Anhedonia in Nicotine Dependence

Pathophysiology of nAChRs: limbic circuits and related disorders

Human epidemiological studies have identified links between nicotine intake and stress disorders, including anxiety, depression and PTSD. Here we review the clinical evidence for activation and desensitization of nicotinic acetylcholine receptors (nAChRs) relevant to affective disorders. We go on to describe clinical and preclinical pharmacological studies suggesting that nAChR function may be involved in the etiology of anxiety and depressive disorders, may be relevant targets for medication development, and may contribute to the antidepressant efficacy of non-nicotinic therapeutics. We then review what is known about nAChR function in a subset of limbic system areas (amygdala, hippocampus and prefrontal cortex), and how this contributes to stress-relevant behaviors in preclinical models that may be relevant to human affective disorders. Taken together, the preclinical and clinical literature point to a clear role for ACh signaling through nAChRs in regulation of behavioral responses to stress. Disruption of nAChR homeostasis is likely to contribute to the psychopathology observed in anxiety and depressive disorders. Targeting specific nAChRs may therefore be a strategy for medication development to treat these disorders or to augment the efficacy of current therapeutics.

Individualized prediction of consummatory anhedonia from functional connectome in major depressive disorder

BACKGROUND

Anhedonia is a key symptom of major depressive disorder (MDD) and other psychiatric diseases. The neural basis of anhedonia has been widely examined, yet the interindividual variability in neuroimaging biomarkers underlying individual-specific symptom severity is not well understood.

METHODS

To establish an individualized prediction model of anhedonia, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity profiles of MDD patients.

RESULTS

The CPM can successfully and reliably predict individual consummatory but not anticipatory anhedonia. The predictive model mainly included salience network (SN), frontoparietal network (FPN), default mode network (DMN), and motor network. Importantly, subsequent computational lesion prediction and consummatory-specific model prediction revealed that connectivity of the SN with DMN and FPN is essential and specific for the prediction of consummatory anhedonia.

CONCLUSIONS

This study shows that brain functional connectivity, especially the connectivity of SN-FPN and SN-DMN, can specifically predict individualized consummatory anhedonia in MDD. These findings suggest the potential of functional connectomes for the diagnosis and prognosis of anhedonia in MDD and other disorders.

Probabilistic Reinforcement Learning and Anhedonia

Despite the prominence of anhedonic symptoms associated with diverse neuropsychiatric conditions, there are currently no approved therapeutics designed to attenuate the loss of responsivity to previously rewarding stimuli. However, the search for improved treatment options for anhedonia has been reinvigorated by a recent reconceptualization of the very construct of anhedonia, including within the Research Domain Criteria (RDoC) initiative. This chapter will focus on the RDoC Positive Valence Systems construct of reward learning generally and sub-construct of probabilistic reinforcement learning specifically. The general framework emphasizes objective measurement of a subject's responsivity to reward via reinforcement learning under asymmetrical probabilistic contingencies as a means to quantify reward learning. Indeed, blunted reward responsiveness and reward learning are central features of anhedonia and have been repeatedly described in major depression. Moreover, these probabilistic reinforcement techniques can also reveal neurobiological mechanisms to aid development of innovative treatment approaches. In this chapter, we describe how investigating reward learning can improve our understanding of anhedonia via the four RDoC-recommended tasks that have been used to probe sensitivity to probabilistic reinforcement contingencies and how such task performance is disrupted in various neuropsychiatric conditions. We also illustrate how reverse translational approaches of probabilistic reinforcement assays in laboratory animals can inform understanding of pharmacological and physiological mechanisms. Next, we briefly summarize the neurobiology of probabilistic reinforcement learning, with a focus on the prefrontal cortex, anterior cingulate cortex, striatum, and amygdala. Finally, we discuss treatment implications and future directions in this burgeoning area.