Performance in a GO/NOGO perceptual task reflects a balance between impulsive and instrumental components of behaviour

Berditchevskaia, Aleksandra; Cazé, Romain D.; Schultz, Simon R.

doi:10.1038/srep27389

Cited by 62 publications

(42 citation statements)

References 41 publications

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“…3 B). Although additional investigation is necessary to determine the cause of this divergence, modulation of response bias has been attributed to the influence of top-down processes, such as motivation 34 , attention, and expectation, on perception and corresponding neural activity in the brain 35 – 37 .…”

Section: Resultsmentioning

confidence: 99%

Automated and rapid self-report of nociception in transgenic mice

Black

Allawala

Bloye

et al. 2020

Sci Rep

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there are currently no rapid, operant pain behaviors in rodents that use a self-report to directly engage higher-order brain circuitry. We have developed a pain detection assay consisting of a lick behavior in response to optogenetic activation of predominantly nociceptive peripheral afferent nerve fibers in head-restrained transgenic mice expressing ChR2 in TRPV1 containing neurons. TRPV1-ChR2-EYFP mice (n = 5) were trained to provide lick reports to the detection of light-evoked nociceptive stimulation to the hind paw. Using simultaneous video recording, we demonstrate that the learned lick behavior may prove more pertinent in investigating brain driven pain processes than the reflex behavior. Within sessions, the response bias of transgenic mice changed with respect to lick behavior but not reflex behavior. Furthermore, response similarity between the lick and reflex behaviors diverged near perceptual threshold. our nociceptive lick-report detection assay will enable a host of investigations into the millisecond, single cell, neural dynamics underlying pain processing in the central nervous system of awake behaving animals. The importance of the brain in nociception has been theorized since Descartes' Treatise of Man 1. Melzack and Wall, whose gate control theory highlights the role of the spinal cord in pain processing, acknowledged the need for the myriad, interconnected brain areas to synthesize the perception of pain 2. Advances in human neural recording technologies have allowed scientists to test theories describing how brain circuits map onto pain 3. Human functional MRI (fMRI) has shown that perceived pain correlates with activation of primary somatosensory cortices, anterior cingulate cortex, and other brain regions implicated in the discriminatory and affective components of pain 4,5. Electroencephalography (EEG) and magnetoencephalography (MEG) have implicated the regulation of specific oscillatory components, for example alpha oscillations in the sensorimotor cortex, in pain perception 6. While techniques such as fMRI, EEG, and MEG paint a macroscopic picture of brain dynamics, these techniques cannot achieve the cellular or temporal resolution necessary for elucidating the neural circuits underlying pain perception. Genetic manipulations 7 and in vivo neural recordings in rodents are foundational in this respect as they afford the granularity required to characterize neural circuit activity and therapeutic outcomes 8-10. Unfortunately, the inability to compare behavioral outputs between animal and human studies presents a significant barrier for clinical translation of experimental results. Pain in humans is primarily measured using verbal self-reports, such as the visual analogue scale 11 , while pain in rodents is inferred from behavioral signals of discomfort, such as reflex behaviors 11,12. Reflex behaviors, like the hind paw withdrawal, are used to characterize evoked pain. Although reflex behaviors have been shown to positively correlate with self-reports of pain in humans 13 , decerebrated ...

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Section: Resultsmentioning

confidence: 99%

Automated and rapid self-report of nociception in transgenic mice

Black

Allawala

Bloye

et al. 2020

Sci Rep

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“…In go/no-go tasks, lick responses can be divided into two components 70 : Initially, upon CS onset, animals display an impulsive Pavlovian lick response that then quickly evolves into an instrumental lick response to CS. We modeled this instrumental lick response as action of a Q-learning model 33 .…”

Section: Methodsmentioning

confidence: 99%

Phasic dopamine reinforces distinct striatal stimulus encoding in the olfactory tubercle driving dopaminergic reward prediction

et al. 2020

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The learning of stimulus-outcome associations allows for predictions about the environment. Ventral striatum and dopaminergic midbrain neurons form a larger network for generating reward prediction signals from sensory cues. Yet, the network plasticity mechanisms to generate predictive signals in these distributed circuits have not been entirely clarified. Also, direct evidence of the underlying interregional assembly formation and information transfer is still missing. Here we show that phasic dopamine is sufficient to reinforce the distinctness of stimulus representations in the ventral striatum even in the absence of reward. Upon such reinforcement, striatal stimulus encoding gives rise to interregional assemblies that drive dopaminergic neurons during stimulus-outcome learning. These assemblies dynamically encode the predicted reward value of conditioned stimuli. Together, our data reveal that ventral striatal and midbrain reward networks form a reinforcing loop to generate reward prediction coding.

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“…Animals could maintain their performance over several days, albeit with fluctuations ( Figure 4A ), again despite day-to-day variability in how the whisker was attached to the stimulator. On every stage of training, improvements in performance occurred mainly through learning to withhold impulsive false alarm responses (licks) ( Figure 3C–D and 4B–C ), in common with other discrimination tasks in mice ( Guo et al, 2014 ; Berditchevskaia et al, 2016 ). Thus, high performance was associated with low false alarm rates ( Figure 4C ).…”

Section: Resultsmentioning

confidence: 78%

Learning and recognition of tactile temporal sequences by mice and humans

Bale

Bitzidou

Pitas

et al. 2017

eLife

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The world around us is replete with stimuli that unfold over time. When we hear an auditory stream like music or speech or scan a texture with our fingertip, physical features in the stimulus are concatenated in a particular order. This temporal patterning is critical to interpreting the stimulus. To explore the capacity of mice and humans to learn tactile sequences, we developed a task in which subjects had to recognise a continuous modulated noise sequence delivered to whiskers or fingertips, defined by its temporal patterning over hundreds of milliseconds. GO and NO-GO sequences differed only in that the order of their constituent noise modulation segments was temporally scrambled. Both mice and humans efficiently learned tactile sequences. Mouse sequence recognition depended on detecting transitions in noise amplitude; animals could base their decision on the earliest information available. Humans appeared to use additional cues, including the duration of noise modulation segments.DOI: http://dx.doi.org/10.7554/eLife.27333.001

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Performance in a GO/NOGO perceptual task reflects a balance between impulsive and instrumental components of behaviour

Cited by 62 publications

References 41 publications

Automated and rapid self-report of nociception in transgenic mice

Automated and rapid self-report of nociception in transgenic mice

Phasic dopamine reinforces distinct striatal stimulus encoding in the olfactory tubercle driving dopaminergic reward prediction

Learning and recognition of tactile temporal sequences by mice and humans

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