Development of Human Emotion Circuits Investigated Using a Big-Data Analytic Approach: Stability, Reliability, and Robustness

Abstract

Emotion perception is fundamental to affective and cognitive development and is thought to involve distributed brain circuits. Efforts to chart neurodevelopmental changes in emotion have been severely hampered by narrowly focused approaches centered on activation of individual brain regions and small sample sizes. Here we investigate the maturation of human functional brain circuits associated with identification of fearful, angry, sad, happy, and neutral faces using a large sample of 759 children, adolescents, and adults (ages 8-23; female/male ϭ 419/340). Network analysis of emotion-related brain circuits revealed three functional modules, encompassing lateral frontoparietal, medial prefrontal-posterior cingulate, and subcortical-posterior insular cortices, with hubs in medial prefrontal, but not posterior cingulate, cortex. This overall network architecture was stable by age 8, and it anchored maturation of circuits important for salience detection and cognitive control, as well as dissociable circuit patterns across distinct emotion categories. Our findings point to similarities and differences in functional circuits associated with identification of fearful, angry, sad, happy, and neutral faces, and reveal aspects of brain circuit organization underlying emotion perception that are stable over development as well as features that change with age. Reliability analyses demonstrated the robustness of our findings and highlighted the importance of large samples for probing functional brain circuit development. Our study emphasizes a need to focus beyond amygdala circuits and provides a robust neurodevelopmental template for investigating emotion perception and identification in psychopathology.

Emotional face processing across neurodevelopmental disorders: a dynamic faces study in children with autism spectrum disorder, attention deficit hyperactivity disorder and obsessive-compulsive disorder

“…Although both happy and angry faces, contrasted to dynamic flowers, demonstrated comparable patterns across participants, the decreased occipital activity was less marked to angry than happy faces in TD, indicating that dynamic angry faces are more salient visual stimuli and require more effort for processing even in TD 68 . This is consistent with the asynchronous maturation of emotional face recognition, where it is later for angry than happy faces 69 . This neural differentiation between angry faces versus flowers shown in TD, however, was not seen in children with ASD or OCD.…”

Childhood Adversity and Neural Development: A Systematic Review

“…However, whether such a pattern truly reflects more mature development remains an open question. This assumption is based on an influential study (Gee et al 2013b), but this age-related pattern has not been replicated in larger studies, including in a recent sample of over 750 children (Zhang et al 2019). To evaluate the stress acceleration model, greater clarity regarding the typical developmental trajectory of amygdala-mPFC connectivity is required, and patterns of task-related functional connectivity may be too variable across tasks to serve as a reliable metric.…”

Interactions Between Methodological and Interindividual Variability: How Monetary Incentive Delay (MID) Task Contrast Maps Vary and Impact Associations with Behavior.

“…Multivariate, cross-validated, pattern analyses can provide a priori activation patterns and locations that can be confirmed out of sample, reducing the possibility of exploring multiple hypotheses. Furthermore, in order to characterize individual variability in neural models, researchers should implement functional organization techniques to explain changes in behavior and cognitive processes (Beltz et al, 2016;Greene et al, 2018;Yip et al, 2019;Zhang et al, 2019). For example, Zhang and colleagues (2019) used a network modeling approach to identify a developmentally stable architecture of emotion related findings, providing some reliable estimates.…”

“…Further, the network models of task-based fMRI may aid researchers in uncovering the neural architecture of cognitive processes (Medaglia et al, 2015), such that connectivity metrics may provide predictive effects of individual traits (Greene et al, 2018). By using individual and group level estimates of connectivity patterns (Beltz et al, 2016), task-based analyses may improve the identification and replication of neural signatures that will aid researchers studying developmental and clinical differences (Yip et al, 2019;Zhang et al, 2019).…”