Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-01-10

AUTHORS

Hitoshi Maezawa, Momoka Fujimoto, Yutaka Hata, Masao Matsuhashi, Hiroaki Hashimoto, Hideki Kashioka, Toshio Yanagida, Masayuki Hirata

ABSTRACT

Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1. More... »

PAGES

388

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-021-04469-0

DOI

http://dx.doi.org/10.1038/s41598-021-04469-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1144547272

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35013521


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Actigraphy", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Biomechanical Phenomena", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Brain Mapping", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Deep Learning", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fingers", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Image Processing, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Magnetic Resonance Imaging", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Magnetoencephalography", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Movement", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Predictive Value of Tests", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sensorimotor Cortex", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Time Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tongue", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Video Recording", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Young Adult", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, 565-0871, Suita, Osaka, Japan", 
          "id": "http://www.grid.ac/institutes/grid.136593.b", 
          "name": [
            "Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, 565-0871, Suita, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Maezawa", 
        "givenName": "Hitoshi", 
        "id": "sg:person.01054457375.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054457375.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Simulation Studies, University of Hyogo, Minatojima-minamimachi 7-1-28, Chuo-ku, 650-0047, Kobe, Hyogo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.266453.0", 
          "name": [
            "Graduate School of Simulation Studies, University of Hyogo, Minatojima-minamimachi 7-1-28, Chuo-ku, 650-0047, Kobe, Hyogo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fujimoto", 
        "givenName": "Momoka", 
        "id": "sg:person.014712143343.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014712143343.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Simulation Studies, University of Hyogo, Minatojima-minamimachi 7-1-28, Chuo-ku, 650-0047, Kobe, Hyogo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.266453.0", 
          "name": [
            "Graduate School of Simulation Studies, University of Hyogo, Minatojima-minamimachi 7-1-28, Chuo-ku, 650-0047, Kobe, Hyogo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hata", 
        "givenName": "Yutaka", 
        "id": "sg:person.010524430071.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010524430071.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Medicine, Human Brain Research Center, Kyoto University, Kawahara-cho 53, Sakyo-ku, 606-8507, Kyoto, Japan", 
          "id": "http://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Graduate School of Medicine, Human Brain Research Center, Kyoto University, Kawahara-cho 53, Sakyo-ku, 606-8507, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Matsuhashi", 
        "givenName": "Masao", 
        "id": "sg:person.01147062626.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01147062626.35"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Neurosurgery, Otemae Hospital, Otemae1-5-34, Chuo-ku, 540-0008, Osaka, Japan", 
          "id": "http://www.grid.ac/institutes/grid.417344.1", 
          "name": [
            "Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, 565-0871, Suita, Osaka, Japan", 
            "Neurosurgery, Otemae Hospital, Otemae1-5-34, Chuo-ku, 540-0008, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hashimoto", 
        "givenName": "Hiroaki", 
        "id": "sg:person.01116210545.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01116210545.91"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Yamadaoka 1-4, 565-0871, Suita, Osaka, Japan", 
          "id": "http://www.grid.ac/institutes/grid.136593.b", 
          "name": [
            "Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Yamadaoka 1-4, 565-0871, Suita, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kashioka", 
        "givenName": "Hideki", 
        "id": "sg:person.012547257733.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012547257733.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Yamadaoka 1-4, 565-0871, Suita, Osaka, Japan", 
          "id": "http://www.grid.ac/institutes/grid.136593.b", 
          "name": [
            "Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Yamadaoka 1-4, 565-0871, Suita, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yanagida", 
        "givenName": "Toshio", 
        "id": "sg:person.015141357621.93", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015141357621.93"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, 565-0871, Suita, Osaka, Japan", 
          "id": "http://www.grid.ac/institutes/grid.136593.b", 
          "name": [
            "Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, 565-0871, Suita, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hirata", 
        "givenName": "Masayuki", 
        "id": "sg:person.01350120573.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01350120573.67"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/s41593-018-0209-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106100351", 
          "https://doi.org/10.1038/s41593-018-0209-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10548-012-0271-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005872938", 
          "https://doi.org/10.1007/s10548-012-0271-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nn1158", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006889744", 
          "https://doi.org/10.1038/nn1158"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00230248", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014460887", 
          "https://doi.org/10.1007/bf00230248"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41596-019-0176-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1117299532", 
          "https://doi.org/10.1038/s41596-019-0176-0"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-01-10", 
    "datePublishedReg": "2022-01-10", 
    "description": "Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.", 
    "genre": "article", 
    "id": "sg:pub.10.1038/s41598-021-04469-0", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8440613", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7523715", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "12"
      }
    ], 
    "keywords": [
      "corticokinematic coherence", 
      "whole-head magnetoencephalographic system", 
      "primary sensorimotor cortex", 
      "tongue movements", 
      "functional localization", 
      "sensorimotor cortex", 
      "contralateral hemisphere", 
      "healthy volunteers", 
      "motion capture system", 
      "finger movements", 
      "magnetic artifacts", 
      "capture system", 
      "movement signals", 
      "cortical localization", 
      "motion capture", 
      "cortex", 
      "volunteers", 
      "hemisphere", 
      "localization", 
      "tongue", 
      "control task", 
      "SM1", 
      "finger", 
      "accelerometer", 
      "movement", 
      "frequency peak", 
      "novel approach", 
      "side", 
      "system", 
      "peak", 
      "task", 
      "videography", 
      "measurements", 
      "video camera", 
      "approach", 
      "signals", 
      "coherence", 
      "source", 
      "artifacts", 
      "capture", 
      "camera", 
      "harmonics"
    ], 
    "name": "Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system", 
    "pagination": "388", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1144547272"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-021-04469-0"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "35013521"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-021-04469-0", 
      "https://app.dimensions.ai/details/publication/pub.1144547272"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:50", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_933.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1038/s41598-021-04469-0"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-021-04469-0'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-021-04469-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-021-04469-0'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-021-04469-0'


 

This table displays all metadata directly associated to this object as RDF triples.

263 TRIPLES      21 PREDICATES      91 URIs      78 LITERALS      26 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-021-04469-0 schema:about N162be3d45fea4c85b10dc26dc51ae8b5
2 N18a7f405cd2446068d388037b7503734
3 N1de73b7464d648719dc172eecafc9e28
4 N488ca943552143cab071fa3b306bbe3a
5 N514a2b2dbc424fef9780f3deb0391fce
6 N52c4f09d2fbd479dbdb30422d74e9233
7 N614ec0e3723243bfa67d42b0c5646886
8 N7b14a64a3d9f403bba653f4dd7032d8b
9 N7c80671a2d2f4041a1c18fa570184f84
10 N8030a92f948145899340d368a60a72a4
11 N88c3816bfa1c4606aec71c7f6324f2bd
12 N92920c2881534bb495596970903f1d5b
13 Na4d4c90cb2a24b969f349d4ef1db6fc3
14 Na8df677f0d364a61b0673cb7e51b18d3
15 Nb96644420d48452ea3d1d21db8695c52
16 Nba6efa75828547aeb1eb30ac6fcff4ea
17 Nc8ecd282fa6d4200a42ec1753770df72
18 Ncaa8e96a1d7b4cd49a2b1e75debee4ae
19 Nfc53474e2f53410e99c41c5bba526651
20 anzsrc-for:17
21 anzsrc-for:1701
22 schema:author N972c1b0f441549aaa57fef15dec91081
23 schema:citation sg:pub.10.1007/bf00230248
24 sg:pub.10.1007/s10548-012-0271-9
25 sg:pub.10.1038/nn1158
26 sg:pub.10.1038/s41593-018-0209-y
27 sg:pub.10.1038/s41596-019-0176-0
28 schema:datePublished 2022-01-10
29 schema:datePublishedReg 2022-01-10
30 schema:description Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.
31 schema:genre article
32 schema:isAccessibleForFree true
33 schema:isPartOf N2fd7cb678c80465fb1923387dfc6cbda
34 Ndb70fdece4644026ae6e61ac30166b5a
35 sg:journal.1045337
36 schema:keywords SM1
37 accelerometer
38 approach
39 artifacts
40 camera
41 capture
42 capture system
43 coherence
44 contralateral hemisphere
45 control task
46 cortex
47 cortical localization
48 corticokinematic coherence
49 finger
50 finger movements
51 frequency peak
52 functional localization
53 harmonics
54 healthy volunteers
55 hemisphere
56 localization
57 magnetic artifacts
58 measurements
59 motion capture
60 motion capture system
61 movement
62 movement signals
63 novel approach
64 peak
65 primary sensorimotor cortex
66 sensorimotor cortex
67 side
68 signals
69 source
70 system
71 task
72 tongue
73 tongue movements
74 video camera
75 videography
76 volunteers
77 whole-head magnetoencephalographic system
78 schema:name Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system
79 schema:pagination 388
80 schema:productId N8a281e34f0bd4722a7dbe04af1a4365e
81 N90ee6bb36ca84d5aa6ddea2b1aff85a0
82 Nabe2029bc96444ab9d48a4c0e1d9b2d4
83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1144547272
84 https://doi.org/10.1038/s41598-021-04469-0
85 schema:sdDatePublished 2022-10-01T06:50
86 schema:sdLicense https://scigraph.springernature.com/explorer/license/
87 schema:sdPublisher N81987ada8bd54ffa9b1e7e3fbba78491
88 schema:url https://doi.org/10.1038/s41598-021-04469-0
89 sgo:license sg:explorer/license/
90 sgo:sdDataset articles
91 rdf:type schema:ScholarlyArticle
92 N162be3d45fea4c85b10dc26dc51ae8b5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
93 schema:name Humans
94 rdf:type schema:DefinedTerm
95 N18a7f405cd2446068d388037b7503734 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Image Processing, Computer-Assisted
97 rdf:type schema:DefinedTerm
98 N1d50bf3267bb4fc8b957ed39627e2261 rdf:first sg:person.014712143343.37
99 rdf:rest Nba31ffbecae0436ebf9e7c20bd9f57d9
100 N1de73b7464d648719dc172eecafc9e28 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
101 schema:name Magnetic Resonance Imaging
102 rdf:type schema:DefinedTerm
103 N2fd7cb678c80465fb1923387dfc6cbda schema:volumeNumber 12
104 rdf:type schema:PublicationVolume
105 N45c83adeab7d4c9d98e7289fd8a63af3 rdf:first sg:person.01350120573.67
106 rdf:rest rdf:nil
107 N488ca943552143cab071fa3b306bbe3a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
108 schema:name Adult
109 rdf:type schema:DefinedTerm
110 N514a2b2dbc424fef9780f3deb0391fce schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Brain Mapping
112 rdf:type schema:DefinedTerm
113 N52c4f09d2fbd479dbdb30422d74e9233 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Video Recording
115 rdf:type schema:DefinedTerm
116 N614ec0e3723243bfa67d42b0c5646886 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Predictive Value of Tests
118 rdf:type schema:DefinedTerm
119 N7b14a64a3d9f403bba653f4dd7032d8b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Time Factors
121 rdf:type schema:DefinedTerm
122 N7c80671a2d2f4041a1c18fa570184f84 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Fingers
124 rdf:type schema:DefinedTerm
125 N8030a92f948145899340d368a60a72a4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Movement
127 rdf:type schema:DefinedTerm
128 N80fbc744147a487cb826a25be11c10be rdf:first sg:person.015141357621.93
129 rdf:rest N45c83adeab7d4c9d98e7289fd8a63af3
130 N81987ada8bd54ffa9b1e7e3fbba78491 schema:name Springer Nature - SN SciGraph project
131 rdf:type schema:Organization
132 N85ff3085fb124841b34705d6de9af836 rdf:first sg:person.012547257733.37
133 rdf:rest N80fbc744147a487cb826a25be11c10be
134 N88c3816bfa1c4606aec71c7f6324f2bd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Deep Learning
136 rdf:type schema:DefinedTerm
137 N8a281e34f0bd4722a7dbe04af1a4365e schema:name pubmed_id
138 schema:value 35013521
139 rdf:type schema:PropertyValue
140 N90ee6bb36ca84d5aa6ddea2b1aff85a0 schema:name doi
141 schema:value 10.1038/s41598-021-04469-0
142 rdf:type schema:PropertyValue
143 N92920c2881534bb495596970903f1d5b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
144 schema:name Actigraphy
145 rdf:type schema:DefinedTerm
146 N972c1b0f441549aaa57fef15dec91081 rdf:first sg:person.01054457375.28
147 rdf:rest N1d50bf3267bb4fc8b957ed39627e2261
148 N9fa287e0ecba4948b5fbfee245d8fee2 rdf:first sg:person.01147062626.35
149 rdf:rest Naa238235888343859265981c7ed87e48
150 Na4d4c90cb2a24b969f349d4ef1db6fc3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
151 schema:name Male
152 rdf:type schema:DefinedTerm
153 Na8df677f0d364a61b0673cb7e51b18d3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
154 schema:name Sensorimotor Cortex
155 rdf:type schema:DefinedTerm
156 Naa238235888343859265981c7ed87e48 rdf:first sg:person.01116210545.91
157 rdf:rest N85ff3085fb124841b34705d6de9af836
158 Nabe2029bc96444ab9d48a4c0e1d9b2d4 schema:name dimensions_id
159 schema:value pub.1144547272
160 rdf:type schema:PropertyValue
161 Nb96644420d48452ea3d1d21db8695c52 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Female
163 rdf:type schema:DefinedTerm
164 Nba31ffbecae0436ebf9e7c20bd9f57d9 rdf:first sg:person.010524430071.79
165 rdf:rest N9fa287e0ecba4948b5fbfee245d8fee2
166 Nba6efa75828547aeb1eb30ac6fcff4ea schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Magnetoencephalography
168 rdf:type schema:DefinedTerm
169 Nc8ecd282fa6d4200a42ec1753770df72 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
170 schema:name Biomechanical Phenomena
171 rdf:type schema:DefinedTerm
172 Ncaa8e96a1d7b4cd49a2b1e75debee4ae schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
173 schema:name Tongue
174 rdf:type schema:DefinedTerm
175 Ndb70fdece4644026ae6e61ac30166b5a schema:issueNumber 1
176 rdf:type schema:PublicationIssue
177 Nfc53474e2f53410e99c41c5bba526651 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
178 schema:name Young Adult
179 rdf:type schema:DefinedTerm
180 anzsrc-for:17 schema:inDefinedTermSet anzsrc-for:
181 schema:name Psychology and Cognitive Sciences
182 rdf:type schema:DefinedTerm
183 anzsrc-for:1701 schema:inDefinedTermSet anzsrc-for:
184 schema:name Psychology
185 rdf:type schema:DefinedTerm
186 sg:grant.7523715 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-021-04469-0
187 rdf:type schema:MonetaryGrant
188 sg:grant.8440613 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-021-04469-0
189 rdf:type schema:MonetaryGrant
190 sg:journal.1045337 schema:issn 2045-2322
191 schema:name Scientific Reports
192 schema:publisher Springer Nature
193 rdf:type schema:Periodical
194 sg:person.010524430071.79 schema:affiliation grid-institutes:grid.266453.0
195 schema:familyName Hata
196 schema:givenName Yutaka
197 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010524430071.79
198 rdf:type schema:Person
199 sg:person.01054457375.28 schema:affiliation grid-institutes:grid.136593.b
200 schema:familyName Maezawa
201 schema:givenName Hitoshi
202 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054457375.28
203 rdf:type schema:Person
204 sg:person.01116210545.91 schema:affiliation grid-institutes:grid.417344.1
205 schema:familyName Hashimoto
206 schema:givenName Hiroaki
207 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01116210545.91
208 rdf:type schema:Person
209 sg:person.01147062626.35 schema:affiliation grid-institutes:grid.258799.8
210 schema:familyName Matsuhashi
211 schema:givenName Masao
212 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01147062626.35
213 rdf:type schema:Person
214 sg:person.012547257733.37 schema:affiliation grid-institutes:grid.136593.b
215 schema:familyName Kashioka
216 schema:givenName Hideki
217 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012547257733.37
218 rdf:type schema:Person
219 sg:person.01350120573.67 schema:affiliation grid-institutes:grid.136593.b
220 schema:familyName Hirata
221 schema:givenName Masayuki
222 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01350120573.67
223 rdf:type schema:Person
224 sg:person.014712143343.37 schema:affiliation grid-institutes:grid.266453.0
225 schema:familyName Fujimoto
226 schema:givenName Momoka
227 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014712143343.37
228 rdf:type schema:Person
229 sg:person.015141357621.93 schema:affiliation grid-institutes:grid.136593.b
230 schema:familyName Yanagida
231 schema:givenName Toshio
232 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015141357621.93
233 rdf:type schema:Person
234 sg:pub.10.1007/bf00230248 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014460887
235 https://doi.org/10.1007/bf00230248
236 rdf:type schema:CreativeWork
237 sg:pub.10.1007/s10548-012-0271-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005872938
238 https://doi.org/10.1007/s10548-012-0271-9
239 rdf:type schema:CreativeWork
240 sg:pub.10.1038/nn1158 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006889744
241 https://doi.org/10.1038/nn1158
242 rdf:type schema:CreativeWork
243 sg:pub.10.1038/s41593-018-0209-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1106100351
244 https://doi.org/10.1038/s41593-018-0209-y
245 rdf:type schema:CreativeWork
246 sg:pub.10.1038/s41596-019-0176-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1117299532
247 https://doi.org/10.1038/s41596-019-0176-0
248 rdf:type schema:CreativeWork
249 grid-institutes:grid.136593.b schema:alternateName Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Yamadaoka 1-4, 565-0871, Suita, Osaka, Japan
250 Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, 565-0871, Suita, Osaka, Japan
251 schema:name Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Yamadaoka 1-4, 565-0871, Suita, Osaka, Japan
252 Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, 565-0871, Suita, Osaka, Japan
253 rdf:type schema:Organization
254 grid-institutes:grid.258799.8 schema:alternateName Graduate School of Medicine, Human Brain Research Center, Kyoto University, Kawahara-cho 53, Sakyo-ku, 606-8507, Kyoto, Japan
255 schema:name Graduate School of Medicine, Human Brain Research Center, Kyoto University, Kawahara-cho 53, Sakyo-ku, 606-8507, Kyoto, Japan
256 rdf:type schema:Organization
257 grid-institutes:grid.266453.0 schema:alternateName Graduate School of Simulation Studies, University of Hyogo, Minatojima-minamimachi 7-1-28, Chuo-ku, 650-0047, Kobe, Hyogo, Japan
258 schema:name Graduate School of Simulation Studies, University of Hyogo, Minatojima-minamimachi 7-1-28, Chuo-ku, 650-0047, Kobe, Hyogo, Japan
259 rdf:type schema:Organization
260 grid-institutes:grid.417344.1 schema:alternateName Neurosurgery, Otemae Hospital, Otemae1-5-34, Chuo-ku, 540-0008, Osaka, Japan
261 schema:name Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, 565-0871, Suita, Osaka, Japan
262 Neurosurgery, Otemae Hospital, Otemae1-5-34, Chuo-ku, 540-0008, Osaka, Japan
263 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...