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.

Smoking Progression and Nicotine-Enhanced Reward Sensitivity Predicted by Resting-State Functional Connectivity in Salience and Executive Control Networks.

INTRODUCTION: The neural underpinnings underlying individual differences in nicotine-enhanced reward sensitivity (NERS) and smoking progression are poorly understood. Thus, we investigated whether brain resting-state functional connectivity (rsFC.) during smoking abstinence predicts NERS and smoking progression in young light smokers. We hypothesized that high rsFC between brain areas with high densities of nicotinic receptors (insula, anterior cingulate cortex [ACC], hippocampus, thalamus) and areas involved in reward-seeking (nucleus accumbens [NAcc], prefrontal cortex [PFC]) would predict NERS and smoking progression. AIMS AND METHODS: Young light smokers (N = 64, age 18-24, M = 1.89 cigarettes/day) participated in the study. These individuals smoked between 5 and 35 cigarettes per week and lifetime use never exceeded 35 cigarettes per week. Their rsFC was assessed using functional magnetic resonance imaging after 14 hours of nicotine deprivation. Subjects also completed a probabilistic reward task after smoking a placebo on 1 day and a regular cigarette on another day. RESULTS: The probabilistic-reward-task assessed greater NERS was associated with greater rsFC between the right anterior PFC and right NAcc, but with reduced rsFC between the ACC and left inferior prefrontal gyrus and the insula and ACC. Decreased rsFC within the salience network (ACC and insula) predicted increased smoking progression across 18 months and greater NERS. CONCLUSIONS: These findings provide the first evidence that differences in rsFCs in young light smokers are associated with nicotine-enhanced reward sensitivity and smoking progression. CLINICAL TRIAL REGISTRATION: NCT02129387 (preregistered hypothesis: www.clinicaltrials.gov). IMPLICATIONS: Weaker rsFC within the salience network predicted greater NERS and smoking progression. These findings suggest that salience network rsFC and drug-enhanced reward sensitivity may be useful tools and potential endophenotypes for reward sensitivity and drug-dependence research.

Depression Severity Moderates Reward Learning Among Smokers with Current or Past Major Depressive Disorder in a Smoking Cessation Randomized Clinical Trial.

INTRODUCTION: Behavioral and pharmacological smoking cessation treatments are hypothesized to increase patients' reward learning to reduce craving. Identifying changes in reward learning processes that support effective tobacco dependence interventions among smokers who experience depression may guide patients towards efficient treatment strategies. The objective was to investigate the extent to which adult daily cigarette smokers with current or past major depressive disorder (MDD) learned to seek reward during 12 weeks of treatment combining behavioral activation and varenicline. We hypothesized that a decline in reward learning would be attenuated (least to most) in the following order: 1) Behavioral activation integrated with ST (BASC) + varenicline, 2) BASC + placebo, 3) Standard behavioral cessation treatment (ST) + varenicline, 4) ST + placebo. METHODS: We ran a Phase 4, placebo-controlled, randomized clinical trial with 300 participants receiving 12 weeks of one of four conditions across two urban medical centers. Depressive symptoms were measured using the Beck Depression Inventory-II (BDI). Reward learning was ascertained at Weeks 1, 7, and 14 using the Probabilistic Reward Task (PRT), a laboratory task that uses an asymmetric reinforcement schedule to assess (a) learning to seek reward (response bias), (b) differentiate between stimuli, and (c) time to react to cues. RESULTS: There was a significant interaction of BDI group x PRT response bias. Response bias declined from Week 7 to 14 among participants with high baseline depression symptoms. The other two BDI groups showed no change in response bias. CONCLUSIONS: Controlling for baseline depression, participants showed a decrease in response bias from Week 1 to 14, and from Weeks 7 to 14. Treatment condition and abstinence status were unassociated with change in reward learning. IMPLICATIONS: Smokers who report greater depression severity show a decline in reward learning despite their participation in smoking cessation treatments, suggesting that depressed populations pose unique challenges with standard smoking cessation approaches.

Effectiveness of Self-guided Tailored Implementation Strategies in Integrating and Embedding Internet-Based Cognitive Behavioral Therapy in Routine Mental Health Care: Results of a Multicenter Stepped-Wedge Cluster Randomized Trial.

BACKGROUND: Internet-based cognitive behavioral therapy (iCBT) services for common mental health disorders have been found to be effective. There is a need for strategies that improve implementation in routine practice. One-size-fits-all strategies are likely to be ineffective. Tailored implementation is considered as a promising approach. The self-guided integrated theory-based Framework for intervention tailoring strategies toolkit (ItFits-toolkit) supports local implementers in developing tailored implementation strategies. Tailoring involves identifying local barriers; matching selected barriers to implementation strategies; developing an actionable work plan; and applying, monitoring, and adapting where necessary. OBJECTIVE: This study aimed to compare the effectiveness of the ItFits-toolkit with implementation-as-usual (IAU) in implementing iCBT services in 12 routine mental health care organizations in 9 countries in Europe and Australia. METHODS: A stepped-wedge cluster randomized trial design with repeated measures was applied. The trial period lasted 30 months. The primary outcome was the normalization of iCBT delivery by service providers (therapists, referrers, IT developers, and administrators), which was measured with the Normalization Measure Development as a proxy for implementation success. A 3-level linear mixed-effects modeling was applied to estimate the effects. iCBT service uptake (referral and treatment completion rates) and implementation effort (hours) were used as secondary outcomes. The perceived satisfaction (Client Satisfaction Questionnaire), usability (System Usability Scale), and impact of the ItFits-toolkit by implementers were used to assess the acceptability of the ItFits-toolkit. RESULTS: In total, 456 mental health service providers were included in this study. Compared with IAU, the ItFits-toolkit had a small positive statistically significant effect on normalization levels in service providers (mean 0.09, SD 0.04; P=.02; Cohen d=0.12). The uptake of iCBT by patients was similar to that of IAU. Implementers did not spend more time on implementation work when using the ItFits-toolkit and generally regarded the ItFits-toolkit as usable and were satisfied with it. CONCLUSIONS: The ItFits-toolkit performed better than the usual implementation activities in implementing iCBT services in routine practice. There is practical utility in the ItFits-toolkit for supporting implementers in developing and applying effective tailored implementation strategies. However, the effect on normalization levels among mental health service providers was small. These findings warrant modesty regarding the effectiveness of self-guided tailored implementation of iCBT services in routine practice. TRIAL REGISTRATION: ClinicalTrials.gov NCT03652883; https://clinicaltrials.gov/ct2/show/NCT03652883. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-020-04686-4.

Uncovering the prevalence and neural substrates of anhedonia in frontotemporal dementia.

Much of human behaviour is motivated by the drive to experience pleasure. The capacity to envisage pleasurable outcomes and to engage in goal-directed behaviour to secure these outcomes depends upon the integrity of frontostriatal circuits in the brain. Anhedonia refers to the diminished ability to experience, and to pursue, pleasurable outcomes, and represents a prominent motivational disturbance in neuropsychiatric disorders. Despite increasing evidence of motivational disturbances in frontotemporal dementia (FTD), no study to date has explored the hedonic experience in these syndromes. Here, we present the first study to document the prevalence and neural correlates of anhedonia in FTD in comparison with Alzheimer's disease, and its potential overlap with related motivational symptoms including apathy and depression. A total of 172 participants were recruited, including 87 FTD, 34 Alzheimer's disease, and 51 healthy older control participants. Within the FTD group, 55 cases were diagnosed with clinically probable behavioural variant FTD, 24 presented with semantic dementia, and eight cases had progressive non-fluent aphasia (PNFA). Premorbid and current anhedonia was measured using the Snaith-Hamilton Pleasure Scale, while apathy was assessed using the Dimensional Apathy Scale, and depression was indexed via the Depression, Anxiety and Stress Scale. Whole-brain voxel-based morphometry analysis was used to examine associations between grey matter atrophy and levels of anhedonia, apathy, and depression in patients. Relative to controls, behavioural variant FTD and semantic dementia, but not PNFA or Alzheimer's disease, patients showed clinically significant anhedonia, representing a clear departure from pre-morbid levels. Voxel-based morphometry analyses revealed that anhedonia was associated with atrophy in an extended frontostriatal network including orbitofrontal and medial prefrontal, paracingulate and insular cortices, as well as the putamen. Although correlated on the behavioural level, the neural correlates of anhedonia were largely dissociable from that of apathy, with only a small region of overlap detected in the right orbitofrontal cortices whilst no overlapping regions were found between anhedonia and depression. This is the first study, to our knowledge, to demonstrate profound anhedonia in FTD syndromes, reflecting atrophy of predominantly frontostriatal brain regions specialized for hedonic tone. Our findings point to the importance of considering anhedonia as a primary presenting feature of behavioural variant FTD and semantic dementia, with distinct neural drivers to that of apathy or depression. Future studies will be essential to address the impact of anhedonia on everyday activities, and to inform the development of targeted interventions to improve quality of life in patients and their families.

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.

Effects of the KCNQ channel opener ezogabine on functional connectivity of the ventral striatum and clinical symptoms in patients with major depressive disorder.

Major depressive disorder (MDD) is a leading cause of disability worldwide, yet current treatment strategies remain limited in their mechanistic diversity. Recent evidence has highlighted a promising novel pharmaceutical target-the KCNQ-type potassium channel-for the treatment of depressive disorders, which may exert a therapeutic effect via functional changes within the brain reward system, including the ventral striatum. The current study assessed the effects of the KCNQ channel opener ezogabine (also known as retigabine) on reward circuitry and clinical symptoms in patients with MDD. Eighteen medication-free individuals with MDD currently in a major depressive episode were enrolled in an open-label study and received ezogabine up to 900 mg/day orally over the course of 10 weeks. Resting-state functional magnetic resonance imaging data were collected at baseline and posttreatment to examine brain reward circuitry. Reward learning was measured using a computerized probabilistic reward task. After treatment with ezogabine, subjects exhibited a significant reduction of depressive symptoms (Montgomery-Asberg Depression Rating Scale score change: -13.7 ± 9.7, p < 0.001, d = 2.08) and anhedonic symptoms (Snaith-Hamilton Pleasure Scale score change: -6.1 ± 5.3, p < 0.001, d = 1.00), which remained significant even after controlling for overall depression severity. Improvement in depression was associated with decreased functional connectivity between the ventral caudate and clusters within the mid-cingulate cortex and posterior cingulate cortex (n = 14, voxel-wise p < 0.005). In addition, a subgroup of patients tested with a probabilistic reward task (n = 9) showed increased reward learning following treatment. These findings highlight the KCNQ-type potassium channel as a promising target for future drug discovery efforts in mood disorders.

Genetic and Depressive Traits Moderate the Reward-Enhancing Effects of Acute Nicotine in Young Light Smokers.

INTRODUCTION: Rates of light smoking have increased in recent years and are associated with adverse health outcomes. Reducing light smoking is a challenge because it is unclear why some but not others, progress to heavier smoking. Nicotine has profound effects on brain reward systems and individual differences in nicotine's reward-enhancing effects may drive variability in smoking trajectories. Therefore, we examined whether a genetic risk factor and personality traits known to moderate reward processing, also moderate the reward-enhancing effects of nicotine. METHODS: Light smokers (n = 116) performed a Probabilistic Reward Task to assess reward responsiveness after receiving nicotine or placebo (order counterbalanced). Individuals were classified as nicotine dependence 'risk' allele carriers (rs16969968 A-allele carriers) or non-carriers (non-A-allele carriers), and self-reported negative affective traits were also measured. RESULTS: Across the sample, reward responsiveness was greater following nicotine compared to placebo (p = 0.045). For Caucasian A-allele carriers but not non-A-allele carriers, nicotine enhanced reward responsiveness compared to placebo for those who received placebo first (p = 0.010). Furthermore, for A-allele carriers but not non-A-allele carriers who received nicotine first, the enhanced reward responsiveness in the nicotine condition carried over to the placebo condition (p < 0.001). Depressive traits also moderated the reward-enhancing effects of nicotine (p = 0.010) and were associated with blunted reward responsiveness following placebo but enhanced reward responsiveness following nicotine. CONCLUSION: These findings suggest that individual differences in a genetic risk factor and depressive traits alter nicotine's effect on reward responsiveness in light smokers and may be important factors underpinning variability in smoking trajectories in this growing population. IMPLICATIONS: Individuals carrying genetic risk factors associated with nicotine dependence(rs16969968 A-allele carriers) and those with higher levels of depressive personality traits, showmore pronounced increases in reward learning following acute nicotine exposure. These findingssuggest that genetic and personality factors may drive individual differences in smoking trajectoriesin young light smokers by altering the degree to which nicotine enhances reward processing. CLINICAL TRIAL REGISTRATION: NCT02129387 (pre-registered hypothesis: www.clinicaltrials.gov).

Baseline reward processing and ventrostriatal dopamine function are associated with pramipexole response in depression.

The efficacy of dopamine agonists in treating major depressive disorder has been hypothesized to stem from effects on ventrostriatal dopamine and reward function. However, an important question is whether dopamine agonists are most beneficial for patients with reward-based deficits. This study evaluated whether measures of reward processing and ventrostriatal dopamine function predicted response to the dopamine agonist, pramipexole (ClinicalTrials.gov Identifier: NCT02033369). Individuals with major depressive disorder (n = 26) and healthy controls (n = 26) (mean ± SD age = 26.5 ± 5.9; 50% female) first underwent assessments of reward learning behaviour and ventrostriatal prediction error signalling (measured using functional MRI). 11C-(+)-PHNO PET before and after oral amphetamine was used to assess ventrostriatal dopamine release. The depressed group then received open-label pramipexole treatment for 6 weeks (0.5 mg/day titrated to a maximum daily dose of 2.5 mg). Symptoms were assessed weekly, and reward learning was reassessed post-treatment. At baseline, relative to controls, the depressed group showed lower reward learning (P = 0.02), a trend towards blunted reward-related prediction error signals (P = 0.07), and a trend towards increased amphetamine-induced dopamine release (P = 0.07). Despite symptom improvements following pramipexole (Cohen's d ranging from 0.51 to 2.16 across symptom subscales), reward learning did not change after treatment. At a group level, baseline reward learning (P = 0.001) and prediction error signalling (P = 0.004) were both associated with symptom improvement, albeit in a direction opposite to initial predictions: patients with stronger pretreatment reward learning and reward-related prediction error signalling improved most. Baseline D2/3 receptor availability (P = 0.02) and dopamine release (P = 0.05) also predicted improvements in clinical functioning, with lower D2/3 receptor availability and lower dopamine release predicting greater improvements. Although these findings await replication, they suggest that measures of reward-related mesolimbic dopamine function may hold promise for identifying depressed individuals likely to respond favourably to dopaminergic pharmacotherapy.

Mental Health Screening in General Practices as a Means for Enhancing Uptake of Digital Mental Health Interventions: Observational Cohort Study.

BACKGROUND: Digital mental health interventions stand to play a critical role in managing the mental health impact of the COVID-19 pandemic. Thus, enhancing their uptake is a key priority. General practitioners (GPs) are well positioned to facilitate access to digital interventions, but tools that assist GPs in identifying suitable patients are lacking. OBJECTIVE: This study aims to evaluate the suitability of a web-based mental health screening and treatment recommendation tool (StepCare) for improving the identification of anxiety and depression in general practice and, subsequently, uptake of digital mental health interventions. METHODS: StepCare screens patients for symptoms of depression (9-item Patient Health Questionnaire) and anxiety (7-item Generalized Anxiety Disorder scale) in the GP waiting room. It provides GPs with stepped treatment recommendations that include digital mental health interventions for patients with mild to moderate symptoms. Patients (N=5138) from 85 general practices across Australia were invited to participate in screening. RESULTS: Screening identified depressive or anxious symptoms in 43.09% (1428/3314) of patients (one-quarter were previously unidentified or untreated). The majority (300/335, 89.6%) of previously unidentified or untreated patients had mild to moderate symptoms and were candidates for digital mental health interventions. Although less than half were prescribed a digital intervention by their GP, when a digital intervention was prescribed, more than two-thirds of patients reported using it. CONCLUSIONS: Implementing web-based mental health screening in general practices can provide important opportunities for GPs to improve the identification of symptoms of mental illness and increase patient access to digital mental health interventions. Although GPs prescribed digital interventions less frequently than in-person psychotherapy or medication, the promising rates of uptake by GP-referred patients suggest that GPs can play a critical role in championing digital interventions and maximizing the associated benefits.

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.

Fear Extinction Recall Modulates Human Frontomedial Theta and Amygdala Activity.

Human functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) studies, as well as animal studies, indicate that the amygdala and frontomedial brain regions are critically involved in conditioned fear and that frontomedial oscillations in the theta range (4-8 Hz) may support communication between these brain regions. However, few studies have used a multimodal approach to probe interactions among these key regions in humans. Here, our goal was to bridge the gap between prior human fMRI, EEG, and animal findings. Using simultaneous EEG-fMRI recordings 24 h after fear conditioning and extinction, conditioned stimuli presented (CS+E, CS-E) and not presented during extinction (CS+N, CS-N) were compared to identify effects specific to extinction versus fear recall. Differential (CS+ vs. CS-) electrodermal, frontomedial theta (EEG) and amygdala responses (fMRI) were reduced for extinguished versus nonextinguished stimuli. Importantly, effects on theta power covaried with effects on amygdala activation. Fear and extinction recall as indicated by theta explained 60% of the variance for the analogous effect in the right amygdala. Our findings show for the first time the interplay of amygdala and frontomedial theta activity during fear and extinction recall in humans and provide insight into neural circuits consistently linked with top-down amygdala modulation in rodents.

Frontostriatal and Dopamine Markers of Individual Differences in Reinforcement Learning: A Multi-modal Investigation.

Prior studies have shown that dopamine (DA) functioning in frontostriatal circuits supports reinforcement learning (RL), as phasic DA activity in ventral striatum signals unexpected reward and may drive coordinated activity of striatal and orbitofrontal regions that support updating of action plans. However, the nature of DA functioning in RL is complex, in particular regarding the role of DA clearance in RL behavior. Here, in a multi-modal neuroimaging study with healthy adults, we took an individual differences approach to the examination of RL behavior and DA clearance mechanisms in frontostriatal learning networks. We predicted that better RL would be associated with decreased striatal DA transporter (DAT) availability and increased intrinsic functional connectivity among DA-rich frontostriatal regions. In support of these predictions, individual differences in RL behavior were related to DAT binding potential in ventral striatum and resting-state functional connectivity between ventral striatum and orbitofrontal cortex. Critically, DAT binding potential had an indirect effect on reinforcement learning behavior through frontostriatal connectivity, suggesting potential causal relationships across levels of neurocognitive functioning. These data suggest that individual differences in DA clearance and frontostriatal coordination may serve as markers for RL, and suggest directions for research on psychopathologies characterized by altered RL.

Cognitive and psychophysiological correlates of disgust in obsessive-compulsive disorder.

OBJECTIVES: Evidence suggests that obsessive-compulsive disorder (OCD) is characterized by heightened self-reported disgust, however, it is unclear if this extends to physiology. The relationship between obsessive beliefs and disgust also remains poorly understood. Therefore, we examined whether the heightened trait and self-reported disgust observed in individuals with OCD is reflected in heightened physiological disgust responses. We also examined whether obsessive beliefs are associated with disgust responding. DESIGN: A 3 (group) × 6 (stimulus) repeated measures design was used to examine disgust responses in individuals with OCD to six categories of image stimuli: body waste, contamination, injury, sociomoral, neutral, negative non-disgust. METHODS: Individuals with OCD (n = 25) were compared to individuals with non-OCD anxiety disorders (n = 21) and healthy participants (n = 25) with respect to trait, self-reported, facial electromyographic and electrodermal disgust responses. RESULTS: Individuals with OCD showed greater disgust propensity and self-reported disgust to images of body waste compared to healthy and anxious participants, however, there were no group differences in physiological responses. After controlling for trait disgust, obsessive beliefs positively correlated with increased self-reported disgust to neutral images and increased levator labii activity to negative non-disgusting images. CONCLUSIONS: Although individuals with OCD showed elevated disgust propensity and self-reported ratings of body waste stimuli, there was little evidence that OCD is characterized by an abnormal physiological disgust response. The intensity of obsessive beliefs was associated with a tendency to respond with disgust in contexts that are non-disgusting, indicating that obsessive beliefs may be implicated in pathological disgust. PRACTITIONER POINTS: Individuals with OCD display greater levels of disgust propensity and self-reported disgust to images of body waste compared to healthy control participants and individuals with non-OCD anxiety disorders. The abnormalities in self-reported disgust observed in those with OCD do not extend to abnormalities in electrodermal activity or facial electromyographic responses. Maladaptive obsessive beliefs commonly associated with OCD predict heightened disgust in contexts where objective sources of disgust are absent, even after controlling for trait disgust. Maladaptive obsessive beliefs may therefore be implicated in pathological disgust. This study used a heterogeneous OCD sample and future research is needed to determine whether the observed effects are greater for those with primarily washing and contamination symptoms. Although group differences emerged in self-reported disgust, further replications using measures of state anxiety are needed to rule out the possibility that heightened self-reported disgust was confounded with co-occurring fear or general negative affect.

Effects of mental health self-efficacy on outcomes of a mobile phone and web intervention for mild-to-moderate depression, anxiety and stress: secondary analysis of a randomised controlled trial.

BACKGROUND: Online psychotherapy is clinically effective yet why, how, and for whom the effects are greatest remain largely unknown. In the present study, we examined whether mental health self-efficacy (MHSE), a construct derived from Bandura's Social Learning Theory (SLT), influenced symptom and functional outcomes of a new mobile phone and web-based psychotherapy intervention for people with mild-to-moderate depression, anxiety and stress. METHODS: STUDY I: Data from 49 people with symptoms of depression, anxiety and/or stress in the mild-to-moderate range were used to examine the reliability and construct validity of a new measure of MHSE, the Mental Health Self-efficacy Scale (MHSES). STUDY II: We conducted a secondary analysis of data from a recently completed randomised controlled trial (N = 720) to evaluate whether MHSE effected post-intervention outcomes, as measured by the Depression, Anxiety and Stress Scales (DASS) and Work and Social Adjustment Scale (WSAS), for people with symptoms in the mild-to-moderate range. RESULTS: STUDY I: The data established that the MHSES comprised a unitary factor, with acceptable internal reliability (Cronbach's alpha = .89) and construct validity. STUDY II: The intervention group showed significantly greater improvement in MHSE at post-intervention relative to the control conditions (p's < = .000). MHSE mediated the effects of the intervention on anxiety and stress symptoms. Furthermore, people with low pre-treatment MHSE reported the greatest post-intervention gains in depression, anxiety and overall distress. No effects were found for MHSE on work and social functioning. CONCLUSION: Mental health self-efficacy influences symptom outcomes of a self-guided mobile phone and web-based psychotherapeutic intervention and may itself be a worthwhile target to increase the effectiveness and efficiency of online treatment programs. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12610000625077.

Using Computational Phenotyping to Identify Divergent Strategies for Effort Allocation Across the Psychosis Spectrum.

BACKGROUND AND HYPOTHESIS: Disturbances in effort-cost decision-making have been highlighted as a potential transdiagnostic process underpinning negative symptoms in individuals with schizophrenia. However, recent studies using computational phenotyping show that individuals employ a range of strategies to allocate effort, and use of different strategies is associated with unique clinical and cognitive characteristics. Building on prior work in schizophrenia, this study evaluated whether effort allocation strategies differed in individuals with distinct psychotic disorders. STUDY DESIGN: We applied computational modeling to effort-cost decision-making data obtained from individuals with psychotic disorders (n = 190) who performed the Effort Expenditure for Rewards Task. The sample included 91 individuals with schizophrenia/schizoaffective disorder, 90 individuals with psychotic bipolar disorder, and 52 controls. STUDY RESULTS: Different effort allocation strategies were observed both across and within different disorders. Relative to individuals with psychotic bipolar disorder, a greater proportion of individuals with schizophrenia/schizoaffective disorder did not use reward value or probability information to guide effort allocation. Furthermore, across disorders, different effort allocation strategies were associated with specific clinical and cognitive features. Those who did not use reward value or probability information to guide effort allocation had more severe positive and negative symptoms, and poorer cognitive and community functioning. In contrast, those who only used reward value information showed a trend toward more severe positive symptoms. CONCLUSIONS: These findings indicate that similar deficits in effort-cost decision-making may arise from different computational mechanisms across the psychosis spectrum.

Profiles of motivational impairment and their relationship to functional decline in frontotemporal dementia.

Motivational disturbances are pervasive in frontotemporal dementia (FTD) and impact negatively on everyday functioning. Despite mounting evidence of anhedonia in FTD, it remains unclear how such changes fit within the broader motivational symptom profile of FTD, or how anhedonia relates to functional outcomes. Here we sought to comprehensively characterize motivational disturbances in FTD and their respective relationships with functional impairment. A cross-sectional study design was used including 211 participants-68 behavioral-variant FTD (bvFTD), 32 semantic dementia (SD), 43 Alzheimer's disease (AD), and 68 healthy older control participants. Anhedonia severity was measured using the Snaith-Hamilton Pleasure Scale while severity of apathy was assessed across Emotional, Executive, and Initiation dimensions using the Dimensional Apathy Scale. Functional impairment was established using the FTD Functional Rating Scale (FRS). Distinct motivational profiles emerged in each dementia syndrome: a domain-general motivational impairment in bvFTD; a predominantly anhedonic profile in SD; and more pronounced initiation and executive apathy in AD. Correlation analyses revealed differential associations between motivational symptoms and severity of functional impairment in each group. Executive apathy was associated with functional impairment in bvFTD, while anhedonia was strongly correlated with functional decline in SD. Finally, executive and emotional apathy were associated with functional decline in AD. Our study indicates distinct profiles of apathy and anhedonia in FTD syndromes, which in turn are differentially associated with functional decline. This detailed characterization of motivational phenotypes can inform patient stratification for targeted interventions to improve functional outcomes.

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.

Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12-25 years): A scoping review.

Digital phenotyping is a promising method for advancing scalable detection and prediction methods in mental health research and practice. However, little is known about how digital phenotyping data are used to make inferences about youth mental health. We conducted a scoping review of 35 studies to better understand how passive sensing (e.g., Global Positioning System, microphone etc) and electronic usage data (e.g., social media use, device activity etc) collected via smartphones are used in detecting and predicting depression and/or anxiety in young people between 12 and 25 years-of-age. GPS and/or Wifi association logs and accelerometers were the most used sensors, although a wide variety of low-level features were extracted and computed (e.g., transition frequency, time spent in specific locations, uniformity of movement). Mobility and sociability patterns were explored in more studies compared to other behaviours such as sleep, phone use, and circadian movement. Studies used machine learning, statistical regression, and correlation analyses to examine relationships between variables. Results were mixed, but machine learning indicated that models using feature combinations (e.g., mobility, sociability, and sleep features) were better able to predict and detect symptoms of youth anxiety and/or depression when compared to models using single features (e.g., transition frequency). There was inconsistent reporting of age, gender, attrition, and phone characteristics (e.g., operating system, models), and all studies were assessed to have moderate to high risk of bias. To increase translation potential for clinical practice, we recommend the development of a standardised reporting framework to improve transparency and replicability of methodology.

Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping.

Introduction: University students are particularly susceptible to developing high levels of stress, which occur when environmental demands outweigh an individual's ability to cope. The growing advent of mental health smartphone apps has led to a surge in use by university students seeking ways to help them cope with stress. Use of these apps has afforded researchers the unique ability to collect extensive amounts of passive sensing data including GPS and step detection. Despite this, little is known about the relationship between passive sensing data and stress. Further, there are no established methodologies or tools to predict stress from passive sensing data in this group. Methods: In this study, we establish a clear machine learning-based methodological pipeline for processing passive sensing data and extracting features that may be relevant in the context of mental health. Results: We then use this methodology to determine the relationship between passive sensing data and stress in university students. Discussion: In doing so, we offer the first proof-of-principle data for the utility of our methodological pipeline and highlight that passive sensing data can indeed digitally phenotype stress in university students. Clinical trial registration: Australia New Zealand Clinical Trials Registry (ANZCTR), identifier ACTRN12621001223820.