Neural Connectivity Underlying Reward and Emotion-Related Processing: Evidence From a Large-Scale Network Analysis

Inter-network Effective Connectivity During An Emotional Working Memory Task in Two Independent Samples of Young Adults

BACKGROUND

Effective connectivity (EC) analysis provides valuable insights into the directionality of neural interactions, which are crucial for understanding the mechanisms underlying cognitive and emotional regulation in depressive and anxiety disorders. In this study, we examined EC within key neural networks during working memory (WM) and emotional regulation (ER) tasks in young adults, both healthy individuals and those seeking help from mental health professionals for emotional distress.

METHODS

Dynamic causal modeling was used to analyze EC in 2 independent samples (n = 97 and n = 94). Participants performed an emotional n-back task to assess EC across the central executive network (CEN), default mode network (DMN), salience network (SN), and face processing network. Group-level parametric empirical Bayes analyses were conducted to examine EC patterns, with subanalyses comparing individuals with and without depression and anxiety.

RESULTS

Consistent patterns of positive (posterior probability > .95) DMN→CEN and DMN→SN EC were observed in both samples, predominantly in low and high WM conditions without ER. However, individuals without depressive or anxiety disorders exhibited a significantly greater number of preserved connections that were replicated across both samples.

CONCLUSIONS

This study highlights the different patterns of DMN→CEN EC in conditions with high and low WM loads with and without ER, suggesting that in higher WM loads with ER, the integration of the DMN with the CEN is reduced to facilitate successful cognitive task performance. The findings also suggest that DMN→CEN and DMN→SN EC are significantly reduced in depressive and anxiety disorders, highlighting this pattern of reduced EC as a potential neural marker of these disorders.

Psychiatric phenotype in neurodevelopmental myoclonus-dystonia is underpinned by abnormality of cerebellar modulation on the cerebral cortex

Psychiatric symptoms are common in neurodevelopmental movement disorders, including some types of dystonia. However, research has mainly focused on motor manifestations and underlying circuits. Myoclonus-dystonia is a rare and homogeneous neurodevelopmental condition serving as an illustrative paradigm of childhood-onset dystonias, associated with psychiatric symptoms. Here, we assessed the prevalence of psychiatric disorders and the severity of depressive symptoms in patients with myoclonus-dystonia and healthy volunteers (HV). Using resting-state functional neuroimaging, we compared the effective connectivity within and among non-motor and motor brain networks between patients and HV. We further explored the hierarchical organization of these networks and examined the relationship between their connectivity and the depressive symptoms. Comparing 19 patients to 25 HV, we found a higher prevalence of anxiety disorders and more depressive symptoms in the patient group. Patients exhibited abnormal modulation of the cerebellum on the cerebral cortex in the sensorimotor, dorsal attention, salience, and default mode networks. Moreover, the salience network activity was directed by the cerebellum in patients and was related to depressive symptoms. Altogether, our findings highlight the role of the cerebellar drive on both motor and non-motor cortical areas in this disorder, suggesting cerebellar involvement in the complex phenotype of such neurodevelopmental movement disorders.

Self-Prioritization Reconsidered: Scrutinizing Three Claims

Such is the power of self-relevance, it has been argued that even arbitrary stimuli (e.g., shapes, lines, colors) with no prior personal connection are privileged during information processing following their association with the self (i.e., self-prioritization). This prioritization effect, moreover, is deemed to be stimulus driven (i.e., automatic), grounded in perception, and supported by specialized processing operations. Here, however, we scrutinize these claims and challenge this viewpoint. Although self-relevance unquestionably influences information processing, we contend that, at least at present, there is limited evidence to suggest that the prioritization of arbitrary self-related stimuli is compulsory, penetrates perception, and is underpinned by activity in a dedicated neural network. Rather, self-prioritization appears to be a task-dependent product of ordinary cognitive processes.