| Title: | Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching |
| Journal: | Imaging Neuroscience |
| Published: | 1 Aug 2024 |
| DOI: | https://doi.org/10.1162/imag_a_00251 |
Publication 12733
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Abstract
Abstract 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.</p>
9 Authors
- Naren Wulan
- Lijun An
- Chen Zhang
- Ru Kong
- Pansheng Chen
- Danilo Bzdok
- Simon B. Eickhoff
- Avram J. Holmes
- B.T. Thomas Yeo
1 Application
| Application ID | Title |
|---|---|
| 25163 | Structural genetic contributions brain function and behavior |
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