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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 | 1021976 | ||
| 005 | 20240130202645.0 | ||
| 024 | 7 | _ | |a 10.1101/2023.12.31.573801 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-01115 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-01115 |
| 100 | 1 | _ | |a Wulan, Naren |0 0009-0005-3974-7528 |b 0 |
| 245 | _ | _ | |a Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1706624600_7629 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Individualized phenotypic prediction based on structural 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), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and 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, as well as 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. |
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| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a An, Lijun |0 0000-0003-1030-4625 |b 1 |
| 700 | 1 | _ | |a Zhang, Chen |b 2 |
| 700 | 1 | _ | |a Kong, Ru |0 0000-0001-7842-0329 |b 3 |
| 700 | 1 | _ | |a Chen, Pansheng |0 0009-0009-8881-8643 |b 4 |
| 700 | 1 | _ | |a Bzdok, Danilo |0 P:(DE-Juel1)136848 |b 5 |
| 700 | 1 | _ | |a Eickhoff, Simon B |0 P:(DE-Juel1)131678 |b 6 |
| 700 | 1 | _ | |a Holmes, Avram J |0 0000-0001-6583-803X |b 7 |
| 700 | 1 | _ | |a Yeo, B. T. Thomas |0 0000-0002-0119-3276 |b 8 |
| 773 | _ | _ | |a 10.1101/2023.12.31.573801 |
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