A guiding interest of the lab is to examine the temporal dynamics of emotion – in particular the temporal dynamics of positive emotion and how it relates to individual differences in functioning. The majority of neuroimaging studies of emotion examine the mean magnitude of neural activity, but there is a substantial amount of information in examining these neurodynamics of affect. We have found that reduced positive emotion (ie., anhedonia) characteristic of many patients with depression appears to be due to a lack of sustained activity in fronto-striatal reward circuits as opposed to simply a reduced mean activity in these circuits (Heller et al., 2009; Heller et al., 2013).
Affective & Neural Dynamics
We have also found that individuals across a wide age range who experience high levels of psychological well-being evidence more sustained activity in these same sustained fronto-striatal reward circuits and have lower levels of stress as evidenced by reduced cortisol output (Heller et al., 2013). These findings strongly suggest that examining the temporal dynamics of affective neural circuits is essential to characterize the neural abnormalities in anhedonia in depression and well-being (Heller, 2016).
Integrating Laboratory and Real-World Assessments
With the potential to improve clinical assessment and interventions, the lab has been interested in how people experience emotion and function in the real-world. To do this, we use mobile Health (mHealth) technology to examine such questions that include Ecological Momentary Assessment (EMA), GPS and social interactions using text messaging methodologies to understand how people function in their everyday life (Heller, 2016).
One novel feature of our mHealth work is the use of real-world tasks whereby participants experience positive and negative affect in the real-world. Doing this, we can examine how individual’s emotional reactions differ after a specified event has occurred. This allows us to examine the individual differences in emotional dynamics (Heller et al., 2015).
Affective Neuroscience of Lifespan Development
The dynamics of emotion change evolve across the lifespan. There are the storms and stress of adolescence – where emotions are more variable and rates of mood and anxiety disorders peak (Heller & Casey, 2016). There are also changes in emotion regulation capacity in midlife (Ryff et al., 2016) as individuals age. It is thus important to take a lifespan developmental perspective when studying the psychology and neuroscience of emotion. When presented with emotional cues, adolescents show greater subcortical connectivity between the amygdala and ventral striatum than adults and these changes are associated with greater behavioral impulsivity in adolescence (Heller et al., 2016).
Using the large MidLife in the United States (MIDUS) dataset, we have found that individuals across a wide age range who experience high levels of psychological well-being evidence more sustained activity in these same sustained fronto-striatal reward circuits and have lower levels of stress as evidenced by cortisol (Heller et al., 2013).
Using Methods that Simultaneously Acquire fMRI with Objective Measures of Affect
Darwin was among the first noted biologists to highlight the importance of facial expression as a read-out of emotion and underscore the continuity across species in its form and function. And of the various psychophysiological methods putatively providing objective measures of emotion (which include eyeblink startle, electrodermal activity, pupil dilation), measurement of facial muscle activity via electromyography (EMG) is the only current measure that is simultaneously valence specific, objective, continuous, unobtrusive and has high temporal resolution. Despite the challenges of recording EMG concurrently with FMRI due to electromagnetic noise, we have developed methods to acquire EMG from the face with fMRI (Heller et al., 2009).
Doing this has allowed us to demonstrate that there is an inverse relationship between the amygdala and ventromedial PFC when people are evidencing more or less frowning, respectively (Heller et al., 2014). Furthermore, an individuals’ capacity for emotion regulation as measured with facial EMG show greater prefrontal-amygdala connectivity (Lee et al., 2012). These methodological developments have the potential to address many additional theoretical questions in affective neuroscience going forward and can enhance our interpretation of fMRI results by integrating these objective measures of affect with better temporal resolution.