Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment Article Swipe
D. Rangaprakash
,
Toluwanimi O. Odemuyiwa
,
D. Narayana Dutt
,
Gopikrishna Deshpande
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1186/s40708-020-00120-2
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1186/s40708-020-00120-2
Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k -means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity.
Related Topics
Concepts
Cluster analysis
Artificial intelligence
Robustness (evolution)
Computer science
Outlier
Pattern recognition (psychology)
Unsupervised learning
DBSCAN
Neuroimaging
Functional magnetic resonance imaging
Dynamic functional connectivity
Noise (video)
Rand index
Machine learning
Correlation clustering
CURE data clustering algorithm
Psychology
Image (mathematics)
Gene
Chemistry
Biochemistry
Neuroscience
Psychiatry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s40708-020-00120-2
- https://braininformatics.springeropen.com/track/pdf/10.1186/s40708-020-00120-2
- OA Status
- gold
- Cited By
- 17
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3108407727
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3108407727Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s40708-020-00120-2Digital Object Identifier
- Title
-
Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairmentWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-11-26Full publication date if available
- Authors
-
D. Rangaprakash, Toluwanimi O. Odemuyiwa, D. Narayana Dutt, Gopikrishna DeshpandeList of authors in order
- Landing page
-
https://doi.org/10.1186/s40708-020-00120-2Publisher landing page
- PDF URL
-
https://braininformatics.springeropen.com/track/pdf/10.1186/s40708-020-00120-2Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://braininformatics.springeropen.com/track/pdf/10.1186/s40708-020-00120-2Direct OA link when available
- Concepts
-
Cluster analysis, Artificial intelligence, Robustness (evolution), Computer science, Outlier, Pattern recognition (psychology), Unsupervised learning, DBSCAN, Neuroimaging, Functional magnetic resonance imaging, Dynamic functional connectivity, Noise (video), Rand index, Machine learning, Correlation clustering, CURE data clustering algorithm, Psychology, Image (mathematics), Gene, Chemistry, Biochemistry, Neuroscience, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 2, 2023: 3, 2022: 6, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
68Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3108407727 |
|---|---|
| doi | https://doi.org/10.1186/s40708-020-00120-2 |
| ids.doi | https://doi.org/10.1186/s40708-020-00120-2 |
| ids.mag | 3108407727 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/33242116 |
| ids.openalex | https://openalex.org/W3108407727 |
| fwci | 1.50631412 |
| type | article |
| title | Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment |
| awards[0].id | https://openalex.org/G4558216502 |
| awards[0].funder_id | https://openalex.org/F4320332161 |
| awards[0].display_name | |
| awards[0].funder_award_id | U01 AG024904 |
| awards[0].funder_display_name | National Institutes of Health |
| biblio.issue | 1 |
| biblio.volume | 7 |
| biblio.last_page | 19 |
| biblio.first_page | 19 |
| topics[0].id | https://openalex.org/T10241 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.9993000030517578 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | Functional Brain Connectivity Studies |
| topics[1].id | https://openalex.org/T10136 |
| topics[1].field.id | https://openalex.org/fields/26 |
| topics[1].field.display_name | Mathematics |
| topics[1].score | 0.9801999926567078 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2613 |
| topics[1].subfield.display_name | Statistics and Probability |
| topics[1].display_name | Statistical Methods and Inference |
| topics[2].id | https://openalex.org/T14393 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9653000235557556 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2307 |
| topics[2].subfield.display_name | Health, Toxicology and Mutagenesis |
| topics[2].display_name | Health, Environment, Cognitive Aging |
| funders[0].id | https://openalex.org/F4320332161 |
| funders[0].ror | https://ror.org/01cwqze88 |
| funders[0].display_name | National Institutes of Health |
| is_xpac | False |
| apc_list.value | 790 |
| apc_list.currency | GBP |
| apc_list.value_usd | 969 |
| apc_paid.value | 790 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 969 |
| concepts[0].id | https://openalex.org/C73555534 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8210460543632507 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[0].display_name | Cluster analysis |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6891295313835144 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C63479239 |
| concepts[2].level | 3 |
| concepts[2].score | 0.668846845626831 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[2].display_name | Robustness (evolution) |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6534507274627686 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C79337645 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6395977735519409 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q779824 |
| concepts[4].display_name | Outlier |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.6230461001396179 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C8038995 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5748003721237183 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1152135 |
| concepts[6].display_name | Unsupervised learning |
| concepts[7].id | https://openalex.org/C46576248 |
| concepts[7].level | 5 |
| concepts[7].score | 0.5409077405929565 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1114630 |
| concepts[7].display_name | DBSCAN |
| concepts[8].id | https://openalex.org/C58693492 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4947735667228699 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q551875 |
| concepts[8].display_name | Neuroimaging |
| concepts[9].id | https://openalex.org/C2779226451 |
| concepts[9].level | 2 |
| concepts[9].score | 0.451130747795105 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q903809 |
| concepts[9].display_name | Functional magnetic resonance imaging |
| concepts[10].id | https://openalex.org/C2781312939 |
| concepts[10].level | 3 |
| concepts[10].score | 0.4434770941734314 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q17088721 |
| concepts[10].display_name | Dynamic functional connectivity |
| concepts[11].id | https://openalex.org/C99498987 |
| concepts[11].level | 3 |
| concepts[11].score | 0.4323781728744507 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2210247 |
| concepts[11].display_name | Noise (video) |
| concepts[12].id | https://openalex.org/C111442797 |
| concepts[12].level | 3 |
| concepts[12].score | 0.41348132491111755 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7291446 |
| concepts[12].display_name | Rand index |
| concepts[13].id | https://openalex.org/C119857082 |
| concepts[13].level | 1 |
| concepts[13].score | 0.3969104588031769 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[13].display_name | Machine learning |
| concepts[14].id | https://openalex.org/C94641424 |
| concepts[14].level | 3 |
| concepts[14].score | 0.3819833993911743 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q5172845 |
| concepts[14].display_name | Correlation clustering |
| concepts[15].id | https://openalex.org/C33704608 |
| concepts[15].level | 4 |
| concepts[15].score | 0.3460972309112549 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q5014717 |
| concepts[15].display_name | CURE data clustering algorithm |
| concepts[16].id | https://openalex.org/C15744967 |
| concepts[16].level | 0 |
| concepts[16].score | 0.10289087891578674 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[16].display_name | Psychology |
| concepts[17].id | https://openalex.org/C115961682 |
| concepts[17].level | 2 |
| concepts[17].score | 0.07360047101974487 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[17].display_name | Image (mathematics) |
| concepts[18].id | https://openalex.org/C104317684 |
| concepts[18].level | 2 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[18].display_name | Gene |
| concepts[19].id | https://openalex.org/C185592680 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[19].display_name | Chemistry |
| concepts[20].id | https://openalex.org/C55493867 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[20].display_name | Biochemistry |
| concepts[21].id | https://openalex.org/C169760540 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[21].display_name | Neuroscience |
| concepts[22].id | https://openalex.org/C118552586 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q7867 |
| concepts[22].display_name | Psychiatry |
| keywords[0].id | https://openalex.org/keywords/cluster-analysis |
| keywords[0].score | 0.8210460543632507 |
| keywords[0].display_name | Cluster analysis |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.6891295313835144 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/robustness |
| keywords[2].score | 0.668846845626831 |
| keywords[2].display_name | Robustness (evolution) |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6534507274627686 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/outlier |
| keywords[4].score | 0.6395977735519409 |
| keywords[4].display_name | Outlier |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.6230461001396179 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/unsupervised-learning |
| keywords[6].score | 0.5748003721237183 |
| keywords[6].display_name | Unsupervised learning |
| keywords[7].id | https://openalex.org/keywords/dbscan |
| keywords[7].score | 0.5409077405929565 |
| keywords[7].display_name | DBSCAN |
| keywords[8].id | https://openalex.org/keywords/neuroimaging |
| keywords[8].score | 0.4947735667228699 |
| keywords[8].display_name | Neuroimaging |
| keywords[9].id | https://openalex.org/keywords/functional-magnetic-resonance-imaging |
| keywords[9].score | 0.451130747795105 |
| keywords[9].display_name | Functional magnetic resonance imaging |
| keywords[10].id | https://openalex.org/keywords/dynamic-functional-connectivity |
| keywords[10].score | 0.4434770941734314 |
| keywords[10].display_name | Dynamic functional connectivity |
| keywords[11].id | https://openalex.org/keywords/noise |
| keywords[11].score | 0.4323781728744507 |
| keywords[11].display_name | Noise (video) |
| keywords[12].id | https://openalex.org/keywords/rand-index |
| keywords[12].score | 0.41348132491111755 |
| keywords[12].display_name | Rand index |
| keywords[13].id | https://openalex.org/keywords/machine-learning |
| keywords[13].score | 0.3969104588031769 |
| keywords[13].display_name | Machine learning |
| keywords[14].id | https://openalex.org/keywords/correlation-clustering |
| keywords[14].score | 0.3819833993911743 |
| keywords[14].display_name | Correlation clustering |
| keywords[15].id | https://openalex.org/keywords/cure-data-clustering-algorithm |
| keywords[15].score | 0.3460972309112549 |
| keywords[15].display_name | CURE data clustering algorithm |
| keywords[16].id | https://openalex.org/keywords/psychology |
| keywords[16].score | 0.10289087891578674 |
| keywords[16].display_name | Psychology |
| keywords[17].id | https://openalex.org/keywords/image |
| keywords[17].score | 0.07360047101974487 |
| keywords[17].display_name | Image (mathematics) |
| language | en |
| locations[0].id | doi:10.1186/s40708-020-00120-2 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2498492389 |
| locations[0].source.issn | 2198-4018, 2198-4026 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2198-4018 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Brain Informatics |
| locations[0].source.host_organization | https://openalex.org/P4310319900 |
| locations[0].source.host_organization_name | Springer Science+Business Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://braininformatics.springeropen.com/track/pdf/10.1186/s40708-020-00120-2 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Brain Informatics |
| locations[0].landing_page_url | https://doi.org/10.1186/s40708-020-00120-2 |
| locations[1].id | pmid:33242116 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Brain informatics |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/33242116 |
| locations[2].id | pmh:oai:eprints.iisc.ac.in:67172 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401429 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | ePrints@IISc (Indian Institute of Science) |
| locations[2].source.host_organization | https://openalex.org/I59270414 |
| locations[2].source.host_organization_name | Indian Institute of Science Bangalore |
| locations[2].source.host_organization_lineage | https://openalex.org/I59270414 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | acceptedVersion |
| locations[2].raw_type | Journal Article |
| locations[2].license_id | |
| locations[2].is_accepted | True |
| locations[2].is_published | False |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | |
| locations[3].id | pmh:oai:doaj.org/article:85c90e057c9e41738da21c942dddfc65 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306401280 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[3].source.host_organization | |
| locations[3].source.host_organization_name | |
| locations[3].license | cc-by-sa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Brain Informatics, Vol 7, Iss 1, Pp 1-13 (2020) |
| locations[3].landing_page_url | https://doaj.org/article/85c90e057c9e41738da21c942dddfc65 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:7691406 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | other-oa |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/other-oa |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Brain Inform |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7691406 |
| locations[5].id | pmh:oai:tspace.library.utoronto.ca:1807/103109 |
| locations[5].is_oa | True |
| locations[5].source | |
| locations[5].license | cc-by |
| locations[5].pdf_url | |
| locations[5].version | submittedVersion |
| locations[5].raw_type | Journal Article |
| locations[5].license_id | https://openalex.org/licenses/cc-by |
| locations[5].is_accepted | False |
| locations[5].is_published | False |
| locations[5].raw_source_name | |
| locations[5].landing_page_url | http://hdl.handle.net/1807/103109 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5090174397 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9553-1354 |
| authorships[0].author.display_name | D. Rangaprakash |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I136199984, https://openalex.org/I63966007 |
| authorships[0].affiliations[0].raw_affiliation_string | Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, MA, USA |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I136199984 |
| authorships[0].affiliations[1].raw_affiliation_string | Department of Radiology, Harvard Medical School, Boston, MA, USA |
| authorships[0].affiliations[2].institution_ids | https://openalex.org/I4210087915, https://openalex.org/I4210127055 |
| authorships[0].affiliations[2].raw_affiliation_string | Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA |
| authorships[0].institutions[0].id | https://openalex.org/I4210127055 |
| authorships[0].institutions[0].ror | https://ror.org/032q5ym94 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I136199984, https://openalex.org/I4210087915, https://openalex.org/I4210127055, https://openalex.org/I48633490, https://openalex.org/I63966007 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Athinoula A. Martinos Center for Biomedical Imaging |
| authorships[0].institutions[1].id | https://openalex.org/I136199984 |
| authorships[0].institutions[1].ror | https://ror.org/03vek6s52 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I136199984 |
| authorships[0].institutions[1].country_code | US |
| authorships[0].institutions[1].display_name | Harvard University |
| authorships[0].institutions[2].id | https://openalex.org/I4210087915 |
| authorships[0].institutions[2].ror | https://ror.org/002pd6e78 |
| authorships[0].institutions[2].type | healthcare |
| authorships[0].institutions[2].lineage | https://openalex.org/I4210087915, https://openalex.org/I48633490 |
| authorships[0].institutions[2].country_code | US |
| authorships[0].institutions[2].display_name | Massachusetts General Hospital |
| authorships[0].institutions[3].id | https://openalex.org/I63966007 |
| authorships[0].institutions[3].ror | https://ror.org/042nb2s44 |
| authorships[0].institutions[3].type | education |
| authorships[0].institutions[3].lineage | https://openalex.org/I63966007 |
| authorships[0].institutions[3].country_code | US |
| authorships[0].institutions[3].display_name | Massachusetts Institute of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | D. Rangaprakash |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA, Department of Radiology, Harvard Medical School, Boston, MA, USA, Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, MA, USA |
| authorships[1].author.id | https://openalex.org/A5028243919 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5346-4744 |
| authorships[1].author.display_name | Toluwanimi O. Odemuyiwa |
| authorships[1].countries | CA |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I185261750 |
| authorships[1].affiliations[0].raw_affiliation_string | Division of Engineering Science, Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada |
| authorships[1].institutions[0].id | https://openalex.org/I185261750 |
| authorships[1].institutions[0].ror | https://ror.org/03dbr7087 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I185261750 |
| authorships[1].institutions[0].country_code | CA |
| authorships[1].institutions[0].display_name | University of Toronto |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Toluwanimi Odemuyiwa |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Division of Engineering Science, Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada |
| authorships[2].author.id | https://openalex.org/A5108614266 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | D. Narayana Dutt |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I59270414 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, India |
| authorships[2].institutions[0].id | https://openalex.org/I59270414 |
| authorships[2].institutions[0].ror | https://ror.org/04dese585 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I59270414 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Indian Institute of Science Bangalore |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | D. Narayana Dutt |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, India |
| authorships[3].author.id | https://openalex.org/A5000419170 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-7471-5357 |
| authorships[3].author.display_name | Gopikrishna Deshpande |
| authorships[3].countries | CN, IN, US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I17301866 |
| authorships[3].affiliations[0].raw_affiliation_string | Alabama Advanced Imaging Consortium, University of Alabama Birmingham, Alabama, USA |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I82497590 |
| authorships[3].affiliations[1].raw_affiliation_string | Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA |
| authorships[3].affiliations[2].institution_ids | https://openalex.org/I82497590 |
| authorships[3].affiliations[2].raw_affiliation_string | Center for Neuroscience, Auburn University, Auburn, AL, USA |
| authorships[3].affiliations[3].institution_ids | https://openalex.org/I82497590 |
| authorships[3].affiliations[3].raw_affiliation_string | Department of Psychological Sciences, Auburn University, Auburn, AL, USA |
| authorships[3].affiliations[4].institution_ids | https://openalex.org/I699696 |
| authorships[3].affiliations[4].raw_affiliation_string | Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India |
| authorships[3].affiliations[5].institution_ids | https://openalex.org/I82497590 |
| authorships[3].affiliations[5].raw_affiliation_string | AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA |
| authorships[3].affiliations[6].institution_ids | https://openalex.org/I96852419 |
| authorships[3].affiliations[6].raw_affiliation_string | School of Psychology, Capital Normal University, Beijing, China |
| authorships[3].affiliations[7].institution_ids | https://openalex.org/I96852419 |
| authorships[3].affiliations[7].raw_affiliation_string | Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China |
| authorships[3].institutions[0].id | https://openalex.org/I96852419 |
| authorships[3].institutions[0].ror | https://ror.org/005edt527 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I96852419 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Capital Normal University |
| authorships[3].institutions[1].id | https://openalex.org/I699696 |
| authorships[3].institutions[1].ror | https://ror.org/0405n5e57 |
| authorships[3].institutions[1].type | healthcare |
| authorships[3].institutions[1].lineage | https://openalex.org/I699696 |
| authorships[3].institutions[1].country_code | IN |
| authorships[3].institutions[1].display_name | National Institute of Mental Health and Neurosciences |
| authorships[3].institutions[2].id | https://openalex.org/I82497590 |
| authorships[3].institutions[2].ror | https://ror.org/02v80fc35 |
| authorships[3].institutions[2].type | education |
| authorships[3].institutions[2].lineage | https://openalex.org/I82497590 |
| authorships[3].institutions[2].country_code | US |
| authorships[3].institutions[2].display_name | Auburn University |
| authorships[3].institutions[3].id | https://openalex.org/I17301866 |
| authorships[3].institutions[3].ror | https://ror.org/03xrrjk67 |
| authorships[3].institutions[3].type | education |
| authorships[3].institutions[3].lineage | https://openalex.org/I17301866 |
| authorships[3].institutions[3].country_code | US |
| authorships[3].institutions[3].display_name | University of Alabama |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Gopikrishna Deshpande |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA, Alabama Advanced Imaging Consortium, University of Alabama Birmingham, Alabama, USA, Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA, Center for Neuroscience, Auburn University, Auburn, AL, USA, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India, Department of Psychological Sciences, Auburn University, Auburn, AL, USA, Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China, School of Psychology, Capital Normal University, Beijing, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://braininformatics.springeropen.com/track/pdf/10.1186/s40708-020-00120-2 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10241 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.9993000030517578 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | Functional Brain Connectivity Studies |
| related_works | https://openalex.org/W2394193399, https://openalex.org/W3134385809, https://openalex.org/W2559422900, https://openalex.org/W2054669762, https://openalex.org/W2387373525, https://openalex.org/W2809859387, https://openalex.org/W2353158678, https://openalex.org/W2377732725, https://openalex.org/W2121149195, https://openalex.org/W2143393242 |
| cited_by_count | 17 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 3 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 6 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 3 |
| locations_count | 6 |
| best_oa_location.id | doi:10.1186/s40708-020-00120-2 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2498492389 |
| best_oa_location.source.issn | 2198-4018, 2198-4026 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2198-4018 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Brain Informatics |
| best_oa_location.source.host_organization | https://openalex.org/P4310319900 |
| best_oa_location.source.host_organization_name | Springer Science+Business Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://braininformatics.springeropen.com/track/pdf/10.1186/s40708-020-00120-2 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Brain Informatics |
| best_oa_location.landing_page_url | https://doi.org/10.1186/s40708-020-00120-2 |
| primary_location.id | doi:10.1186/s40708-020-00120-2 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2498492389 |
| primary_location.source.issn | 2198-4018, 2198-4026 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2198-4018 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Brain Informatics |
| primary_location.source.host_organization | https://openalex.org/P4310319900 |
| primary_location.source.host_organization_name | Springer Science+Business Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://braininformatics.springeropen.com/track/pdf/10.1186/s40708-020-00120-2 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Brain Informatics |
| primary_location.landing_page_url | https://doi.org/10.1186/s40708-020-00120-2 |
| publication_date | 2020-11-26 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W1905081362, https://openalex.org/W2112532472, https://openalex.org/W2086381765, https://openalex.org/W2123923307, https://openalex.org/W2146594014, https://openalex.org/W2007483873, https://openalex.org/W1974731011, https://openalex.org/W2043699623, https://openalex.org/W2004622816, https://openalex.org/W2049955503, https://openalex.org/W2141141646, https://openalex.org/W2024933578, https://openalex.org/W2160045918, https://openalex.org/W2008895995, https://openalex.org/W2106773151, https://openalex.org/W1537312536, https://openalex.org/W2161078209, https://openalex.org/W1991458215, https://openalex.org/W2097747115, https://openalex.org/W2148601182, https://openalex.org/W2495632124, https://openalex.org/W2143003018, https://openalex.org/W2158485497, https://openalex.org/W1999823999, https://openalex.org/W2004363938, https://openalex.org/W1964858122, https://openalex.org/W2100198805, https://openalex.org/W2168125451, https://openalex.org/W2077786358, https://openalex.org/W1998031351, https://openalex.org/W2153233077, https://openalex.org/W6631872075, https://openalex.org/W2160642098, https://openalex.org/W2088272457, https://openalex.org/W2495614755, https://openalex.org/W2019288483, https://openalex.org/W2157106546, https://openalex.org/W1970695058, https://openalex.org/W2024671524, https://openalex.org/W2625805440, https://openalex.org/W2766833230, https://openalex.org/W1970628480, https://openalex.org/W3184801616, https://openalex.org/W2122457251, https://openalex.org/W2071768612, https://openalex.org/W2600734209, https://openalex.org/W1666942233, https://openalex.org/W1983855464, https://openalex.org/W2034841618, https://openalex.org/W2168824581, https://openalex.org/W2102697279, https://openalex.org/W4237407686, https://openalex.org/W1970261918, https://openalex.org/W2129066856, https://openalex.org/W2042440292, https://openalex.org/W1673310716, https://openalex.org/W2108585670, https://openalex.org/W2057550180, https://openalex.org/W1578711506, https://openalex.org/W1582673689, https://openalex.org/W2573848376, https://openalex.org/W3105762472, https://openalex.org/W2143661428, https://openalex.org/W2012739722, https://openalex.org/W3100570787, https://openalex.org/W1976623182, https://openalex.org/W2161829094, https://openalex.org/W1528119265 |
| referenced_works_count | 68 |
| abstract_inverted_index.a | 43, 53, 156 |
| abstract_inverted_index.k | 85 |
| abstract_inverted_index.In | 96 |
| abstract_inverted_index.To | 146 |
| abstract_inverted_index.We | 125 |
| abstract_inverted_index.an | 203 |
| abstract_inverted_index.be | 94 |
| abstract_inverted_index.in | 13, 112, 179, 202 |
| abstract_inverted_index.is | 52 |
| abstract_inverted_index.of | 38, 90, 103, 119, 142, 150, 181 |
| abstract_inverted_index.to | 9, 33, 41, 73, 77, 152, 187 |
| abstract_inverted_index.we | 99, 154 |
| abstract_inverted_index.Its | 69 |
| abstract_inverted_index.and | 15, 35, 75, 110, 128, 139, 185, 197 |
| abstract_inverted_index.are | 31 |
| abstract_inverted_index.can | 195 |
| abstract_inverted_index.for | 60, 133 |
| abstract_inverted_index.has | 64 |
| abstract_inverted_index.not | 65, 93 |
| abstract_inverted_index.the | 83, 88, 101, 137, 148 |
| abstract_inverted_index.two | 104 |
| abstract_inverted_index.use | 27 |
| abstract_inverted_index.way | 205 |
| abstract_inverted_index.This | 189 |
| abstract_inverted_index.been | 6, 66 |
| abstract_inverted_index.both | 167 |
| abstract_inverted_index.data | 40, 63 |
| abstract_inverted_index.have | 5 |
| abstract_inverted_index.need | 92 |
| abstract_inverted_index.that | 30, 166, 192 |
| abstract_inverted_index.this | 97 |
| abstract_inverted_index.used | 126 |
| abstract_inverted_index.were | 170 |
| abstract_inverted_index.with | 79, 116, 174 |
| abstract_inverted_index.work | 78 |
| abstract_inverted_index.These | 24 |
| abstract_inverted_index.brain | 11, 143, 207 |
| abstract_inverted_index.data. | 81 |
| abstract_inverted_index.novel | 157 |
| abstract_inverted_index.study | 190 |
| abstract_inverted_index.terms | 180 |
| abstract_inverted_index.these | 50 |
| abstract_inverted_index.three | 117 |
| abstract_inverted_index.using | 18, 161, 206 |
| abstract_inverted_index.which | 48, 135 |
| abstract_inverted_index.whose | 58 |
| abstract_inverted_index.-means | 86 |
| abstract_inverted_index.DBSCAN | 109 |
| abstract_inverted_index.OPTICS | 173 |
| abstract_inverted_index.Unlike | 82 |
| abstract_inverted_index.assess | 147 |
| abstract_inverted_index.called | 159 |
| abstract_inverted_index.method | 158 |
| abstract_inverted_index.model. | 45 |
| abstract_inverted_index.number | 89 |
| abstract_inverted_index.purity | 183 |
| abstract_inverted_index.stages | 118 |
| abstract_inverted_index.states | 12 |
| abstract_inverted_index.static | 127 |
| abstract_inverted_index.study, | 98 |
| abstract_inverted_index.(fMRI). | 23 |
| abstract_inverted_index.OPTICS, | 111 |
| abstract_inverted_index.Results | 164 |
| abstract_inverted_index.Various | 1 |
| abstract_inverted_index.ability | 76 |
| abstract_inverted_index.classes | 201 |
| abstract_inverted_index.cluster | 182 |
| abstract_inverted_index.compare | 100 |
| abstract_inverted_index.disease | 16 |
| abstract_inverted_index.dynamic | 129, 175 |
| abstract_inverted_index.healthy | 14 |
| abstract_inverted_index.imaging | 22 |
| abstract_inverted_index.include | 71 |
| abstract_inverted_index.issues, | 51 |
| abstract_inverted_index.methods | 25 |
| abstract_inverted_index.popular | 54, 84, 105 |
| abstract_inverted_index.propose | 155 |
| abstract_inverted_index.require | 36 |
| abstract_inverted_index.utility | 59 |
| abstract_inverted_index.(95.46%) | 184 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.although | 172 |
| abstract_inverted_index.approach | 57 |
| abstract_inverted_index.captures | 136 |
| abstract_inverted_index.classify | 10 |
| abstract_inverted_index.clusters | 91 |
| abstract_inverted_index.disease. | 124 |
| abstract_inverted_index.employed | 7 |
| abstract_inverted_index.features | 132, 177 |
| abstract_inverted_index.generate | 42 |
| abstract_inverted_index.identify | 199 |
| abstract_inverted_index.labeling | 37 |
| abstract_inverted_index.learning | 56 |
| abstract_inverted_index.magnetic | 20 |
| abstract_inverted_index.methods, | 108 |
| abstract_inverted_index.outliers | 34, 74 |
| abstract_inverted_index.robustly | 198 |
| abstract_inverted_index.strength | 138 |
| abstract_inverted_index.temporal | 140 |
| abstract_inverted_index.training | 39 |
| abstract_inverted_index.(R-CLAN). | 163 |
| abstract_inverted_index.cognitive | 120 |
| abstract_inverted_index.generally | 26 |
| abstract_inverted_index.including | 122 |
| abstract_inverted_index.overcomes | 49 |
| abstract_inverted_index.resonance | 21 |
| abstract_inverted_index.sensitive | 32 |
| abstract_inverted_index.unlabeled | 80 |
| abstract_inverted_index.variation | 141 |
| abstract_inverted_index.accurately | 113, 196 |
| abstract_inverted_index.advantages | 70 |
| abstract_inverted_index.algorithms | 169 |
| abstract_inverted_index.clustering | 107, 151, 168, 194 |
| abstract_inverted_index.diagnostic | 200 |
| abstract_inverted_index.effective, | 171 |
| abstract_inverted_index.evaluated. | 68 |
| abstract_inverted_index.functional | 19, 130 |
| abstract_inverted_index.predictive | 44 |
| abstract_inverted_index.previously | 8, 67 |
| abstract_inverted_index.robustness | 149, 186 |
| abstract_inverted_index.specified. | 95 |
| abstract_inverted_index.supervised | 28 |
| abstract_inverted_index.techniques | 4 |
| abstract_inverted_index.classifiers | 29 |
| abstract_inverted_index.clustering, | 47, 87, 134 |
| abstract_inverted_index.identifying | 114 |
| abstract_inverted_index.impairment, | 121 |
| abstract_inverted_index.individuals | 115 |
| abstract_inverted_index.performance | 102 |
| abstract_inverted_index.populations | 17 |
| abstract_inverted_index.connectivity | 131, 144, 176 |
| abstract_inverted_index.demonstrated | 165 |
| abstract_inverted_index.demonstrates | 191 |
| abstract_inverted_index.neuroimaging | 62 |
| abstract_inverted_index.outperformed | 178 |
| abstract_inverted_index.unsupervised | 55, 204 |
| abstract_inverted_index.Alzheimer’s | 123 |
| abstract_inverted_index.Density-based | 46 |
| abstract_inverted_index.connectivity. | 208 |
| abstract_inverted_index.density-based | 106, 193 |
| abstract_inverted_index.insensitivity | 72 |
| abstract_inverted_index.respectively. | 145 |
| abstract_inverted_index.additive-noise | 162 |
| abstract_inverted_index.classification | 3 |
| abstract_inverted_index.noise/outliers, | 153 |
| abstract_inverted_index.noise/outliers. | 188 |
| abstract_inverted_index.high-dimensional | 61 |
| abstract_inverted_index.machine-learning | 2 |
| abstract_inverted_index.recursive-clustering | 160 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.8121696 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |