Predicting episodic and spatial memory performance from hippocampal resting-state functional connectivity: Evidence...

fMRI studies have identified distinct resting-state functional connectivity (RSFC) networks associated with the anterior and posterior hippocampus. However, the functional relevance of these two networks is still largely unknown. Hippocampal lesion studies and task-related fMRI point to a role for the anterior hippocampus in nonspatial episodic memory and the posterior hippocampus in spatial memory. We used Relevance Vector Regression (RVR), a machine-learning method that enables predictions of continuous outcome measures from multivariate patterns of brain imaging data, to test the hypothesis that patterns of whole-brain RSFC associated with the anterior hippocampus predict episodic memory performance, while patterns of whole-brain RSFC associated with the posterior hippocampus predict spatial memory performance. Magnetic resonance imaging and memory assessment took place at two separate occasions. The anterior and posterior RSFC largely corresponded with previous findings, and showed no effect of laterality. Supporting the hypothesis, RVR produced accurate predictions of episodic performance from anterior, but not posterior, RSFC, and accurate predictions of spatial performance from posterior, but not anterior, RSFC. In contrast, a univariate approach could not predict performance from resting-state connectivity. This supports a functional dissociation between the anterior and posterior hippocampus, and indicates a multivariate relationship between intrinsic functional networks and cognitive performance within specific domains, that is relatively stable over time.