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  • Title: ➤  Synthesizing Evidence On Machine Learning-Based Prediction Of Human Cognition Using MRI Of Different Modalities: A Meta-Analytic Review
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The application of machine learning (ML) models in cognitive neuroscience has expanded significantly over the past decade. In neuroimaging research, ML techniques have been widely employed to predict human phenotypes from brain imaging data. A particularly active area of investigation is the use of various MRI modalities to predict cognitive function. Despite this growing interest, only one meta-analysis has addressed this topic to date, and it included studies published only up to 2020—a period that preceded the widespread availability of large-scale MRI datasets and the rapid evolution of ML methodologies (Vieira et al., 2022). In light of the field’s continued growth and technological advancements, we propose a comprehensive meta-analysis with an expanded scope. This study will include research utilizing any MRI modality to predict a broad spectrum of cognitive outcomes using diverse ML algorithms. With a more extensive and up-to-date body of literature, we will also investigate potential moderating factors, including sample characteristics, preprocessing techniques, definitions of cognition, types of ML algorithms, MRI modalities, validation methods, control of confounding variables, and participant demographics such as health status and age.

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