Amygdala tractography predicts functional connectivity and learning during feedback-guided decision-making.
Flexibly adapting behavior in dynamic environments relies on fronto-limbic networks that include the amygdala, orbitofrontal cortex, and striatum. Animal work demonstrates that interactions among these regions are critical for flexible feedback-guided learning, but it remains unknown to what extent such anatomical-functional interactions operate in humans. Here, we use connectivity analyses in both structural and functional MRI to further our understanding of how brain circuits work in conjunction to promote goal-directed behavior. In particular, fiber tracking based on diffusion-weighted imaging provides information about anatomical connectivity between brain structures, and functional MRI provides estimates of functional connectivity between structures. We found that, during a feedback-guided reversal learning task, the strength of estimated white matter tracts from the amygdala to the hippocampus, orbitofrontal cortex, and ventral striatum predicted both how subjects adapted their behavior following positive and negative feedback, and the functional connectivity (estimated from functional MRI time series) between the amygdala and these regions. In addition, we identified a dissociation between an amygdala-hippocampus circuit that predicted response switching, and an amygdala-orbitofrontal cortex circuit that predicted learning following rule reversals. These findings provide novel insights into how the anatomy and functioning of amygdala-related brain circuits mediate different aspects of feedback-guided learning behavior.
Cohen
Michael X
MX
Department of Epileptology, University of Bonn, Germany. mcohen@ucdavis.edu
Elger
Christian E
CE
Weber
Bernd
B
D000328:Adult; D000679:Amygdala / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D038524:Diffusion Magnetic Resonance Imaging; D005246:Feedback / Q000502:physiology*; D005260:Female; D006801:Humans; D007206:Individuality; D007858:Learning / Q000502:physiology*; D008279:Magnetic Resonance Imaging; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology; D012193:Reversal Learning / Q000502:physiology; D012201:Reward