On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting | bioRxiv

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting | bioRxiv  https://www.biorxiv.org/content/10.1101/2021.10.19.462649v1AbstractHuman intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we present a systematic review of this growing area of research, based on studies that employ structural, functional, and/or diffusion MRI to predict human intelligence in cognitively normal subjects using machine-learning. We performed a systematic assessment of methodological and reporting quality, using the PROBAST and TRIPOD assessment forms and 30 studies identified through a systematic search. We observed that fMRI is the most employed modality, resting-state functional connectivity (RSFC) is the most studied predictor, and the Human Connectome Project is the most employed dataset. A meta-analysis revealed a significant difference between the performance obtained in the prediction of general and fluid intelligence from fMRI data, confirming that the quality of measurement moderates this association. The expected performance of studies predicting general intelligence from fMRI was estimated to be r = 0.42 (CI95% = [0.35, 0.50]) while...
Source: Intelligent Insights on Intelligence Theories and Tests (aka IQ's Corner) - Category: Neuroscience Source Type: blogs