Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Wulan, Naren; Zhang, Chen; Bzdok, Danilo; Eickhoff, Simon B.; Kong, Ru; An, Lijun; Yeo, B. T. Thomas; Holmes, Avram J.; Chen, Pansheng

doi:10.1162/imag_a_00251

Journal Article

FZJ-2024-05517

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Wulan, N. ; An, L. ; Zhang, C. ; Kong, R. ; Chen, P. ; Bzdok, D. ; Eickhoff, S. B.FZJ* ; Holmes, A. J. ; Yeo, B. T. T. (Corresponding author)

2024
MIT Press Cambridge, MA

Imaging neuroscience 2, 1 - 21 (2024) [10.1162/imag_a_00251]

This record in other databases:

Please use a persistent id in citations: doi:10.1162/imag_a_00251 doi:10.34734/FZJ-2024-05517

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.

Classification: