Generalizable predictive modeling of semantic processing ability from functional brain connectivity

Abstract

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating conceptual and meaningful information. Neuroimaging studies of SP typically collapse data from many subjects, but its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural network organizations contribute to the individual differences in SP behaviors. We aim to identify the neural signatures underlying SP variabilities by analyzing functional connectivity (FC) patterns based on a large‐sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two‐stage predictive modeling approach to build an internally cross‐validated model and to test the model's generalizability with unseen data from different HCP samples and other out‐of‐sample datasets. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five SP‐related behavioral tests. This cross‐validated model can be used to predict unseen HCP data. The model generalizability was enhanced in the language task compared with other tasks used during scanning and was better for females than males. The model constructed from the HCP dataset can be partially generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out‐of‐sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education for healthy individuals and intervention for SP and language deficits patients.

Functional magnetic resonance imaging data for the neural dynamics underlying the acquisition of distinct auditory categories

“…Significant individual differences in learning speed and outcome have been revealed by the behavioral analyses for each learning task. Potential neuromarkers linked to learners’ behavioral performance could be identified with predictive modeling approaches [2] . These putative neuromarkers have the potential to be used to build generalizable predictive models to predict unseen learners’ future learning behaviors [2] , [3] , [4] , [5] .…”