Trajectory of aberrant reward processing in patients with bipolar disorder - A longitudinal fMRI study

Neural basis of reward expectancy inducing proactive aggression

Proactive aggression refers to deliberate and unprovoked behavior, typically motivated by personal gain or expected reward. Reward expectancy is generally recognized as a critical factor that may influence proactive aggression, but its neural mechanisms remain unknown. We conducted a task-based functional magnetic resonance imaging (fMRI) experiment to investigate the relationship between reward expectancy and proactive aggression. 37 participants (20 females, mean age = 20.8 ± 1.42, age range = 18-23 years) completed a reward-harm task. In the experiment, reward valence expectancy and reward possibility expectancy were manipulated respectively by varying amounts (low: 0.5-1.5 yuan; high: 10.5-11.5 yuan) and possibilities (low: 10%-30%; high: 70%-90%) of money that participants could obtain by choosing to aggress. Participants received fMRI scans throughout the experiment. Brain activation regions associated with reward expectancy mainly involve the middle frontal gyrus, lingual gyrus, inferior temporal gyrus, anterior cuneus, caudate nucleus, inferior frontal gyrus, cingulate gyrus, anterior central gyrus, and posterior central gyrus. Associations between brain activation and reward expectancy in the left insula, left middle frontal gyrus, left thalamus, and right middle frontal gyrus were found to be related to proactive aggression. Furthermore, the brain activation regions primarily involved in proactive aggression induced by reward expectancy were the insula, inferior frontal gyrus, inferior temporal gyrus, pallidum, and caudate nucleus. Under conditions of high reward expectancy, participants engage in more proactive aggressive behavior. Reward expectancy involves the activation of reward- and social-cognition-related brain regions, and these associations are instrumental in proactive aggressive decisions.

Improving Quality of Life in Bipolar Disorders with an Immersive Virtual Reality Remediation Training Randomized Controlled Trial (RCT)

Background: Health-related quality of life (H-QoL) is a critical measure in bipolar disorder (BD). Recent trials using virtual reality (VR) have shown potential in improving H-QoL. However, VR's effect on the H-QoL of people with BD needs to be further explored. Methods: This study involved a secondary analysis of a feasibility randomized controlled trial, focusing on "quality of life". Participants (aged 18-75) diagnosed with bipolar disorder were randomized into two groups. The experimental group used the CEREBRUM VR app, while the control group received the usual care. Quality of life was assessed using the Short-Form Health Survey (SF-12). Results: A total of 39 individuals in the experimental group and 25 in the control group represent the final samples. The results showed a greater improvement in the SF-12 total score in the experimental group (8.7%) compared to the control group (F = 66.851 p < 0.0001), specifically in the dimension of physical activity limitation, emotional impact, concentration, pain, calmness, energy levels, discouragement, and social activities. Conclusions: This study demonstrated an improvement in QoL for individuals with BD following a VR intervention. As a feasibility study, this secondary outcome needs to be confirmed by further phase III studies. If confirmed, VR could offer valuable rehabilitation tools and insights into the pathogenesis and treatment of BD.

Trajectory of reward-related abnormalities in unaffected relatives of patients with bipolar disorder - A longitudinal fMRI study

First-degree relatives of patients with bipolar disorder are at heightened risk of mood episodes, which may be attributed to the existence of endophenotypes i.e., heritable (neuro)biological changes present in patients and their unaffected relatives (UR). In this longitudinal MRI study, we aim to investigate the trajectories of aberrant reward-related functional changes identified in UR vs healthy controls (HC). Sixty-eight UR and 65 HC of similar age and gender distribution underwent MRI at baseline while performing a card guessing task. Of these, 29 UR and 36 HC were investigated with the same protocol following a 16-month period in average. We first identified brain regions showing group differences in the neural response to expected value (EV) and reward prediction error (PE) at baseline and analyzed how the reward-related response in these regions changed over time in UR vs HC. Relative to HC at baseline, UR showed lower EV signal in the right ventrolateral prefrontal cortex (vlPFC) and paracingulate gyrus and lower PE signal in the left vlPFC and dorsomedial PFC. The trajectories of these abnormalities in UR showed a normalization of the prefrontal EV signals, whereas the PE signals which correlated with depressive symptoms remained stable over time. While the UR showed both blunted EV and PE signals, none of these abnormalities increased over time, which is consistent with the observed stable mood symptoms.

Reward and punishment learning deficits among bipolar disorder subtypes

BACKGROUND

Reward sensitivity is an essential dimension related to mood fluctuations in bipolar disorder (BD), but there is currently a debate around hypersensitivity or hyposensitivity hypotheses to reward in BD during remission, probably related to a heterogeneous population within the BD spectrum and a lack of reward bias evaluation. Here, we examine reward maximization vs. punishment avoidance learning within the BD spectrum during remission.

METHODS

Patients with BD-I (n = 45), BD-II (n = 34) and matched (n = 30) healthy controls (HC) were included. They performed an instrumental learning task designed to dissociate reward-based from punishment-based reinforcement learning. Computational modeling was used to identify the mechanisms underlying reinforcement learning performances.

RESULTS

Behavioral results showed a significant reward learning deficit across BD subtypes compared to HC, captured at the computational level by a lower sensitivity to rewards compared to punishments in both BD subtypes. Computational modeling also revealed a higher choice randomness in BD-II compared to BD-I that reflected a tendency of BD-I to perform better during punishment avoidance learning than BD-II.

LIMITATIONS

Our patients were not naive to antipsychotic treatment and were not euthymic (but in syndromic remission) according to the International Society for Bipolar Disorder definition.

CONCLUSIONS

Our results are consistent with the reward hyposensitivity theory in BD. Computational modeling suggests distinct underlying mechanisms that produce similar observable behaviors, making it a useful tool for distinguishing how symptoms interact in BD versus other disorders. In the long run, a better understanding of these processes could contribute to better prevention and management of BD.

Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting

A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.

Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives

Based on functional magnetic resonance imaging and multilayer dynamic network model, the brain network’s quantified temporal stability has shown potential in predicting altered brain functions. This manuscript aims to summarize current knowledge, clinical research progress, and future perspectives on brain network’s temporal stability. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date which measure is the best. The temporal stability of brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, circadian rhythm, and data preprocessing/analyzing strategies, which should be considered in clinical studies. Multiple common psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disorder-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric disorders partly because of the divergent results. Further studies with larger samples and in transdiagnostic (including schizoaffective disorder) subjects are warranted.