This title appears in the Scientific Report :
2024
Please use the identifier:
http://dx.doi.org/10.34734/FZJ-2024-01115 in citations.
Please use the identifier: http://dx.doi.org/10.1101/2023.12.31.573801 in citations.
Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching
Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching
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...
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Personal Name(s): | Wulan, Naren |
---|---|
An, Lijun / Zhang, Chen / Kong, Ru / Chen, Pansheng / Bzdok, Danilo / Eickhoff, Simon B / Holmes, Avram J / Yeo, B. T. Thomas | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Imprint: |
2024
|
DOI: |
10.34734/FZJ-2024-01115 |
DOI: |
10.1101/2023.12.31.573801 |
Document Type: |
Preprint |
Research Program: |
Multilevel Brain Organization and Variability |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1101/2023.12.31.573801 in citations.
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. |