Influence of data sampling methods on the representation of neural spiking activity in vivo

In vivo single-unit recordings distinguish the basal spiking properties of neurons in different experimental settings and disease states. Here, we examined over 300 spike trains recorded from Purkinje cells and cerebellar nuclei neurons to test whether data sampling approaches influence the extraction of rich descriptors of firing properties. Our analyses included neurons recorded in awake and anesthetized control mice, as well as disease models of ataxia, dystonia, and tremor. We find that recording duration circumscribes overall representations of firing rate and pattern. Notably, shorter recording durations skew estimates for global firing rate variability towards lower values. We also find that only some populations of neurons in the same mouse are more similar to each other than to neurons recorded in different mice. These data reveal that recording duration and approach are primary considerations when interpreting task-independent single-neuron firing properties. If not accounted for, group differences may be concealed or exaggerated.

The Effect of Neural Entropy during Deep Brain Stimulation of Cortex-Basal Ganglia Network Model.

Researchers reported decreased nerve entropy Patients with Parkinson's disease (PD) have abnormalities in their basal ganglia (BG). Studies of local field potentials (LFPs) recorded from the hypothalamus and single unit recordings of GP neurons showed this reduction to be significant. According to this hypothesis, these changes are consistent with changes in the ability of the basal ganglion network to encode PD information. Our deep brain stimulation of cortical basal ganglia (DBS) model includes single LFP recordings and shows how entropy changes during DBS. In addition to the extracellular stimulation of supplied STN fibers and LFP mimetics, which are detected differently on a registered electrode, this model includes osteoclast activation and anti-apoptosis. In the DBS network, the firing pattern fluctuated between high-frequency and low-frequency stimuli, since gp neurons in the network showed a decrease in entropy when a high-frequency stimulus was applied and an increase in entropy when a low-frequency stimulus was applied. Second hand. Changes in neural entropy after DBS have been reported experimentally. The simulation results were consistent

Neural correlates and determinants of approach-avoidance conflict in the prelimbic prefrontal cortex

The recollection of environmental cues associated with threat or reward allows animals to select the most appropriate behavioral responses. Neurons in the prelimbic cortex (PL) respond to both threat- and reward-associated cues. However, it remains unknown whether PL regulates threat-avoidance vs. reward-approaching responses when an animals' decision depends on previously associated memories. Using a conflict model in which male Long-Evans rats retrieve memories of shock- and food-paired cues, we observed two distinct phenotypes during conflict: i) rats that continued to press a lever for food (Pressers); and ii) rats that exhibited a complete suppression in food seeking (Non-pressers). Single-unit recordings revealed that increased risk-taking behavior in Pressers is associated with persistent food-cue responses in PL, and reduced spontaneous activity in PL glutamatergic (PLGLUT) neurons during conflict. Activating PLGLUT neurons in Pressers attenuated food-seeking responses in a neutral context, whereas inhibiting PLGLUT neurons in Non-pressers reduced defensive responses and increased food approaching during conflict. Our results establish a causal role for PLGLUT neurons in mediating individual variability in memory-based risky decision making by regulating threat-avoidance vs. reward-approach behaviors.

Disrupted-in-Schizophrenia-1 is required for proper pyramidal cell-interneuron communication and network dynamics in the prefrontal cortex

We interrogated prefrontal circuit function in mice lacking Disrupted-in-schizophrenia-1 (Disc1-mutant mice), a risk factor for psychiatric disorders. Single-unit recordings in awake mice revealed reduced average firing rates of fast-spiking interneurons (INTs), including optogenetically identified parvalbumin-positive cells, and a lower proportion of INTs phase-coupled to ongoing gamma oscillations. Moreover, we observed decreased spike transmission efficacy at local pyramidal cell (PYR)-INT connections in vivo, suggesting a reduced excitatory effect of local glutamatergic inputs as a potential mechanism of lower INT rates. On the network level, impaired INT function resulted in altered activation of PYR assemblies: While assembly activations were observed equally often, the expression strength of individual assembly patterns was significantly higher in Disc1-mutant mice. Our data thus reveal a role of Disc1 in shaping the properties of prefrontal assembly patterns by setting prefrontal INT responsiveness to glutamatergic drive.

A spike-sorting method based on hierarchical clustering to discriminate extracellularly recorded simple spikes and complex spikes from cerebellar Purkinje cells

Background: In the cerebellar cortex, Purkinje cells are the only output neurons and exhibit two types of discharge. Most Purkinje cell discharges are simple spikes, which are commonly appearing action potentials exhibiting a rich variety of firing patterns with a rate of up to 400 Hz. More infrequent discharges are complex spikes, which consist of a short burst of impulses accompanied by a massive increase in dendritic Ca2+ with a firing rate of around 1 Hz. The discrimination of these spikes in extracellular single-unit recordings is not always straightforward, as their waveforms vary depending on recording conditions and intrinsic fluctuations. New Method: To discriminate complex spikes from simple spikes in the extracellular single-unit data, we developed a semiautomatic spike-sorting method based on divisive hierarchical clustering. Results: Quantitative evaluation using parallel in vivo two-photon Ca2+ imaging of Purkinje cell dendrites indicated that 96.6% of the complex spikes were detected using our spike-sorting method from extracellular single-unit recordings obtained from anesthetized mice. Comparison with Existing Method(s): No reports have conducted a quantitative evaluation of spike-sorting algorithms used for the classification of extracellular spikes recorded from cerebellar Purkinje cells. Conclusions: Our method could be expected to contribute to research in information processing in the cerebellar cortex and the development of a fully automatic spike-sorting algorithm by providing ground-truth data useful for deep learning.

Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings

Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.

Neural correlates and determinants of approach-avoidance conflict in the prelimbic prefrontal cortex

The recollection of environmental cues associated with threat or reward allows animals to select the most appropriate behavioral responses. Neurons in the prelimbic cortex (PL) respond to both threat- and reward-associated cues. However, it remains unknown whether PL regulates threat-avoidance vs. reward-approaching responses when an animal decision depends on previously associated memories. Using a conflict model in which rats retrieve memories of shock- and food-paired cues, we observed two distinct phenotypes during conflict: i) rats that continued to press a lever for food (Pressers); and ii) rats that exhibited a complete suppression in food seeking (Non-Pressers). Single-unit recordings revealed that increased risk-taking behavior in Pressers is associated with persistent food-cue responses in PL, and reduced spontaneous activity in PL glutamatergic (PLGLUT) neurons during conflict. Activating PLGLUT neurons in Pressers attenuated food-seeking responses in a neutral context, whereas inhibiting PLGLUT neurons in Non-Pressers reduced defensive responses and increased food approaching during conflict. Our results establish a causal role for PLGLUT neurons in mediating individual variability in memory-based risky decision making by regulating threat-avoidance vs. reward-approach behaviors.

Common coding of expected value and value uncertainty memories in the prefrontal cortex and basal ganglia output

Recent evidence implicates both basal ganglia and ventrolateral prefrontal cortex (vlPFC) in encoding value memories. However, comparative roles of cortical and basal nodes in value memory are not well understood. Here, single-unit recordings in vlPFC and substantia nigra reticulata (SNr), within macaque monkeys, revealed a larger value signal in SNr that was nevertheless correlated with and had a comparable onset to the vlPFC value signal. The value signal was maintained for many objects (>90) many weeks after reward learning and was resistant to extinction in both regions and to repetition suppression in vlPFC. Both regions showed comparable granularity in encoding expected value and value uncertainty, which was paralleled by enhanced gaze bias during free viewing. The value signal dynamics in SNr could be predicted by combining responses of vlPFC neurons according to their value preferences consistent with a scheme in which cortical neurons reached SNr via direct and indirect pathways.