Functional MRI registration with tissue-specific patch-based functional correlation tensors Article Swipe
Yujia Zhou
,
Han Zhang
,
Lichi Zhang
,
Xiaohuan Cao
,
Ru Yang
,
Qianjin Feng
,
Pew‐Thian Yap
,
Dinggang Shen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.17615/6rvf-m593
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.17615/6rvf-m593
Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high-resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs-fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole-brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white-matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals using tissue-specific patch-based functional correlation tensors (ts-PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi-channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17615/6rvf-m593
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400432665
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400432665Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.17615/6rvf-m593Digital Object Identifier
- Title
-
Functional MRI registration with tissue-specific patch-based functional correlation tensorsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-09Full publication date if available
- Authors
-
Yujia Zhou, Han Zhang, Lichi Zhang, Xiaohuan Cao, Ru Yang, Qianjin Feng, Pew‐Thian Yap, Dinggang ShenList of authors in order
- Landing page
-
https://doi.org/10.17615/6rvf-m593Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.17615/6rvf-m593Direct OA link when available
- Concepts
-
Correlation, Computer science, Artificial intelligence, Pattern recognition (psychology), Nuclear magnetic resonance, Medicine, Mathematics, Physics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2019: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4400432665 |
|---|---|
| doi | https://doi.org/10.17615/6rvf-m593 |
| ids.doi | https://doi.org/10.17615/6rvf-m593 |
| ids.openalex | https://openalex.org/W4400432665 |
| fwci | 0.0 |
| type | article |
| title | Functional MRI registration with tissue-specific patch-based functional correlation tensors |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11304 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.992900013923645 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Advanced Neuroimaging Techniques and Applications |
| topics[1].id | https://openalex.org/T10052 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9839000105857849 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Medical Image Segmentation Techniques |
| topics[2].id | https://openalex.org/T12303 |
| topics[2].field.id | https://openalex.org/fields/26 |
| topics[2].field.display_name | Mathematics |
| topics[2].score | 0.941100001335144 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2605 |
| topics[2].subfield.display_name | Computational Mathematics |
| topics[2].display_name | Tensor decomposition and applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C117220453 |
| concepts[0].level | 2 |
| concepts[0].score | 0.54352205991745 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5172842 |
| concepts[0].display_name | Correlation |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.39038801193237305 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.3807535767555237 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C153180895 |
| concepts[3].level | 2 |
| concepts[3].score | 0.37780383229255676 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[3].display_name | Pattern recognition (psychology) |
| concepts[4].id | https://openalex.org/C46141821 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3380274176597595 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q209402 |
| concepts[4].display_name | Nuclear magnetic resonance |
| concepts[5].id | https://openalex.org/C71924100 |
| concepts[5].level | 0 |
| concepts[5].score | 0.32292598485946655 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[5].display_name | Medicine |
| concepts[6].id | https://openalex.org/C33923547 |
| concepts[6].level | 0 |
| concepts[6].score | 0.25416260957717896 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[6].display_name | Mathematics |
| concepts[7].id | https://openalex.org/C121332964 |
| concepts[7].level | 0 |
| concepts[7].score | 0.18949031829833984 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[7].display_name | Physics |
| concepts[8].id | https://openalex.org/C2524010 |
| concepts[8].level | 1 |
| concepts[8].score | 0.1092621386051178 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[8].display_name | Geometry |
| keywords[0].id | https://openalex.org/keywords/correlation |
| keywords[0].score | 0.54352205991745 |
| keywords[0].display_name | Correlation |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.39038801193237305 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.3807535767555237 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/pattern-recognition |
| keywords[3].score | 0.37780383229255676 |
| keywords[3].display_name | Pattern recognition (psychology) |
| keywords[4].id | https://openalex.org/keywords/nuclear-magnetic-resonance |
| keywords[4].score | 0.3380274176597595 |
| keywords[4].display_name | Nuclear magnetic resonance |
| keywords[5].id | https://openalex.org/keywords/medicine |
| keywords[5].score | 0.32292598485946655 |
| keywords[5].display_name | Medicine |
| keywords[6].id | https://openalex.org/keywords/mathematics |
| keywords[6].score | 0.25416260957717896 |
| keywords[6].display_name | Mathematics |
| keywords[7].id | https://openalex.org/keywords/physics |
| keywords[7].score | 0.18949031829833984 |
| keywords[7].display_name | Physics |
| keywords[8].id | https://openalex.org/keywords/geometry |
| keywords[8].score | 0.1092621386051178 |
| keywords[8].display_name | Geometry |
| language | en |
| locations[0].id | doi:10.17615/6rvf-m593 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S7407051488 |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | UNC Libraries |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | article-journal |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.17615/6rvf-m593 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5016388137 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0889-2261 |
| authorships[0].author.display_name | Yujia Zhou |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zhou, Yujia |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5100399276 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0166-1973 |
| authorships[1].author.display_name | Han Zhang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhang, Han |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5006447298 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4396-4566 |
| authorships[2].author.display_name | Lichi Zhang |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zhang, Lichi |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5012899268 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-2413-114X |
| authorships[3].author.display_name | Xiaohuan Cao |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Cao, Xiaohuan |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5103572723 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Ru Yang |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yang, Ru |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5100772585 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-8647-0596 |
| authorships[5].author.display_name | Qianjin Feng |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Feng, Qianjin |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5015071060 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-1489-2102 |
| authorships[6].author.display_name | Pew‐Thian Yap |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Yap, Pew-Thian |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5000937401 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-7934-5698 |
| authorships[7].author.display_name | Dinggang Shen |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Shen, Dinggang |
| authorships[7].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.17615/6rvf-m593 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-07-09T00:00:00 |
| display_name | Functional MRI registration with tissue-specific patch-based functional correlation tensors |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11304 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.992900013923645 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Advanced Neuroimaging Techniques and Applications |
| related_works | https://openalex.org/W2748952813, https://openalex.org/W3031052312, https://openalex.org/W4389568370, https://openalex.org/W3032375762, https://openalex.org/W1995515455, https://openalex.org/W2080531066, https://openalex.org/W3108674512, https://openalex.org/W1506200166, https://openalex.org/W2033914206, https://openalex.org/W2042327336 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2023 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2019 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 1 |
| best_oa_location.id | doi:10.17615/6rvf-m593 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S7407051488 |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | UNC Libraries |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | article-journal |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.17615/6rvf-m593 |
| primary_location.id | doi:10.17615/6rvf-m593 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S7407051488 |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | UNC Libraries |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | article-journal |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.17615/6rvf-m593 |
| publication_date | 2024-07-09 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 65 |
| abstract_inverted_index.GM | 174 |
| abstract_inverted_index.In | 118 |
| abstract_inverted_index.To | 61 |
| abstract_inverted_index.be | 130 |
| abstract_inverted_index.by | 182 |
| abstract_inverted_index.in | 172 |
| abstract_inverted_index.is | 21, 179 |
| abstract_inverted_index.of | 2, 17, 67, 79, 90, 114, 158 |
| abstract_inverted_index.on | 13, 72 |
| abstract_inverted_index.to | 111, 138 |
| abstract_inverted_index.we | 121, 152 |
| abstract_inverted_index.GM, | 140 |
| abstract_inverted_index.WM. | 176 |
| abstract_inverted_index.and | 34, 101, 175 |
| abstract_inverted_index.are | 54 |
| abstract_inverted_index.can | 128 |
| abstract_inverted_index.for | 86, 145 |
| abstract_inverted_index.has | 41 |
| abstract_inverted_index.not | 55, 136 |
| abstract_inverted_index.our | 203 |
| abstract_inverted_index.the | 99, 133, 139, 142, 147, 159, 184, 190 |
| abstract_inverted_index.(FC) | 84 |
| abstract_inverted_index.This | 20 |
| abstract_inverted_index.also | 129 |
| abstract_inverted_index.been | 76 |
| abstract_inverted_index.both | 173 |
| abstract_inverted_index.data | 74 |
| abstract_inverted_index.from | 98, 132, 186 |
| abstract_inverted_index.grey | 105 |
| abstract_inverted_index.have | 75 |
| abstract_inverted_index.more | 31 |
| abstract_inverted_index.most | 78, 89 |
| abstract_inverted_index.only | 97 |
| abstract_inverted_index.rely | 12 |
| abstract_inverted_index.show | 201 |
| abstract_inverted_index.such | 44 |
| abstract_inverted_index.that | 43, 123, 202 |
| abstract_inverted_index.then | 180 |
| abstract_inverted_index.thin | 100 |
| abstract_inverted_index.this | 63, 119 |
| abstract_inverted_index.well | 50 |
| abstract_inverted_index.with | 5, 30, 58, 211 |
| abstract_inverted_index.(GM), | 107 |
| abstract_inverted_index.(WM), | 144 |
| abstract_inverted_index.align | 47 |
| abstract_inverted_index.areas | 53 |
| abstract_inverted_index.based | 70 |
| abstract_inverted_index.blood | 160 |
| abstract_inverted_index.brain | 3 |
| abstract_inverted_index.great | 109 |
| abstract_inverted_index.large | 192 |
| abstract_inverted_index.local | 154 |
| abstract_inverted_index.these | 91 |
| abstract_inverted_index.using | 26, 165, 189 |
| abstract_inverted_index.which | 80 |
| abstract_inverted_index.whole | 134 |
| abstract_inverted_index.(BOLD) | 163 |
| abstract_inverted_index.areas. | 19 |
| abstract_inverted_index.better | 35 |
| abstract_inverted_index.brain, | 135 |
| abstract_inverted_index.cannot | 46 |
| abstract_inverted_index.curved | 103 |
| abstract_inverted_index.highly | 102 |
| abstract_inverted_index.images | 29 |
| abstract_inverted_index.matter | 106 |
| abstract_inverted_index.method | 204 |
| abstract_inverted_index.metric | 195 |
| abstract_inverted_index.number | 66 |
| abstract_inverted_index.posing | 108 |
| abstract_inverted_index.tissue | 36 |
| abstract_inverted_index.useful | 125 |
| abstract_inverted_index.because | 51 |
| abstract_inverted_index.details | 33 |
| abstract_inverted_index.extract | 94 |
| abstract_inverted_index.fields. | 117 |
| abstract_inverted_index.imaging | 10 |
| abstract_inverted_index.mapping | 196 |
| abstract_inverted_index.methods | 92 |
| abstract_inverted_index.overall | 148 |
| abstract_inverted_index.regions | 49 |
| abstract_inverted_index.results | 200 |
| abstract_inverted_index.rs-fMRI | 73 |
| abstract_inverted_index.signals | 164 |
| abstract_inverted_index.spatial | 32 |
| abstract_inverted_index.studies | 1 |
| abstract_inverted_index.tensors | 170 |
| abstract_inverted_index.through | 24 |
| abstract_inverted_index.tissues | 188 |
| abstract_inverted_index.usually | 93 |
| abstract_inverted_index.utilize | 81 |
| abstract_inverted_index.(mLDDMM) | 197 |
| abstract_inverted_index.However, | 38, 88 |
| abstract_inverted_index.accurate | 14, 112 |
| abstract_inverted_index.achieved | 23 |
| abstract_inverted_index.achieves | 205 |
| abstract_inverted_index.article, | 120 |
| abstract_inverted_index.compared | 210 |
| abstract_inverted_index.cortical | 104 |
| abstract_inverted_index.directly | 71 |
| abstract_inverted_index.evidence | 40 |
| abstract_inverted_index.features | 85, 96, 127, 185 |
| abstract_inverted_index.function | 4 |
| abstract_inverted_index.magnetic | 8 |
| abstract_inverted_index.methods. | 214 |
| abstract_inverted_index.patterns | 157 |
| abstract_inverted_index.problem, | 64 |
| abstract_inverted_index.quantify | 153 |
| abstract_inverted_index.strategy | 45 |
| abstract_inverted_index.superior | 206 |
| abstract_inverted_index.(rs-fMRI) | 11 |
| abstract_inverted_index.alleviate | 62 |
| abstract_inverted_index.contrast. | 37 |
| abstract_inverted_index.different | 187 |
| abstract_inverted_index.extracted | 131 |
| abstract_inverted_index.improving | 146 |
| abstract_inverted_index.performed | 181 |
| abstract_inverted_index.resonance | 9 |
| abstract_inverted_index.suggested | 42 |
| abstract_inverted_index.typically | 22 |
| abstract_inverted_index.(ts-PFCTs) | 171 |
| abstract_inverted_index.Functional | 177 |
| abstract_inverted_index.Population | 0 |
| abstract_inverted_index.additional | 124 |
| abstract_inverted_index.algorithm. | 198 |
| abstract_inverted_index.algorithms | 69 |
| abstract_inverted_index.anatomical | 59 |
| abstract_inverted_index.challenges | 110 |
| abstract_inverted_index.consistent | 57 |
| abstract_inverted_index.developed, | 77 |
| abstract_inverted_index.estimation | 113 |
| abstract_inverted_index.functional | 7, 18, 48, 52, 82, 95, 126, 149, 168, 207 |
| abstract_inverted_index.restricted | 137 |
| abstract_inverted_index.structural | 28 |
| abstract_inverted_index.anisotropic | 155 |
| abstract_inverted_index.correlation | 156, 169 |
| abstract_inverted_index.deformation | 116, 193 |
| abstract_inverted_index.demonstrate | 122 |
| abstract_inverted_index.integrating | 183 |
| abstract_inverted_index.necessarily | 56 |
| abstract_inverted_index.oxygenation | 161 |
| abstract_inverted_index.patch-based | 167 |
| abstract_inverted_index.structures. | 60 |
| abstract_inverted_index.whole-brain | 115 |
| abstract_inverted_index.Experimental | 199 |
| abstract_inverted_index.accumulating | 39 |
| abstract_inverted_index.connectivity | 83 |
| abstract_inverted_index.conventional | 212 |
| abstract_inverted_index.intersubject | 15 |
| abstract_inverted_index.particularly | 141 |
| abstract_inverted_index.performance, | 209 |
| abstract_inverted_index.registration | 16, 25, 68, 178, 208, 213 |
| abstract_inverted_index.white-matter | 143 |
| abstract_inverted_index.Specifically, | 151 |
| abstract_inverted_index.diffeomorphic | 194 |
| abstract_inverted_index.multi-channel | 191 |
| abstract_inverted_index.registration. | 87, 150 |
| abstract_inverted_index.resting-state | 6 |
| abstract_inverted_index.high-resolution | 27 |
| abstract_inverted_index.level-dependent | 162 |
| abstract_inverted_index.tissue-specific | 166 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.17394645 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |