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.

Urged to feel certain again: The role of emotion-related impulsivity on the relationships between intolerance of uncertainty and OCD symptom severity.

OBJECTIVES: Obsessive-compulsive disorder (OCD) is a debilitating mental disorder characterized by persistent and intrusive thoughts accompanied by repetitive mental or physical acts. While both intolerance of uncertainty and emotion-related impulsivity have been consistently evidenced as cognitive risk factors of OCD, no studies have considered their joint effects. The current study examined the interaction between intolerance of uncertainty and two forms of emotion-related impulsivity-including both a behavioural and cognitive form-in predicting OCD symptoms. DESIGN: Cross-sectional data were collected online from community-based adult participants. METHODS: Participants (N = 673) completed a battery of self-report measures of OCD symptom severity, intolerance of uncertainty, and emotion-related impulsivity. RESULTS: The behavioural form of emotion-related impulsivity positively moderated the relationship between intolerance of uncertainty and OCD symptoms. Elevated levels of both factors predicted the most severe symptoms, particularly checking, washing, and obsessing. This interaction effect was not found for the cognitive form of emotion-related impulsivity, which still emerged as a unique predictor of OCD symptom severity, specifically obsessing symptoms. CONCLUSIONS: Current findings furthered the understanding of the link between intolerance of uncertainty and OCD symptoms by highlighting the role of emotion-related impulsivity. When uncertainty triggers distress in individuals with high intolerance of uncertainty, the urge to behaviourally alleviate this distress could promote the use of maladaptive obsessions and compulsions, leading to greater OCD symptoms. Results also indicated the potentially differential effects from the behavioural versus cognitive forms of emotion-related impulsivity on different symptom domains, and the mechanistic link here is worthy of further investigation.

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.

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

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

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

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

Identity in bipolar disorder: Self-worth and achievement.

This article considers self and self-concept in bipolar disorder. Bipolar disorder, defined on the basis of manic symptoms, is a highly debilitating psychopathology. It is heavily grounded in biology but symptom course is still very responsive to psychological and social forces in the lives of persons who have the disorder. This review assumes an overall view of the self that is typical of personality psychology: self as traits, self as goals and aspirations, and ongoing efforts to attain those goals. In this review, we will discuss two different facets of self and identity in bipolar disorder. First, we review a body of goal pursuit literature suggesting that persons with bipolar disorder endorse heightened ambitions for attaining goals and recognition from others. Second, we will review multiple findings which suggest that among persons with bipolar disorder, self-worth depends on measurable success in an extreme way. We will consider how the intersection of these two themes may lead to unique identity challenges for people with bipolar disorder, drawing from self-report, behavioral, and neuroscience findings to critically examine this viewpoint.

Evidence of a diurnal rhythm in implicit reward learning.

Many aspects of hedonic behavior, including self-administration of natural and drug rewards, as well as human positive affect, follow a diurnal cycle that peaks during the species-specific active period. This variation has been linked to circadian modulation of the mesolimbic dopamine system, and is hypothesized to serve an adaptive function by driving an organism to engage with the environment during times where the opportunity for obtaining rewards is high. However, relatively little is known about whether more complex facets of hedonic behavior - in particular, reward learning - follow the same diurnal cycle. The current study aimed to address this gap by examining evidence for diurnal variation in reward learning on a well-validated probabilistic reward learning task (PRT). PRT data from a large normative sample (N = 516) of non-clinical individuals, recruited across eight studies, were examined for the current study. The PRT uses an asymmetrical reinforcement ratio to induce a behavioral response bias, and reward learning was operationalized as the strength of this response bias across blocks of the task. Results revealed significant diurnal variation in reward learning, however in contrast to patterns previously observed in other aspects of hedonic behavior, reward learning was lowest in the middle of the day. Although a diurnal pattern was also observed on a measure of more general task performance (discriminability), this did not account for the variation observed in reward learning. Taken together, these findings point to a distinct diurnal pattern in reward learning that differs from that observed in other aspects of hedonic behavior. The results of this study have important implications for our understanding of clinical disorders characterized by both circadian and reward learning disturbances, and future research is needed to confirm whether this diurnal variation has a truly circadian origin.

Electroencephalography Source Functional Connectivity Reveals Abnormal High-Frequency Communication Among Large-Scale Functional Networks in Depression.

BACKGROUND: Functional magnetic resonance imaging studies of resting-state functional connectivity have shown that major depressive disorder (MDD) is characterized by increased connectivity within the default mode network (DMN) and between the DMN and the frontoparietal network (FPN). However, much remains unknown about abnormalities in higher frequency (>1 Hz) synchronization. Findings of abnormal synchronization in specific frequencies would contribute to a better understanding of the potential neurophysiological origins of disrupted functional connectivity in MDD. METHODS: We used the high temporal resolution of electroencephalography to compare the spectral properties of resting-state functional connectivity in individuals with MDD (n = 65) with healthy control subjects (n = 79) and examined the extent to which connectivity disturbances were evident in a third sample of individuals in remission from depression (n = 30). Exact low resolution electromagnetic tomography was used to compute intracortical activity from regions within the DMN and FPN, and functional connectivity was computed using lagged phase synchronization. RESULTS: Compared to control subjects, the MDD group showed greater within-DMN beta 2 band (18.5-21 Hz) connectivity and greater beta 1 band (12.5-18 Hz) connectivity between the DMN and FPN. This hyperconnectivity was not observed in the remitted MDD group. However, greater beta 1 band DMN-FPN connectivity was associated with more frequent depressive episodes since first depression onset, even after controlling for current symptom severity. CONCLUSIONS: These findings extend our understanding of the neurophysiological basis of abnormal resting-state functional connectivity in MDD and indicate that elevations in high-frequency DMN-FPN connectivity may be a neural marker linked to a more recurrent illness course.

Acute stress impairs frontocingulate activation during error monitoring in remitted depression.

Deficits in cognitive control are a hallmark characteristic of depression, however less is known about the degree to which they persist beyond symptom remission and might contribute to symptom recurrence in remitted individuals (rMDD). Evidence indicates that stress interferes with cognitive control, highlighting a potential mechanism by which stress precipitates depression relapse. Therefore, this study examined whether stress exposure elicits deficits in error monitoring - a component of cognitive control thought to be particularly implicated in the ability to adaptively respond to negative feedback - in individuals with rMDD. Unmedicated individuals with rMDD (n=30) and healthy controls (n=34) performed an Eriksen Flanker task before and 45min after an acute stressor while 128-channel event-related potentials (ERPs) were recorded. Flanker interference effects and post-error adjustments were examined, and ERP analyses focused on the error-related negativity (ERN) and error positivity (Pe). Standardized low resolution electromagnetic tomography (sLORETA) was used to examine stress-induced changes in current source density. Individuals with rMDD showed blunted cortisol reactivity to the stressor, coupled with heightened self-reported stress reactivity. Although no significant effects of group or stress were observed in scalp-level ERPs, source-level analyses indicated that among the rMDD group only, stress caused a reduction in activation in frontocingulate regions critically implicated in error monitoring. The magnitude of stress-induced decreases in frontocingulate activation correlated with heightened self-reported stress reactivity, and also predicted heightened levels of stress and depression 18 months later in the entire sample. These findings suggest that individuals with rMDD show a stress-induced disruption in frontocingulate function that is linked to heightened stress reactivity, and this disruption prospectively predicts heightened levels of future stress and depressive symptomatology.