Predicting Alzheimer’s disease progression using multi-modal deep learning approach View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Garam Lee, Kwangsik Nho, Byungkon Kang, Kyung-Ah Sohn, Dokyoon Kim,

ABSTRACT

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials. More... »

PAGES

1952

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-37769-z

DOI

http://dx.doi.org/10.1038/s41598-018-37769-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112093901

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30760848


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/2008/1109", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Neurosciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Ajou University", 
          "id": "https://www.grid.ac/institutes/grid.251916.8", 
          "name": [
            "Department of Software and Computer Engineering, Ajou University, Suwon, South Korea", 
            "Biomedical & Translational Informatics Institute, Geisinger, Danville, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Garam", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Indiana University \u2013 Purdue University Indianapolis", 
          "id": "https://www.grid.ac/institutes/grid.257413.6", 
          "name": [
            "Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN USA", 
            "Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nho", 
        "givenName": "Kwangsik", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ajou University", 
          "id": "https://www.grid.ac/institutes/grid.251916.8", 
          "name": [
            "Department of Software and Computer Engineering, Ajou University, Suwon, South Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kang", 
        "givenName": "Byungkon", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ajou University", 
          "id": "https://www.grid.ac/institutes/grid.251916.8", 
          "name": [
            "Department of Software and Computer Engineering, Ajou University, Suwon, South Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sohn", 
        "givenName": "Kyung-Ah", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pennsylvania State University", 
          "id": "https://www.grid.ac/institutes/grid.29857.31", 
          "name": [
            "Biomedical & Translational Informatics Institute, Geisinger, Danville, USA", 
            "The Huck Institute of the Life Sciences, Pennsylvania State University, University Park, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Dokyoon", 
        "type": "Person"
      }, 
      {}
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/srep39880", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005854479", 
          "https://doi.org/10.1038/srep39880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2011.09.069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007778081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jamia/ocw112", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007784435"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/geriatrics1020011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009276284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/geriatrics1020011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009276284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature14539", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010020120", 
          "https://doi.org/10.1038/nature14539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10548-012-0246-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010581750", 
          "https://doi.org/10.1007/s10548-012-0246-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jalz.2015.02.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012338779"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2013.06.033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014761691"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jalz.2011.03.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015324218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg3868", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017872565", 
          "https://doi.org/10.1038/nrg3868"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jalz.2011.03.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018411398"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00994018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025150743", 
          "https://doi.org/10.1007/bf00994018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jalz.2011.03.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025279802"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/hbm.22156", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026932483"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11682-012-9203-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027547946", 
          "https://doi.org/10.1007/s11682-012-9203-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11682-012-9203-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027547946", 
          "https://doi.org/10.1007/s11682-012-9203-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.nicl.2013.05.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027936716"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/archneur.56.3.303", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030365018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0033182", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031408726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-02126-3_16", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033734679", 
          "https://doi.org/10.1007/978-3-319-02126-3_16"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jalz.2015.05.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036459340"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jalz.2015.05.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036459340"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/neco.1997.9.8.1735", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038140272"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jalz.2010.03.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038951865"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jalz.2010.03.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038951865"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neurobiolaging.2010.10.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039999182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2011.09.085", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042106073"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.290.5500.2323", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051806676"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0025446", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053445243"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0025446", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053445243"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/72.279181", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061218416"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2015.2404809", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061529805"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1212/wnl.0b013e3182343314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064356558"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1212/wnl.0b013e3182343314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064356558"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1212/wnl.0b013e3182343314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064356558"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078353811", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3233/jad-131928", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078893958"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3115/v1/d14-1179", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099110544"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3115/v1/d14-1179", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099110544"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-018-22871-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103198095", 
          "https://doi.org/10.1038/s41598-018-22871-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-018-22871-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103198095", 
          "https://doi.org/10.1038/s41598-018-22871-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.2517-6161.1996.tb02080.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110458978"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.2517-6161.1996.tb02080.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110458978"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC)\u2009=\u20090.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC\u2009=\u20090.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41598-018-37769-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6617992", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.6953223", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2696250", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2687006", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7132465", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "Predicting Alzheimer\u2019s disease progression using multi-modal deep learning approach", 
    "pagination": "1952", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d74b6e3c4a2786d2c12dbbe35090e63203f0fca3ed06497211858113fe351b2e"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30760848"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101563288"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-018-37769-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112093901"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-018-37769-z", 
      "https://app.dimensions.ai/details/publication/pub.1112093901"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:33", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000346_0000000346/records_99812_00000004.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41598-018-37769-z"
  }
]
 

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/s41598-018-37769-z'

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/s41598-018-37769-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37769-z'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37769-z'


 

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

221 TRIPLES      21 PREDICATES      63 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-018-37769-z schema:about anzsrc-for:11
2 anzsrc-for:1109
3 schema:author Na185a74c856a47a89d9e149feda66a93
4 schema:citation sg:pub.10.1007/978-3-319-02126-3_16
5 sg:pub.10.1007/bf00994018
6 sg:pub.10.1007/s10548-012-0246-x
7 sg:pub.10.1007/s11682-012-9203-2
8 sg:pub.10.1038/nature14539
9 sg:pub.10.1038/nrg3868
10 sg:pub.10.1038/s41598-018-22871-z
11 sg:pub.10.1038/srep39880
12 https://app.dimensions.ai/details/publication/pub.1078353811
13 https://doi.org/10.1001/archneur.56.3.303
14 https://doi.org/10.1002/hbm.22156
15 https://doi.org/10.1016/j.jalz.2010.03.013
16 https://doi.org/10.1016/j.jalz.2011.03.003
17 https://doi.org/10.1016/j.jalz.2011.03.004
18 https://doi.org/10.1016/j.jalz.2011.03.008
19 https://doi.org/10.1016/j.jalz.2015.02.003
20 https://doi.org/10.1016/j.jalz.2015.05.009
21 https://doi.org/10.1016/j.neurobiolaging.2010.10.019
22 https://doi.org/10.1016/j.neuroimage.2011.09.069
23 https://doi.org/10.1016/j.neuroimage.2011.09.085
24 https://doi.org/10.1016/j.neuroimage.2013.06.033
25 https://doi.org/10.1016/j.nicl.2013.05.004
26 https://doi.org/10.1093/jamia/ocw112
27 https://doi.org/10.1109/72.279181
28 https://doi.org/10.1109/tbme.2015.2404809
29 https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
30 https://doi.org/10.1126/science.290.5500.2323
31 https://doi.org/10.1162/neco.1997.9.8.1735
32 https://doi.org/10.1212/wnl.0b013e3182343314
33 https://doi.org/10.1371/journal.pone.0025446
34 https://doi.org/10.1371/journal.pone.0033182
35 https://doi.org/10.3115/v1/d14-1179
36 https://doi.org/10.3233/jad-131928
37 https://doi.org/10.3390/geriatrics1020011
38 schema:datePublished 2019-12
39 schema:datePublishedReg 2019-12-01
40 schema:description Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.
41 schema:genre research_article
42 schema:inLanguage en
43 schema:isAccessibleForFree true
44 schema:isPartOf N4e68b20a7e334c73aec263bf2ea786b0
45 Ndaf2d1fd98ae46d285d3236c2cf8f37e
46 sg:journal.1045337
47 schema:name Predicting Alzheimer’s disease progression using multi-modal deep learning approach
48 schema:pagination 1952
49 schema:productId N20eceaa24b4c44a6b30a98ea9b2a3b8b
50 N3744dc23d3c3459e8b6a887150d052ed
51 Nbc1eeb08d03e4b9ba92a0a9887ac55b5
52 Neb70a6b0b17343e7b12900fa34974231
53 Nf596266c92a24d75978735e73891f8bf
54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112093901
55 https://doi.org/10.1038/s41598-018-37769-z
56 schema:sdDatePublished 2019-04-11T09:33
57 schema:sdLicense https://scigraph.springernature.com/explorer/license/
58 schema:sdPublisher N272824142d124dd4a857937fb5eb399b
59 schema:url https://www.nature.com/articles/s41598-018-37769-z
60 sgo:license sg:explorer/license/
61 sgo:sdDataset articles
62 rdf:type schema:ScholarlyArticle
63 N1a1fd16a6e4c4a6886faa15de505f026 rdf:first N8ccbebba2e6a4ff9aefddb5ebcfc0c7d
64 rdf:rest Ne1a0d7db2d1148689dbaa47f71da1ce0
65 N20eceaa24b4c44a6b30a98ea9b2a3b8b schema:name doi
66 schema:value 10.1038/s41598-018-37769-z
67 rdf:type schema:PropertyValue
68 N272824142d124dd4a857937fb5eb399b schema:name Springer Nature - SN SciGraph project
69 rdf:type schema:Organization
70 N3744dc23d3c3459e8b6a887150d052ed schema:name dimensions_id
71 schema:value pub.1112093901
72 rdf:type schema:PropertyValue
73 N4e68b20a7e334c73aec263bf2ea786b0 schema:issueNumber 1
74 rdf:type schema:PublicationIssue
75 N79ff98f99661449a9efce0dbac936937 schema:affiliation https://www.grid.ac/institutes/grid.257413.6
76 schema:familyName Nho
77 schema:givenName Kwangsik
78 rdf:type schema:Person
79 N8ccbebba2e6a4ff9aefddb5ebcfc0c7d schema:affiliation https://www.grid.ac/institutes/grid.29857.31
80 schema:familyName Kim
81 schema:givenName Dokyoon
82 rdf:type schema:Person
83 Na185a74c856a47a89d9e149feda66a93 rdf:first Nc154907cbe994ac4a43cd1a5424752af
84 rdf:rest Ne97bbde9c71842219f0dd0016ef406c1
85 Na543ccd1091b463db4c289c246738bde schema:affiliation https://www.grid.ac/institutes/grid.251916.8
86 schema:familyName Sohn
87 schema:givenName Kyung-Ah
88 rdf:type schema:Person
89 Nbc1eeb08d03e4b9ba92a0a9887ac55b5 schema:name readcube_id
90 schema:value d74b6e3c4a2786d2c12dbbe35090e63203f0fca3ed06497211858113fe351b2e
91 rdf:type schema:PropertyValue
92 Nc154907cbe994ac4a43cd1a5424752af schema:affiliation https://www.grid.ac/institutes/grid.251916.8
93 schema:familyName Lee
94 schema:givenName Garam
95 rdf:type schema:Person
96 Nc9833d624a1d4026a91e991228620000 schema:affiliation https://www.grid.ac/institutes/grid.251916.8
97 schema:familyName Kang
98 schema:givenName Byungkon
99 rdf:type schema:Person
100 Nda5cea9316c147bfb20c973df6e4e214 rdf:first Nc9833d624a1d4026a91e991228620000
101 rdf:rest Ned3246a21dc14a78af143dfb4c9211d9
102 Ndaf2d1fd98ae46d285d3236c2cf8f37e schema:volumeNumber 9
103 rdf:type schema:PublicationVolume
104 Ne1a0d7db2d1148689dbaa47f71da1ce0 rdf:first Nfbe085d8490b4232a5b54552fb7f18c2
105 rdf:rest rdf:nil
106 Ne97bbde9c71842219f0dd0016ef406c1 rdf:first N79ff98f99661449a9efce0dbac936937
107 rdf:rest Nda5cea9316c147bfb20c973df6e4e214
108 Neb70a6b0b17343e7b12900fa34974231 schema:name nlm_unique_id
109 schema:value 101563288
110 rdf:type schema:PropertyValue
111 Ned3246a21dc14a78af143dfb4c9211d9 rdf:first Na543ccd1091b463db4c289c246738bde
112 rdf:rest N1a1fd16a6e4c4a6886faa15de505f026
113 Nf596266c92a24d75978735e73891f8bf schema:name pubmed_id
114 schema:value 30760848
115 rdf:type schema:PropertyValue
116 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
117 schema:name Medical and Health Sciences
118 rdf:type schema:DefinedTerm
119 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
120 schema:name Neurosciences
121 rdf:type schema:DefinedTerm
122 sg:grant.2687006 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-37769-z
123 rdf:type schema:MonetaryGrant
124 sg:grant.2696250 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-37769-z
125 rdf:type schema:MonetaryGrant
126 sg:grant.6617992 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-37769-z
127 rdf:type schema:MonetaryGrant
128 sg:grant.6953223 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-37769-z
129 rdf:type schema:MonetaryGrant
130 sg:grant.7132465 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-37769-z
131 rdf:type schema:MonetaryGrant
132 sg:journal.1045337 schema:issn 2045-2322
133 schema:name Scientific Reports
134 rdf:type schema:Periodical
135 sg:pub.10.1007/978-3-319-02126-3_16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033734679
136 https://doi.org/10.1007/978-3-319-02126-3_16
137 rdf:type schema:CreativeWork
138 sg:pub.10.1007/bf00994018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025150743
139 https://doi.org/10.1007/bf00994018
140 rdf:type schema:CreativeWork
141 sg:pub.10.1007/s10548-012-0246-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1010581750
142 https://doi.org/10.1007/s10548-012-0246-x
143 rdf:type schema:CreativeWork
144 sg:pub.10.1007/s11682-012-9203-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027547946
145 https://doi.org/10.1007/s11682-012-9203-2
146 rdf:type schema:CreativeWork
147 sg:pub.10.1038/nature14539 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010020120
148 https://doi.org/10.1038/nature14539
149 rdf:type schema:CreativeWork
150 sg:pub.10.1038/nrg3868 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017872565
151 https://doi.org/10.1038/nrg3868
152 rdf:type schema:CreativeWork
153 sg:pub.10.1038/s41598-018-22871-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1103198095
154 https://doi.org/10.1038/s41598-018-22871-z
155 rdf:type schema:CreativeWork
156 sg:pub.10.1038/srep39880 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005854479
157 https://doi.org/10.1038/srep39880
158 rdf:type schema:CreativeWork
159 https://app.dimensions.ai/details/publication/pub.1078353811 schema:CreativeWork
160 https://doi.org/10.1001/archneur.56.3.303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030365018
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1002/hbm.22156 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026932483
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/j.jalz.2010.03.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038951865
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/j.jalz.2011.03.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018411398
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1016/j.jalz.2011.03.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015324218
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.jalz.2011.03.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025279802
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/j.jalz.2015.02.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012338779
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/j.jalz.2015.05.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036459340
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/j.neurobiolaging.2010.10.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039999182
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1016/j.neuroimage.2011.09.069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007778081
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1016/j.neuroimage.2011.09.085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042106073
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1016/j.neuroimage.2013.06.033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014761691
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1016/j.nicl.2013.05.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027936716
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1093/jamia/ocw112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007784435
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1109/72.279181 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061218416
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1109/tbme.2015.2404809 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061529805
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1111/j.2517-6161.1996.tb02080.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1110458978
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1126/science.290.5500.2323 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051806676
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1162/neco.1997.9.8.1735 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038140272
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1212/wnl.0b013e3182343314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064356558
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1371/journal.pone.0025446 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053445243
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1371/journal.pone.0033182 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031408726
203 rdf:type schema:CreativeWork
204 https://doi.org/10.3115/v1/d14-1179 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099110544
205 rdf:type schema:CreativeWork
206 https://doi.org/10.3233/jad-131928 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078893958
207 rdf:type schema:CreativeWork
208 https://doi.org/10.3390/geriatrics1020011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009276284
209 rdf:type schema:CreativeWork
210 https://www.grid.ac/institutes/grid.251916.8 schema:alternateName Ajou University
211 schema:name Biomedical & Translational Informatics Institute, Geisinger, Danville, USA
212 Department of Software and Computer Engineering, Ajou University, Suwon, South Korea
213 rdf:type schema:Organization
214 https://www.grid.ac/institutes/grid.257413.6 schema:alternateName Indiana University – Purdue University Indianapolis
215 schema:name Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN USA
216 Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
217 rdf:type schema:Organization
218 https://www.grid.ac/institutes/grid.29857.31 schema:alternateName Pennsylvania State University
219 schema:name Biomedical & Translational Informatics Institute, Geisinger, Danville, USA
220 The Huck Institute of the Life Sciences, Pennsylvania State University, University Park, USA
221 rdf:type schema:Organization
 




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


...