Distinct profiles of anhedonia and reward processing and their prospective associations with quality of life among individuals with mood disorders.

Leading professional health bodies have called for the wider adoption of Patient Reported Outcome Measures, such as quality of life, in research and clinical practice as a means for understanding why the global burden of depression continues to climb despite increased rates of treatment use. Here, we examined whether anhedonia-an often recalcitrant and impairing symptom of depression-along with its neural correlates, was associated with longitudinal changes in patient-reported quality of life among individuals seeking treatment for mood disorders. We recruited 112 participants, including n = 80 individuals with mood disorders (58 unipolar, 22 bipolar) and n = 32 healthy controls (63.4% female). We assessed anhedonia severity along with two electroencephalographic markers of neural reward responsiveness (scalp-level 'Reward Positivity' amplitude and source-localized reward-related activation in the dorsal anterior cingulate cortex), and assessed quality of life at baseline, 3- and 6-month follow-up. Anhedonia emerged as a robust correlate of quality of life cross-sectionally and longitudinally among individuals with mood disorders. Furthermore, increased neural reward responsiveness at baseline was associated with greater improvements in quality of life over time, and this improvement was mediated by longitudinal improvements in anhedonia severity. Finally, differences in quality of life observed between individuals with unipolar and bipolar mood disorders were mediated by differences in anhedonia severity. Our findings indicate that anhedonia and its reward-related neural correlates are linked to variability in quality of life over time in individuals with mood disorders. Treatments capable of improving anhedonia and normalizing brain reward function may be necessary for improving broader health outcomes for individuals seeking treatment for depression.ClinicalTrials.gov identifier: NCT01976975.

Exploration of baseline and early changes in neurocognitive characteristics as predictors of treatment response to bupropion, sertraline, and placebo in the EMBARC clinical trial.

BACKGROUND: Treatment for major depressive disorder (MDD) is imprecise and often involves trial-and-error to determine the most effective approach. To facilitate optimal treatment selection and inform timely adjustment, the current study investigated whether neurocognitive variables could predict an antidepressant response in a treatment-specific manner. METHODS: In the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial, outpatients with non-psychotic recurrent MDD were first randomized to an 8-week course of sertraline selective serotonin reuptake inhibitor or placebo. Behavioral measures of reward responsiveness, cognitive control, verbal fluency, psychomotor, and cognitive processing speeds were collected at baseline and week 1. Treatment responders then continued on another 8-week course of the same medication, whereas non-responders to sertraline or placebo were crossed-over under double-blinded conditions to bupropion noradrenaline/dopamine reuptake inhibitor or sertraline, respectively. Hamilton Rating for Depression scores were also assessed at baseline, weeks 8, and 16. RESULTS: Greater improvements in psychomotor and cognitive processing speeds within the first week, as well as better pretreatment performance in these domains, were specifically associated with higher likelihood of response to placebo. Moreover, better reward responsiveness, poorer cognitive control and greater verbal fluency were associated with greater likelihood of response to bupropion in patients who previously failed to respond to sertraline. CONCLUSION: These exploratory results warrant further scrutiny, but demonstrate that quick and non-invasive behavioral tests may have substantial clinical value in predicting antidepressant treatment response.

Effect of Exenatide Use on Cognitive and Affective Functioning in Obese Patients With Type 2 Diabetes Mellitus: Exenatide Use Mediates Depressive Scores Through Increased Perceived Stress Levels.

PURPOSE/BACKGROUND: Glucagon-like peptide-1 (GLP-1) is a molecule used to treat type 2 diabetes mellitus (T2DM). Given their widespread expression in the nervous system, GLP-1 receptors also play a role in regulating mood and cognitive function. Here, we aimed to compare obese patients with T2DM, with or without exenatide (a GLP-1R agonist) use on cognitive and affective functioning. METHODS/PROCEDURES: A total of 43 patients with T2DM (23 on exenatide and 20 without exenatide) were evaluated with the Snaith-Hamilton Pleasure Scale, Cognitive Failures Questionnaire, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7, Childhood Trauma Questionnaire, Perceived Stress Scale (PSS), and Chronic Stress Scale, in addition to laboratory-based measures of reward learning (the probabilistic reward task) and working memory (Letter-N-Back task). FINDINGS/RESULTS: Patients on exenatide had higher body mass index (BMI) (37.88 ± 5.44 vs 35.29 ± 6.30; P = 0.015), PHQ-9 (9.70 ± 4.92 vs 6.70 ± 4.66; P = 0.026), and PSS (29.39 ± 6.70 vs 23.35 ± 7.69; P = 0.015) scores. Other stress scales (Childhood Trauma Questionnaire and Chronic Stress Scale), Generalized Anxiety Disorder-7 scores, response bias, or discriminability as assessed by probabilistic reward task and self-report (Cognitive Failures Questionnaire) and laboratory-based (Letter-N-Back) cognitive measures were not significantly different between groups (both Ps > 0.05). Multivariate linear regression analyses adding BMI and PSS as covariates revealed that although BMI had no effect (P = 0.5), PSS significantly predicted PHQ-9 scores (P = 0.004). Mediation analysis showed that exenatide users reported higher PSS, with greater PSS associated with higher PHQ-9 levels (b = 0.236). There was no evidence on exenatide directly influencing PHQ-9 independent of PSS (c' = 1.573; P = 0.305; 95% bootstrap confidence interval, -1.487 to 4.634). IMPLICATIONS/CONCLUSIONS: Based on previous research and our findings, exenatide use might be mediating depression scores through disrupting stress responses.

Diagnostic and dimensional evaluation of implicit reward learning in social anxiety disorder and major depression.

OBJECTIVE: Increasing evidence supports the presence of an anhedonic endophenotype in major depressive disorder (MDD), characterized by impairments in various components of reward processing, particularly incentive motivation, effort-based decision making, and reward learning. In addition to its prominent role in MDD, reward processing dysregulation has been proposed as a transdiagnostic risk and/or maintenance factor for a range of other forms of psychopathology. Individuals with social anxiety disorder (SAD)-a condition that frequently co-occurs with MDD-demonstrate low trait positive affectivity and altered processing of rewards and positively valenced information. However, no studies to date have directly tested reward learning-the ability to modulate behavior in response to rewards-in this population. MATERIALS AND METHODS: The current study evaluated reward learning in MDD, SAD, and healthy control subjects (N = 90) using a well-validated signal detection task. Given increasing data supporting transdiagnostic features of psychopathology, we also evaluated associations between anhedonia and task performance transdiagnostically in the patient sample. RESULTS: Contrary to expectations, results indicated no significant group differences in response bias in the full sample, suggesting no diagnostic differences in reward learning. However, dimensional analyses revealed that higher self-reported anhedonia (but not general distress or anxious arousal) was associated with worse reward learning in both the MDD and SAD groups explaining about 11% of the variance. CONCLUSION: Deficits in implicit reward learning are associated with anhedonia but not necessarily with major depressive disorder as a diagnosis, which supports the use of transdiagnostic approaches to understanding psychopathology.

Success inhibits preschoolers' ability to establish selective trust.

A number of studies have shown that preschoolers make inferences about potential informants based on the informants' past behavior, selectively trusting an informant who has been helpful in the past, for example, over one who has been unhelpful. Here we used a hiding game to show that 4- and 5-year-olds' selective trust can also be influenced by inferences they make about their own abilities. Children do not prefer a previously helpful informant over a previously unhelpful one when informant helpfulness is decoupled from children's success in finding hidden objects (Studies 1 and 3). Indeed, children do not seem to track informant helpfulness when their success at finding hidden objects has never depended on it (Study 2). A single failure to find a hidden object when offered information by the unhelpful informant can, however, lead them to selectively trust the previously helpful one later (Study 4). Children's selective trust is based not only on differences between informants but also on their sense of illusory control-their inferences about whether they need assistance from those informants in the first place.

Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome.

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

"Don't [ruminate], be happy": A cognitive perspective linking depression and anhedonia.

Anhedonia, a lack of pleasure in things an individual once enjoyed, and rumination, the process of perseverative and repetitive attention to specific thoughts, are hallmark features of depression. Though these both contribute to the same debilitating disorder, they have often been studied independently and through different theoretical lenses (e.g., biological vs. cognitive). Cognitive theories and research on rumination have largely focused on understanding negative affect in depression with much less focus on the etiology and maintenance of anhedonia. In this paper, we argue that by examining the relation between cognitive constructs and deficits in positive affect, we may better understand anhedonia in depression thereby improving prevention and intervention efforts. We review the extant literature on cognitive deficits in depression and discuss how these dysfunctions may not only lead to sustained negative affect but, importantly, interfere with an ability to attend to social and environmental cues that could restore positive affect. Specifically, we discuss how rumination is associated to deficits in working memory and propose that these deficits in working memory may contribute to anhedonia in depression. We further argue that analytical approaches such as computational modeling are needed to study these questions and, finally, discuss implications for treatment.

Seeing red: Distraction influences visual attention for anger but not for other negative emotions.

Emotion regulation is a vital skill that improves psychological well-being and overall functioning. Distraction (the purposeful internal disengagement from an emotional stimulus) and cognitive reappraisal (the process of changing one's thoughts about an emotional event/stimulus) are two well-established regulation strategies that can effectively decrease negative affect. Less understood, however, are the attention allocation strategies that occur when engaging in these emotion regulation strategies-specifically, do people visually scan emotional information differently when distracting vs. reappraising? In the current study, community participants were randomly assigned to either distract, reappraise, or view naturally while watching four emotional film clips that each elicited a different negative emotional state: anger, fear, sadness, and disgust. Eye tracking was used to record total time spent gazing ("dwell time") at faces within the emotion-eliciting film clips. An effect of condition was found for anger-eliciting material only: participants in the distraction condition exhibited shorter dwell times compared with reappraisal and natural viewing. Importantly, this effect was moderated by state anxiety, such that it was found at low but not high levels of state anxiety. These results show that emotion regulation strategies differentially affect attention to emotion-eliciting stimuli and points to the role of current affective states in impacting how distraction is used. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Brain-based graph-theoretical predictive modeling to map the trajectory of transdiagnostic symptoms of anhedonia, impulsivity, and hypomania from the human functional connectome.

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM's mean square error (MSE) to that of a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region for information spread) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders, highlighting transdiagnostic generalization. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level. ClinicalTrials.gov identifier: NCT01976975.

Mapping Disease Course Across the Mood Disorder Spectrum Through a Research Domain Criteria Framework.

BACKGROUND: The National Institute of Mental Health Research Domain Criteria (RDoC) initiative aims to establish a neurobiologically valid framework for classifying mental illness. Here, we examined whether the RDoC construct of reward learning and three aspects of its underlying neurocircuitry predicted symptom trajectories in individuals with mood pathology. METHODS: Aligning with the RDoC approach, we recruited individuals (n = 80 with mood disorders [58 unipolar and 22 bipolar] and n = 32 control subjects; 63.4% female) based on their performance on a laboratory-based reward learning task rather than clinical diagnosis. We then assessed 1) anterior cingulate cortex prediction errors using electroencephalography, 2) striatal reward prediction errors using functional magnetic resonance imaging, and 3) medial prefrontal cortex glutamatergic function (mPFC Gln/Glu) using 1H magnetic resonance spectroscopy. Severity of anhedonia, (hypo)mania, and impulsivity were measured at baseline, 3 months, and 6 months. RESULTS: Greater homogeneity in aspects of brain function (mPFC Gln/Glu) was observed when individuals were classified according to reward learning ability rather than diagnosis. Furthermore, mPFC Gln/Glu levels predicted more severe (hypo)manic symptoms cross-sectionally, predicted worsening (hypo)manic symptoms longitudinally, and explained greater variance in future (hypo)manic symptoms than diagnostic information. However, rather than being transdiagnostic, this effect was specific to individuals with bipolar disorder. Prediction error indices were unrelated to symptom severity. CONCLUSIONS: Although findings are preliminary and require replication, they suggest that heightened mPFC Gln/Glu warrants further consideration as a predictor of future (hypo)mania. Importantly, this work highlights the value of an RDoC approach that works in tandem with, rather than independent of, traditional diagnostic frameworks.

Psychophysiological correlates of anxious apprehension: Trait worry is associated with startle response to threat.

Worry is a form of repetitive negative thought that is closely associated with anxiety disorders. Worry has been described as anxious apprehension and conceptualized as reflecting heightened anticipation of potentially threatening future events. However, it is unclear whether people who tend to worry show heightened physiological reactivity when anticipating threat, especially if the threat is uncertain. In the current study, community participants (n = 52) completed a threat anticipation task featuring uncertain threat, certain threat, and safety while the startle response to auditory probes was measured. Self-reported tendency to worry was assessed using the Penn State Worry Questionnaire, and anxiety disorder status was assessed via a clinical interview. A repeated-measures general linear model showed a main effect of threat level on the startle response, as well as a significant three-way interaction among threat level, worry, and anxiety disorder status. Follow-up tests showed that higher worry was associated with blunted startle responses to threat but particularly to uncertain threat among participants with a history of anxiety disorders. Worry did not moderate startle responding in participants without a history of anxiety disorders. These results indicate that psychophysiological correlates of worry depend on clinical status and suggest that trait worry is associated with physiological blunting to threat in individuals with a history of anxiety disorders, particularly when threat is uncertain. Implications for theoretical models of worry are discussed.

Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder.

Neuroimaging studies have shown that major depressive disorder (MDD) is characterized by abnormal neural activity and connectivity. However, hemodynamic imaging techniques lack the temporal resolution needed to resolve the dynamics of brain mechanisms underlying MDD. Moreover, it is unclear whether putative abnormalities persist after remission. To address these gaps, we used microstate analysis to study resting-state brain activity in major depressive disorder (MDD). Electroencephalographic (EEG) "microstates" are canonical voltage topographies that reflect brief activations of components of resting-state brain networks. We used polarity-insensitive k-means clustering to segment resting-state high-density (128-channel) EEG data into microstates. Data from 79 healthy controls (HC), 63 individuals with MDD, and 30 individuals with remitted MDD (rMDD) were included. The groups produced similar sets of five microstates, including four widely-reported canonical microstates (A-D). The proportion of microstate D was decreased in MDD and rMDD compared to the HC group (Cohen's d = 0.63 and 0.72, respectively) and the duration and occurrence of microstate D was reduced in the MDD group compared to the HC group (Cohen's d = 0.43 and 0.58, respectively). Among the MDD group, proportion and duration of microstate D were negatively correlated with symptom severity (Spearman's rho = -0.34 and -0.46, respectively). Finally, microstate transition probabilities were nonrandom and the MDD group, relative to the HC and the rMDD groups, exhibited multiple distinct transition probabilities, primarily involving microstates A and C. Our findings highlight both state and trait abnormalities in resting-state brain activity in MDD.

Pretreatment Rostral Anterior Cingulate Cortex Connectivity With Salience Network Predicts Depression Recovery: Findings From the EMBARC Randomized Clinical Trial.

BACKGROUND: Baseline rostral anterior cingulate cortex (rACC) activity is a well-replicated nonspecific predictor of depression improvement. The rACC is a key hub of the default mode network, which prior studies indicate is hyperactive in major depressive disorder. Because default mode network downregulation is reliant on input from the salience network and frontoparietal network, an important question is whether rACC connectivity with these systems contributes to depression improvement. METHODS: Our study evaluated this hypothesis in outpatients (N = 238; 151 female) enrolled in the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) 8-week randomized clinical trial of sertraline versus placebo for major depressive disorder. Depression severity was measured using the Hamilton Rating Scale for Depression, and electroencephalography was recorded at baseline and week 1. Exact low-resolution electromagnetic tomography was used to compute activity from the rACC, and key regions within the default mode network (posterior cingulate cortex), frontoparietal network (left dorsolateral prefrontal cortex), and salience network (right anterior insula [rAI]). Connectivity in the theta band (4.5-7 Hz) and beta band (12.5-21 Hz) was computed using lagged phase synchronization. RESULTS: Stronger baseline theta-band rACC-rAI (salience network hub) connectivity predicted greater depression improvement across 8 weeks of treatment for both treatment arms (B = -0.57, 95% confidence interval = -1.07, -0.08, p = .03). Early increases in theta-band rACC-rAI connectivity predicted greater likelihood of achieving remission at week 8 (odds ratio = 2.90, p = .03). CONCLUSIONS: Among patients undergoing treatment, theta-band rACC-rAI connectivity is a prognostic, albeit treatment-nonspecific, indicator of depression improvement, and early connectivity changes may predict clinically meaningful outcomes.