Joseph Leshin

Loneliness is associated with age-related health issues through its impact on the brain and body. The brain’s allostatic-interoceptive system (AIS) consists of regions that predictively regulate bodily functions in reaction to affectively or socially salient situations. We hypothesized that loneliness would interact with age to exacerbate normative age-related differences in the static (i.e. average) and dynamic (i.e. time-varying) functional connectivity of the AIS during socially salient scenarios. Participants (N = 73) rated their loneliness and underwent functional magnetic resonance imaging (fMRI) during a task involving socially neutral, pleasant, and unpleasant conditions. Static and dynamic functional connectivity of the AIS were each quantified using participation coefficient (PC) and within-module degree (WD), which are graph-theoretical metrics indicating integration of the AIS with the rest of the brain and coherence within itself, respectively. Static AIS PC was positively associated with age across conditions. In the socially pleasant condition, static AIS WD decreased with increasing age, but only for those with high loneliness. In both the pleasant and unpleasant conditions, dynamic PC and WD showed steeper negative associations with age in individuals with higher loneliness. Overall, loneliness may exacerbate age-related differences in brain systems that are essential for physiological regulation and responding to social cues.

The brain’s processing of affective states involves complex interactions across brain regions, rather than localized to a specific region. The examination of the neural mechanisms of affect has shifted from a functional localization approach towards a distributed network neuroscience. Participants (N=73) underwent fMRI while engaging in a novel affect-induction task where they mentally visualized neutral, positive, and negative self-nominated acquaintances. We examined the topology of brain networks relevant to affective processing (i.e., the default mode [DMN], salience [SN], ventral attention [VAN], dorsal attention [DAN], and frontoparietal [FPN] networks). For each network in each task condition, we used graph-theory metrics to compute their integration with the rest of the brain (using participation coefficient; PC) and within-network coherence (using within-module degree; WD). Both static (i.e., mean) and dynamic (i.e., standard deviation) PC and WD were computed for each network. Several networks showed differences in static PC between affective conditions, while the VAN displayed differences in dynamic PC between conditions. FC of the networks during the tasks were also associated with subjective affect reported immediately after each task. These results are consistent with the constructionist account that emotions emerge from flexible network configurations and contribute to growing network-based understandings of affective processing.

Anxiety disorders are characterized by maladaptive defensive responses to distal or uncertain threats. Elucidating neural mechanisms of anxiety is essential to understand the development and maintenance of anxiety disorders. In fMRI, patients with pathological anxiety (ANX, n = 23) and healthy controls (HC, n = 28) completed a contextual threat learning paradigm in which they picked flowers in a virtual environment comprising a danger zone in which flowers were paired with shock and a safe zone (no shock). ANX compared with HC showed 1) decreased ventromedial prefrontal cortex and anterior hippocampus activation during the task, particularly in the safe zone, 2) increased insula and dorsomedial prefrontal cortex activation during the task, particularly in the danger zone, and 3) increased amygdala and midbrain/periaqueductal gray activation in the danger zone prior to potential shock delivery. Findings suggest that ANX engage brain areas differently to modulate context-appropriate emotional responses when learning to discriminate cues within an environment.

Situated models of emotion hypothesize that emotions are optimized for the context at hand, but most neuroimaging approaches ignore context. For the first time, we applied Granger causality (GC) analysis to determine how an emotion is affected by a person’s cultural background and situation. Electroencephalographic recordings were obtained from mainland Chinese (CHN) and US participants as they viewed and rated fearful and neutral images displaying either social or non-social contexts. Independent component analysis and GC analysis were applied to determine the epoch of peak effect for each condition and to identify sources and sinks among brain regions of interest. We found that source–sink couplings differed across culture, situation and culture × situation. Mainland CHN participants alone showed preference for an early-onset source–sink pairing with the supramarginal gyrus as a causal source, suggesting that, relative to US participants, CHN participants more strongly prioritized a scene’s social aspects in their response to fearful scenes. Our findings suggest that the neural representation of fear indeed varies according to both culture and situation and their interaction in ways that are consistent with norms instilled by cultural background.