The default mode network (DMN) consists of several regions that selectively interact to support distinct domains of cognition. Of the various sites that partake in DMN function, the posterior cingulate cortex (PCC), temporal parietal junction (TPJ), and medial prefrontal cortex (MPFC) are frequently identified as key contributors. Yet, it remains unclear whether these subcomponents of the DMN make unique contributions to specific cognitive processes and health conditions. To address this issue, we applied a meta-analytic parcellation approach used in prior work. This approach used the Neurosynth database and classification methods to quantify the association between PCC, TPJ, and MPFC activation and specific topics related to cognition and health (e.g., decision making and smoking). Our analyses replicated prior observations that the PCC, TPJ, and MPFC collectively support multiple cognitive functions such as decision making, memory, and awareness. To gain insight into the functional organization of each region, we parceled each region based on its coactivation pattern with the rest of the brain. This analysis indicated that each region could be further subdivided into functionally distinct subcomponents. Taken together, we further delineate DMN function by demonstrating the relative strengths of association among subcomponents across a range of cognitive processes and health conditions. A continued attentiveness to the specialization within the DMN allows future work to consider the nuances in sub-regional contributions necessary for healthy cognition, as well as create the potential for more targeted treatment protocols in various health conditions.

Background: A family history of major depressive disorder (MDD) increases the likelihood of a future depressive episode, which itself poses a significant risk for disruptions in reward processing and social cognition. However, it is unclear whether a family history of MDD is associated with alterations in the neural circuitry underlying reward processing and social cognition.Methods: We subdivided 279 participants from the Human Connectome Project into three groups: 71 with a lifetime history of MDD, 103 with a family history (FH) of MDD, and 105 healthy controls (HCs). We then evaluated task-based functional magnetic resonance imaging data on a social cognition and a reward processing task and found a region of the ventromedial prefrontal cortex (vmPFC) that responded to both tasks, independent of the group. To investigate whether the vmPFC shows alterations in functional connectivity between groups, we conducted psychophysiological interaction analyses using the vmPFC as a seed region.Results: We found that FH (relative to HC) was associated with increased sadness scores, and MDD (relative to both FH and HC) was associated with increased sadness and MDD symptoms. Additionally, the FH group had increased vmPFC functional connectivity within the nucleus accumbens, left dorsolateral PFC, and subregions of the cerebellum relative to HC during the social cognition task.Conclusions: These findings suggest that aberrant neural mechanisms among those with a familial risk of MDD may underlie vulnerability to altered social cognition.

People express emotions via a variety of behaviors, including facial muscle movements, body poses and gestures, vocal prosody, and speech. To understand how people experience and perceive emotion, it is crucial to quantify and model these behaviors. However, existing methods are insufficient to address this need. Manually annotating behavior is very time-consuming, making it infeasible to do at scale. Moreover, common linear models cannot fully capture the complex, nonlinear, and interactive affective processes embodied by these behaviors. In this methodology review, we describe how deep learning addresses these challenges and thereby promises to advance naturalistic affective science. First, deep learning provides accessible and efficient tools to annotate dynamic, complex, multi-modal behaviors. These automated annotation tools can scale up behavioral quantification to a degree impossible with human coders, enabling many new, more naturalistic approaches to affective science. Second, deep learning offers innovative paradigms for optimizing and manipulating naturalistic stimuli. This application makes it possible to generate experiment designs with greater generalizability, statistical power, and external validity. Third, deep learning can support flexible, powerful cognitive models of naturalistic affective processing. These novel cognitive models make it possible to explain how the mind and brain engage in the emotional world in ways that are both broader and more precise. However, deep learning is not without its limitations, so we also explore important failure cases, practical issues, and ethical concerns. By detailing the promise and the peril of deep learning, this review paves the way for a more naturalistic and generalizable affective science.

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A family history of major depressive disorder (MDD) increases the likelihood of a future depressive episode, which itself poses a significant risk for disruptions in reward processing and social cognition. However, it is unclear whether a family history of MDD is associated with alterations in the neural circuitry underlying reward processing and social cognition. To address this gap, we subdivided 279 participants from the Human Connectome Project into three groups: 71 with a lifetime history of MDD (Dep), 103 with a family history of MDD (Fam), and 105 healthy controls (HC). We found that Fam (relative to HCs) were associated with increased sadness scores, and Dep (relative to both Fam and HC) were associated with increased sadness and MDD symptoms. We then evaluated task-based fMRI data on a social cognition and reward processing task and found a region of the ventromedial prefrontal cortex (vmPFC) that responded to both tasks, independent of group. To investigate whether the vmPFC shows alterations in functional connectivity between groups, we conducted psychophysiological interaction (PPI) analyses using the vmPFC as a seed region. These analyses revealed that Fam groups had increased vmPFC functional connectivity within the nucleus accumbens, left dorsolateral PFC and subregions of the cerebellum relative to HCs during the social cognition task. These findings suggest that aberrant neural mechanisms among those with a familial risk of MDD may underlie vulnerability to altered social cognition.