Gast ::
| Hauptseite > Publikationsdatenbank > GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox > print |
| Hauptseite > Publikationsdatenbank > GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox > print |
| 001 | 1024846 | ||
| 005 | 20250204113831.0 | ||
| 024 | 7 | _ | |a 10.1016/j.neuroimage.2024.120595 |2 doi |
| 024 | 7 | _ | |a 1053-8119 |2 ISSN |
| 024 | 7 | _ | |a 1095-9572 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-02512 |2 datacite_doi |
| 024 | 7 | _ | |a 38554782 |2 pmid |
| 024 | 7 | _ | |a WOS:001218479400001 |2 WOS |
| 037 | _ | _ | |a FZJ-2024-02512 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Park, Yeongjun |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox |
| 260 | _ | _ | |a Orlando, Fla. |c 2024 |b Academic Press |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1712721821_14826 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community. |
| 536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
| 536 | _ | _ | |a 5252 - Brain Dysfunction and Plasticity (POF4-525) |0 G:(DE-HGF)POF4-5252 |c POF4-525 |f POF IV |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Lee, Mi Ji |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Yoo, Seulki |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Kim, Chae Yeon |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Namgung, Jong Young |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Park, Yunseo |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Park, Hyunjin |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Lee, Eun-Chong |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Yoon, Yeo Dong |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Paquola, Casey |0 P:(DE-Juel1)187055 |b 9 |u fzj |
| 700 | 1 | _ | |a Bernhardt, Boris C. |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Park, Bo-yong |0 P:(DE-HGF)0 |b 11 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.neuroimage.2024.120595 |g Vol. 291, p. 120595 - |0 PERI:(DE-600)1471418-8 |p 120595 - |t NeuroImage |v 291 |y 2024 |x 1053-8119 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1024846/files/1-s2.0-S1053811924000909-main.pdf |
| 856 | 4 | _ | |y OpenAccess |x icon |u https://juser.fz-juelich.de/record/1024846/files/1-s2.0-S1053811924000909-main.gif?subformat=icon |
| 856 | 4 | _ | |y OpenAccess |x icon-1440 |u https://juser.fz-juelich.de/record/1024846/files/1-s2.0-S1053811924000909-main.jpg?subformat=icon-1440 |
| 856 | 4 | _ | |y OpenAccess |x icon-180 |u https://juser.fz-juelich.de/record/1024846/files/1-s2.0-S1053811924000909-main.jpg?subformat=icon-180 |
| 856 | 4 | _ | |y OpenAccess |x icon-640 |u https://juser.fz-juelich.de/record/1024846/files/1-s2.0-S1053811924000909-main.jpg?subformat=icon-640 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1024846 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 9 |6 P:(DE-Juel1)187055 |
| 910 | 1 | _ | |a Department of Data Science, Inha University, Incheon, South Korea. boyong.park@inha.ac.kr |0 I:(DE-HGF)0 |b 11 |6 P:(DE-HGF)0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5254 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5252 |x 1 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2023-10-21 |
| 915 | _ | _ | |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0 |0 LIC:(DE-HGF)CCBYNCND4 |2 HGFVOC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2023-05-02T08:47:40Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2023-05-02T08:47:40Z |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2023-10-21 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2023-10-21 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2023-10-21 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2023-05-02T08:47:40Z |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2024-12-16 |w ger |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b NEUROIMAGE : 2022 |d 2024-12-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2024-12-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2024-12-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2024-12-16 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2024-12-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2024-12-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2024-12-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |d 2024-12-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2024-12-16 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b NEUROIMAGE : 2022 |d 2024-12-16 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-7-20090406 |k INM-7 |l Gehirn & Verhalten |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-7-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|