Reliability of Fronto-Amygdala Coupling during Emotional Face Processing.
Camilla L NordAlan GrayOliver Joe RobinsonJonathan P RoiserPublished in: Brain sciences (2019)
One of the most exciting translational prospects for brain imaging research is the potential use of functional magnetic resonance imaging (fMRI) 'biomarkers' to predict an individual's risk of developing a neuropsychiatric disorder or the likelihood of responding to a particular intervention. This proposal depends critically on reliable measurements at the level of the individual. Several previous studies have reported relatively poor reliability of amygdala activation during emotional face processing, a key putative fMRI 'biomarker'. However, the reliability of amygdala connectivity measures is much less well understood. Here, we assessed the reliability of task-modulated coupling between three seed regions (left and right amygdala and the subgenual anterior cingulate cortex) and the dorsomedial frontal/cingulate cortex (DMFC), measured using a psychophysiological interaction analysis in 29 healthy individuals scanned approximately two weeks apart. We performed two runs on each day of three different emotional face-processing tasks: emotion identification, emotion matching, and gender classification. We tested both between-day reliability and within-day (between-run) reliability. We found good-to-excellent within-subject reliability of amygdala-DMFC coupling, both between days (in two tasks), and within day (in one task). This suggests that disorder-relevant regional coupling may be sufficiently reliable to be used as a predictor of treatment response or clinical risk in future clinical studies.
Keyphrases
- functional connectivity
- resting state
- magnetic resonance imaging
- room temperature
- randomized controlled trial
- autism spectrum disorder
- depressive symptoms
- magnetic resonance
- mass spectrometry
- multiple sclerosis
- deep learning
- brain injury
- white matter
- blood brain barrier
- contrast enhanced
- subarachnoid hemorrhage
- diffusion weighted imaging