Deconstructing empathy: Neuroanatomical dissociations between affect sharing and prosocial motivation using a patient lesion model

Abstract

Affect sharing and prosocial motivation are integral parts of empathy that are conceptually and mechanistically distinct. We used a neurodegenerative disease (NDG) lesion model to examine the neural correlates of these two aspects of real-world empathic responding. The study enrolled 275 participants, including 44 healthy older controls and 231 patients diagnosed with one of five neurodegenerative diseases (75 Alzheimer's disease, 58 behavioral variant frontotemporal dementia (bvFTD), 42 semantic variant primary progressive aphasia (svPPA), 28 progressive supranuclear palsy, and 28 non-fluent variant primary progressive aphasia (nfvPPA). Informants completed the Revised Self-Monitoring Scale's Sensitivity to the Expressive Behavior of Others (RSMS-EX) subscale and the Interpersonal Reactivity Index's Empathic Concern (IRI-EC) subscale describing the typical empathic behavior of the participants in daily life. Using regression modeling of the voxel based morphometry of T1 brain scans prepared using SPM8 DARTEL-based preprocessing, we isolated the variance independently contributed by the affect sharing and the prosocial motivation elements of empathy as differentially measured by the two scales. We found that the affect sharing component uniquely correlated with volume in right > left medial and lateral temporal lobe structures, including the amygdala and insula, that support emotion recognition, emotion generation, and emotional awareness. Prosocial motivation, in contrast, involved structures such as the nucleus accumbens (NaCC), caudate head, and inferior frontal gyrus (IFG), which suggests that an individual must maintain the capacity to experience reward, to resolve ambiguity, and to inhibit their own emotional experience in order to effectively engage in spontaneous altruism as a component of their empathic response to others.

Regional and hemispheric susceptibility of the temporal lobe to FTLD-TDP type C pathology

“…A neurologist is usually consulted only when atrophy reaches the left temporal lobe and aphasic symptoms become apparent. The brain-behavior correlations illustrated by our ROIs, corroborate previous findings establishing the crucial role played by the right temporal lobe in empathic behavior (R. Perry et al, 2001;Rankin et al, 2006), in particular affect sharing (Shdo et al, 2018), as well as emotions comprehension, especially for negative ones (H. J. Rosen, 2002;Howard J. Rosen et al, 2006).…”

Reduced capacity for empathy in corticobasal syndrome and its impact on carer burden

“…Turning to affective empathy, both empathic concern and personal distress were altered in CBS patients. Reduced empathic concern in CBS is indicative of a deterioration in other‐oriented emotional responses, with similar deficits in affect sharing also noted in progressive supranuclear palsy . However, a subsequent increase in personal distress within tense interpersonal situations can be paradoxically attributed to empathic overarousal, resulting in “mirroring” of these emotions in the form of negative self‐oriented responses such as anxiety and emotional uncertainty .…”

“…These take the form of a primary emotion processing disturbance (ie, impaired facial and dynamic emotion recognition) as well as a compromised ability to express emotions, characterised by facial apraxia and flat aprosodic speech . Similar disturbances in emotion recognition have been recognised in progressive supranuclear palsy, with further deficits noted in theory of mind and empathic capacity . The extent to which empathy is affected in CBS is, however, unclear.…”

The complexity of empathy during medical school training: evidence for positive changes

“…replacing missing values with substitute values based on the distribution of the dataset), which made it well suited to the current dataset as we did not have complete data for each subject at every time‐point . All models were run using the nlme package for r , developed for building hierarchical linear models, with full maximum likelihood estimation (method of computing estimates of population parameters that jointly maximise the likelihood of observing a particular sample of data; for more explanation, see Shdo et al …”