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.

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 and smoking progression are poorly understood. Thus, we investigated whether brain resting-state functional connectivity (rsFC) during smoking abstinence predicts nicotine-enhanced reward sensitivity 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 nicotine-enhanced reward sensitivity and smoking progression. METHODS: Young light smokers (N=64, age 18-24, M = 1.89 cigarettes/day) participated in the study. These individuals smoked between 5 to 35 cigarettes per week and lifetime use never exceeded 35 cigarettes per week. Their rsFC was assessed using functional magnetic resonance imaging after 14-hour nicotine-deprivation. Subjects also completed a probabilistic reward task after smoking a placebo on one day and a regular cigarette on another day. RESULTS: The probabilistic-reward-task assessed greater nicotine-enhanced reward sensitivity 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 nicotine-enhanced reward sensitivity. 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. IMPLICATIONS: Weaker rsFC within the salience network predicted greater nicotine-enhanced reward sensitivity 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.

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.

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.

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.

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.

Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app.

Background: Advances in smartphone technology have allowed people to access mental healthcare via digital apps from wherever and whenever they choose. University students experience a high burden of mental health concerns. Although these apps improve mental health symptoms, user engagement has remained low. Studies have shown that users can be subgrouped based on unique characteristics that just-in-time adaptive interventions (JITAIs) can use to improve engagement. To date, however, no studies have examined the effect of the COVID-19 pandemic on these subgroups. Objective: Here, we sought to examine user subgroup characteristics across three COVID-19-specific timepoints: during lockdown, immediately following lockdown, and three months after lockdown ended. Methods: To do this, we used a two-step machine learning approach combining unsupervised and supervised machine learning. Results: We demonstrate that there are three unique subgroups of university students who access mental health apps. Two of these, with either higher or lower mental well-being, were defined by characteristics that were stable across COVID-19 timepoints. The third, situational well-being, had characteristics that were timepoint-dependent, suggesting that they are highly influenced by traumatic stressors and stressful situations. This subgroup also showed feelings and behaviours consistent with burnout. Conclusions: Overall, our findings clearly suggest that user subgroups are unique: they have different characteristics and therefore likely have different mental healthcare goals. Our findings also highlight the importance of including questions and additional interventions targeting traumatic stress(ors), reason(s) for use, and burnout in JITAI-style mental health apps to improve engagement.

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.

Using latent profile analyses to classify subjects with anhedonia based on reward-related measures obtained in the FAST-MAS study.

BACKGROUND: Growing evidence indicates that anhedonia is a multifaceted construct. This study examined the possibility of identifying subgroups of people with anhedonia using multiple reward-related measures to provide greater understanding the Research Domain Criteria's Positive Valence Systems Domain and pathways for developing treatments. METHODS: Latent profile analysis of baseline data from a study that examined the effects of a novel kappa opioid receptor (KOR) antagonist drug on measures and biomarkers associated with anhedonia was used to identify subgroups. Measures included ventral striatal activation during the Monetary Incentive Delay task, response bias in the Probabilistic Reward Task, reward valuation scores from the Effort-Expenditure for Rewards Task, and scores from reward-related self-report measures. RESULTS: Two subgroups were identified, which differed on self-report measures of reward. Participants in the subgroup reporting more anhedonia also reported more depression and had greater illness severity and functional impairments. Graphs of change with treatment showed a trend for the less severe subgroup to demonstrate higher response to KOR antagonist treatment on the neuroimaging measure, probabilistic reward task, and ratings of functioning; the subgroup with greater severity showed a trend for higher treatment response on reward-related self-report measures. LIMITATIONS: The main limitations include the small sample size and exploratory nature of analyses. CONCLUSIONS: Evidence of possible dissociation between self-reported measures of anhedonia and other measures with respect to treatment response emerged. These results highlight the importance for future research to consider severity of self-reported reward-related deficits and how the relationship across measurement methods may vary with severity.

Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up study.

INTRODUCTION: Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. METHODS AND ANALYSIS: The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. ETHICS AND DISSEMINATION: Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). TRIAL REGISTRATION NUMBER: ACTRN12621001223820.

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.

Stress and reward: A multimodal assessment of childhood sexual abuse.

Background: Childhood adversity has been found to impact stress and brain reward systems but it is unclear whether interactions between these systems might explain resilient vs. non-resilient trajectories following childhood sexual abuse (CSA). To address this gap, we adopted a multimodal approach in which cortisol reactivity to an acute stressor was assessed in conjunction with behavioral and neural measures of reward responsiveness in females with major depressive disorder (MDD) or no psychiatric disorders (i.e., resilient) who experienced CSA compared to females with and without MDD who did not experience abuse. Methods: Latent Class Mixed Modelling (LCMM) identified classes of adults (n = 62; MAge = 26.48, SD = 5.68) characterized by distinct cortisol trajectories in response to a combined social evaluative cold pressor task. Classes were examined for their history of CSA and resilience as well as behavioral and neural measures of reward responsiveness using 128-channel electroencephalography (event-related potentials and source localization analysis). Results: LCMM analysis identified two distinct classes of individuals with increased (Responders) or blunted (Non-Responders) cortisol reactivity to an acute stressor. Unlike Responders, Non-Responders did not modulate reward responses throughout the stress manipulation. No differences emerged between Responders and Non-Responders in terms of CSA or resilience. However, exploratory results showed that blunted cortisol response and non-modulation of reward responses emerged for those who experienced CSA at a younger age. Conclusions: Co-occurring blunted stress and reward reactivity emerged irrespective of adults' experience of CSA or resilience. However, preliminary findings showed that CSA ending during peripubertal development was associated with blunted cortisol and reward responsiveness. Future research needs to replicate findings in larger samples and could investigate if increasing reward responsiveness during critical times of neurodevelopment could normalize stress reactivity to future stressors and thus promote resilience.

Anhedonia in Depression and Bipolar Disorder.

Anhedonia is a hallmark feature of depression and is highly prevalent among individuals with mood disorders. The history and neurobiology of anhedonia has been most extensively studied in the context of unipolar Major Depressive Disorder (MDD), with converging lines of evidence indicating that marked anhedonia heralds a more chronic and treatment-refractory illness course. Furthermore, findings from neuroimaging studies suggest that anhedonia in MDD is associated with aberrant reward-related activation in key brain reward regions, particularly blunted reward anticipation-related activation in the ventral striatum. However, the ongoing clinical challenge of treating anhedonia in the context of Bipolar Disorder (BD) also highlights important gaps in our understanding of anhedonia's prevalence, severity, and pathophysiology along the entire mood disorder spectrum. In addition, although current theoretical models posit a key role for reward hyposensitivity in BD depression, unlike studies in MDD, studies in BD do not clearly show evidence for reduced reward-related activation in striatal or other brain regions. Although further research is needed, the evidence to date hints at a divergent pathophysiology for anhedonia in unipolar and bipolar mood disorders, which, if better understood, could lead to significant improvements in the diagnosis and treatment of MDD and BD.

Smoking as a Common Modulator of Sensory Gating and Reward Learning in Individuals with Psychotic Disorders.

Motivational and perceptual disturbances co-occur in psychosis and have been linked to aberrations in reward learning and sensory gating, respectively. Although traditionally studied independently, when viewed through a predictive coding framework, these processes can both be linked to dysfunction in striatal dopaminergic prediction error signaling. This study examined whether reward learning and sensory gating are correlated in individuals with psychotic disorders, and whether nicotine-a psychostimulant that amplifies phasic striatal dopamine firing-is a common modulator of these two processes. We recruited 183 patients with psychotic disorders (79 schizophrenia, 104 psychotic bipolar disorder) and 129 controls and assessed reward learning (behavioral probabilistic reward task), sensory gating (P50 event-related potential), and smoking history. Reward learning and sensory gating were correlated across the sample. Smoking influenced reward learning and sensory gating in both patient groups; however, the effects were in opposite directions. Specifically, smoking was associated with improved performance in individuals with schizophrenia but impaired performance in individuals with psychotic bipolar disorder. These findings suggest that reward learning and sensory gating are linked and modulated by smoking. However, disorder-specific associations with smoking suggest that nicotine may expose pathophysiological differences in the architecture and function of prediction error circuitry in these overlapping yet distinct psychotic disorders.

Peripheral immune cell reactivity and neural response to reward in patients with depression and anhedonia.

Increased levels of peripheral cytokines have been previously associated with depression in preclinical and clinical research. Although the precise nature of peripheral immune dysfunction in depression remains unclear, evidence from animal studies points towards a dysregulated response of peripheral leukocytes as a risk factor for stress susceptibility. This study examined dynamic release of inflammatory blood factors from peripheral blood mononuclear cells (PBMC) in depressed patients and associations with neural and behavioral measures of reward processing. Thirty unmedicated patients meeting criteria for unipolar depressive disorder and 21 healthy control volunteers were enrolled. PBMCs were isolated from whole blood and stimulated ex vivo with lipopolysaccharide (LPS). Olink multiplex assay was used to analyze a large panel of inflammatory proteins. Participants completed functional magnetic resonance imaging with an incentive flanker task to probe neural responses to reward anticipation, as well as clinical measures of anhedonia and pleasure including the Temporal Experience of Pleasure Scale (TEPS) and the Snaith-Hamilton Pleasure Scale (SHAPS). LPS stimulation revealed larger increases in immune factors in depressed compared to healthy subjects using an aggregate immune score (t49 = 2.83, p = 0.007). Higher peripheral immune score was associated with reduced neural responses to reward anticipation within the ventral striatum (VS) (r = -0.39, p = 0.01), and with reduced anticipation of pleasure as measured with the TEPS anticipatory sub-score (r = -0.318, p = 0.023). Our study provides new evidence suggesting that dynamic hyper-reactivity of peripheral leukocytes in depressed patients is associated with blunted activation of the brain reward system and lower subjective anticipation of pleasure.

Examining the Preliminary Effectiveness and Acceptability of a Web-Based Training Program for Australian Secondary School Teachers: Pilot Study of the BEAM (Building Educators' Skills in Adolescent Mental Health) Program.

BACKGROUND: Secondary schools are increasingly supporting adolescents' mental health and well-being, yet many teachers report that they lack the skills and confidence to do so. Building Educators' skills in Adolescent Mental Health (BEAM) is a web-based training program developed to improve secondary school teachers' knowledge and confidence in caring for students' mental health. OBJECTIVE: This pilot study examined the preliminary effectiveness and acceptability of the BEAM program for improving mental health knowledge, attitudes, confidence, helping behaviors, and psychological distress among secondary school teachers. METHODS: A single-arm pilot trial was conducted from July to December 2019 among secondary school teachers located in New South Wales, Australia, who were employed in leadership positions responsible for managing student well-being (ie, Year Advisors). Participants had access to the BEAM program for 6 weeks. Self-report surveys, delivered at baseline, postintervention (6-weeks post baseline) and 3-month follow-up (19 weeks post baseline) were used to measure changes in training outcomes. Acceptability was assessed by program use, barriers, satisfaction, and participants' perceptions of program effectiveness. RESULTS: A total of 70 secondary school teachers took part (mean age 36.5 years, SD 9.41 years, range 24-60 years). Significant improvements in confidence were reported at postintervention and 3-month follow-up. Significant improvements in helping behaviors were reported at 3-month follow-up only. There was also a significant reduction in psychological distress at postintervention. Participants agreed that the program content was easy to understand and relevant, but program completion was challenged by lack of time, competing priorities, and forgetfulness. CONCLUSIONS: Findings indicated that a web-based training program may be beneficial for improving secondary school teachers' abilities to care for students' mental health; however, program modifications are required to increase training completions. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619000821190, Universal Trial Number U1111-1232-7680; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377529.

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.

Differential reinforcement learning responses to positive and negative information in unmedicated individuals with depression.

Major depressive disorder (MDD) is characterized by behavioral and neural abnormalities in processing both rewarding and aversive stimuli, which may impact motivational and affective symptoms. Learning paradigms have been used to assess reinforcement encoding abnormalities in MDD and their association with dysfunctional incentive-based behavior, but how the valence and context of information modulate this learning is not well understood. To address these gaps, we examined responses to positive and negative reinforcement across multiple temporal phases of information processing. While undergoing functional magnetic resonance imaging (fMRI), 47 participants (23 unmedicated, predominantly medication-naïve participants with MDD and 24 demographically-matched HC participants) completed a probabilistic, feedback-based reinforcement learning task that allowed us to separate neural activation during motor response (choice) from reinforcement feedback and monetary outcome across two independent conditions: pursuing gains and avoiding losses. In the gain condition, MDD participants showed overall blunted learning responses (prediction error) in the dorsal striatum when receiving monetary outcome, and reduced responses in ventral striatum for positive, but not negative, prediction error. The MDD group showed enhanced sensitivity to negative information, and symptom severity was associated with better behavioral performance in the loss condition. These findings suggest that striatal responses during learning are abnormal in individuals with MDD but vary with the valence of information.

Anhedonia in Semantic Dementia-Exploring Right Hemispheric Contributions to the Loss of Pleasure.

Semantic dementia (SD) is a younger-onset neurodegenerative disease characterised by progressive deterioration of the semantic knowledge base in the context of predominantly left-lateralised anterior temporal lobe (ATL) atrophy. Mounting evidence indicates the emergence of florid socioemotional changes in SD as atrophy encroaches into right temporal regions. How lateralisation of temporal lobe pathology impacts the hedonic experience in SD remains largely unknown yet has important implications for understanding socioemotional and functional impairments in this syndrome. Here, we explored how lateralisation of temporal lobe atrophy impacts anhedonia severity on the Snaith-Hamilton Pleasure Scale in 28 SD patients presenting with variable right- (SD-R) and left-predominant (SD-L) profiles of temporal lobe atrophy compared to that of 30 participants with Alzheimer's disease and 30 healthy older Control participants. Relative to Controls, SD-R but not SD-L or Alzheimer's patients showed clinically significant anhedonia, representing a clear departure from premorbid levels. Overall, anhedonia was more strongly associated with functional impairment on the Frontotemporal Dementia Functional Rating Scale and motivational changes on the Cambridge Behavioural Inventory in SD than in Alzheimer's disease patients. Voxel-based morphometry analyses revealed that anhedonia severity correlated with reduced grey matter intensity in a restricted set of regions centred on right orbitofrontal and temporopolar cortices, bilateral posterior temporal cortices, as well as the anterior cingulate gyrus and parahippocampal gyrus, bilaterally. Finally, regression and mediation analysis indicated a unique role for right temporal lobe structures in modulating anhedonia in SD. Our findings suggest that degeneration of predominantly right-hemisphere structures deleteriously impacts the capacity to experience pleasure in SD. These findings offer important insights into hemispheric lateralisation of motivational disturbances in dementia and suggest that anhedonia may emerge at different timescales in the SD disease trajectory depending on the integrity of the right hemisphere.