Model specification and the reliability of fMRI results: implications for longitudinal neuroimaging studies in psychiatry

Free-viewing gaze patterns reveal a mood-congruency bias in MDD during an affective fMRI/eye-tracking task

Major depressive disorder (MDD) has been related to abnormal amygdala activity during emotional face processing. However, a recent large-scale study (n = 28,638) found no such correlation, which is probably due to the low precision of fMRI measurements. To address this issue, we used simultaneous fMRI and eye-tracking measurements during a commonly employed emotional face recognition task. Eye-tracking provide high-precision data, which can be used to enrich and potentially stabilize fMRI readouts. With the behavioral response, we additionally divided the active task period into a task-related and a free-viewing phase to explore the gaze patterns of MDD patients and healthy controls (HC) and compare their respective neural correlates. Our analysis showed that a mood-congruency attentional bias could be detected in MDD compared to healthy controls during the free-viewing phase but without parallel amygdala disruption. Moreover, the neural correlates of gaze patterns reflected more prefrontal fMRI activity in the free-viewing than the task-related phase. Taken together, spontaneous emotional processing in free viewing might lead to a more pronounced mood-congruency bias in MDD, which indicates that combined fMRI with eye-tracking measurement could be beneficial for our understanding of the underlying psychopathology of MDD in different emotional processing phases.Trial Registration: The BeCOME study is registered on ClinicalTrials (gov: NCT03984084) by the Max Planck Institute of Psychiatry in Munich, Germany.

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 Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research

Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; discuss how various modeling principles, including those from hierarchical and structural equation modeling frameworks, can help to improve reliability; and demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.

Reliability and stability challenges in ABCD task fMRI data

Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and long-term stability of individual differences in task-fMRI measures using fMRI measures of brain activation provided by the adolescent brain cognitive development (ABCD) Study Release v4.0 as an individual's average regional activity, using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability (quantified as the ratio of non-scanner related stable variance to all variances) was poor in virtually all brain regions, with an average value of 0.088 and 0.072 for short term (within-session) reliability and long-term (between-session) stability, respectively, in regions of interest (ROIs) historically-recruited by the tasks. Only one reliability or stability value in ROIs exceeded the 'poor' cut-off of 0.4, and in fact rarely exceeded 0.2 (only 4.9%). Motion had a pronounced effect on estimated reliability/stability, with the lowest motion quartile of participants having a mean reliability/stability 2.5 times higher (albeit still 'poor') than the highest motion quartile. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue urgently needs to be addressed through optimization of task design, scanning parameters, data acquisition protocols, preprocessing pipelines, and data denoising methods.

Reliability of task-evoked neural activation during face-emotion paradigms: Effects of scanner and psychological processes

Assessing and improving test-retest reliability is critical to efforts to address concerns about replicability of task-based functional magnetic resonance imaging. The current study uses two statistical approaches to examine how scanner and task-related factors influence reliability of neural response to face-emotion viewing. Forty healthy adult participants completed two face-emotion paradigms at up to three scanning sessions across two scanners of the same build over approximately 2 months. We examined reliability across the main task contrasts using Bayesian linear mixed-effects models performed voxel-wise across the brain. We also used a novel Bayesian hierarchical model across a predefined whole-brain parcellation scheme and subcortical anatomical regions. Scanner differences accounted for minimal variance in temporal signal-to-noise ratio and task contrast maps. Regions activated during task at the group level showed higher reliability relative to regions not activated significantly at the group level. Greater reliability was found for contrasts involving conditions with clearly distinct visual stimuli and associated cognitive demands (e.g., face vs. nonface discrimination) compared to conditions with more similar demands (e.g., angry vs. happy face discrimination). Voxel-wise reliability estimates tended to be higher than those based on predefined anatomical regions. This work informs attempts to improve reliability in the context of task activation patterns and specific task contrasts. Our study provides a new method to estimate reliability across a large number of regions of interest and can inform researchers' selection of task conditions and analytic contrasts.

Informing the study of suicidal thoughts and behaviors in distressed young adults: The use of a machine learning approach to identify neuroimaging, psychiatric, behavioral, and demographic correlates

Young adults are at high risk for suicide, yet there is limited ability to predict suicidal thoughts and behaviors. Machine learning approaches are better able to examine a large number of variables simultaneously to identify combinations of factors associated with suicidal thoughts and behaviors. The current study used LASSO regression to investigate extent to which a number of demographic, psychiatric, behavioral, and functional neuroimaging variables are associated with suicidal thoughts and behaviors during young adulthood. 78 treatment seeking young adults (ages 18-25) completed demographic, psychiatric, behavioral, and suicidality measures. Participants also completed an implicit emotion regulation functional neuroimaging paradigm. Report of recent suicidal thoughts and behaviors served as the dependent variable. Five variables were identified by the LASSO regression: Two were demographic variables (age and level of education), two were psychiatric variables (depression and general psychiatric distress), and one was a neuroimaging variable (left amygdala activity during sad faces). Amygdala function was significantly associated with suicidal thoughts and behaviors above and beyond the other factors. Findings inform the study of suicidal thoughts and behaviors among treatment seeking young adults, and also highlight the importance of investigating neurobiological markers.

Importance of test-retest reliability for promoting fMRI based screening and interventions in major depressive disorder

Proponents of personalized medicine have promoted neuroimaging in three areas of clinical application for major depression: clinical prediction, outcome evaluation, and treatment, via neurofeedback. Whereas psychometric considerations such as test-retest reliability are basic precursors to clinical adoption for most clinical instruments, we show, in this article, that basic psychometrics have not been regularly attended to in fMRI of depression. For instance, no fMRI neurofeedback study has included measures of test-retest reliability, despite the implicit assumption that brain signals are stable enough to train. We consider several factors that could be useful to aid clinical translation, including (1) attending to how the BOLD response is parameterized, (2) identifying and promoting regions or voxels with stronger psychometric properties, (3) accounting for within-individual changes (e.g., in symptomatology) across time, and (4) focusing on tasks and clinical populations that are relevant for the intended clinical application. We apply these principles to published prognostic and neurofeedback data sets. The broad implication of this work is that attention to psychometrics is important for clinical adoption of mechanistic assessment, is feasible, and may improve the underlying science.

Internal reliability of blame-related functional MRI measures in major depressive disorder

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Self-blame-related fMRI measures were previously validated in depressive disorders.

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Reproducibility and internal consistency as a measure of reliability were examined.

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Whilst simple fMRI measures exhibited fair reliability, complex measures did not.

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Yet, complex measures showed reproducible clinical validity at the group level.

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Connectivity measures, that balance reliability and validity better, are needed.

Background

Methods

Results

Conclusions

Trauma Affects Prospective Relationships Between Reward-Related Ventral Striatal and Amygdala Activation and 1-Year Future Hypo/Mania Trajectories

BACKGROUND

Trauma exposure is associated with a more severe, persistent course of affective and anxiety symptoms. Markers of reward neural circuitry function, specifically activation to reward prediction error (RPE), are impacted by trauma and predict the future course of affective symptoms. This study's purpose was to determine how lifetime trauma exposure influences relationships between reward neural circuitry function and the course of future affective and anxiety symptoms in a naturalistic, transdiagnostic observational context.

METHODS

A total of 59 young adults aged 18-25 (48 female and 11 male participants, mean ± SD = 21.5 ± 2.0 years) experiencing psychological distress completed the study. Participants were evaluated at baseline, 6, and 12 months. At baseline, the participants reported lifetime trauma events and completed a monetary reward functional magnetic resonance imaging task. Affective and anxiety symptoms were reported at each visit, and trajectories were calculated using MPlus. Neural activation during RPE and other phases of reward processing were determined using SPM8. Trauma and reward neural activation were entered as predictors of symptom trajectories.

RESULTS

Trauma exposure moderated prospective relationships between left ventral striatum (β = -1.29, p = .02) and right amygdala (β = 0.58, p = .04) activation to RPE and future hypo/mania severity trajectory: the interaction between greater trauma and greater left ventral striatum activation to RPE was associated with a shallower increase in hypo/mania severity, whereas the interaction between greater trauma and greater right amygdala activation to RPE was associated with increasing hypo/mania severity.

CONCLUSIONS

Trauma exposure affects prospective relationships between markers of reward circuitry function and affective symptom trajectories. Evaluating trauma exposure is thus crucial in naturalistic and treatment studies aiming to identify neural predictors of future affective symptom course.

Predictive modeling of neurobehavioral state and trait variation across development

A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.

Reliability of a functional magnetic resonance imaging task of emotional conflict in healthy participants

Task-based functional neuroimaging methods are increasingly being used to identify biomarkers of treatment response in psychiatric disorders. To facilitate meaningful interpretation of neural correlates of tasks and their potential changes with treatment over time, understanding the reliability of the blood-oxygen-level dependent (BOLD) signal of such tasks is essential. We assessed test-retest reliability of an emotional conflict task in healthy participants collected as part of the Canadian Biomarker Integration Network in Depression. Data for 36 participants, scanned at three time points (weeks 0, 2, and 8) were analyzed, and intra-class correlation coefficients (ICC) were used to quantify reliability. We observed moderate reliability (median ICC values between 0.5 and 0.6), within occipital, parietal, and temporal regions, specifically for conditions of lower cognitive complexity, that is, face, congruent or incongruent trials. For these conditions, activation was also observed within frontal and sub-cortical regions, however, their reliability was poor (median ICC < 0.2). Clinically relevant prognostic markers based on task-based fMRI require high predictive accuracy at an individual level. For this to be achieved, reliability of BOLD responses needs to be high. We have shown that reliability of the BOLD response to an emotional conflict task in healthy individuals is moderate. Implications of these findings to further inform studies of treatment effects and biomarker discovery are discussed.

Test-retest reliability of brain responses to risk-taking during the balloon analogue risk task

The Balloon Analogue Risk Task (BART) provides a reliable and ecologically valid model for the assessment of individual risk-taking propensity and is frequently used in neuroimaging and developmental research. Although the test-retest reliability of risk-taking behavior during the BART is well established, the reliability of brain activation patterns in response to risk-taking during the BART remains elusive. In this study, we used functional magnetic resonance imaging (fMRI) and evaluated the test-retest reliability of brain responses in 34 healthy adults during a modified BART by calculating the intraclass correlation coefficients (ICC) and Dice’s similarity coefficients (DSC). Analyses revealed that risk-induced brain activation patterns showed good test-retest reliability (median ICC = 0.62) and moderate to high spatial consistency, while brain activation patterns associated with win or loss outcomes only had poor to fair reliability (median ICC = 0.33 for win and 0.42 for loss). These findings have important implications for future utility of the BART in fMRI to examine brain responses to risk-taking and decision-making.

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.

Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach

BACKGROUND

It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogeneity poses challenges for identifying biological markers associated with dimensions of symptoms and behaviour that could provide targets to guide treatment choice and novel treatment. In response, the research domain criteria (RDoC) (Insel et al., 2010) was developed to advocate a dimensional approach which omits any disease definitions, disorder thresholds, or cut-points for various levels of psychopathology to understanding the pathophysiological processes underlying psychiatry disorders. In the present study we aimed to apply pattern regression analysis to identify brain signatures during dynamic emotional face processing that are predictive of anxiety and depression symptoms in a continuum that ranges from normal to pathological levels, cutting across categorically-defined diagnoses.

METHODS

The sample was composed of one-hundred and fifty-four young adults (mean age=21.6 and s.d.=2.0, 103 females) consisting of eighty-two young adults seeking treatment for psychological distress that cut across categorically-defined diagnoses and 72 matched healthy young adults. Participants performed a dynamic face task involving fearful, angry and happy faces (and geometric shapes) while undergoing functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Gaussian Process Regression (GPR) implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r) and normalized mean squared error (MSE) to evaluate the models' performance. Permutation test was applied to estimate significance levels.

RESULTS

GPR identified patterns of neural activity to dynamic emotional face processing predictive of self-report anxiety in the whole sample, which covered a continuum that ranged from healthy to different levels of distress, including subthreshold to fully-syndromal psychiatric diagnoses. Results were significant using two different cross validation strategies (two-fold: r=0.28 (p-value=0.001), MSE=4.47 (p-value=0.001) and five fold r=0.28 (p-value=0.002), MSE=4.62 (p-value=0.003). The contributions of individual regions to the predictive model were very small, demonstrating that predictions were based on the overall pattern rather than on a small combination of regions.

CONCLUSIONS

These findings represent early evidence that neuroimaging techniques may inform clinical assessment of young adults irrespective of diagnoses by allowing accurate and objective quantitative estimation of psychopathology.

Test-retest reliability in an fMRI study of naming in dementia

fMRI has been used as an outcome measure in dementia treatment studies, with many previous studies comparing only single pre- and post-treatment fMRI scans to determine treatment-induced neural changes, while utilizing single subject experimental designs. The purpose of the current study was to evaluate fMRI test-retest reliability in dementia patients and typical older adults using noun and verb confrontation naming to evaluate the validity of using a single pre/post-treatment scan comparison. Seven individuals with dementia and 9 control participants were tested three times over two months using the same fMRI procedures. Differences in individual and group level activation patterns were observed that varied across time. Additionally, the extent of variability fluctuated across individuals, groups, and the grammatical category of target words. Our findings suggested that one time fMRI scanning may inadequately represent an individual's typical brain activation pattern, particularly an individual with dementia. Thus, multiple imaging baselines are recommended.

Network abnormalities in generalized anxiety pervade beyond the amygdala-pre-frontal cortex circuit: Insights from graph theory

Generalized anxiety disorder (GAD) has excessive anxiety and uncontrollable worry as core symptoms. Abnormal cerebral functioning underpins the expression and perhaps pathogenesis of GAD:. Studies implicate impaired communication between the amygdala and the pre-frontal cortex (PFC). Our aim was to longitudinally investigate whether such network abnormalities are spatially restricted to this circuit or if the integrity of functional brain networks is globally disrupted in GAD. We acquired resting-state functional magnetic resonance imaging data from 16 GAD patients and 16 matched controls at baseline and after 1 year. Using network modeling and graph-theory, whole-brain connectivity was characterized from local and global perspectives. Overall lower global efficiency, indicating sub-optimal brain-wide organization and integration, was present in patients with GAD compared to controls. The amygdala and midline cortices showed higher betweenness centrality, reflecting functional dominance of these brain structures. Third, lower betweenness centrality and lower degree emerged for PFC, suggesting weakened inhibitory control. Overall, network organization showed impairments consistent with neurobiological models of GAD (involving amygdala, PFC, and cingulate cortex) and further pointed to an involvement of temporal regions. Such impairments tended to progress over time and predict anxiety symptoms. A graph-analytic approach represents a powerful approach to deepen our understanding of GAD.

Experimental and methodological factors affecting test-retest reliability of amygdala BOLD responses

Previous studies reported poor to fair test-retest reliability of amygdala BOLD responses to emotional stimuli. However, these findings are very heterogeneous across and within studies. The present study sought to systematically examine experimental and methodological factors that contribute to this heterogeneity. Forty-six young subjects were scanned twice with a mean test-retest interval of 7 weeks. We compared amygdala reliability across three tasks: A face-matching task, passive viewing of emotional faces, and passive viewing of emotional scenes. We also explored whether extraction of physiological noise can affect the stability of amygdala responses. We assessed test-retest reliability of amygdala mean amplitudes at the individual level and spatial repeatability (i.e., stability of the spatial distribution of activation) of the amygdala BOLD signal at the group and individual level. All three tasks evoked robust amygdala activation at the group level. At the individual level, amygdala spatial repeatability was poor during passive viewing of scenes and faces and fair or close to fair in the face-matching task. On the other hand, reliability of amygdala mean responses was very poor in the face-matching task while it was significantly higher during passive viewing of faces and scenes. Physiological noise correction changed reliability rates but not uniformly across the three tasks. The current work suggests that the presence of a concurrent task during emotion processing affects amygdala reliability. The dissociation between spatial repeatability and reliability of mean amplitudes highlights the importance of taking into account both measures for a multidimensional assessment of the reliability of BOLD responses.

Intraclass correlation: Improved modeling approaches and applications for neuroimaging

Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, and so on. The popular definitions and interpretations of ICC are usually framed statistically under the conventional ANOVA platform. Here, we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging, and we show that the standard ANOVA framework is often limited, rigid, and inflexible in modeling capabilities. These intrinsic limitations motivate several improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degenerative circumstance, and second, incorporating precision information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), regularized mixed-effects (RME), multilevel mixed-effects (MME), and regularized multilevel mixed-effects (RMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects and their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data and fixed effects for confounding variables. More importantly, we show that the MME and RMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or RMME is preferable when precision information (i.e., weights that more accurately allocate the variances in the data) is available for the effect estimate; when precision information is unavailable, ICC estimation through LME or the RME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole-brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.

Haste or Speed? Alterations in the Impact of Incentive Cues on Task Performance in Remitted and Depressed Patients With Bipolar Disorder

A variety of evidence suggests that bipolar disorder is associated with disruptions of reward related processes, although the properties, and scope of these changes are not well understood. In the present study, we aimed to address this question by examining performance of patients with bipolar disorder (30 depressed bipolar; 35 euthymic bipolar) on a motivated choice reaction time task. We compared performance with a group of healthy control individuals (n = 44) and a group of patients with unipolar depression (n = 41), who were matched on several demographic variables. The task consists of an "odd-one-out" discrimination, in the presence of a cue signaling the probability of reward on a given trial (10, 50, or 90%) given a sufficiently fast response. All groups showed similar reaction time (RT) performance, and similar shortening of RT following the presentation of a reward predictive cue. However, compared to healthy individuals, the euthymic bipolar group showed a relative increase in commission errors during the high reward compared to low condition. Further correlational analysis revealed that in the healthy control and unipolar depression groups, participants tended either to shorten RTs for the high rather than low reward cue a relatively large amount with an increase in error rate, or to shorten RTs to a lesser extent but without increasing errors to the same degree. By contrast, reward-related speeding and reward-related increase in errors were less well coupled in the bipolar groups, significantly so in the BPD group. These findings suggest that although RT performance on the present task is relatively well matched, there may be a specific failure of individuals with bipolar disorder to calibrate RT speed and accuracy in a strategic way in the presence of reward-related stimuli.

Mediation by anxiety of the relationship between amygdala activity during emotion processing and poor quality of life in young adults

Young adults often experience psychological distress and poor quality of life (QoL). Yet, there are no objective neural markers to accurately guide interventions to help improve these measures. We thus aimed to identify directional relationships between frontoamygdala emotional regulation circuitry activity during emotion processing, personality traits, and symptoms associated with psychological distress, and QoL. One hundred twenty 18-25-year olds, n=51 psychologically distressed and n=69 healthy individuals, completed a face emotion-processing task during functional magnetic resonance imaging, clinical and behavioral measures, and QoL assessment. Penalized regression, accounting for large numbers of independent variables, showed that increased state and trait anxiety, cohort and measures of general and anhedonic depression severity predicted poorer QoL (all exponents>0.87). Only state and trait anxiety predicted emotion processing-related frontoamygdala activity (all exponents=1.00). State and trait anxiety fully mediated the relationship between amygdala activity and QoL (P-value increased from 0.001 to 0.29: left amygdala, and from 0.003 to 0.94: right amygdala). State anxiety fully mediated the relationship between left ventrolateral prefrontal cortical (vlPFC) activity and QoL (P-value increased from 0.01 to 0.18). Testing an alternative mediational pathway showed that the relationship between state and trait anxiety and QoL was not mediated by amygdala or left vlPFC activity. We thereby identify specific, directional relationships linking amygdala and left vlPFC activity, state and trait anxiety, and poor QoL across different diagnoses. Our findings highlight roles of amygdala and left vlPFC activity as neural predictors of anxiety and poor QoL, and as potentially important targets for novel interventions to reduce anxiety and, in turn, improve QoL in young adults.

Unreliability of putative fMRI biomarkers during emotional face processing

There is considerable need to develop tailored approaches to psychiatric treatment. Numerous researchers have proposed using functional magnetic resonance imaging (fMRI) biomarkers to predict therapeutic response, in particular by measuring task-evoked subgenual anterior cingulate (sgACC) and amygdala activation in mood and anxiety disorders. Translating this to the clinic relies on the assumption that blood-oxygen-level dependent (BOLD) responses in these regions are stable within individuals. To test this assumption, we scanned a group of 29 volunteers twice (mean test-retest interval=14.3 days) and calculated the within-subject reliability of the amplitude of the amygdalae and sgACC BOLD responses to emotional faces using three paradigms: emotion identification; emotion matching; and gender classification. We also calculated the reliability of activation in a control region, the right fusiform face area (FFA). All three tasks elicited robust group activations in the amygdalae and sgACC (which changed little on average over scanning sessions), but within-subject reliability was surprisingly low, despite excellent reliability in the control right FFA region. Our findings demonstrate low statistical reliability of two important putative treatment biomarkers in mood and anxiety disorders.

A pathway linking reward circuitry, impulsive sensation-seeking and risky decision-making in young adults: identifying neural markers for new interventions

High trait impulsive sensation seeking (ISS) is common in 18-25-year olds, and is associated with risky decision-making and deleterious outcomes. We examined relationships among: activity in reward regions previously associated with ISS during an ISS-relevant context, uncertain reward expectancy (RE), using fMRI; ISS impulsivity and sensation-seeking subcomponents; and risky decision-making in 100, transdiagnostically recruited 18-25-year olds. ISS, anhedonia, anxiety, depression and mania were measured using self-report scales; clinician-administered scales also assessed the latter four. A post-scan risky decision-making task measured 'risky' (possible win/loss/mixed/neutral) fMRI-task versus 'sure thing' stimuli. 'Bias' reflected risky over safe choices. Uncertain RE-related activity in left ventrolateral prefrontal cortex and bilateral ventral striatum was positively associated with an ISS composite score, comprising impulsivity and sensation-seeking-fun-seeking subcomponents (ISSc; P⩽0.001). Bias positively associated with sensation seeking-experience seeking (ES; P=0.003). This relationship was moderated by ISSc (P=0.009): it was evident only in high ISSc individuals. Whole-brain analyses showed a positive relationship between: uncertain RE-related left ventrolateral prefrontal cortical activity and ISSc; uncertain RE-related visual attention and motor preparation neural network activity and ES; and uncertain RE-related dorsal anterior cingulate cortical activity and bias, specifically in high ISSc participants (all ps<0.05, peak-level, family-wise error corrected). We identify an indirect pathway linking greater levels of uncertain RE-related activity in reward, visual attention and motor networks with greater risky decision-making, via positive relationships with impulsivity, fun seeking and ES. These objective neural markers of high ISS can guide new treatment developments for young adults with high levels of this debilitating personality trait.

Within- and Between-Session Changes in Neural Activity During Emotion Processing in Unipolar and Bipolar Depression

BACKGROUND

Bipolar disorder (BD) and unipolar depression (UD) can be difficult to distinguish clinically, particularly during episodes of depression. In this study we test for differences between BD, UD, and healthy control (HC) adults regarding within- and between-session changes in BOLD response during implicit emotional processing.

METHODS

During fMRI, HC adults (N=19) and depressed adults with UD (N=19) and BD (N=16) performed an implicit emotion-processing task. Each participant was scanned twice, separated by 6-months, resulting in 108 scans. BOLD response and linear change in BOLD response were examined within and between sessions.

RESULTS

We observed within-session linear decreases in BOLD signal (irrespective of group, condition, or session) in the left amygdala, a right-sided temporo-parietal region, and a right-sided fronto-insular region. Furthermore, we observed group differences in within-session BOLD signal change (p<0.05, FWE corrected) in a left-sided striatal-insular-thalamic region. Individuals with BD demonstrated a linear decrease in BOLD signal compared to HC (p<0.008, FWE corrected) across this region and compared to UD in the posterior insula portion of the region (p<0.008, FWE corrected). Finally, we observed main effects of emotional valence in bilateral visuo-spatial processing regions as well as in the left and right amygdala.

CONCLUSIONS

Individuals with BD demonstrated linear attenuation of BOLD response to emotional stimuli within left-sided striatal-insular-thalamic regions. Individuals with BD may either have experienced abnormal habituation in this region or disengaged quickly from processing the emotional stimuli, despite comparable task performance. This pattern may represent an underlying pathophysiological process associated with BD that differs from UD.

Accounting for Dynamic Fluctuations across Time when Examining fMRI Test-Retest Reliability: Analysis of a Reward Paradigm in the EMBARC Study

Longitudinal investigation of the neural correlates of reward processing in depression may represent an important step in defining effective biomarkers for antidepressant treatment outcome prediction, but the reliability of reward-related activation is not well understood. Thirty-seven healthy control participants were scanned using fMRI while performing a reward-related guessing task on two occasions, approximately one week apart. Two main contrasts were examined: right ventral striatum (VS) activation fMRI BOLD signal related to signed prediction errors (PE) and reward expectancy (RE). We also examined bilateral visual cortex activation coupled to outcome anticipation. Significant VS PE-related activity was observed at the first testing session, but at the second testing session, VS PE-related activation was significantly reduced. Conversely, significant VS RE-related activity was observed at time 2 but not time 1. Increases in VS RE-related activity from time 1 to time 2 were significantly associated with decreases in VS PE-related activity from time 1 to time 2 across participants. Intraclass correlations (ICCs) in VS were very low. By contrast, visual cortex activation had much larger ICCs, particularly in individuals with high quality data. Dynamic changes in brain activation are widely predicted, and failure to account for these changes could lead to inaccurate evaluations of the reliability of functional MRI signals. Conventional measures of reliability cannot distinguish between changes specified by algorithmic models of neural function and noisy signal. Here, we provide evidence for the former possibility: reward-related VS activations follow the pattern predicted by temporal difference models of reward learning but have low ICCs.