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Performance reserves in brain-imaging-based phenotype prediction

This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increas...

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Bibliographic Details
Published in:Cell reports (Cambridge) 2024-01, Vol.43 (1), p.113597-113597, Article 113597
Main Authors: Schulz, Marc-Andre, Bzdok, Danilo, Haufe, Stefan, Haynes, John-Dylan, Ritter, Kerstin
Format: Article
Language:English
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Summary:This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data. Additionally, we find that integrating multiple imaging modalities boosts prediction accuracy, often equivalent to doubling the sample size. Interestingly, the most informative imaging modality often varied with increasing sample size, emphasizing the need to consider multiple modalities. Despite significant performance reserves for phenotype prediction, achieving substantial improvements may necessitate prohibitively large sample sizes, thus casting doubt on the practical or clinical utility of machine learning in some areas of neuroimaging. [Display omitted] •Full predictive information in brain images not utilized even at 1 M samples•Multiple imaging modalities improve accuracy akin to doubling sample size•Most informative modality varies with larger sample sizes•Achieving practical utility may require prohibitively large samples Schulz et al. shed light on the role of sample size and imaging modalities in predicting cognitive and mental health traits from brain images. They underscore the potential of multimodal imaging to boost accuracy and caution about the challenges in achieving practical utility despite increasing sample sizes.
ISSN:2211-1247
2211-1247
DOI:10.1016/j.celrep.2023.113597