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A product of the immediate early gene Arc (Activity-regulated cytoskeleton-associated protein or Arc protein) of retroviral ancestry resides in the genome of all tetrapods for millions of years and is expressed endogenously in neurons. It is a well-known protein, very important for synaptic plasticity and memory consolidation. Activity-dependent Arc expression concentrated in glutamatergic synapses affects the long-time synaptic strength of those excitatory synapses. Because it modulates excitatory-inhibitory balance in a neuronal network, the Arc gene itself was found to be related to the pathogenesis of epilepsy. General Arc knockout rodent models develop a susceptibility to epileptic seizures. Because of activity dependence, synaptic Arc protein synthesis also is affected by seizures. Interestingly, it was found that Arc protein in synapses of active neurons self-assemble in capsids of retrovirus-like particles, which can transfer genetic information between neurons, at least across neuronal synaptic boutons. Released Arc particles can be accumulated in astrocytes after seizures. It is still not known how capsid assembling and transmission timescale is affected by seizures. This scientific field is relatively novel and is experiencing swift transformation as it grapples with difficult concepts in light of evolving experimental findings. We summarize the emergent literature on the subject and also discuss the specific rodent models for studying Arc effects in epilepsy. We summarized both to clarify the possible role of Arc-related pseudo-viral particles in epileptic disorders, which may be helpful to researchers interested in this growing area of investigation. [ABSTRACT FROM AUTHOR]

A novel system to cultivate and record from organotypic brain slices directly on high-density microelectrode arrays (HD-MEA) was developed. This system allows for continuous recording of electrical activity of specific individual neurons at high spatial resolution while monitoring at the same time, neuronal network activity. For the first time, the electrical activity patterns of single neurons and the corresponding neuronal network in an organotypic hippocampal slice culture were studied during several consecutive weeks at daily intervals. An unsupervised iterative spike-sorting algorithm, based on PCA and k-means clustering, was developed to assign the activities to the single units. Spike-triggered average extracellular waveforms of an action potential recorded across neighboring electrodes, termed "footprints" of single-units were generated and tracked over weeks. The developed system offers the potential to study chronic impacts of drugs or genetic modifications on individual neurons in slice preparations over extended times. [ABSTRACT FROM AUTHOR]

This study investigated how the activity of neostriatal neurons is related to the kinematics of movement when monkeys performed visually and vibratory cued wrist extensions and flexions. Single-unit recordings of 142/236 neostriatal neurons showed pre-movement activity (PMA) in a reaction time task with unpredictable reward. Monkeys were pseudo-randomly (75%) rewarded for correct performance. A regression model was used to determine whether the correlation between neostriatal neuronal activity and the kinematic variables (position, velocity, and acceleration) of wrist movement changes as a function of reward contingency, sensory cues, and movement direction. The coefficients of determination (CoD) representing the proportion of the variance in neuronal activity explained by the regression model on a trial by trial basis, together with their temporal occurrences (time of best regression/correlation, ToC) were compared across sensory modality, movement direction, and reward contingency. The best relationship (correlation) between neuronal activity and movement kinematic variables, given by the average coefficient of determination (CoD), was: (a) greater during trials in which rewards were certain, called "A" trials, as compared with those in which reward was uncertain called ("R") trials, (b) greater during flexion (Flex) trials as compared with extension (Ext) trials, and (c) greater during visual (VIS) cued trials than during vibratory (VIB) cued trials, for the same type of trial and the same movement direction. These results are consistent with the hypothesis that predictability of reward for correct performance is accompanied by faster linkage between neostriatal PMA and the vigor of wrist movement kinematics. Furthermore, the results provide valuable insights for building an upper-limb neuroprosthesis. [ABSTRACT FROM AUTHOR]

This study investigated how the activity of neostriatal neurons is related to the kinematics of movement when monkeys performed visually and vibratory cued wrist extensions and flexions. Single-unit recordings of 142/236 neostriatal neurons showed pre-movement activity (PMA) in a reaction time task with unpredictable reward. Monkeys were pseudo-randomly (75%) rewarded for correct performance. A regression model was used to determine whether the correlation between neostriatal neuronal activity and the kinematic variables (position, velocity, and acceleration) of wrist movement changes as a function of reward contingency, sensory cues, and movement direction. The coefficients of determination (CoD) representing the proportion of the variance in neuronal activity explained by the regression model on a trial by trial basis, together with their temporal occurrences (time of best regression/correlation, ToC) were compared across sensory modality, movement direction, and reward contingency. The best relationship (correlation) between neuronal activity and movement kinematic variables, given by the average coefficient of determination (CoD), was: (a) greater during trials in which rewards were certain, called "A" trials, as compared with those in which reward was uncertain called ("R") trials, (b) greater during flexion (Flex) trials as compared with extension (Ext) trials, and (c) greater during visual (VIS) cued trials than during vibratory (VIB) cued trials, for the same type of trial and the same movement direction. These results are consistent with the hypothesis that predictability of reward for correct performance is accompanied by faster linkage between neostriatal PMA and the vigor of wrist movement kinematics. Furthermore, the results provide valuable insights for building an upper-limb neuroprosthesis. [ABSTRACT FROM AUTHOR]

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data. [ABSTRACT FROM AUTHOR]