001     912513
005     20230217124543.0
024 7 _ |a 10.1523/JNEUROSCI.2290-20.2022
|2 doi
024 7 _ |a 0270-6474
|2 ISSN
024 7 _ |a 1529-2401
|2 ISSN
024 7 _ |a 2128/33068
|2 Handle
024 7 _ |a 35545434
|2 pmid
024 7 _ |a WOS:000817218600006
|2 WOS
037 _ _ |a FZJ-2022-05686
082 _ _ |a 610
100 1 _ |a Grabowska, Martyna
|0 P:(DE-HGF)0
|b 0
245 _ _ |a Existence of a Long-Range Caudo-Rostral Sensory Influence in Terrestrial Locomotion
260 _ _ |a Washington, DC
|c 2022
|b Soc.
264 _ 1 |3 online
|2 Crossref
|b Society for Neuroscience
|c 2022-05-11
264 _ 1 |3 print
|2 Crossref
|b Society for Neuroscience
|c 2022-06-15
264 _ 1 |3 print
|2 Crossref
|b Society for Neuroscience
|c 2022-06-15
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1671712843_12005
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a In multisegmented locomotion, coordination of all appendages is crucial for the generation of a proper motor output. In running for example, leg coordination is mainly based on the central interaction of rhythm generating networks, called central pattern generators (CPGs). In slower forms of locomotion, however, sensory feedback, which originates from sensory organs that detect changes in position, velocity and load of the legs' segments, has been shown to play a more crucial role. How exactly sensory feedback influences the activity of the CPGs to establish functional neuronal connectivity is not yet fully understood. Using the female stick insect Carausius morosus, we show for the first time that a long-range caudo-rostral sensory connection exists and highlight that load as sensory signal is sufficient to entrain rhythmic motoneuron (MN) activity in the most rostral segment. So far, mainly rostro-caudal influencing pathways have been investigated where the strength of activation, expressed by the MN activity in the thoracic ganglia, decreases with the distance from the stepping leg to these ganglia. Here, we activated CPGs, producing rhythmic neuronal activity in the thoracic ganglia by using the muscarinic agonist pilocarpine and enforced the stepping of a single, remaining leg. This enabled us to study sensory influences on the CPGs' oscillatory activity. Using this approach, we show that, in contrast to the distance-dependent activation of the protractor-retractor CPGs in different thoracic ganglia, there is no such dependence for the entrainment of the rhythmic activity of active protractor-retractor CPG networks by individual stepping legs.SIGNIFICANCE STATEMENT We show for the first time that sensory information is transferred not only to the immediate adjacent segmental ganglia but also to those farther away, indicating the existence of a long-range caudo-rostral sensory influence. This influence is dependent on stepping direction but independent of whether the leg is actively or passively moved. We suggest that the sensory information comes from unspecific load signals sensed by cuticle mechanoreceptors (campaniform sensilla) of a leg. Our results provide a neuronal basis for the long-established behavioral rules of insect leg coordination. We thus provide a breakthrough in understanding the neuronal networks underlying multilegged locomotion and open new vistas into the neuronal functional connectivity of multisegmented locomotion systems across the animal kingdom.Keywords: CPG; entrainment; inter-segmental coordination; locomotion; six-legged walking.
536 _ _ |a 5252 - Brain Dysfunction and Plasticity (POF4-525)
|0 G:(DE-HGF)POF4-5252
|c POF4-525
|f POF IV
|x 0
588 _ _ |a Dataset connected to DataCite
700 1 _ |a Toth, Tibor I.
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Büschges, Ansgar
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Daun, Silvia
|0 P:(DE-Juel1)162297
|b 3
|e Corresponding author
|u fzj
773 1 8 |a 10.1523/jneurosci.2290-20.2022
|b Society for Neuroscience
|d 2022-05-11
|n 24
|p 4841-4851
|3 journal-article
|2 Crossref
|t The Journal of Neuroscience
|v 42
|y 2022
|x 0270-6474
773 _ _ |a 10.1523/JNEUROSCI.2290-20.2022
|g Vol. 42, no. 24, p. 4841 - 4851
|0 PERI:(DE-600)1475274-8
|n 24
|p 4841-4851
|t The journal of neuroscience
|v 42
|y 2022
|x 0270-6474
856 4 _ |u https://juser.fz-juelich.de/record/912513/files/Invoice_JNeurosci07798.pdf
856 4 _ |u https://juser.fz-juelich.de/record/912513/files/4841.full.pdf
|y Published on 2022-06-15. Available in OpenAccess from 2022-12-15.
856 4 _ |u https://juser.fz-juelich.de/record/912513/files/Post-print.pdf
|y Published on 2022-06-15. Available in OpenAccess from 2022-12-15.
909 C O |o oai:juser.fz-juelich.de:912513
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)162297
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5252
|x 0
914 1 _ |y 2022
915 p c |a Local Funding
|0 PC:(DE-HGF)0001
|2 APC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2021-01-30
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-30
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2021-01-30
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-01-30
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2021-01-30
915 _ _ |a Embargoed OpenAccess
|0 StatID:(DE-HGF)0530
|2 StatID
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b J NEUROSCI : 2019
|d 2021-01-30
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2021-01-30
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-30
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2021-01-30
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2021-01-30
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b J NEUROSCI : 2019
|d 2021-01-30
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2021-01-30
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2021-01-30
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2021-01-30
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-3-20090406
|k INM-3
|l Kognitive Neurowissenschaften
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-3-20090406
980 _ _ |a APC
980 _ _ |a UNRESTRICTED
980 1 _ |a APC
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21