Meissner corpuscles and their spatially intermingled afferents underlie gentle touch perception

Neubarth, Nicole; Emanuel, Alan J.; Liu, Yin; Springel, Mark W.; Handler, Annie; Zhang, Qiyu; Lehnert, Brendan P.; Guo, Chong; Orefice, Lauren L.; Abdelaziz, Amira; DeLisle, Michelle M.; Iskols, Michael; Rhyins, Julia; Kim, Soo J.; Cattel, Stuart; Regehr, Wade G.; Harvey, Christopher D.; Drugowitsch, Jan; Ginty, David D.

doi:10.1126/science.abb2751

Cited by 109 publications

(154 citation statements)

References 44 publications

(36 reference statements)

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“…Of note, however, representation of aversive pressure in S1 contralateral to the tactile manipulation indicates that some nociceptive information does reach this area, though this representation likely includes both discriminatory and hedonic signals. This effect is consistent with evidence of pre-cortical integration of signals carried along A and C-fiber pathways at the dorsal horn (Abraira et al, 2017;Marshall and McGlone, 2020;Neubarth et al, 2020). Interestingly, similar integration and representation of signals carried along CT-fiber afferents is not observed, suggesting divergent projections for C and CT-fiber pathways.…”

Section: Discussionsupporting

confidence: 87%

“…Interestingly, unilateral investigation of left S1 did identify a representation pattern associated with aversive pressure (see Supplementary material S1), though this was observed in the absence of specific tactile discriminability (i.e., ST or rST). This suggests either that discriminative and nociceptive information are integrated prior to reaching S1 (for candidate regions, see Abraira et al, 2017;Marshall and McGlone, 2020;Neubarth et al, 2020), or alternatively, that sustained changes in tonic firing of rates of slow adapting mechanoreceptors (for review, see Knibestol, 1975;Abraham and Mathew, 2019) in response to the strong pressure manipulation (right hand) result in a tactile representation state in left S1 during CS-trials (pressure task) that is unique from the representation of scanner generic sensation (right hand) experienced during the brush manipulation (which was applied to the left hand). With substantive evidence to support both interpretations, it is likely that the observed IPC contributions reflect a combination of these processes.…”

Section: Dissociable Cortical Representations For Tactile Informationmentioning

confidence: 99%

“…Yet, evidence for peripheral labeling of affective information suggests not all affective modulation of sensory signals is the result of central feedback (Qiu et al, 2006). Interestingly, while recent evidence has demonstrated that signals of hedonic and discriminative touch are integrated as early as the dorsal horn of the spinal cord (Abraira et al, 2017;Marshall and McGlone, 2020;Neubarth et al, 2020), the cortical instantiation of these signals does not appear to depend on this integration. For example, in patients lacking peripheral pathways for discriminatory touch (Aβ fiber), modulation of cortical activity by hedonic tactile signals (carried by C-fiber) is still observed in frontotemporal affectrelated structures (Olausson et al, 2002;McGlone et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Decoding representations of discriminatory and hedonic information during appetitive and aversive touch

Kryklywy¹,

Ehlers

Beukers³

et al. 2020

Preprint

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In the somatosensory system, hedonic information is coded by mechanoreceptors at the point of contact. Pleasure and pain signals travel along peripheral nerve pathways distinct from those for discriminative touch. Yet it remains unknown whether the central nervous system represents tactile hedonic information in sensory cortices as another dimension of exteroceptive information, similar to discriminative touch signals, or if tactile hedonic information is instantiated in regions mediating internal interoceptive states. Employing representational similarity analysis with a new approach of pattern component modeling, we decomposed multivoxel patterns to demonstrate that signals of painful but not pleasurable touch are represented in primary somatosensory cortices. By contrast, all hedonic touch representations were identified in regions associated with affect and interoception. This suggests that touch should be divided into external-exteroceptive and internal-interoceptive dimensions, with hedonic touch represented as an internal state, even though evoked by external stimulation.

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Section: Discussionsupporting

confidence: 87%

Section: Dissociable Cortical Representations For Tactile Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decoding representations of discriminatory and hedonic information during appetitive and aversive touch

Kryklywy¹,

Ehlers

Beukers³

et al. 2020

Preprint

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“…In some cases, behavioral tests allow quantification of behavior through tracking an animal's location in space, such as in the threechamber assay, open field arena, Morris water maze, and elevated plus maze 2 . Increasingly researchers are finding that important details of behavior involve subtle actions that are hard to quantify, such as changes in the prevalence of grooming in models of anxiety 3 , licking a limb in models of pain 4 , and manipulation of food objects for fine sensorimotor control 5,6 . In these cases, researchers often closely observe videos of animals and then develop a list of behaviors they want to measure.…”

Section: Introductionmentioning

confidence: 99%

DeepEthogram: a machine learning pipeline for supervised behavior classification from raw pixels

Bohnslav

Wimalasena

Clausing

et al. 2020

Preprint

Self Cite

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Researchers commonly acquire videos of animal behavior and quantify the prevalence of behaviors of interest to study nervous system function, the effects of gene mutations, and the efficacy of pharmacological therapies. This analysis is typically performed manually and is therefore immensely time consuming, often limited to a small number of behaviors, and variable across researchers. Here, we created DeepEthogram: software that takes raw pixel values of videos as input and uses machine learning to output an ethogram, the set of user-defined behaviors of interest present in each frame of a video. We used convolutional neural network models that compute motion in a video, extract features from motion and single frames, and classify these features into behaviors. These models classified behaviors with greater than 90% accuracy on single frames in videos of flies and mice, matching expert-level human performance. The models accurately predicted even extremely rare behaviors, required little training data, and generalized to new videos and subjects. DeepEthogram runs rapidly on common scientific computer hardware and has a graphical user interface that does not require programming by the end-user. We anticipate DeepEthogram will enable the rapid, automated, and reproducible assignment of behavior labels to every frame of a video, thus accelerating all those studies that quantify behaviors of interest.Code is available at: https://github.com/jbohnslav/deepethogram

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“…Both types are innervated by myelinated mechanoreceptors that arise from somatosensory ganglia. Neuronal mechanoreceptors are thought to be the only touch sensors within corpuscles and produce rapidly-adapting firing patterns when their mechanically-gated ion channels are activated by touch 2,4,5 . In layered corpuscles, the mechanoreceptor is surrounded by onion-like sheaths formed by lamellar cells, whereas it is sandwiched between two or more lamellar cells in non-layered corpuscles.…”

mentioning

confidence: 99%

Lamellar cells in Pacinian and Meissner corpuscles are touch sensors

Nikolaev

Feketa

Anderson

et al. 2020

Preprint

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The skin covering the human palm and other specialized tactile organs contains a high density of mechanosensory corpuscles tuned to detect transient pressure and vibration. These corpuscles comprise a sensory afferent neuron surrounded by lamellar cells. The neuronal afferent is thought to be the mechanical sensor within the corpuscle, whereas the function of lamellar cells is unknown. Here we show that lamellar cells within Meissner and Pacinian corpuscles detect tactile stimuli. We develop a preparation of bill skin from tactile-specialist ducks that permits electrophysiological recordings from lamellar cells and demonstrate that they contain mechanically-gated ion channels. We also show that lamellar cells from Meissner corpuscles generate mechanically-evoked action potentials using R-type voltage-gated calcium channels. These findings provide the first evidence for R-type channel-dependent action potentials in non-neuronal cells and demonstrate that lamellar cells are active detectors of touch. We propose that Meissner and Pacinian corpuscles use both neuronal and non-neuronal mechanoreception to detect mechanical signals.

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Meissner corpuscles and their spatially intermingled afferents underlie gentle touch perception

Cited by 109 publications

References 44 publications

Decoding representations of discriminatory and hedonic information during appetitive and aversive touch

Decoding representations of discriminatory and hedonic information during appetitive and aversive touch

DeepEthogram: a machine learning pipeline for supervised behavior classification from raw pixels

Lamellar cells in Pacinian and Meissner corpuscles are touch sensors

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