  Decrypting intelligence from the human brain construct is vital in the
detection of particular neurological disorders. Recently, functional brain
connectomes have been used successfully to predict behavioral scores. However,
state-of-the-art methods, on one hand, neglect the topological properties of
the connectomes and, on the other hand, fail to solve the high inter-subject
brain heterogeneity. To address these limitations, we propose a novel
regression graph neural network through meta-learning namely Meta-RegGNN for
predicting behavioral scores from brain connectomes. The parameters of our
proposed regression GNN are explicitly trained so that a small number of
gradient steps combined with a small training data amount produces a good
generalization to unseen brain connectomes. Our results on verbal and
full-scale intelligence quotient (IQ) prediction outperform existing methods in
both neurotypical and autism spectrum disorder cohorts. Furthermore, we show
that our proposed approach ensures generalizability, particularly for autistic
subjects. Our Meta-RegGNN source code is available at
https://github.com/basiralab/Meta-RegGNN.
