Disentangle the group and individual components of functional connectome with autoencoders.

One of the central goals of neuroscience is to understand the group commonality and individual variability in functional connectome. However, the entangled nature of the group and individual components in functional connectome poses challenges. Some methods have attempted to disentangle these group and individual components, typically using functional connectivity (FC).

Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity.

Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation.