Semiparametric Bayesian local functional models for diffusion tensor tract statistics Article Swipe
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.17615/vjq3-wt60
We propose a semiparametric Bayesian local functional model (BFM) for the analysis of multiple diffusion properties (e.g., fractional anisotropy) along white matter fiber bundles with a set of covariates of interest, such as age and gender. BFM accounts for heterogeneity in the shape of the fiber bundle diffusion properties among subjects, while allowing the impact of the covariates to vary across subjects. A nonparametric Bayesian LPP2 prior facilitates global and local borrowings of information among subjects, while an infinite factor model flexibly represents low-dimensional structure. Local hypothesis testing and credible bands are developed to identify fiber segments, along which multiple diffusion properties are significantly associated with covariates of interest, while controlling for multiple comparisons. Moreover, BFM naturally group subjects into more homogeneous clusters. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFM. We apply BFM to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment in new born infants.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17615/vjq3-wt60
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4298213042
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4298213042Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.17615/vjq3-wt60Digital Object Identifier
- Title
-
Semiparametric Bayesian local functional models for diffusion tensor tract statisticsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-11-06Full publication date if available
- Authors
-
Zhaowei Hua, David B. Dunson, John H. Gilmore, Martin Styner, Hongtu ZhuList of authors in order
- Landing page
-
https://doi.org/10.17615/vjq3-wt60Publisher 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/vjq3-wt60Direct OA link when available
- Concepts
-
Bayesian probability, Statistics, Diffusion MRI, Mathematics, Tensor (intrinsic definition), Econometrics, Statistical physics, Diffusion, Computer science, Physics, Geometry, Medicine, Magnetic resonance imaging, Thermodynamics, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2014: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4298213042 |
|---|---|
| doi | https://doi.org/10.17615/vjq3-wt60 |
| ids.doi | https://doi.org/10.17615/vjq3-wt60 |
| ids.openalex | https://openalex.org/W4298213042 |
| fwci | 0.0 |
| type | article |
| title | Semiparametric Bayesian local functional models for diffusion tensor tract statistics |
| 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 | 0.9977999925613403 |
| 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/T11901 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9470999836921692 |
| 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 | Bayesian Methods and Mixture Models |
| 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 | 0.9301999807357788 |
| 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/C107673813 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6401333808898926 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q812534 |
| concepts[0].display_name | Bayesian probability |
| concepts[1].id | https://openalex.org/C105795698 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5350699424743652 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[1].display_name | Statistics |
| concepts[2].id | https://openalex.org/C149550507 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5331494212150574 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q899360 |
| concepts[2].display_name | Diffusion MRI |
| concepts[3].id | https://openalex.org/C33923547 |
| concepts[3].level | 0 |
| concepts[3].score | 0.4764935374259949 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[3].display_name | Mathematics |
| concepts[4].id | https://openalex.org/C155281189 |
| concepts[4].level | 2 |
| concepts[4].score | 0.46378716826438904 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3518150 |
| concepts[4].display_name | Tensor (intrinsic definition) |
| concepts[5].id | https://openalex.org/C149782125 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4522361159324646 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[5].display_name | Econometrics |
| concepts[6].id | https://openalex.org/C121864883 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4367658495903015 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q677916 |
| concepts[6].display_name | Statistical physics |
| concepts[7].id | https://openalex.org/C69357855 |
| concepts[7].level | 2 |
| concepts[7].score | 0.43211162090301514 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q163214 |
| concepts[7].display_name | Diffusion |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.32566630840301514 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.21104741096496582 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C2524010 |
| concepts[10].level | 1 |
| concepts[10].score | 0.1053139865398407 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[10].display_name | Geometry |
| concepts[11].id | https://openalex.org/C71924100 |
| concepts[11].level | 0 |
| concepts[11].score | 0.09919145703315735 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[11].display_name | Medicine |
| concepts[12].id | https://openalex.org/C143409427 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q161238 |
| concepts[12].display_name | Magnetic resonance imaging |
| concepts[13].id | https://openalex.org/C97355855 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[13].display_name | Thermodynamics |
| concepts[14].id | https://openalex.org/C126838900 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[14].display_name | Radiology |
| keywords[0].id | https://openalex.org/keywords/bayesian-probability |
| keywords[0].score | 0.6401333808898926 |
| keywords[0].display_name | Bayesian probability |
| keywords[1].id | https://openalex.org/keywords/statistics |
| keywords[1].score | 0.5350699424743652 |
| keywords[1].display_name | Statistics |
| keywords[2].id | https://openalex.org/keywords/diffusion-mri |
| keywords[2].score | 0.5331494212150574 |
| keywords[2].display_name | Diffusion MRI |
| keywords[3].id | https://openalex.org/keywords/mathematics |
| keywords[3].score | 0.4764935374259949 |
| keywords[3].display_name | Mathematics |
| keywords[4].id | https://openalex.org/keywords/tensor |
| keywords[4].score | 0.46378716826438904 |
| keywords[4].display_name | Tensor (intrinsic definition) |
| keywords[5].id | https://openalex.org/keywords/econometrics |
| keywords[5].score | 0.4522361159324646 |
| keywords[5].display_name | Econometrics |
| keywords[6].id | https://openalex.org/keywords/statistical-physics |
| keywords[6].score | 0.4367658495903015 |
| keywords[6].display_name | Statistical physics |
| keywords[7].id | https://openalex.org/keywords/diffusion |
| keywords[7].score | 0.43211162090301514 |
| keywords[7].display_name | Diffusion |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.32566630840301514 |
| keywords[8].display_name | Computer science |
| keywords[9].id | https://openalex.org/keywords/physics |
| keywords[9].score | 0.21104741096496582 |
| keywords[9].display_name | Physics |
| keywords[10].id | https://openalex.org/keywords/geometry |
| keywords[10].score | 0.1053139865398407 |
| keywords[10].display_name | Geometry |
| keywords[11].id | https://openalex.org/keywords/medicine |
| keywords[11].score | 0.09919145703315735 |
| keywords[11].display_name | Medicine |
| language | en |
| locations[0].id | doi:10.17615/vjq3-wt60 |
| 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/vjq3-wt60 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5108670559 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Zhaowei Hua |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hua, Zhaowei |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5108072328 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | David B. Dunson |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Dunson, David B. |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5052698941 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-0939-6764 |
| authorships[2].author.display_name | John H. Gilmore |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Gilmore, John H. |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5065838160 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8747-5118 |
| authorships[3].author.display_name | Martin Styner |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Styner, Martin A. |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5100567697 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Hongtu Zhu |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Zhu, Hongtu |
| authorships[4].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/vjq3-wt60 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Semiparametric Bayesian local functional models for diffusion tensor tract statistics |
| has_fulltext | True |
| 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 | 0.9977999925613403 |
| 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/W1768769760, https://openalex.org/W2157123855, https://openalex.org/W2413360119, https://openalex.org/W2131307089, https://openalex.org/W2356293051, https://openalex.org/W1576270090, https://openalex.org/W2508419374, https://openalex.org/W1717632717, https://openalex.org/W57104752, https://openalex.org/W3106021769 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2014 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.17615/vjq3-wt60 |
| 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/vjq3-wt60 |
| primary_location.id | doi:10.17615/vjq3-wt60 |
| 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/vjq3-wt60 |
| publication_date | 2020-11-06 |
| publication_year | 2020 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 62, 134 |
| abstract_inverted_index.a | 2, 25, 173 |
| abstract_inverted_index.We | 0, 147 |
| abstract_inverted_index.an | 77, 127 |
| abstract_inverted_index.as | 32 |
| abstract_inverted_index.in | 40, 172, 178 |
| abstract_inverted_index.is | 137 |
| abstract_inverted_index.of | 12, 27, 29, 43, 55, 72, 107, 145, 154, 161, 176 |
| abstract_inverted_index.to | 58, 93, 139, 150 |
| abstract_inverted_index.BFM | 36, 115, 149 |
| abstract_inverted_index.age | 33 |
| abstract_inverted_index.and | 34, 69, 88, 166 |
| abstract_inverted_index.are | 91, 102 |
| abstract_inverted_index.for | 9, 38, 111 |
| abstract_inverted_index.new | 179 |
| abstract_inverted_index.set | 26 |
| abstract_inverted_index.the | 10, 41, 44, 53, 56, 141, 152, 159, 162, 167 |
| abstract_inverted_index.via | 126 |
| abstract_inverted_index.BFM. | 146 |
| abstract_inverted_index.LPP2 | 65 |
| abstract_inverted_index.born | 180 |
| abstract_inverted_index.into | 119 |
| abstract_inverted_index.more | 120 |
| abstract_inverted_index.such | 31 |
| abstract_inverted_index.vary | 59 |
| abstract_inverted_index.with | 24, 105 |
| abstract_inverted_index.(BFM) | 8 |
| abstract_inverted_index.Carlo | 132 |
| abstract_inverted_index.Local | 85 |
| abstract_inverted_index.Monte | 131 |
| abstract_inverted_index.along | 19, 97, 158 |
| abstract_inverted_index.among | 49, 74 |
| abstract_inverted_index.apply | 148 |
| abstract_inverted_index.bands | 90 |
| abstract_inverted_index.chain | 130 |
| abstract_inverted_index.fiber | 22, 45, 95 |
| abstract_inverted_index.group | 117 |
| abstract_inverted_index.local | 5, 70 |
| abstract_inverted_index.model | 7, 80 |
| abstract_inverted_index.prior | 66 |
| abstract_inverted_index.right | 168 |
| abstract_inverted_index.shape | 42 |
| abstract_inverted_index.study | 136, 175 |
| abstract_inverted_index.tract | 165, 171 |
| abstract_inverted_index.which | 98 |
| abstract_inverted_index.while | 51, 76, 109 |
| abstract_inverted_index.white | 20, 155 |
| abstract_inverted_index.(e.g., | 16 |
| abstract_inverted_index.Markov | 129 |
| abstract_inverted_index.across | 60 |
| abstract_inverted_index.bundle | 46 |
| abstract_inverted_index.corpus | 163 |
| abstract_inverted_index.factor | 79 |
| abstract_inverted_index.finite | 142 |
| abstract_inverted_index.global | 68 |
| abstract_inverted_index.impact | 54 |
| abstract_inverted_index.matter | 21, 156 |
| abstract_inverted_index.sample | 143 |
| abstract_inverted_index.bundles | 23 |
| abstract_inverted_index.capsule | 170 |
| abstract_inverted_index.gender. | 35 |
| abstract_inverted_index.propose | 1 |
| abstract_inverted_index.testing | 87 |
| abstract_inverted_index.Bayesian | 4, 64 |
| abstract_inverted_index.accounts | 37 |
| abstract_inverted_index.allowing | 52 |
| abstract_inverted_index.analysis | 11 |
| abstract_inverted_index.callosum | 164 |
| abstract_inverted_index.clinical | 174 |
| abstract_inverted_index.credible | 89 |
| abstract_inverted_index.evaluate | 140 |
| abstract_inverted_index.flexibly | 81 |
| abstract_inverted_index.identify | 94 |
| abstract_inverted_index.infants. | 181 |
| abstract_inverted_index.infinite | 78 |
| abstract_inverted_index.internal | 169 |
| abstract_inverted_index.multiple | 13, 99, 112 |
| abstract_inverted_index.proceeds | 125 |
| abstract_inverted_index.splenium | 160 |
| abstract_inverted_index.subjects | 118 |
| abstract_inverted_index.Moreover, | 114 |
| abstract_inverted_index.Posterior | 123 |
| abstract_inverted_index.clusters. | 122 |
| abstract_inverted_index.developed | 92 |
| abstract_inverted_index.diffusion | 14, 47, 100 |
| abstract_inverted_index.efficient | 128 |
| abstract_inverted_index.interest, | 30, 108 |
| abstract_inverted_index.naturally | 116 |
| abstract_inverted_index.performed | 138 |
| abstract_inverted_index.segments, | 96 |
| abstract_inverted_index.subjects, | 50, 75 |
| abstract_inverted_index.subjects. | 61 |
| abstract_inverted_index.algorithm. | 133 |
| abstract_inverted_index.associated | 104 |
| abstract_inverted_index.borrowings | 71 |
| abstract_inverted_index.covariates | 28, 57, 106 |
| abstract_inverted_index.fractional | 17 |
| abstract_inverted_index.functional | 6 |
| abstract_inverted_index.hypothesis | 86 |
| abstract_inverted_index.properties | 15, 48, 101 |
| abstract_inverted_index.represents | 82 |
| abstract_inverted_index.simulation | 135 |
| abstract_inverted_index.structure. | 84 |
| abstract_inverted_index.anisotropy) | 18 |
| abstract_inverted_index.computation | 124 |
| abstract_inverted_index.controlling | 110 |
| abstract_inverted_index.development | 153 |
| abstract_inverted_index.facilitates | 67 |
| abstract_inverted_index.homogeneous | 121 |
| abstract_inverted_index.information | 73 |
| abstract_inverted_index.investigate | 151 |
| abstract_inverted_index.performance | 144 |
| abstract_inverted_index.comparisons. | 113 |
| abstract_inverted_index.diffusivities | 157 |
| abstract_inverted_index.heterogeneity | 39 |
| abstract_inverted_index.nonparametric | 63 |
| abstract_inverted_index.significantly | 103 |
| abstract_inverted_index.semiparametric | 3 |
| abstract_inverted_index.low-dimensional | 83 |
| abstract_inverted_index.neurodevelopment | 177 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.35898377 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |