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.

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.

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.

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.

Reward Behavior Disengagement, a Neuroeconomic Model-Based Objective Measure of Reward Pathology in Depression: Findings from the EMBARC Trial.

The probabilistic reward task (PRT) has identified reward learning impairments in those with major depressive disorder (MDD), as well as anhedonia-specific reward learning impairments. However, attempts to validate the anhedonia-specific impairments have produced inconsistent findings. Thus, we seek to determine whether the Reward Behavior Disengagement (RBD), our proposed economic augmentation of PRT, differs between MDD participants and controls, and whether there is a level at which RBD is high enough for depressed participants to be considered objectively disengaged. Data were gathered as part of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a double-blind, placebo-controlled clinical trial of antidepressant response. Participants included 195 individuals with moderate to severe MDD (Quick Inventory of Depressive Symptomatology (QIDS-SR) score ≥ 15), not in treatment for depression, and with complete PRT data. Healthy controls (n = 40) had no history of psychiatric illness, a QIDS-SR score < 8, and complete PRT data. Participants with MDD were treated with sertraline or placebo for 8 weeks (stage I of the EMBARC trial). RBD was applied to PRT data using discriminant analysis, and classified MDD participants as reward task engaged (n = 137) or reward task disengaged (n = 58), relative to controls. Reward task engaged/disengaged groups were compared on sociodemographic features, reward-behavior, and sertraline/placebo response (Hamilton Depression Rating Scale scores). Reward task disengaged MDD participants responded only to sertraline, whereas those who were reward task engaged responded to sertraline and placebo (F(1293) = 4.33, p = 0.038). Reward task engaged/disengaged groups did not differ otherwise. RBD was predictive of reward impairment in depressed patients and may have clinical utility in identifying patients who will benefit from antidepressants.

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.

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 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.

Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response.

BACKGROUND: Delivery of effective antidepressant treatment has been hampered by a lack of objective tools for predicting or monitoring treatment response. This study aimed to address this gap by testing novel dynamic resting-state functional network markers of antidepressant response. METHODS: The Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study randomized adults with major depressive disorder to 8 weeks of either sertraline or placebo, and depression severity was evaluated longitudinally. Participants completed resting-state neuroimaging pretreatment and again after 1 week of treatment (n = 259 eligible for analyses). Coactivation pattern analyses identified recurrent whole-brain states of spatial coactivation, and computed time spent in each state for each participant was the main dynamic measure. Multilevel modeling estimated the associations between pretreatment network dynamics and sertraline response and between early (pretreatment to 1 week) changes in network dynamics and sertraline response. RESULTS: Dynamic network markers of early sertraline response included increased time in network states consistent with canonical default and salience networks, together with decreased time in network states characterized by coactivation of cingulate and ventral limbic or temporal regions. The effect of sertraline on depression recovery was mediated by these dynamic network changes. In contrast, early changes in dynamic functioning of corticolimbic and frontoinsular-default networks were related to patterns of symptom recovery common across treatment groups. CONCLUSIONS: Dynamic resting-state markers of early antidepressant response or general recovery may assist development of clinical tools for monitoring and predicting effective intervention.

A checklist for assessing the methodological quality of concurrent tES-fMRI studies (ContES checklist): a consensus study and statement.

Low-intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation, applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional MRI (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies. The objective of this work was to develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency and reproducibility (ContES checklist). A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists through the International Network of the tES-fMRI Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC on the basis of a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed by using the checklist. Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (i) technological factors, (ii) safety and noise tests and (iii) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article. In conclusion, use of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies and increase methodological transparency and reproducibility.

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.

Dissociating default mode network resting state markers of suicide from familial risk factors for depression.

Neural signatures of suicide risk likely reflect a combination of specific and non-specific factors, and clarifying specific factors may facilitate development of novel treatments. Previously, we demonstrated an altered pattern of resting state connectivity between the dorsal and ventral posterior cingulate cortex (d/vPCC) and the dorsal anterior cingulate cortex (dACC), as well as altered low frequency oscillations in these regions, in individuals with a history of suicidal thoughts and behaviors (STBs) compared to healthy controls. It remains uncertain, however, whether these markers were directly related to STBs or, more generally, reflect a trait-level risk factor for depression. Here, we examined data from a 3-generational longitudinal study of depression where resting state fMRI data were analyzed from 2nd and 3rd generation offspring of probands with (FH+ = 44: STB+ = 32, STB- = 12) and without (FH- = 25: STB+ = 15, STB- = 10) a family history of major depressive disorder (MDD). Standard seed-based methods and a frequency-based analysis of intrinsic neural activity (ALFF/fALFF) were employed. FH of MDD, but not a personal history of STBs or MDD, was associated with relatively reduced dPCC-dACC, and enhanced vPCC-dACC functional connectivity. FH of MDD showed a pattern of reduced ALFF in the dPCC whereas an STB history was associated with an increase. All findings were invariant to confounding by lifetime MDD and current depression severity. Overall, contrary to predictions, resting state functional connectivity within the default mode network (DMN) was associated with FH of depression rather than STBs. These findings confirm the relevance of DMN functional connectivity for mood disorders and underscore the importance of disambiguating biological factors that differentially relate to mental disorders versus STBs.

The functional neural architecture of dysfunctional reward processing in autism.

Functional imaging studies have found differential neural activation patterns during reward-paradigms in patients with autism spectrum disorder (ASD) compared to neurotypical controls. However, publications report conflicting results on the directionality and location of these aberrant activations. We here quantitatively summarized relevant fMRI papers in the field using the anatomical likelihood estimation (ALE) algorithm. Patients with ASD consistently showed hypoactivations in the striatum across studies, mainly in the right putamen and accumbens. These regions are functionally involved in the processing of rewards and are enrolled in extensive neural networks involving limbic, cortical, thalamic and mesencephalic regions. The striatal hypo-activations found in our ALE meta-analysis, which pooled over contrasts derived from the included studies on reward-processing in ASD, highlight the role of the striatum as a key neural correlate of impaired reward processing in autism. These changes were present for studies using social and non-social stimuli alike. The involvement of these regions in extensive networks associated with the processing of both positive and negative emotion alike might hint at broader impairments of emotion processing in the disorder.

Trait sensation seeking is associated with heightened beta-band oscillatory dynamics over left ventrolateral prefrontal cortex during reward expectancy.

BACKGROUND: Sensation Seeking, the proclivity toward novel and stimulating experiences, is associated with greater left ventrolateral prefrontal cortex (vlPFC) activity during uncertain reward expectancy. Here, we examined relationships between sensation seeking and vlPFC oscillatory dynamics using electroencephalography (EEG). METHODS: In 26 adolescents/young adults (16 female; 22.3 ± 1.7yrs), EEG was measured during uncertain reward expectancy. Event-related spectral perturbations (ERSP) from 15-80 Hz (beta/gamma bands) were compared as a function of uncertain reward expected value and assessed for relationships with feedback-related negativity (FRN) response to outcome feedback and response tendency measures of risk for BD. RESULTS: Event-related synchronization (ERS) between 15-25 Hz (beta) over left vlPFC was sensitive to the expected value of uncertain reward (rho=0.46; p = 0.048), and correlated with sensation seeking (r = 0.49, p < 0.01) and feedback-related negativity (FRN), where greater beta ERS was related to larger FRN (r = -0.39, p = 0.047). FRN was also related to behavioral inhibition (r = 0.49, p < 0.01). LIMITATIONS: It is unknown whether results may extrapolate to clinical populations, given the healthy sample used here. Further, although we have confidence that the beta-band signal we measure in this study arises from left prefrontal cortex, we largely infer a left vlPFC source. CONCLUSIONS: These findings highlight the role of left vlPFC in evaluation of immediate rewards. We now provide a link between reward expectancy-related left vlPFC activity and the well-characterized FRN, with a known role in attentive processing. These findings can guide treatment development for mania/hypomania at-risk individuals, including transcranial alternating current stimulation.

How representative are neuroimaging samples? Large-scale evidence for trait anxiety differences between fMRI and behaviour-only research participants.

Over the past three decades, functional magnetic resonance imaging (fMRI) has become crucial to study how cognitive processes are implemented in the human brain. However, the question of whether participants recruited into fMRI studies differ from participants recruited into other study contexts has received little to no attention. This is particularly pertinent when effects fail to generalize across study contexts: for example, a behavioural effect discovered in a non-imaging context not replicating in a neuroimaging environment. Here, we tested the hypothesis, motivated by preliminary findings (N = 272), that fMRI participants differ from behaviour-only participants on one fundamental individual difference variable: trait anxiety. Analysing trait anxiety scores and possible confounding variables from healthy volunteers across multiple institutions (N = 3317), we found robust support for lower trait anxiety in fMRI study participants, consistent with a sampling or self-selection bias. The bias was larger in studies that relied on phone screening (compared with full in-person psychiatric screening), recruited at least partly from convenience samples (compared with community samples), and in pharmacology studies. Our findings highlight the need for surveying trait anxiety at recruitment and for appropriate screening procedures or sampling strategies to mitigate this bias.

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 (ß = -1.29, p = .02) and right amygdala (ß = 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.

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.