Harmonic Holes as the Submodules of Brain Network and Network Dissimilarity View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2018-12-22

AUTHORS

Hyekyoung Lee , Moo K. Chung , Hongyoon Choi , Hyejin Kang , Seunggyun Ha , Yu Kyeong Kim , Dong Soo Lee

ABSTRACT

Persistent homology has been applied to brain network analysis for finding the shape of brain networks across multiple thresholds. In the persistent homology, the shape of networks is often quantified by the sequence of k-dimensional holes and Betti numbers. The Betti numbers are more widely used than holes themselves in topological brain network analysis. However, the holes show the local connectivity of networks, and they can be very informative features in analysis. In this study, we propose a new method of measuring network differences based on the dissimilarity measure of harmonic holes (HHs). The HHs, which represent the substructure of brain networks, are extracted by the Hodge Laplacian of brain networks. We also find the most contributed HHs to the network difference based on the HH dissimilarity. We applied our proposed method to clustering the networks of 4 groups, normal controls (NC), stable and progressive mild cognitive impairment (sMCI and pMCI), and Alzheimer’s disease (AD). The results showed that the clustering performance of the proposed method was better than that of network distances based on only the global change of topology. More... »

PAGES

110-122

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-10828-1_9

DOI

http://dx.doi.org/10.1007/978-3-030-10828-1_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110854419


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/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1109", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Neurosciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Seoul National University Hospital, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Hyekyoung", 
        "id": "sg:person.0626601031.23", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0626601031.23"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Wisconsin-Madison, Madison, WI, USA", 
          "id": "http://www.grid.ac/institutes/grid.14003.36", 
          "name": [
            "University of Wisconsin-Madison, Madison, WI, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chung", 
        "givenName": "Moo K.", 
        "id": "sg:person.01323327342.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323327342.11"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Seoul National University Hospital, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Choi", 
        "givenName": "Hongyoon", 
        "id": "sg:person.0631257534.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0631257534.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "Seoul National University, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kang", 
        "givenName": "Hyejin", 
        "id": "sg:person.0675240200.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0675240200.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Seoul National University Hospital, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ha", 
        "givenName": "Seunggyun", 
        "id": "sg:person.01107464404.68", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01107464404.68"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "SMG-SNU Boramae Medical Center, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.412479.d", 
          "name": [
            "SMG-SNU Boramae Medical Center, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Yu Kyeong", 
        "id": "sg:person.013505754404.21", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013505754404.21"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "Seoul National University Hospital, Seoul, Republic of Korea", 
            "Seoul National University, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Dong Soo", 
        "id": "sg:person.015617314175.88", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617314175.88"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2018-12-22", 
    "datePublishedReg": "2018-12-22", 
    "description": "Persistent homology has been applied to brain network analysis for finding the shape of brain networks across multiple thresholds. In the persistent homology, the shape of networks is often quantified by the sequence of k-dimensional holes and Betti numbers. The Betti numbers are more widely used than holes themselves in\u00a0topological brain network analysis. However, the holes show the local connectivity of networks, and they can be very informative features in analysis. In this study, we propose a new method of measuring network differences based on the dissimilarity measure of harmonic holes (HHs). The HHs, which represent the substructure of brain networks, are extracted by the Hodge Laplacian of brain networks. We also find the most contributed HHs to the network difference based on the HH dissimilarity. We applied our proposed method to clustering the networks of 4 groups, normal controls (NC), stable and progressive mild cognitive impairment (sMCI and pMCI), and Alzheimer\u2019s disease (AD). The results showed that the clustering performance of the proposed method was better than that of network distances based on only the global change of topology.", 
    "editor": [
      {
        "familyName": "Marfil", 
        "givenName": "Rebeca", 
        "type": "Person"
      }, 
      {
        "familyName": "Calder\u00f3n", 
        "givenName": "Mariletty", 
        "type": "Person"
      }, 
      {
        "familyName": "D\u00edaz del R\u00edo", 
        "givenName": "Fernando", 
        "type": "Person"
      }, 
      {
        "familyName": "Real", 
        "givenName": "Pedro", 
        "type": "Person"
      }, 
      {
        "familyName": "Bandera", 
        "givenName": "Antonio", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-030-10828-1_9", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-030-10827-4", 
        "978-3-030-10828-1"
      ], 
      "name": "Computational Topology in Image Context", 
      "type": "Book"
    }, 
    "keywords": [
      "Betti numbers", 
      "persistent homology", 
      "harmonic holes", 
      "Hodge Laplacian", 
      "dimensional holes", 
      "brain network analysis", 
      "network distance", 
      "local connectivity", 
      "dissimilarity measure", 
      "new method", 
      "network dissimilarity", 
      "holes", 
      "multiple thresholds", 
      "Laplacian", 
      "brain networks", 
      "network", 
      "informative features", 
      "topology", 
      "network analysis", 
      "submodules", 
      "shape", 
      "network differences", 
      "number", 
      "shape of network", 
      "analysis", 
      "substructure", 
      "distance", 
      "connectivity", 
      "performance", 
      "dissimilarity", 
      "results", 
      "control", 
      "threshold", 
      "features", 
      "sequence", 
      "measures", 
      "differences", 
      "study", 
      "progressive mild cognitive impairment", 
      "changes", 
      "homology", 
      "group", 
      "global change", 
      "normal controls", 
      "Alzheimer's disease", 
      "mild cognitive impairment", 
      "cognitive impairment", 
      "disease", 
      "method", 
      "impairment"
    ], 
    "name": "Harmonic Holes as the Submodules of Brain Network and Network Dissimilarity", 
    "pagination": "110-122", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1110854419"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-030-10828-1_9"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-030-10828-1_9", 
      "https://app.dimensions.ai/details/publication/pub.1110854419"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-10-01T06:57", 
    "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/chapter/chapter_371.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-030-10828-1_9"
  }
]
 

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.10.1007/978-3-030-10828-1_9'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-10828-1_9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-10828-1_9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-10828-1_9'


 

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

181 TRIPLES      22 PREDICATES      74 URIs      67 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-030-10828-1_9 schema:about anzsrc-for:11
2 anzsrc-for:1109
3 schema:author Nfa9287f3932944f19209eb1d3eba7bda
4 schema:datePublished 2018-12-22
5 schema:datePublishedReg 2018-12-22
6 schema:description Persistent homology has been applied to brain network analysis for finding the shape of brain networks across multiple thresholds. In the persistent homology, the shape of networks is often quantified by the sequence of k-dimensional holes and Betti numbers. The Betti numbers are more widely used than holes themselves in topological brain network analysis. However, the holes show the local connectivity of networks, and they can be very informative features in analysis. In this study, we propose a new method of measuring network differences based on the dissimilarity measure of harmonic holes (HHs). The HHs, which represent the substructure of brain networks, are extracted by the Hodge Laplacian of brain networks. We also find the most contributed HHs to the network difference based on the HH dissimilarity. We applied our proposed method to clustering the networks of 4 groups, normal controls (NC), stable and progressive mild cognitive impairment (sMCI and pMCI), and Alzheimer’s disease (AD). The results showed that the clustering performance of the proposed method was better than that of network distances based on only the global change of topology.
7 schema:editor N0fc9d898e5e5444eac75b2d75f6c7b7c
8 schema:genre chapter
9 schema:isAccessibleForFree true
10 schema:isPartOf Nff8c2a735d694e8eba6ef106fe11f324
11 schema:keywords Alzheimer's disease
12 Betti numbers
13 Hodge Laplacian
14 Laplacian
15 analysis
16 brain network analysis
17 brain networks
18 changes
19 cognitive impairment
20 connectivity
21 control
22 differences
23 dimensional holes
24 disease
25 dissimilarity
26 dissimilarity measure
27 distance
28 features
29 global change
30 group
31 harmonic holes
32 holes
33 homology
34 impairment
35 informative features
36 local connectivity
37 measures
38 method
39 mild cognitive impairment
40 multiple thresholds
41 network
42 network analysis
43 network differences
44 network dissimilarity
45 network distance
46 new method
47 normal controls
48 number
49 performance
50 persistent homology
51 progressive mild cognitive impairment
52 results
53 sequence
54 shape
55 shape of network
56 study
57 submodules
58 substructure
59 threshold
60 topology
61 schema:name Harmonic Holes as the Submodules of Brain Network and Network Dissimilarity
62 schema:pagination 110-122
63 schema:productId Naee4fce04a934f4cbfdc41b085d6a0b9
64 Nbb5dd61792b54c1c8cbad3692c9dbace
65 schema:publisher N159e7118e0ba4b9cb9ec6c2958404c83
66 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110854419
67 https://doi.org/10.1007/978-3-030-10828-1_9
68 schema:sdDatePublished 2022-10-01T06:57
69 schema:sdLicense https://scigraph.springernature.com/explorer/license/
70 schema:sdPublisher N9c32fc68ddcf4578a209134033e54297
71 schema:url https://doi.org/10.1007/978-3-030-10828-1_9
72 sgo:license sg:explorer/license/
73 sgo:sdDataset chapters
74 rdf:type schema:Chapter
75 N09038b3453e3471697a4cae594ba351d schema:familyName Real
76 schema:givenName Pedro
77 rdf:type schema:Person
78 N0fc9d898e5e5444eac75b2d75f6c7b7c rdf:first N8a732e1dbb444c0bb09582d7a9eca7d2
79 rdf:rest Nd658194ea1874bbca58771daf961f8e4
80 N159e7118e0ba4b9cb9ec6c2958404c83 schema:name Springer Nature
81 rdf:type schema:Organisation
82 N2896f4c9936f4b3bb98cbcf622ff3293 rdf:first sg:person.013505754404.21
83 rdf:rest Nbb84380b1caf42d195f2df8ac027be91
84 N2c0e0ccec8a2437a82b99776edb41272 rdf:first sg:person.0631257534.28
85 rdf:rest Ne2e371901f9941f990a0d4843dc39faf
86 N5319fe6806734666bde37d2c73af5e83 schema:familyName Bandera
87 schema:givenName Antonio
88 rdf:type schema:Person
89 N572392621c2648c19a088fda7935daac rdf:first sg:person.01107464404.68
90 rdf:rest N2896f4c9936f4b3bb98cbcf622ff3293
91 N5c53b301687b4deb915b59e4a8ab0b10 rdf:first sg:person.01323327342.11
92 rdf:rest N2c0e0ccec8a2437a82b99776edb41272
93 N81c3e7af738948a1ac38b316573a8323 rdf:first N09038b3453e3471697a4cae594ba351d
94 rdf:rest Nc16c78166b3d40f4a27420e6d99dc228
95 N8a732e1dbb444c0bb09582d7a9eca7d2 schema:familyName Marfil
96 schema:givenName Rebeca
97 rdf:type schema:Person
98 N97b119c8b9fd4b60846387c46ca309e4 schema:familyName Calderón
99 schema:givenName Mariletty
100 rdf:type schema:Person
101 N9c32fc68ddcf4578a209134033e54297 schema:name Springer Nature - SN SciGraph project
102 rdf:type schema:Organization
103 Naee4fce04a934f4cbfdc41b085d6a0b9 schema:name dimensions_id
104 schema:value pub.1110854419
105 rdf:type schema:PropertyValue
106 Nb291b99179f642098e364df973275c7c rdf:first Nf6ad734aa4654447aaead8228045e1ac
107 rdf:rest N81c3e7af738948a1ac38b316573a8323
108 Nbb5dd61792b54c1c8cbad3692c9dbace schema:name doi
109 schema:value 10.1007/978-3-030-10828-1_9
110 rdf:type schema:PropertyValue
111 Nbb84380b1caf42d195f2df8ac027be91 rdf:first sg:person.015617314175.88
112 rdf:rest rdf:nil
113 Nc16c78166b3d40f4a27420e6d99dc228 rdf:first N5319fe6806734666bde37d2c73af5e83
114 rdf:rest rdf:nil
115 Nd658194ea1874bbca58771daf961f8e4 rdf:first N97b119c8b9fd4b60846387c46ca309e4
116 rdf:rest Nb291b99179f642098e364df973275c7c
117 Ne2e371901f9941f990a0d4843dc39faf rdf:first sg:person.0675240200.25
118 rdf:rest N572392621c2648c19a088fda7935daac
119 Nf6ad734aa4654447aaead8228045e1ac schema:familyName Díaz del Río
120 schema:givenName Fernando
121 rdf:type schema:Person
122 Nfa9287f3932944f19209eb1d3eba7bda rdf:first sg:person.0626601031.23
123 rdf:rest N5c53b301687b4deb915b59e4a8ab0b10
124 Nff8c2a735d694e8eba6ef106fe11f324 schema:isbn 978-3-030-10827-4
125 978-3-030-10828-1
126 schema:name Computational Topology in Image Context
127 rdf:type schema:Book
128 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
129 schema:name Medical and Health Sciences
130 rdf:type schema:DefinedTerm
131 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
132 schema:name Neurosciences
133 rdf:type schema:DefinedTerm
134 sg:person.01107464404.68 schema:affiliation grid-institutes:grid.412484.f
135 schema:familyName Ha
136 schema:givenName Seunggyun
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01107464404.68
138 rdf:type schema:Person
139 sg:person.01323327342.11 schema:affiliation grid-institutes:grid.14003.36
140 schema:familyName Chung
141 schema:givenName Moo K.
142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323327342.11
143 rdf:type schema:Person
144 sg:person.013505754404.21 schema:affiliation grid-institutes:grid.412479.d
145 schema:familyName Kim
146 schema:givenName Yu Kyeong
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013505754404.21
148 rdf:type schema:Person
149 sg:person.015617314175.88 schema:affiliation grid-institutes:grid.31501.36
150 schema:familyName Lee
151 schema:givenName Dong Soo
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617314175.88
153 rdf:type schema:Person
154 sg:person.0626601031.23 schema:affiliation grid-institutes:grid.412484.f
155 schema:familyName Lee
156 schema:givenName Hyekyoung
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0626601031.23
158 rdf:type schema:Person
159 sg:person.0631257534.28 schema:affiliation grid-institutes:grid.412484.f
160 schema:familyName Choi
161 schema:givenName Hongyoon
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0631257534.28
163 rdf:type schema:Person
164 sg:person.0675240200.25 schema:affiliation grid-institutes:grid.31501.36
165 schema:familyName Kang
166 schema:givenName Hyejin
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0675240200.25
168 rdf:type schema:Person
169 grid-institutes:grid.14003.36 schema:alternateName University of Wisconsin-Madison, Madison, WI, USA
170 schema:name University of Wisconsin-Madison, Madison, WI, USA
171 rdf:type schema:Organization
172 grid-institutes:grid.31501.36 schema:alternateName Seoul National University, Seoul, Republic of Korea
173 schema:name Seoul National University Hospital, Seoul, Republic of Korea
174 Seoul National University, Seoul, Republic of Korea
175 rdf:type schema:Organization
176 grid-institutes:grid.412479.d schema:alternateName SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
177 schema:name SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
178 rdf:type schema:Organization
179 grid-institutes:grid.412484.f schema:alternateName Seoul National University Hospital, Seoul, Republic of Korea
180 schema:name Seoul National University Hospital, Seoul, Republic of Korea
181 rdf:type schema:Organization
 




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


...