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Apathy is a debilitating but poorly understood disorder characterized by a reduction in motivation. As well as being associated with several brain disorders, apathy is also prevalent in varying degrees in healthy people. Whilst many tools have been developed to assess levels of apathy in clinical disorders, surprisingly there are no measures of apathy suitable for healthy people. Moreover, although apathy is commonly comorbid with symptoms of depression, anhedonia and fatigue, how and why these symptoms are associated is unclear. Here we developed the Apathy-Motivation Index (AMI), a brief self-report index of apathy and motivation. Using exploratory factor analysis (in a sample of 505 people), and then confirmatory analysis (in a different set of 479 individuals), we identified subtypes of apathy in behavioural, social and emotional domains. Latent profile analyses showed four different profiles of apathy that were associated with varying levels of depression, anhedonia and fatigue. The AMI is a novel and reliable measure of individual differences in apathy and might provide a useful means of probing different mechanisms underlying sub-clinical lack of motivation in otherwise healthy individuals. Moreover, associations between apathy and comorbid states may be reflective of problems in different emotional, social and behavioural domains.

Anhedonia, a transdiagnostic feature common to both Major Depressive Disorder (MDD) and Schizophrenia (SCZ), is characterized by abnormalities in hedonic experience. Previous studies have used machine learning (ML) algorithms without focusing on disorder-specific characteristics to independently classify SCZ and MDD. This study aimed to classify MDD and SCZ using ML models that integrate components of hedonic processing. We recruited 99 patients with MDD, 100 patients with SCZ, and 113 healthy controls (HC) from four sites. The patient groups were allocated to distinct training and testing datasets. All participants completed a modified Monetary Incentive Delay (MID) task, which yielded features categorized into five hedonic components, two reward consequences, and three reward magnitudes. We employed a stacking ensemble model with SHapley Additive exPlanations (SHAP) values to identify key features distinguishing MDD, SCZ, and HC across binary and multi-class classifications. The stacking model demonstrated high classification accuracy, with Area Under the Curve (AUC) values of 96.08% (MDD versus HC) and 91.77% (SCZ versus HC) in the main dataset. However, the MDD versus SCZ classification had an AUC of 57.75%. The motivation reward component, loss reward consequence, and high reward magnitude were the most influential features within respective categories for distinguishing both MDD and SCZ from HC (  < 0.001). A refined model using only the top eight features maintained robust performance, achieving AUCs of 96.06% (MDD versus HC) and 95.18% (SCZ versus HC). The stacking model effectively classified SCZ and MDD from HC, contributing to understanding transdiagnostic mechanisms of anhedonia.

Abstract Negative symptoms are characteristic of schizophrenia and closely linked to numerous outcomes. A body of work has sought to identify homogenous negative symptom subgroups—a strategy that can promote mechanistic understanding and precision medicine. However, our knowledge of negative symptom subgroups among individuals at clinical high-risk (CHR) for psychosis is limited. Here, we investigated distinct negative symptom profiles in a large CHR sample (N = 244) using a cluster analysis approach. Subgroups were compared on external validators that are (1) commonly observed in the schizophrenia literature and/or (2) may be particularly relevant for CHR individuals, informing early prevention and prediction. We observed 4 distinct negative symptom subgroups, including individuals with (1) lower symptom severity, (2) deficits in emotion, (3) impairments in volition, and (4) global elevations. Analyses of external validators suggested a pattern in which individuals with global impairments and volitional deficits exhibited more clinical pathology. Furthermore, the Volition group endorsed more disorganized, anxious, and depressive symptoms and impairments in functioning compared to the Emotion group. These data suggest there are unique negative symptom profiles in CHR individuals, converging with studies in schizophrenia indicating motivational deficits may be central to this symptom dimension. Furthermore, observed differences in CHR relevant external validators may help to inform early identification and treatment efforts.

Multiple studies have found a reduced reward positivity (RewP) among individuals with major depressive disorder (MDD). Event-related potential studies have also reported blunted neural responses to pleasant pictures in MDD as reflected by the late positive potential (LPP). These deficits have been interpreted broadly in terms of anhedonia and decreased emotional engagement characteristic of depression. In the current study, a community-based sample of 83 participants with current MDD and 45 healthy individuals performed both a guessing task and a picture viewing paradigm with neutral and pleasant pictures to assess the RewP and the LPP, respectively. We found that both RewP and LPP to pleasant pictures were reduced in the MDD group; moreover, RewP and LPP were both independent predictors of MDD status. Within the MDD group, a smaller RewP predicted impaired mood reactivity in younger but not older participants. Smaller LPP amplitudes were associated with increased anhedonia severity in the MDD group. These data replicate and merge separate previous lines of research, and suggest that a blunted RewP and LPP reflect independent neural deficits in MDD - which could be used in conjunction to improve the classification of depression.

Abstract Objective Negative symptoms are currently viewed as having a 2-dimensional structure, with factors reflecting diminished expression (EXP) and motivation and pleasure (MAP). However, several factor-analytic studies suggest that the consensus around a 2-dimensional model is premature. The current study investigated and cross-culturally validated the factorial structure of BNSS-rated negative symptoms across a range of cultures and languages. Method Participants included individuals diagnosed with a psychotic disorder who had been rated on the Brief Negative Symptom Scale (BNSS) from 5 cross-cultural samples, with a total N = 1691. First, exploratory factor analysis was used to extract up to 6 factors from the data. Next, confirmatory factor analysis evaluated the fit of 5 models: (1) a 1-factor model, 2) a 2-factor model with factors of MAP and EXP, 3) a 3-factor model with inner world, external, and alogia factors; 4) a 5-factor model with separate factors for blunted affect, alogia, anhedonia, avolition, and asociality, and 5) a hierarchical model with 2 second-order factors reflecting EXP and MAP, as well as 5 first-order factors reflecting the 5 aforementioned domains. Results Models with 4 factors or less were mediocre fits to the data. The 5-factor, 6-factor, and the hierarchical second-order 5-factor models provided excellent fit with an edge to the 5-factor model. The 5-factor structure demonstrated invariance across study samples. Conclusions Findings support the validity of the 5-factor structure of BNSS-rated negative symptoms across diverse cultures and languages. These findings have important implications for the diagnosis, assessment, and treatment of negative symptoms.

Aberrant brain structural connectivity in major depressive disorder (MDD) has been repeatedly reported, yet many previous studies lack integration of different features of MDD with structural connectivity in multivariate modeling approaches. In n = 595 MDD patients, we used structural equation modeling (SEM) to test the intercorrelations between anhedonia, anxiety, neuroticism, and cognitive control in one comprehensive model. We then separately analyzed diffusion tensor imaging (DTI) connectivity measures in association with those clinical variables, and finally integrated brain connectivity associations, clinical/cognitive variables into a multivariate SEM. We first confirmed our clinical/cognitive SEM. DTI analyses (FWE‐corrected) showed a positive correlation of anhedonia with fractional anisotropy (FA) in the right anterior thalamic radiation (ATR) and forceps minor/corpus callosum, while neuroticism was negatively correlated with axial diffusivity (AD) in the left uncinate fasciculus (UF) and inferior fronto‐occipital fasciculus (IFOF). An extended SEM confirmed the associations of ATR FA with anhedonia and UF/IFOF AD with neuroticism impacting on cognitive control. Our findings provide evidence for a differential impact of state and trait variables of MDD on brain connectivity and cognition. The multivariate approach shows feasibility of explaining heterogeneity within MDD and tracks this to specific brain circuits, thus adding to better understanding of heterogeneity on the biological level. In this article, we analyzed a large cohort of n = 595 major depressive disorder (MDD) patients using structural equation modeling of brain connectivity, clinical, and cognitive parameters to identify the relation of anhedonia, neuroticism, and state anxiety as well as cognitive control. Results show a brain structural overlap of anhedonia and cognitive control as well as of neuroticism and cognitive control, contributing to disconnection in MDD.

Main Problem Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia. Methods A predictive model used connectome‐based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre‐treatment functional connectivity (FC) data from 59 patients with MDD. Node‐based FC analysis was employed to compare differences in FC patterns between melancholic and non‐melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients. Results CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non‐melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non‐melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method. Conclusions This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non‐melancholic MDD patients. These findings provide guidance for clinical treatment. Our study aimed to identify a predictive model for anhedonia symptoms using connectome‐based predictive modeling (CPM) in major depressive disorder (MDD). The abnormal functional connectivity patterns between the default mode network and the limbic network in the predictive networks serve as potential neurobiological markers to distinguish between melancholic and non‐melancholic MDD. These findings revealed distinct neural substrates for anhedonia symptoms in melancholic and non‐melancholic MDD.

The relationship between developmental exposure to adversity and affective disorders is reviewed. Adversity discussed herein includes physical and sexual abuse, neglect, or loss of a caregiver in humans. While these stressors can occur at any point during development, the unique temporal relationship to specific depressive symptoms was the focus of discussion. Further influences of stress exposure during sensitive periods can vary by gender and duration of abuse as well. Data from animal studies are presented to provide greater translational and causal understanding of how sensitive periods, different types of psychosocial stressors, and sex interact to produce depressive-like behaviors. Findings from maternal separation, isolation rearing, chronic variable stress, and peer–peer rearing paradigms clarify interpretation about how various depressive behaviors are influenced by age of exposure. Depressive behaviors are broken down into the following categories: mood and affect, anhedonia, energy, working memory, sleep–wake, appetite changes, suicide, and general malaise. Cross-species evidence from humans, nonhuman primates, rats, and mice within each of these categories is discussed. In conclusion, sensitive periods for affective-related behaviors (anxiety, mood, and controllability) occur earlier in life, while other aspects of depression are associated with adversity later during adolescence.

Background Major Depressive Disorder (MDD) involves complex disturbances in hedonic processing, which contribute to anhedonia—a core symptom of the disorder. Although anhedonia is well recognized, the relative contributions of distinct hedonic components remain poorly understood. Advances in machine learning (ML) provide powerful tools to model high-dimensional data and may clarify the most critical components for differentiating MDD from healthy individuals. Methods Sixty-six MDD patients and 249 healthy controls completed the Monetary Incentive Delay task. A voting classifier was trained on the entire dataset to develop an integrated MDD classification model. To evaluate component-level importance, the classifier was applied to feature subsets reflecting different combinations of hedonic components. Individual-level importance was further examined using importance coefficients, stability selection, and statistical tests. Results The integrated model demonstrated strong diagnostic performance, achieving an area under the curve (AUC) of 0.806, sensitivity of 78.5%, and specificity of 82.6%. Across all optimal models that achieved the highest AUC (0.806), sensitivity (81.1%), or specificity (90.1%), anticipatory pleasure consistently emerged as a key predictive feature component. Meanwhile, five of the six top-ranked features were also derived from anticipatory pleasure. Conclusions These findings underscore the importance of integrating multi-dimensional hedonic processing to classify MDD from healthy individuals and emphasize anticipatory pleasure as a core deficit within hedonic processing of MDD.

In DSM-5, all symptoms of depression are considered equal representations of severity. In ICD-10, the type of symptom is considered in classifying severity. It is important to better understand if the defining symptoms of depression are differentially associated with overall severity so that severity categorization in diagnostic systems is most valid. In the present study from the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project we examined the association between the individual diagnostic criteria for major depressive disorder (MDD) and global ratings of depression severity. We thus examined whether there is support for the ICD-10 approach in which some symptoms are better indicators of severity than are other symptoms. Patients were evaluated with a semi-structured interview and the presence of each symptom of MDD was recorded. Patients were also rated on the Clinical Global Index of severity (CGI-S). All 9 DSM-5 criteria were significantly correlated with the CGI with suicidality having the highest correlation. A regression analysis found that all 9 criteria were significant predictors of the CGI. At the symptom level, 15 of the 17 symptoms were significantly correlated with the CGI (all except increased appetite and increased weight). There were differences between the symptoms of depression in their association with severity with suicidal ideation, depressed mood, and anhedonia having the highest correlations with severity whereas some symptoms were not significantly associated with severity distinctions. Future descriptions of the severity of depression should not consider all criteria as equal representations of severity. •All 9 DSM-5 criteria were significantly correlated with global ratings of depression severity.•However, all correlations were below 0.20 except for the suicidal ideation criterion.•Future descriptions of the severity of depression should include suicidality as a component of the definition.