Graph convolutional deep learning has emerged as a promising method to explore the
functional organization of the human brain in neuroscience research. This paper presents a …

Using machine-learning tools to predict individual phenotypes from neuroimaging data is
one of the most promising and hence dynamic fields in systems neuroscience. Here, we …

Complex deep learning models have shown their impressive power in analyzing high-
dimensional medical image data. To increase the trust of applying deep learning models in …

Multivariate prediction of human behavior from resting state data is gaining increasing
popularity in the neuroimaging community, with far-reaching translational implications in …

There is significant interest in using resting-state functional connectivity (RSFC) to predict
human behavior. Good behavioral prediction should in theory require RSFC to be …

How behavior arises from brain physiology has been one central topic of investigation in
neuroscience. Considering the recent interest in predicting behavior from brain imaging …

Intelligence is a socially and scientifically interesting topic because of its prominence in
human behavior, yet there is little clarity on how the neuroimaging and neurobiological …

Mapping individual differences in brain function has been hampered by poor reliability as
well as limited interpretability. Leveraging patterns of brain‐wide functional connectivity (FC) …

The field of neuroimaging has increasingly sought to develop artificial intelligence-based
models for neurological and neuropsychiatric disorder automated diagnosis and clinical …

Abstract Representational Similarity Analysis (RSA) has emerged as a popular method for
relating representational spaces from human brain activity, behavioral data, and …