Representational learning of brain responses in executive function and higher-order cognition using deep graph convolutions

Brain decoding aims to infer human cognition from recordings of neural activity using modern neuroimaging techniques. Studies so far often concentrated on a limited number of cognitive states and aimed to classifying patterns of brain activity within a local area. This procedure demonstrated a great success on classifying motor and sensory processes but showed limited power over higher cognitive functions. In this work, we investigate a high-order graph convolution model, named ChebNet, to model the segregation and integration organizational principles in neural dynamics, and to decode brain activity across a large number of cognitive domains. By leveraging our prior knowledge on brain organization using a graph-based model, ChebNet graph convolution learns a new representation from task-evoked neural activity, which demonstrates a highly predictive signature of cognitive states and task performance. Our results reveal that between-network integration significantly boosts the decoding of high-order cognition such as visual working memory tasks, while the segregation of localized brain activity is sufficient to classify motor and sensory processes. Using twin and family data from the Human Connectome Project (n = 1,070), we provide evidence that individual variability in the graph representations of working-memory tasks are under genetic control and strongly associated with participants in-scanner behaviors. These findings uncover the essential role of functional integration in brain decoding, especially when decoding high-order cognition other than sensory and motor functions.

Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A Brain Decoding Analysis

Despite its relevance for health and education, the neurocognitive mechanism of real-life self-control is largely unknown. While recent research revealed a prominent role of the ventromedial prefrontal cortex in the computation of an integrative value signal, the contribution and relevance of other brain regions for real-life self-control remains unclear. To investigate neural correlates of decisions in line with long-term consequences and to assess the potential of brain decoding methods for the individual prediction of real-life self-control, we combined functional magnetic resonance imaging during preference decision making with ecological momentary assessment of daily self-control in a large community sample (N = 266). Decisions in line with long-term consequences were associated with increased activity in bilateral angular gyrus and precuneus, regions involved in different forms of perspective taking, such as imagining one’s own future and the perspective of others. Applying multivariate pattern analysis to the same clusters revealed that individual patterns of activity predicted the probability of real-life self-control. Brain activations are discussed in relation to episodic future thinking and mentalizing as potential mechanisms mediating real-life self-control.