Prefrontal, parietal, and limbic condition-dependent differences in bipolar disorder: a large-scale meta-analysis of functional neuroimaging studies.

BACKGROUND: Over the past few decades, neuroimaging research in Bipolar Disorder (BD) has identified neural differences underlying cognitive and emotional processing. However, substantial clinical and methodological heterogeneity present across neuroimaging experiments potentially hinders the identification of consistent neural biomarkers of BD. This meta-analysis aims to comprehensively reassess brain activation and connectivity in BD in order to identify replicable differences that converge across and within resting-state, cognitive, and emotional neuroimaging experiments. METHODS: Neuroimaging experiments (using fMRI, PET, or arterial spin labeling) reporting whole-brain results in adults with BD and controls published from December 1999-June 18, 2019 were identified via PubMed search. Coordinates showing significant activation and/or connectivity differences between BD participants and controls during resting-state, emotional, or cognitive tasks were extracted. Four parallel, independent meta-analyses were calculated using the revised activation likelihood estimation algorithm: all experiment types, all resting-state experiments, all cognitive experiments, and all emotional experiments. To confirm reliability of identified clusters, two different meta-analytic significance tests were employed. RESULTS: 205 published studies yielding 506 individual neuroimaging experiments (150 resting-state, 134 cognitive, 222 emotional) comprising 5745 BD and 8023 control participants were included. Five regions survived both significance tests. Individuals with BD showed functional differences in the right posterior cingulate cortex during resting-state experiments, the left amygdala during emotional experiments, including those using a mixed (positive/negative) valence manipulation, and the left superior and right inferior parietal lobules during cognitive experiments, while hyperactivating the left medial orbitofrontal cortex during cognitive experiments. Across all experiments, there was convergence in the right caudate extending to the ventral striatum, surviving only one significance test. CONCLUSIONS: Our findings indicate reproducible localization of prefrontal, parietal, and limbic differences distinguishing BD from control participants that are condition-dependent, despite heterogeneity, and point towards a framework for identifying reproducible differences in BD that may guide diagnosis and treatment.

A specific neural substrate predicting current and future impulsivity in young adults.

Impulsivity (rash action with deleterious outcomes) is common to many psychiatric disorders. While some studies indicate altered amygdala and prefrontal cortical (PFC) activity associated with impulsivity, it remains unclear whether these patterns of neural activity are specific to impulsivity or common to a range of affective and anxiety symptoms. To elucidate neural markers specific to impulsivity, we aimed to differentiate patterns of amygdala-PFC activity and functional connectivity associated with impulsivity from those associated with affective and anxiety symptoms, and identify measures of this circuitry predicting future worsening of impulsivity. Using a face emotion processing task that reliably activates amygdala-PFC circuitry, neural activity and connectivity were assessed in a transdiagnostically-recruited sample of young adults, including healthy (N = 47) and treatment-seeking individuals (N = 67). Relationships were examined between neural measures and impulsivity, anhedonia, and affective and anxiety symptoms at baseline (N = 114), and at 6 months post scan (N = 30). Impulsivity, particularly negative urgency and lack of perseverance, was related to greater amygdala activity (beta = 0.82, p = 0.003; beta = 0.68, p = 0.004; respectively) and lower amygdala-medial PFC functional connectivity (voxels = 60, tpeak = 4.45, pFWE = 0.017; voxels = 335, tpeak = 5.26, pFWE = 0.001; respectively) to facial fear. Left vlPFC, but not amygdala, activity to facial anger was inversely associated with mania/hypomania (beta = -2.08, p = 0.018). Impulsivity 6 months later was predicted by amygdala activity to facial sadness (beta = 0.50, p = 0.017). There were no other significant relationships between neural activity and 6-month anhedonia, affective, and anxiety symptoms. Our findings are the first to associate amygdala-PFC activity and functional connectivity with impulsivity in a large, transdiagnostic sample, providing neural targets for future interventions to reduce predisposition to impulsivity and related future mental health problems in young adults.

Reduced frontostriatal response to expected value and reward prediction error in remitted monozygotic twins with mood disorders and their unaffected high-risk co-twins.

BACKGROUND: Depressive episodes experienced in unipolar (UD) and bipolar (BD) disorders are characterized by anhedonia and have been associated with abnormalities in reward processes related to reward valuation and error prediction. It remains however unclear whether these deficits are associated with familial vulnerability to mood disorders. METHODS: In a functional magnetic resonance imaging study, we evaluated differences in the expected value (EV) and reward prediction error (RPE) signals in ventral striatum (VS) and prefrontal cortex between three groups of monozygotic twins: affected twins in remission for either UD or BD (n = 53), their high-risk unaffected co-twins (n = 34), and low-risk twins with no family history of mood disorders (n = 25). RESULTS: Compared to low-risk twins, affected twins showed lower EV signal bilaterally in the frontal poles and lower RPE signal bilaterally in the VS, left frontal pole and superior frontal gyrus. The high-risk group did not show a significant change in the EV or RPE signals in frontostriatal regions, yet both reward signals were consistently lower compared with low-risk twins in all regions where the affected twins showed significant reductions. CONCLUSION: Our findings strengthen the notion that reduced valuation of expected rewards and reduced error-dependent reward learning may underpin core symptom of depression such as loss of interest in rewarding activities. The trend reduction in reward-related signals in unaffected co-twins warrants further investigation of this effect in larger samples and prospective follow-up to confirm possible association with increased familial vulnerability to mood disorders.

Transcranial direct current stimulation: a roadmap for research, from mechanism of action to clinical implementation.

Transcranial direct current stimulation (tDCS) is a promising method for altering the function of neural systems, cognition, and behavior. Evidence is emerging that it can also influence psychiatric symptomatology, including major depression and schizophrenia. However, there are many open questions regarding how the method might have such an effect, and uncertainties surrounding its influence on neural activity, and human cognition and functioning. In the present critical review, we identify key priorities for future research into major depression and schizophrenia, including studies of the mechanism(s) of action of tDCS at the neuronal and systems levels, the establishment of the cognitive impact of tDCS, as well as investigations of the potential clinical efficacy of tDCS. We highlight areas of progress in each of these domains, including data that appear to favor an effect of tDCS on neural oscillations rather than spiking, and findings that tDCS administration to the prefrontal cortex during task training may be an effective way to enhance behavioral performance. Finally, we provide suggestions for further empirical study that will elucidate the impact of tDCS on brain and behavior, and may pave the way for efficacious clinical treatments for psychiatric disorders.

Reward related ventral striatal activity and differential response to sertraline versus placebo in depressed individuals.

Medications to treat major depressive disorder (MDD) are not equally effective across patients. Given that neural response to rewards is altered in MDD and given that reward-related circuitry is modulated by dopamine and serotonin, we examined, for the first time, whether reward-related neural activity moderated response to sertraline, an antidepressant medication that targets these neurotransmitters. A total of 222 unmedicated adults with MDD randomized to receive sertraline (n = 110) or placebo (n = 112) in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study completed demographic and clinical assessments, and pretreatment functional magnetic resonance imaging while performing a reward task. We tested whether an index of reward system function in the ventral striatum (VS), a key reward circuitry region, moderated differential response to sertraline versus placebo, assessed with the Hamilton Rating Scale for Depression (HSRD) over 8 weeks. We observed a significant moderation effect of the reward index, reflecting the temporal dynamics of VS activity, on week-8 depression levels (Fs ≥ 9.67, ps ≤ 0.002). Specifically, VS responses that were abnormal with respect to predictions from reinforcement learning theory were associated with lower week-8 depression symptoms in the sertraline versus placebo arms. Thus, a more abnormal pattern of pretreatment VS dynamic response to reward expectancy (expected outcome value) and prediction error (difference between expected and actual outcome), likely reflecting serotonergic and dopaminergic deficits, was associated with better response to sertraline than placebo. Pretreatment measures of reward-related VS activity may serve as objective neural markers to advance efforts to personalize interventions by guiding individual-level choice of antidepressant treatment.

Functional differentiation in the human ventromedial frontal lobe: A data-driven parcellation.

Ventromedial regions of the frontal lobe (vmFL) are thought to play a key role in decision-making and emotional regulation. However, aspects of this area's functional organization, including the presence of a multiple subregions, their functional and anatomical connectivity, and the cross-species homologies of these subregions with those of other species, remain poorly understood. To address this uncertainty, we employed a two-stage parcellation of the region to identify six distinct structures within the region on the basis of data-driven classification of functional connectivity patterns obtained using the meta-analytic connectivity modeling (MACM) approach. From anterior to posterior, the derived subregions included two lateralized posterior regions, an intermediate posterior region, a dorsal and ventral central region, and a single anterior region. The regions were characterized further by functional connectivity derived using resting-state fMRI and functional decoding using the Brain Map database. In general, the regions could be differentiated on the basis of different patterns of functional connectivity with canonical "default mode network" regions and/or subcortical regions such as the striatum. Together, the findings suggest the presence of functionally distinct neural structures within vmFL, consistent with data from experimental animals as well prior demonstrations of anatomical differences within the region. Detailed correspondence with the anterior cingulate, medial orbitofrontal cortex, and rostroventral prefrontal cortex, as well as specific animal homologs are discussed. The findings may suggest future directions for resolving potential functional and structural correspondence of subregions within the frontal lobe across behavioral contexts, and across mammalian species.

Abnormal prefrontal cortex processing of reward prediction errors in recently diagnosed patients with bipolar disorder and their unaffected relatives.

OBJECTIVE: Bipolar disorder (BD) has been associated with abnormal reward functioning including pleasure-seeking and impulsivity. Here we sought to clarify whether these changes can be attributed to abnormalities in the neural processing of reward valuation or error prediction. Moreover, we tested whether abnormalities in these processes are associated with familial vulnerability to BD. METHODS: We obtained functional magnetic resonance imaging data from patients with recently diagnosed BD (n = 85), their unaffected first-degree relatives (n = 44), and healthy control participants (n = 66) while they were performing a monetary card game. We used a region-of-interest approach to test for group differences in the activation of the midbrain, the ventral striatum, and the prefrontal cortex during reward valuation and error prediction. RESULTS: Patients with BD showed decreased prediction error signal in ventrolateral prefrontal cortex and the unaffected relatives showed decreased prediction error signal in the supplementary motor area in comparison to healthy controls. There were no significant group differences in the activation of the ventral striatum during the task. In healthy controls, prediction error signal in dorsal anterior cingulate cortex correlated with an out-of-scanner measure of motor inhibition but this association was absent in patients and relatives. CONCLUSIONS: The findings indicate that abnormal reward processing in BD is primarily related to deficits in the engagement of prefrontal regions involved in inhibitory control during error prediction. In contrast, deficient activation in supplementary motor cortex involved in planning of movement emerged as a familial vulnerability to BD.

Trauma-associated anterior cingulate connectivity during reward learning predicts affective and anxiety states in young adults.

BACKGROUND: Trauma exposure is associated with development of depression and anxiety; yet, some individuals are resilient to these trauma-associated effects. Differentiating mechanisms underlying development of negative affect and resilience following trauma is critical for developing effective interventions. One pathway through which trauma could exert its effects on negative affect is reward-learning networks. In this study, we examined relationships among lifetime trauma, reward-learning network function, and emotional states in young adults. METHODS: One hundred eleven young adults self-reported trauma and emotional states and underwent functional magnetic resonance imaging during a monetary reward task. Trauma-associated neural activation and functional connectivity were analyzed during reward prediction error (RPE). Relationships between trauma-associated neural functioning and affective and anxiety symptoms were examined. RESULTS: Number of traumatic events was associated with greater ventral anterior cingulate cortex (vACC) activation, and lower vACC connectivity with the right insula, frontopolar, inferior parietal, and temporoparietal regions, during RPE. Lower trauma-associated vACC connectivity with frontoparietal regions implicated in regulatory and decision-making processes was associated with heightened affective and anxiety symptoms; lower vACC connectivity with insular regions implicated in interoception was associated with lower affective and anxiety symptoms. CONCLUSIONS: In a young adult sample, two pathways linked the impact of trauma on reward-learning networks with higher v. lower negative affective and anxiety symptoms. The disconnection between vACC and regions implicated in decision-making and self-referential processes may reflect aberrant regulatory but appropriate self-focused mechanisms, respectively, conferring risk for v. resilience against negative affective and anxiety symptoms.

Meta-analytic evidence for altered mesolimbic responses to reward in schizophrenia.

Dysfunction of reward-related neural circuitry in schizophrenia (SCZ) has been widely reported, and may provide insight into the motivational and cognitive disturbances that characterize the disorder. Although previous meta-analyses of reward learning paradigms in SCZ have been performed, a meta-analysis of whole-brain coordinate maps in SCZ alone has not been conducted. In this study, we performed an activation likelihood estimate (ALE) meta-analysis, and performed a follow-up analysis of functional connectivity and functional decoding of identified regions. We report several salient findings that extend prior work in this area. First, an alteration in reward-related activation was observed in the right ventral striatum, but this was not solely driven by hypoactivation in the SCZ group compared to healthy controls. Second, the region was characterized by functional connectivity primarily with the lateral prefrontal cortex and pre-supplementary motor area (preSMA), as well as subcortical regions such as the thalamus which show structural deficits in SCZ. Finally, although the meta-analysis showed no regions outside the ventral striatum to be significantly altered, regions with higher functional connectivity with the ventral striatum showed a greater number of subthreshold foci. Together, these findings confirm the alteration of ventral striatal function in SCZ, but suggest that a network-based approach may assist future analysis of the functional underpinnings of the disorder.

Multimodal evaluation of the amygdala's functional connectivity.

The amygdala is one of the most extensively studied human brain regions and undisputedly plays a central role in many psychiatric disorders. However, an outstanding question is whether connectivity of amygdala subregions, specifically the centromedial (CM), laterobasal (LB) and superficial (SF) nuclei, are modulated by brain state (i.e., task vs. rest). Here, using a multimodal approach, we directly compared meta-analytic connectivity modeling (MACM) and specific co-activation likelihood estimation (SCALE)-derived estimates of CM, LB and SF task-based co-activation to the functional connectivity of these nuclei as assessed by resting state fmri (rs-fmri). Finally, using a preexisting resting state functional connectivity-derived cortical parcellation, we examined both MACM and rs-fmri amygdala subregion connectivity with 17 large-scale networks, to explicitly address how the amygdala interacts with other large-scale neural networks. Analyses revealed strong differentiation of CM, LB and SF connectivity patterns with other brain regions, both in task-dependent and task-independent contexts. All three regions, however, showed convergent connectivity with the right ventrolateral prefrontal cortex (VLPFC) that was not driven by high base rate levels of activation. Similar patterns of connectivity across rs-fmri and MACM were observed for each subregion, suggesting a similar network architecture of amygdala connectivity with the rest of the brain across tasks and resting state for each subregion, that may be modified in the context of specific task demands. These findings support animal models that posit a parallel model of amygdala functioning, but importantly, also modify this position to suggest integrative processing in the amygdala.

Evidence for an anterior-posterior differentiation in the human hippocampal formation revealed by meta-analytic parcellation of fMRI coordinate maps: focus on the subiculum.

Previous studies, predominantly in experimental animals, have suggested the presence of a differentiation of function across the hippocampal formation. In rodents, ventral regions are thought to be involved in emotional behavior while dorsal regions mediate cognitive or spatial processes. Using a combination of modeling the co-occurrence of significant activations across thousands of neuroimaging experiments and subsequent data-driven clustering of these data we were able to provide evidence of distinct subregions within a region corresponding to the human subiculum, a critical hub within the hippocampal formation. This connectivity-based model consists of a bilateral anterior region, as well as separate posterior and intermediate regions on each hemisphere. Functional connectivity assessed both by meta-analytic and resting fMRI approaches revealed that more anterior regions were more strongly connected to the default mode network, and more posterior regions were more strongly connected to 'task positive' regions. In addition, our analysis revealed that the anterior subregion was functionally connected to the ventral striatum, midbrain and amygdala, a circuit that is central to models of stress and motivated behavior. Analysis of a behavioral taxonomy provided evidence for a role for each subregion in mnemonic processing, as well as implication of the anterior subregion in emotional and visual processing and the right posterior subregion in reward processing. These findings lend support to models which posit anterior-posterior differentiation of function within the human hippocampal formation and complement other early steps toward a comparative (cross-species) model of the region.

Reinforcement learning models and their neural correlates: An activation likelihood estimation meta-analysis.

Reinforcement learning describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments-prediction error-is thought to update the expected value of actions and predictive stimuli. Electrophysiological and lesion studies have suggested that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and orbitofrontal cortex are implicated in representing expected value. To obtain unbiased maps of these representations in the human brain, we performed a meta-analysis of functional magnetic resonance imaging studies that had employed algorithmic reinforcement learning models across a variety of experimental paradigms. We found that the ventral striatum (medial and lateral) and midbrain/thalamus represented reward prediction errors, consistent with animal studies. Prediction error signals were also seen in the frontal operculum/insula, particularly for social rewards. In Pavlovian studies, striatal prediction error signals extended into the amygdala, whereas instrumental tasks engaged the caudate. Prediction error maps were sensitive to the model-fitting procedure (fixed or individually estimated) and to the extent of spatial smoothing. A correlate of expected value was found in a posterior region of the ventromedial prefrontal cortex, caudal and medial to the orbitofrontal regions identified in animal studies. These findings highlight a reproducible motif of reinforcement learning in the cortico-striatal loops and identify methodological dimensions that may influence the reproducibility of activation patterns across studies.

Differential role of fusiform gyrus coupling in depressive and anxiety symptoms during emotion perception.

Anxiety and depression co-occur; the neural substrates of shared and unique components of these symptoms are not understood. Given emotional alterations in internalizing disorders, we hypothesized that function of regions associated with emotion processing/regulation, including the anterior cingulate cortex (ACC), amygdala and fusiform gyrus (FG), would differentiate these symptoms. Forty-three adults with depression completed an emotional functional magnetic resonance imaging task and the Hamilton Depression and Anxiety Scales. We transformed these scales to examine two orthogonal components, one representing internalizing symptom severity and the other the type of internalizing symptoms (anxiety vs depression). We extracted blood oxygen level dependent signal from FG subregions, ACC, and amygdala and performed generalized psychophysiological interaction analyses to assess relationships between symptoms and brain function. Type of internalizing symptoms was associated with FG3-FG1 coupling (F = 8.14, P = 0.007). More coupling was associated with a higher concentration of depression, demonstrating that intra-fusiform coupling is differentially associated with internalizing symptom type (anxiety vs depression). We found an interaction between task condition and internalizing symptoms and dorsal (F = 4.51, P = 0.014) and rostral ACC activity (F = 4.27, P = 0.012). Post hoc comparisons revealed that less activity was associated with greater symptom severity during emotional regulation. Functional coupling differences during emotional processing are associated with depressive relative to anxiety symptoms and internalizing symptom severity. These findings could inform future treatments for depression.

Patterns of Neural Network Functional Connectivity Associated With Mania/Hypomania and Depression Risk in 3 Independent Young Adult Samples.

Importance: Mania/hypomania is the pathognomonic feature of bipolar disorder (BD). Established, reliable neural markers denoting mania/hypomania risk to help with early risk detection and diagnosis and guide the targeting of pathophysiologically informed interventions are lacking. Objective: To identify patterns of neural responses associated with lifetime mania/hypomania risk, the specificity of such neural responses to mania/hypomania risk vs depression risk, and the extent of replication of findings in 2 independent test samples. Design, Setting, and Participants: This cross-sectional study included 3 independent samples of young adults aged 18 to 30 years without BD or active substance use disorder within the past 3 months who were recruited from the community through advertising. Of 603 approached, 299 were ultimately included and underwent functional magnetic resonance imaging at the University of Pittsburgh, Pittsburgh, Pennsylvania, from July 2014 to May 2023. Main Outcomes and Measures: Activity and functional connectivity to approach-related emotions were examined using a region-of-interest mask supporting emotion processing and emotional regulation. The Mood Spectrum Self-Report assessed lifetime mania/hypomania risk and depression risk. In the discovery sample, elastic net regression models identified neural variables associated with mania/hypomania and depression risk; multivariable regression models identified the extent to which selected variables were significantly associated with each risk measure. Multivariable regression models then determined whether associations in the discovery sample replicated in both test samples. Results: A total of 299 participants were included. The discovery sample included 114 individuals (mean [SD] age, 21.60 [1.91] years; 80 female and 34 male); test sample 1, 103 individuals (mean [SD] age, 21.57 [2.09] years; 30 male and 73 female); and test sample 2, 82 individuals (mean [SD] age, 23.43 [2.86] years; 48 female, 29 male, and 5 nonbinary). Associations between neuroimaging variables and Mood Spectrum Self-Report measures were consistent across all 3 samples. Bilateral amygdala-left amygdala functional connectivity and bilateral ventrolateral prefrontal cortex-right dorsolateral prefrontal cortex functional connectivity were positively associated with mania/hypomania risk: discovery omnibus χ2 = 1671.7 (P < .001); test sample 1 omnibus χ2 = 1790.6 (P < .001); test sample 2 omnibus χ2 = 632.7 (P < .001). Bilateral amygdala-left amygdala functional connectivity and right caudate activity were positively associated and negatively associated with depression risk, respectively: discovery omnibus χ2 = 2566.2 (P < .001); test sample 1 omnibus χ2 = 2935.9 (P < .001); test sample 2 omnibus χ2 = 1004.5 (P < .001). Conclusions and Relevance: In this study of young adults, greater interamygdala functional connectivity was associated with greater risk of both mania/hypomania and depression. By contrast, greater functional connectivity between ventral attention or salience and central executive networks and greater caudate deactivation were reliably associated with greater risk of mania/hypomania and depression, respectively. These replicated findings indicate promising neural markers distinguishing mania/hypomania-specific risk from depression-specific risk and may provide neural targets to guide and monitor interventions for mania/hypomania and depression in at-risk individuals.

Specific Patterns of Endogenous Functional Connectivity Are Associated With Harm Avoidance in Obsessive-Compulsive Disorder.

BACKGROUND: Individuals with obsessive-compulsive disorder (OCD) show persistent avoidance behaviors, often in the absence of actual threat. Quality-of-life costs and heterogeneity support the need for novel brain-behavior intervention targets. Informed by mechanistic and anatomical studies of persistent avoidance in rodents and nonhuman primates, our goal was to test whether connections within a hypothesized persistent avoidance-related network predicted OCD-related harm avoidance (HA), a trait measure of persistent avoidance. We hypothesized that 1) HA, not an OCD diagnosis, would be associated with altered endogenous connectivity in at least one connection in the network; 2) HA-specific findings would be robust to comorbid symptoms; and 3) reliable findings would replicate in a holdout testing subsample. METHODS: Using resting-state functional connectivity magnetic resonance imaging, cross-validated elastic net for feature selection, and Poisson generalized linear models, we tested which connections significantly predicted HA in our training subsample (n = 73; 71.8% female; healthy control group n = 36, OCD group n = 37); robustness to comorbidities; and replicability in a testing subsample (n = 30; 56.7% female; healthy control group n = 15, OCD group n = 15). RESULTS: Stronger inverse connectivity between the right dorsal anterior cingulate cortex and right basolateral amygdala and stronger positive connectivity between the right ventral anterior insula and left ventral striatum were associated with greater HA across groups. Network connections did not discriminate OCD diagnostic status or predict HA-correlated traits, suggesting sensitivity to trait HA. The dorsal anterior cingulate cortex-basolateral amygdala relationship was robust to controlling for comorbidities and medication in individuals with OCD and was also predictive of HA in our testing subsample. CONCLUSIONS: Stronger inverse dorsal anterior cingulate cortex-basolateral amygdala connectivity was robustly and reliably associated with HA across groups and in OCD. Results support the relevance of a cross-species persistent avoidance-related network to OCD, with implications for precision-based approaches and treatment.

Neural markers of mania that distinguish inpatient adolescents with bipolar disorder from those with other psychopathology.

Pediatric bipolar disorder (BD) is difficult to distinguish from other psychiatric disorders, a challenge which can result in delayed or incorrect interventions. Using neuroimaging we aimed to identify neural measures differentiating a rarified sample of inpatient adolescents with BD from other inpatient psychopathology (OP) and healthy adolescents (HC) during a reward task. We hypothesized reduced subcortical and elevated cortical activation in BD relative to other groups, and that these markers will be related to self-reported mania scores. We examined inpatient adolescents with diagnosis of BD-I/II (n = 29), OP (n = 43), and HC (n = 20) from the Inpatient Child and Adolescent Bipolar Spectrum Imaging study. Inpatient adolescents with BD showed reduced activity in right thalamus, left thalamus, and left amygdala, relative to inpatient adolescents with OP and HC. This reduced neural function explained 21% of the variance in past month and 23% of the variance in lifetime mania scores. Lower activity in regions associated with the reward network, during reward processing, differentiates BD from OP in inpatient adolescents and explains >20% of the variance in mania scores. These findings highlight potential targets to aid earlier identification of, and guide new treatment developments for, pediatric BD.

Dissociable patterns of abnormal frontal cortical activation during anticipation of an uncertain reward or loss in bipolar versus major depression.

OBJECTIVES: Recent research has found abnormalities in reward-related neural activation in bipolar disorder (BD), during both manic and euthymic phases. However, reward-related neural activation in currently depressed individuals with BD and that in currently depressed individuals with major depressive disorder (MDD) have yet to be directly compared. Here, we studied these groups, examining the neural activation elicited during a guessing task in fronto-striatal regions identified by previous studies. METHODS: We evaluated neural activation during a reward task using fMRI in two groups of depressed individuals, one with bipolar I disorder (BD-I) (n = 23) and one with MDD (n = 40), with similar levels of illness severity, and a group of healthy individuals (n = 37). RESULTS: Reward expectancy-related activation in the anterior cingulate cortex was observed in the healthy individuals, but was significantly reduced in depressed patients (BD-I and MDD together). Anticipation-related activation was increased in the left ventrolateral prefrontal cortex in the BD-I depressed group compared with the other two groups. There were no significant differences in prediction error-related activation in the ventral striatum across the three groups. CONCLUSIONS: The findings extend previous research which has identified dysfunction within the ventrolateral prefrontal cortex in BD, and show that abnormally elevated activity in this region during anticipation of either reward or loss may distinguish depressed individuals with BD-I from those with MDD. Altered activation of the anterior cingulate cortex during reward expectancy characterizes both types of depression. These findings have important implications for identifying both common and distinct properties of the neural circuitry underlying BD-I and MDD.

A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI.

Introduction: Computational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of psychiatric neuroimaging. In particular, reinforcement learning (RL) techniques have been widely adopted to examine neural responses to reward prediction errors and stimulus or action values, and how these might vary as a function of clinical status. However, there is a lack of consensus around the importance of the precision of free parameter estimation for these methods, particularly with regard to the learning rate. In the present study, I introduce a novel technique which may be used within a general linear model (GLM) to model the effect of mis-estimation of the learning rate on reward prediction error (RPE)-related neural responses. Methods: Simulations employed a simple RL algorithm, which was used to generate hypothetical neural activations that would be expected to be observed in functional magnetic resonance imaging (fMRI) studies of RL. Similar RL models were incorporated within a GLM-based analysis method including derivatives, with individual differences in the resulting GLM-derived beta parameters being evaluated with respect to the free parameters of the RL model or being submitted to other validation analyses. Results: Initial simulations demonstrated that the conventional approach to fitting RL models to RPE responses is more likely to reflect individual differences in a reinforcement efficacy construct (lambda) rather than learning rate (alpha). The proposed method, adding a derivative regressor to the GLM, provides a second regressor which reflects the learning rate. Validation analyses were performed including examining another comparable method which yielded highly similar results, and a demonstration of sensitivity of the method in presence of fMRI-like noise. Conclusion: Overall, the findings underscore the importance of the lambda parameter for interpreting individual differences in RPE-coupled neural activity, and validate a novel neural metric of the modulation of such activity by individual differences in the learning rate. The method is expected to find application in understanding aberrant reinforcement learning across different psychiatric patient groups including major depression and substance use disorder.

From Computation to Clinic.

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

Neurobehavioral Reward and Sleep-Circadian Profiles Predict Present and Next-Year Mania/Hypomania Symptoms.

BACKGROUND: Heightened reward sensitivity/impulsivity, related neural activity, and sleep-circadian disruption are important risk factors for bipolar spectrum disorders, the defining feature of which is mania/hypomania. Our goal was to identify neurobehavioral profiles based on reward and sleep-circadian features and examine their specificity to mania/hypomania versus depression vulnerability. METHODS: At baseline, a transdiagnostic sample of 324 adults (18-25 years) completed trait measures of reward sensitivity (Behavioral Activation Scale), impulsivity (UPPS-P-Negative Urgency), and a functional magnetic resonance imaging card-guessing reward task (left ventrolateral prefrontal activity to reward expectancy, a neural correlate of reward motivation and impulsivity, was extracted). At baseline, 6-month follow-up, and 12-month follow-up, the Mood Spectrum Self-Report Measure - Lifetime Version assessed lifetime predisposition to subthreshold-syndromal mania/hypomania, depression, and sleep-circadian disturbances (insomnia, sleepiness, reduced sleep need, rhythm disruption). Mixture models derived profiles from baseline reward, impulsivity, and sleep-circadian variables. RESULTS: Three profiles were identified: 1) healthy (no reward or sleep-circadian disruption; n = 162); 2) moderate-risk (moderate reward and sleep-circadian disruption; n = 109); and 3) high-risk (high impulsivity and sleep-circadian disruption; n = 53). At baseline, the high-risk group had significantly higher mania/hypomania scores than the other groups but did not differ from the moderate-risk group in depression scores. Over the follow-up period, the high-risk and moderate-risk groups exhibited elevated mania/hypomania scores, whereas depression scores increased at a faster rate in the healthy group than in the other groups. CONCLUSIONS: Cross-sectional and next-year predisposition to mania/hypomania is associated with a combination of heightened reward sensitivity and impulsivity, related reward circuitry activity, and sleep-circadian disturbances. These measures can be used to detect mania/hypomania risk and provide targets to guide and monitor interventions.