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": "2022-12-01T06:51", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/chapter/chapter_305.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 N0d9615bc76a84d11bc50f900ea9b3e88
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 N340a042789c3465f896b87fbca43a736
8 schema:genre chapter
9 schema:isAccessibleForFree true
10 schema:isPartOf Nf01d26b6b1754f2b9b51ed23479f5136
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 N84b55445f2694b839ccd82943f531c8d
64 Nf2ef6540dd694360b8a501efe9a30abc
65 schema:publisher N784ec4ecc7ac4930afe266cb2619e62c
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 2022-12-01T06:51
69 schema:sdLicense https://scigraph.springernature.com/explorer/license/
70 schema:sdPublisher N9bf02c2fad8e4d95ace9125c9d9ee5f8
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 N03c0d270a37c4fdfa0980ba3827b49cf rdf:first sg:person.01107464404.68
76 rdf:rest N50d32921d87643e3aeef54556ae8dcc1
77 N0d9615bc76a84d11bc50f900ea9b3e88 rdf:first sg:person.0626601031.23
78 rdf:rest N1ba561365e544a86b1561479bb32fc43
79 N0fb28bbb74f3476fa75e5bd02641d4e4 rdf:first Nc45a889cd33e4b5ab9250915f829188d
80 rdf:rest N36c2dcf36db546f4a7c3cee4beb688e2
81 N1ba561365e544a86b1561479bb32fc43 rdf:first sg:person.01323327342.11
82 rdf:rest Nfae82a28c05b4ad89ed7c8f57f7c4755
83 N340a042789c3465f896b87fbca43a736 rdf:first Nbf3535234d414ecf830a0efaf6484eb2
84 rdf:rest N91167cad612b40dcb5ff50b797c647ca
85 N36c2dcf36db546f4a7c3cee4beb688e2 rdf:first N6fba678b97e54a45a3b0d6a1e47b92fa
86 rdf:rest rdf:nil
87 N50d32921d87643e3aeef54556ae8dcc1 rdf:first sg:person.013505754404.21
88 rdf:rest N9780b8230f1d4079b088179bd7c1629a
89 N682168b7892d4c5a87cb4b16f931eaa0 rdf:first Nf64e6a380c85482aba42fc93d1b91608
90 rdf:rest N0fb28bbb74f3476fa75e5bd02641d4e4
91 N6fba678b97e54a45a3b0d6a1e47b92fa schema:familyName Bandera
92 schema:givenName Antonio
93 rdf:type schema:Person
94 N784ec4ecc7ac4930afe266cb2619e62c schema:name Springer Nature
95 rdf:type schema:Organisation
96 N81b13ef4ae324f67a55880776a357e7e schema:familyName Calderón
97 schema:givenName Mariletty
98 rdf:type schema:Person
99 N84b55445f2694b839ccd82943f531c8d schema:name dimensions_id
100 schema:value pub.1110854419
101 rdf:type schema:PropertyValue
102 N91167cad612b40dcb5ff50b797c647ca rdf:first N81b13ef4ae324f67a55880776a357e7e
103 rdf:rest N682168b7892d4c5a87cb4b16f931eaa0
104 N9780b8230f1d4079b088179bd7c1629a rdf:first sg:person.015617314175.88
105 rdf:rest rdf:nil
106 N9bf02c2fad8e4d95ace9125c9d9ee5f8 schema:name Springer Nature - SN SciGraph project
107 rdf:type schema:Organization
108 Na3066bedddc740f1a017c912c0fd4083 rdf:first sg:person.0675240200.25
109 rdf:rest N03c0d270a37c4fdfa0980ba3827b49cf
110 Nbf3535234d414ecf830a0efaf6484eb2 schema:familyName Marfil
111 schema:givenName Rebeca
112 rdf:type schema:Person
113 Nc45a889cd33e4b5ab9250915f829188d schema:familyName Real
114 schema:givenName Pedro
115 rdf:type schema:Person
116 Nf01d26b6b1754f2b9b51ed23479f5136 schema:isbn 978-3-030-10827-4
117 978-3-030-10828-1
118 schema:name Computational Topology in Image Context
119 rdf:type schema:Book
120 Nf2ef6540dd694360b8a501efe9a30abc schema:name doi
121 schema:value 10.1007/978-3-030-10828-1_9
122 rdf:type schema:PropertyValue
123 Nf64e6a380c85482aba42fc93d1b91608 schema:familyName Díaz del Río
124 schema:givenName Fernando
125 rdf:type schema:Person
126 Nfae82a28c05b4ad89ed7c8f57f7c4755 rdf:first sg:person.0631257534.28
127 rdf:rest Na3066bedddc740f1a017c912c0fd4083
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)


...