We applied matched filtering, a quasi-optimum signal detection technique, to decouple and extract movement-related signals from local field potentials (LFPs) recorded in monkey motor cortical areas during the execution of a visually instructed reach-out task. Using a matched-filterbank, we examined coupling and interference of pre-movement and initial steady-state oscillations with movement-induced signals. Once these signal contributions were eliminated, we were able to identify significant correlations of the residual signals with behavioral parameters, which appeared attenuated by pre-movement signal interference in the raw LFPs. Specifically, the maximum and minimum amplitudes of filtered LFPs were directly modulated by peak movement velocity and micro-movements, respectively, identified in recorded hand velocity profiles. In addition, we identified phase correlations between signals during the delay (when the instructional cue was presented) and movement intervals, as well as modulation of LFP phase by movement direction. For pairs of orthogonal movement directions, corresponding LFP signals were consistently out of phase. Finally, β-band energy which is typically reduced during movement execution, possibly partly due to destructive interference between the modulated by behavior signal and unrelated oscillations, appeared to be recovered in the filtered signals.     

Cortical Neurovascular Coupling Driven by Stimulation of Channelrhodopsin-2

While functional imaging is widely used in studies of the brain, how well the hemodynamic signal represents the underlying neural activity is still unclear. And there is a debate on whether hemodynamic signal is more tightly related to synaptic activity or action potentials. This study intends to address these questions by examining neurovascular coupling driven by pyramidal cells in the motor cortex of rats. Pyramidal cells in the motor cortex of rats were selectively transduced with the light sensitive cation channel channelrhodopsin-2 (ChR2). Electrophysiological recordings and optical intrinsic signal imaging were performed simultaneously and synchronously to capture the neural activity and hemodynamics induced by optical stimulation of ChR2-expressing pyramidal cells. Our results indicate that both synaptic activity (local field potential, LFP) and action potentials (multi-unit activity, MUA) are tightly related to hemodynamic signals. While LFPs in γ band are better in predicting hemodynamic signals elicited by short stimuli, MUA has better predictions to hemodynamic signals elicited by long stimuli. Our results also indicate that strong nonlinearity exists in neurovascular coupling.     

Persistent neural activity: prevalence and mechanisms

Persistent neural activity refers to a sustained change in action potential discharge that long outlasts a stimulus. It is found in a diverse set of brain regions and organisms and several in vitro systems, suggesting that it can be considered a universal form of circuit dynamics that can be used as a mechanism for short-term storage and accumulation of sensory or motor information. Both single cell and network mechanisms are likely to co-operate in generating persistent activity in many brain areas.     

Cortical control of reaching movements

Recent studies provide further support for the hypothesis that spatial representations of limb position, target locations, and potential motor actions are expressed in the neuronal activity in parietal cortex. In contrast, precentral cortical activity more strongly expresses processes involved in the selection and execution of motor actions. As a general conceptual framework, these processes may be interpreted in terms of such formalisms as sensorimotor transformation and ‘internal models’.     

Pacemaker neurons and neuronal networks: an integrative view

Rhythmically active neuronal networks give rise to rhythmic motor activities but also to seemingly non-rhythmic behaviors such as sleep, arousal, addiction, memory and cognition. Many of these networks contain pacemaker neurons. The ability of these neurons to generate bursts of activity intrinsically lies in voltage- and time-dependent ion fluxes resulting from a dynamic interplay among ion channels, second messenger pathways and intracellular Ca2+ concentrations, and is influenced by neuromodulators and synaptic inputs. This complex intrinsic and extrinsic modulation of pacemaker activity exerts a dynamic effect on network activity. The nonlinearity of bursting activity might enable pacemaker neurons to facilitate the onset of excitatory states or to synchronize neuronal ensembles--an interactive process that is intimately regulated by synaptic and modulatory processes.     

Functional microcircuitry in the accumbens underlying drug addiction: insights from real-time signaling during behavior

An understanding of the neurobiological basis of drug addiction requires examination of real-time (subsecond) cellular and chemical responses in the brain reward system during drug-seeking and drug-taking behavior. Electrophysiological and electrochemical studies in the rodent nucleus accumbens have examined changes in cell firing and rapid dopamine signaling during crucial periods of behavioral responding for drugs, and show the associative nature of those signals. These findings are considered with respect to the functional microcircuitry in the nucleus accumbens that underlies goal-directed behavior and the role of this circuit in drug addiction.     

Polyubiquitin chains: polymeric protein signals

The 76-residue protein ubiquitin exists within eukaryotic cells both as a monomer and in the form of isopeptide-linked polymers called polyubiquitin chains. In two well-described cases, structurally distinct polyubiquitin chains represent functionally distinct intracellular signals. Recently, additional polymeric structures have been detected in vivo and in vitro, and several large families of proteins with polyubiquitin chain-binding activity have been discovered. Although the molecular mechanisms governing specificity in chain synthesis and recognition are still incompletely understood, the scope of signaling by polyubiquitin chains is likely to be broader than originally envisioned.     

Signal acquisition and analysis for cortical control of neuroprosthetics

Work in cortically controlled neuroprosthetic systems has concentrated on decoding natural behaviors from neural activity, with the idea that if the behavior could be fully decoded it could be duplicated using an artificial system. Initial estimates from this approach suggested that a high-fidelity signal comprised of many hundreds of neurons would be required to control a neuroprosthetic system successfully. However, recent studies are showing hints that these systems can be controlled effectively using only a few tens of neurons. Attempting to decode the pre-existing relationship between neural activity and natural behavior is not nearly as important as choosing a decoding scheme that can be more readily deployed and trained to generate the desired actions of the artificial system. These artificial systems need not resemble or behave similarly to any natural biological system. Effective matching of discrete and continuous neural command signals to appropriately configured device functions will enable effective control of both natural and abstract artificial systems using compatible thought processes.     

Inertial vestibular coding of motion: concepts and evidence

Central processing of inertial sensory information about head attitude and motion in space is crucial for motor control. Vestibular signals are coded relative to a non-inertial system, the head, that is virtually continuously in motion. Evidence for transformation of vestibular signals from head-fixed sensory coordinates to gravity-centered coordinates have been provided by studies of the vestibulo-ocular reflex. The underlying central processing depends on otolith afferent information that needs to be resolved in terms of head translation related inertial forces and head attitude dependent pull of gravity. Theoretical solutions have been suggested, but experimental evidence is still scarce. It appears, along these lines, that gaze control systems are intimately linked to motor control of head attitude and posture.     

Mental imaging of motor activity in humans

Motor imagery corresponds to a subliminal activation of the motor system, a system that appears to be involved not only in producing movements, but also in imagining actions, recognising tools and learning by observation, as well as in understanding the behaviour of other people. Recent advances in the field include the use of techniques for mapping brain activity and probing cortical excitability, as well as observation of brain lesioned patients during imaging tasks; these advances provide new insights into the covert aspects of motor activity.     

Electrophysiological correlates of rest and activity in Drosophila melanogaster

Extended periods of rest in Drosophila melanogaster resemble mammalian sleep states in that they are characterized by heightened arousal thresholds and specific alterations in gene expression. Defined as inactivity periods spanning 5 or more min, amounts of this sleep-like state are, as in mammals, sensitive to prior amounts of waking activity, time of day, and pharmacological intervention. Clearly recognizable changes in the pattern and amount of brain electrical activity accompany changes in motor activity and arousal thresholds originally used to identify mammalian sleeping behavior. Electroencephalograms (EEGs) and/or local field potentials (LFPs) are now widely used to quantify sleep state amounts and define types of sleep. Thus, slow-wave sleep (SWS) is characterized by EEG spindles and large-amplitude delta-frequency (0-3.5 Hz) waves. Rapid-eye movement (REM) sleep is characterized by irregular gamma-frequency cortical EEG patterns and rhythmic theta-frequency (5-9 Hz) hippocampal EEG activity. It is unknown whether rest and activity in Drosophila are associated with distinct electrophysiological correlates. To address this issue, we monitored motor activity levels and recorded LFPs in the medial brain between the mushroom bodies, structures implicated in the modulation of locomotor activity, of Drosophila. The results indicate that LFPs can be reliably recorded from the brains of awake, moving fruit flies, that targeted genetic manipulations can be used to localize sources of LFP activity, and that brain electrical activity of Drosophila is reliably correlated with activity state.