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| Hauptseite > Publikationsdatenbank > Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching > print |
| Hauptseite > Publikationsdatenbank > Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching > print |
| 001 | 1030924 | ||
| 005 | 20250912110159.0 | ||
| 024 | 7 | _ | |a 10.1162/imag_a_00251 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-05517 |2 datacite_doi |
| 024 | 7 | _ | |a 40800257 |2 pmid |
| 024 | 7 | _ | |a WOS:001531565300003 |2 WOS |
| 037 | _ | _ | |a FZJ-2024-05517 |
| 082 | _ | _ | |a 050 |
| 100 | 1 | _ | |a Wulan, Naren |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching |
| 260 | _ | _ | |a Cambridge, MA |c 2024 |b MIT 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 1737100324_7619 |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 Individualized phenotypic prediction based on structural magnetic resonance imaging (MRI) is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a “meta-matching” framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants (“meta-matching finetune” and “meta-matching stacking”) from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), the Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017), and the HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset and when translating models across datasets with different MRI scanners, acquisition protocols, and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = –0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework. |
| 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 An, Lijun |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Zhang, Chen |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Kong, Ru |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Chen, Pansheng |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Bzdok, Danilo |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 6 |u fzj |
| 700 | 1 | _ | |a Holmes, Avram J. |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Yeo, B. T. Thomas |0 P:(DE-HGF)0 |b 8 |e Corresponding author |
| 773 | _ | _ | |a 10.1162/imag_a_00251 |g Vol. 2, p. 1 - 21 |0 PERI:(DE-600)3167925-0 |p 1 - 21 |t Imaging neuroscience |v 2 |y 2024 |x 2837-6056 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1030924/files/imag_a_00251.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1030924 |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 6 |6 P:(DE-Juel1)131678 |
| 910 | 1 | _ | |a HHU Düsseldorf |0 I:(DE-HGF)0 |b 6 |6 P:(DE-Juel1)131678 |
| 910 | 1 | _ | |a Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore *Corresponding Author: B.T. Thomas Yeo (yeoyeo02+INau@gmail.com) |0 I:(DE-HGF)0 |b 8 |6 P:(DE-HGF)0 |
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| 914 | 1 | _ | |y 2024 |
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