Multifaceted Neuroimaging Data Integration via Analysis of Subspaces Article Swipe
Andrew Ackerman
,
Zhengwu Zhang
,
Jan Hannig
,
Jack Prothero
,
J. S. Marron
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1017/psy.2025.10020
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1017/psy.2025.10020
Neuroimaging studies, such as the Human Connectome Project (HCP), often collect multifaceted data to study the human brain. However, these data are often analyzed in a pairwise fashion, which can hinder our understanding of how different brain-related measures interact. In this study, we analyze the multi-block HCP data using data integration via analysis of subspaces (DIVAS). We integrate structural and functional brain connectivity, substance use, cognition, and genetics in an exhaustive five-block analysis. This gives rise to the important finding that genetics is the single data modality most predictive of brain connectivity, outside of brain connectivity itself. Nearly 14% of the variation in functional connectivity (FC) and roughly 12% of the variation in structural connectivity (SC) is attributed to shared spaces with genetics. Moreover, investigations of shared space loadings provide interpretable associations between particular brain regions and drivers of variability. Novel Jackstraw hypothesis tests are developed for the DIVAS framework to establish statistically significant loadings. For example, in the (FC, SC, and substance use) subspace, these novel hypothesis tests highlight largely negative functional and structural connections suggesting the brain’s role in physiological responses to increased substance use. Our findings are validated on genetically relevant subjects not studied in the main analysis.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1017/psy.2025.10020
- https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DF96CB30ABCAD057482692C43DCB3435/S0033312325100203a.pdf/div-class-title-multifaceted-neuroimaging-data-integration-via-analysis-of-subspaces-div.pdf
- OA Status
- hybrid
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411357498
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4411357498Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1017/psy.2025.10020Digital Object Identifier
- Title
-
Multifaceted Neuroimaging Data Integration via Analysis of SubspacesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-15Full publication date if available
- Authors
-
Andrew Ackerman, Zhengwu Zhang, Jan Hannig, Jack Prothero, J. S. MarronList of authors in order
- Landing page
-
https://doi.org/10.1017/psy.2025.10020Publisher landing page
- PDF URL
-
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DF96CB30ABCAD057482692C43DCB3435/S0033312325100203a.pdf/div-class-title-multifaceted-neuroimaging-data-integration-via-analysis-of-subspaces-div.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DF96CB30ABCAD057482692C43DCB3435/S0033312325100203a.pdf/div-class-title-multifaceted-neuroimaging-data-integration-via-analysis-of-subspaces-div.pdfDirect OA link when available
- Concepts
-
Neuroimaging, Linear subspace, Computer science, Psychology, Data science, Artificial intelligence, Econometrics, Cognitive psychology, Machine learning, Mathematics, Neuroscience, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4411357498 |
|---|---|
| doi | https://doi.org/10.1017/psy.2025.10020 |
| ids.doi | https://doi.org/10.1017/psy.2025.10020 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40518568 |
| ids.openalex | https://openalex.org/W4411357498 |
| fwci | 0.0 |
| type | article |
| title | Multifaceted Neuroimaging Data Integration via Analysis of Subspaces |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 22 |
| biblio.first_page | 1 |
| topics[0].id | https://openalex.org/T12859 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 0.8867999911308289 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1304 |
| topics[0].subfield.display_name | Biophysics |
| topics[0].display_name | Cell Image Analysis Techniques |
| topics[1].id | https://openalex.org/T10862 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.8439000248908997 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | AI in cancer detection |
| topics[2].id | https://openalex.org/T10799 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.8403000235557556 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Data Visualization and Analytics |
| is_xpac | False |
| apc_list.value | 2190 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2790 |
| apc_paid.value | 2190 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2790 |
| concepts[0].id | https://openalex.org/C58693492 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7842148542404175 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q551875 |
| concepts[0].display_name | Neuroimaging |
| concepts[1].id | https://openalex.org/C12362212 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5603931546211243 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q728435 |
| concepts[1].display_name | Linear subspace |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.4562793970108032 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C15744967 |
| concepts[3].level | 0 |
| concepts[3].score | 0.4414721727371216 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[3].display_name | Psychology |
| concepts[4].id | https://openalex.org/C2522767166 |
| concepts[4].level | 1 |
| concepts[4].score | 0.435669869184494 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[4].display_name | Data science |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4095646142959595 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C149782125 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3948338031768799 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[6].display_name | Econometrics |
| concepts[7].id | https://openalex.org/C180747234 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3834845721721649 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q23373 |
| concepts[7].display_name | Cognitive psychology |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3234579563140869 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.26552602648735046 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C169760540 |
| concepts[10].level | 1 |
| concepts[10].score | 0.16552025079727173 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[10].display_name | Neuroscience |
| concepts[11].id | https://openalex.org/C2524010 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[11].display_name | Geometry |
| keywords[0].id | https://openalex.org/keywords/neuroimaging |
| keywords[0].score | 0.7842148542404175 |
| keywords[0].display_name | Neuroimaging |
| keywords[1].id | https://openalex.org/keywords/linear-subspace |
| keywords[1].score | 0.5603931546211243 |
| keywords[1].display_name | Linear subspace |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.4562793970108032 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/psychology |
| keywords[3].score | 0.4414721727371216 |
| keywords[3].display_name | Psychology |
| keywords[4].id | https://openalex.org/keywords/data-science |
| keywords[4].score | 0.435669869184494 |
| keywords[4].display_name | Data science |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4095646142959595 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/econometrics |
| keywords[6].score | 0.3948338031768799 |
| keywords[6].display_name | Econometrics |
| keywords[7].id | https://openalex.org/keywords/cognitive-psychology |
| keywords[7].score | 0.3834845721721649 |
| keywords[7].display_name | Cognitive psychology |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.3234579563140869 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.26552602648735046 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/neuroscience |
| keywords[10].score | 0.16552025079727173 |
| keywords[10].display_name | Neuroscience |
| language | en |
| locations[0].id | doi:10.1017/psy.2025.10020 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S186480540 |
| locations[0].source.issn | 0033-3123, 1860-0980 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0033-3123 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Psychometrika |
| 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-nc-nd |
| locations[0].pdf_url | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DF96CB30ABCAD057482692C43DCB3435/S0033312325100203a.pdf/div-class-title-multifaceted-neuroimaging-data-integration-via-analysis-of-subspaces-div.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Psychometrika |
| locations[0].landing_page_url | https://doi.org/10.1017/psy.2025.10020 |
| locations[1].id | pmid:40518568 |
| 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 | Psychometrika |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40518568 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5087814754 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9218-3224 |
| authorships[0].author.display_name | Andrew Ackerman |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I114027177 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
| authorships[0].institutions[0].id | https://openalex.org/I114027177 |
| authorships[0].institutions[0].ror | https://ror.org/0130frc33 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I114027177 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of North Carolina at Chapel Hill |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Andrew Ackerman |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
| authorships[1].author.id | https://openalex.org/A5050808730 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-9047-8838 |
| authorships[1].author.display_name | Zhengwu Zhang |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I114027177 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
| authorships[1].institutions[0].id | https://openalex.org/I114027177 |
| authorships[1].institutions[0].ror | https://ror.org/0130frc33 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I114027177 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of North Carolina at Chapel Hill |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhengwu Zhang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
| authorships[2].author.id | https://openalex.org/A5084746497 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-4164-0173 |
| authorships[2].author.display_name | Jan Hannig |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I114027177 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
| authorships[2].institutions[0].id | https://openalex.org/I114027177 |
| authorships[2].institutions[0].ror | https://ror.org/0130frc33 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I114027177 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of North Carolina at Chapel Hill |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Jan Hannig |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
| authorships[3].author.id | https://openalex.org/A5065532288 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4766-1741 |
| authorships[3].author.display_name | Jack Prothero |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I1321296531 |
| authorships[3].affiliations[0].raw_affiliation_string | Statistical Engineering Division, National Institute of Standards and Technology, Boulder, CO, USA |
| authorships[3].institutions[0].id | https://openalex.org/I1321296531 |
| authorships[3].institutions[0].ror | https://ror.org/05xpvk416 |
| authorships[3].institutions[0].type | government |
| authorships[3].institutions[0].lineage | https://openalex.org/I1321296531, https://openalex.org/I1343035065 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | National Institute of Standards and Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jack Prothero |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Statistical Engineering Division, National Institute of Standards and Technology, Boulder, CO, USA |
| authorships[4].author.id | https://openalex.org/A5060651495 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | J. S. Marron |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I114027177 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
| authorships[4].institutions[0].id | https://openalex.org/I114027177 |
| authorships[4].institutions[0].ror | https://ror.org/0130frc33 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I114027177 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | University of North Carolina at Chapel Hill |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | J.S. Marron |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DF96CB30ABCAD057482692C43DCB3435/S0033312325100203a.pdf/div-class-title-multifaceted-neuroimaging-data-integration-via-analysis-of-subspaces-div.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Multifaceted Neuroimaging Data Integration via Analysis of Subspaces |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12859 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 0.8867999911308289 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1304 |
| primary_topic.subfield.display_name | Biophysics |
| primary_topic.display_name | Cell Image Analysis Techniques |
| related_works | https://openalex.org/W2391321755, https://openalex.org/W2949110394, https://openalex.org/W2896134808, https://openalex.org/W1825472806, https://openalex.org/W4289378085, https://openalex.org/W2071055957, https://openalex.org/W4211228991, https://openalex.org/W2353790329, https://openalex.org/W3103861421, https://openalex.org/W2279743781 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1017/psy.2025.10020 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S186480540 |
| best_oa_location.source.issn | 0033-3123, 1860-0980 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0033-3123 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Psychometrika |
| 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-nc-nd |
| best_oa_location.pdf_url | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DF96CB30ABCAD057482692C43DCB3435/S0033312325100203a.pdf/div-class-title-multifaceted-neuroimaging-data-integration-via-analysis-of-subspaces-div.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Psychometrika |
| best_oa_location.landing_page_url | https://doi.org/10.1017/psy.2025.10020 |
| primary_location.id | doi:10.1017/psy.2025.10020 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S186480540 |
| primary_location.source.issn | 0033-3123, 1860-0980 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0033-3123 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Psychometrika |
| 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-nc-nd |
| primary_location.pdf_url | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DF96CB30ABCAD057482692C43DCB3435/S0033312325100203a.pdf/div-class-title-multifaceted-neuroimaging-data-integration-via-analysis-of-subspaces-div.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Psychometrika |
| primary_location.landing_page_url | https://doi.org/10.1017/psy.2025.10020 |
| publication_date | 2025-06-15 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2953295585, https://openalex.org/W4224212374, https://openalex.org/W1981218237, https://openalex.org/W2092460018, https://openalex.org/W2122173473, https://openalex.org/W3137540260, https://openalex.org/W2791409461, https://openalex.org/W2789530810, https://openalex.org/W2132361314, https://openalex.org/W2137969290, https://openalex.org/W2101135654, https://openalex.org/W2785762584, https://openalex.org/W1828334443, https://openalex.org/W2143769908, https://openalex.org/W2123840305, https://openalex.org/W2766639217, https://openalex.org/W1963991144, https://openalex.org/W4392883213, https://openalex.org/W3179229384, https://openalex.org/W2963758681, https://openalex.org/W2922337419, https://openalex.org/W2121637463, https://openalex.org/W2605944716, https://openalex.org/W2018040121, https://openalex.org/W4392817507, https://openalex.org/W4306165631, https://openalex.org/W2110543343, https://openalex.org/W4308696487, https://openalex.org/W2963954571, https://openalex.org/W2111902267, https://openalex.org/W3217229701, https://openalex.org/W2004927913, https://openalex.org/W2806627596, https://openalex.org/W2020519533, https://openalex.org/W2924069433, https://openalex.org/W4221004863, https://openalex.org/W2136550122, https://openalex.org/W2024729467, https://openalex.org/W4311817261, https://openalex.org/W2025341678, https://openalex.org/W2065760681, https://openalex.org/W2378809385, https://openalex.org/W2164081174, https://openalex.org/W4210888701, https://openalex.org/W3106834533, https://openalex.org/W2004293194, https://openalex.org/W2063212164, https://openalex.org/W2386638426, https://openalex.org/W2962946304 |
| referenced_works_count | 49 |
| abstract_inverted_index.a | 26 |
| abstract_inverted_index.In | 40 |
| abstract_inverted_index.We | 57 |
| abstract_inverted_index.an | 70 |
| abstract_inverted_index.as | 4 |
| abstract_inverted_index.in | 25, 69, 103, 113, 158, 181, 198 |
| abstract_inverted_index.is | 83, 117 |
| abstract_inverted_index.of | 34, 54, 90, 94, 100, 110, 126, 139 |
| abstract_inverted_index.on | 192 |
| abstract_inverted_index.to | 14, 77, 119, 151, 184 |
| abstract_inverted_index.we | 43 |
| abstract_inverted_index.12% | 109 |
| abstract_inverted_index.14% | 99 |
| abstract_inverted_index.For | 156 |
| abstract_inverted_index.HCP | 47 |
| abstract_inverted_index.Our | 188 |
| abstract_inverted_index.SC, | 161 |
| abstract_inverted_index.and | 60, 67, 107, 137, 162, 174 |
| abstract_inverted_index.are | 22, 145, 190 |
| abstract_inverted_index.can | 30 |
| abstract_inverted_index.for | 147 |
| abstract_inverted_index.how | 35 |
| abstract_inverted_index.not | 196 |
| abstract_inverted_index.our | 32 |
| abstract_inverted_index.the | 5, 16, 45, 78, 84, 101, 111, 148, 159, 178, 199 |
| abstract_inverted_index.via | 52 |
| abstract_inverted_index.(FC) | 106 |
| abstract_inverted_index.(FC, | 160 |
| abstract_inverted_index.(SC) | 116 |
| abstract_inverted_index.This | 74 |
| abstract_inverted_index.data | 13, 21, 48, 50, 86 |
| abstract_inverted_index.main | 200 |
| abstract_inverted_index.most | 88 |
| abstract_inverted_index.rise | 76 |
| abstract_inverted_index.role | 180 |
| abstract_inverted_index.such | 3 |
| abstract_inverted_index.that | 81 |
| abstract_inverted_index.this | 41 |
| abstract_inverted_index.use) | 164 |
| abstract_inverted_index.use, | 65 |
| abstract_inverted_index.use. | 187 |
| abstract_inverted_index.with | 122 |
| abstract_inverted_index.DIVAS | 149 |
| abstract_inverted_index.Human | 6 |
| abstract_inverted_index.Novel | 141 |
| abstract_inverted_index.brain | 62, 91, 95, 135 |
| abstract_inverted_index.gives | 75 |
| abstract_inverted_index.human | 17 |
| abstract_inverted_index.novel | 167 |
| abstract_inverted_index.often | 10, 23 |
| abstract_inverted_index.space | 128 |
| abstract_inverted_index.study | 15 |
| abstract_inverted_index.tests | 144, 169 |
| abstract_inverted_index.these | 20, 166 |
| abstract_inverted_index.using | 49 |
| abstract_inverted_index.which | 29 |
| abstract_inverted_index.(HCP), | 9 |
| abstract_inverted_index.Nearly | 98 |
| abstract_inverted_index.brain. | 18 |
| abstract_inverted_index.hinder | 31 |
| abstract_inverted_index.shared | 120, 127 |
| abstract_inverted_index.single | 85 |
| abstract_inverted_index.spaces | 121 |
| abstract_inverted_index.study, | 42 |
| abstract_inverted_index.Project | 8 |
| abstract_inverted_index.analyze | 44 |
| abstract_inverted_index.between | 133 |
| abstract_inverted_index.collect | 11 |
| abstract_inverted_index.drivers | 138 |
| abstract_inverted_index.finding | 80 |
| abstract_inverted_index.itself. | 97 |
| abstract_inverted_index.largely | 171 |
| abstract_inverted_index.outside | 93 |
| abstract_inverted_index.provide | 130 |
| abstract_inverted_index.regions | 136 |
| abstract_inverted_index.roughly | 108 |
| abstract_inverted_index.studied | 197 |
| abstract_inverted_index.(DIVAS). | 56 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.analysis | 53 |
| abstract_inverted_index.analyzed | 24 |
| abstract_inverted_index.example, | 157 |
| abstract_inverted_index.fashion, | 28 |
| abstract_inverted_index.findings | 189 |
| abstract_inverted_index.genetics | 68, 82 |
| abstract_inverted_index.loadings | 129 |
| abstract_inverted_index.measures | 38 |
| abstract_inverted_index.modality | 87 |
| abstract_inverted_index.negative | 172 |
| abstract_inverted_index.pairwise | 27 |
| abstract_inverted_index.relevant | 194 |
| abstract_inverted_index.studies, | 2 |
| abstract_inverted_index.subjects | 195 |
| abstract_inverted_index.Jackstraw | 142 |
| abstract_inverted_index.Moreover, | 124 |
| abstract_inverted_index.analysis. | 73, 201 |
| abstract_inverted_index.brain’s | 179 |
| abstract_inverted_index.developed | 146 |
| abstract_inverted_index.different | 36 |
| abstract_inverted_index.establish | 152 |
| abstract_inverted_index.framework | 150 |
| abstract_inverted_index.genetics. | 123 |
| abstract_inverted_index.highlight | 170 |
| abstract_inverted_index.important | 79 |
| abstract_inverted_index.increased | 185 |
| abstract_inverted_index.integrate | 58 |
| abstract_inverted_index.interact. | 39 |
| abstract_inverted_index.loadings. | 155 |
| abstract_inverted_index.responses | 183 |
| abstract_inverted_index.subspace, | 165 |
| abstract_inverted_index.subspaces | 55 |
| abstract_inverted_index.substance | 64, 163, 186 |
| abstract_inverted_index.validated | 191 |
| abstract_inverted_index.variation | 102, 112 |
| abstract_inverted_index.Connectome | 7 |
| abstract_inverted_index.attributed | 118 |
| abstract_inverted_index.cognition, | 66 |
| abstract_inverted_index.exhaustive | 71 |
| abstract_inverted_index.five-block | 72 |
| abstract_inverted_index.functional | 61, 104, 173 |
| abstract_inverted_index.hypothesis | 143, 168 |
| abstract_inverted_index.particular | 134 |
| abstract_inverted_index.predictive | 89 |
| abstract_inverted_index.structural | 59, 114, 175 |
| abstract_inverted_index.suggesting | 177 |
| abstract_inverted_index.connections | 176 |
| abstract_inverted_index.genetically | 193 |
| abstract_inverted_index.integration | 51 |
| abstract_inverted_index.multi-block | 46 |
| abstract_inverted_index.significant | 154 |
| abstract_inverted_index.Neuroimaging | 1 |
| abstract_inverted_index.associations | 132 |
| abstract_inverted_index.connectivity | 96, 105, 115 |
| abstract_inverted_index.multifaceted | 12 |
| abstract_inverted_index.variability. | 140 |
| abstract_inverted_index.brain-related | 37 |
| abstract_inverted_index.connectivity, | 63, 92 |
| abstract_inverted_index.interpretable | 131 |
| abstract_inverted_index.physiological | 182 |
| abstract_inverted_index.statistically | 153 |
| abstract_inverted_index.understanding | 33 |
| abstract_inverted_index.investigations | 125 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5060651495 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I114027177 |
| citation_normalized_percentile.value | 0.16092341 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |