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Mobile technologies can be used for behavioral assessments to associate changes in behavior with environmental context and its influence on mental health and disease. Research on real-time motor control with a joystick, analyzed using a computational proportion-derivative (PD) modeling approach, has shown that model parameters can be estimated with high reliability and are related both to self-reported fear and to brain structures important for affective regulation, such as the anterior cingulate cortex. Here we introduce a mobile version of this paradigm, the rapid assessment of motor processing (RAMP) paradigm, and show that it provides robust, reliable, and accessible behavioral measurements relevant to mental health. A smartphone version of a previous joystick sensorimotor task was developed in which participants control a virtual car to a stop sign and stop. A sample of 89 adults performed the task, with 66 completing a second retest session. A PD modeling approach was applied to compute Kp (drive) and Kd (damping) parameters. Both Kp and Kd exhibited high test-retest reliabilities (ICC .81 and .78, respectively). Replicating a previous finding from a different sample with the joystick version of the task, both Kp and Kd were negatively associated with self-reported fear. The RAMP paradigm, a mobile sensorimotor assessment, can be used to assess drive and damping during motor control, which is robustly associated with subjective affect. This paradigm could be useful for examining dynamic contextual modulation of affect-related processing, which could improve assessment of the effects of interventions for psychiatric disorders in a real-world context.

Objective To identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students. Methods 356 first-year university students completed a large battery of demographic and clinically-relevant self-report measures during the first semester of college and end-of-year (n = 228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A machine learning (ML) pipeline using stacking and nested cross-validation examined correlates of SBQ-R scores. Results 9.6% of students were identified at significant STBs risk by the SBQ-R. The ML algorithm explained 28.3% of variance (95%CI: 28-28.5%) in baseline SBQ-R scores, with depression severity, social isolation, meaning and purpose in life, and positive affect among the most important factors. There was a significant reduction in STBs at end-of-year with only 1.8% of students identified at significant risk. Conclusion Analyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.

We have previously reported activation in reward, salience and executive control regions during functional MRI (fMRI) using an approach-avoidance conflict (AAC) decision-making task with healthy adults. Further investigations into how anxiety and depressive disorders relate to differences in neural responses during AAC can inform their understanding and treatment. We tested the hypothesis that people with anxiety or depression have altered neural activation during AAC.We compared 118 treatment-seeking adults with anxiety or depression and 58 healthy adults using linear mixed-effects models to examine group-level differences in neural activation (fMRI) during AAC decision-making. Correlational analyses examined relationships between behavioural and neural measures.Adults with anxiety or depression had greater striatal engagement when reacting to affective stimuli (Our transdiagnostic approach did not allow for comparisons between specific anxiety disorders, and our cross-sectional approach did not allow for causal inference.Anxiety and depression were associated with altered neural responses to AAC. Findings were consistent with the role of the striatum in action selection and reward responsivity, and they point toward striatal reactivity as a future treatment target. Blunting of amygdala activity in anxiety or depression may indicate a compensatory response to inhibit affective salience and maintain approach.

Introduction Treatment effectiveness for major depressive disorder (MDD) is often affected by client non-adherence, dropout, and non-response. Identification of client characteristics predicting successful treatment completion and/or response (i.e., symptom reduction) may be an important tool to increase intervention effectiveness. It is unclear whether neural attenuations in reward processing associated with MDD predict behavioral treatment outcome. Methods This study aimed to determine whether blunted neural responses to reward at baseline differentiate MDD (n = 60; 41 with comorbid anxiety) and healthy control (HC; n = 40) groups; and predict MDD completion of and response to 7–10 sessions of behavior therapy. Participants completed a monetary incentive delay (MID) task. The N200, P300, contingent negative variation (CNV) event related potentials (ERPs) and behavioral responses (reaction time [RT], correct hits) were quantified and extracted for cross-sectional group analyses. ERPs and behavioral responses demonstrating group differences were then used to predict therapy completion and response within MDD. Results MDD exhibited faster RT and smaller P300 amplitudes than HC across conditions. Within the MDD group, treatment completers (n = 37) exhibited larger P300 amplitudes than non-completers (n = 21). Limitations : This study comprises secondary analyses of EEG data; thus task parameters are not optimized to examine feedback ERPs from the paradigm. We did not examine heterogenous presentations of MDD; however, severity and comorbidity did not influence findings. Conclusions Previous studies suggest that P300 is an index of motivational salience and stimulus resource allocation. In sum, individuals who deploy greater neural resources to task demands are more likely to persevere in behavioral therapy.

Neural and behavioral mechanisms during approach‐avoidance conflict decision‐making are relevant across various psychiatric disorders, particularly anxiety disorders. Studies using approach‐avoidance conflict paradigms in healthy adults have identified preliminary neural mechanisms, but findings must be replicated and demonstrated as reliable before further application. This study sought to replicate previous findings and examine test–retest reliability of behavioral (approach behavior, reaction time) and neural (regions of interest [ROIs]) responses during an approach‐avoidance conflict task conducted during functional magnetic resonance imaging (fMRI). Thirty healthy adults completed an approach‐avoidance conflict task during fMRI on two occasions (mean interval: 17 days; range: 11–32). Effects of task condition during three task phases (decision‐making, affective outcome and monetary reward) and intraclass correlation coefficients (ICCs) were calculated across time points. Results replicated that approach behavior was modulated by conflict during decision‐making. ROI activations were replicated such that dorsal anterior cingulate cortex (dACC) was modulated by conflict during decision‐making, and dACC, striatum, and anterior insula were modulated by valence during affective outcomes (p's, The following article examines the test‐retest reliability of behavioral and neural responses to an approach‐avoidance conflict task during functional magnetic resonance imaging. Specific responses with sufficient reliability were characterized across multiple levels of analysis.

Background: An estimated 1100 college students die by suicide each year. Our ability to predict who is at risk for suicide, as well as our knowledge of resilience factors protecting against it, remains limited. We used a machine learning (ML) framework in conjunction with a large battery of self-report and demographic measures to select features contributing most to observed variability in suicidal thoughts and behaviors (STBs) in college.Method: First-year university students completed demographic and clinically-relevant self-report measures at the beginning of the first semester of college (baseline; n=356), and at end-of-year (n=228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A ML pipeline with 55 and 57 variables using stacking and nested cross-validation to avoid overfitting was conducted to examine predictors of baseline and end-of-year STBs, respectively. Results: For baseline SBQ-R score, the identified ML algorithm explained 28.3% of variance (95%CI: 28-28.5%), with depression severity, meaning and purpose in life, and social isolation among the most important predictors. For end-of-year SBQ-R score, the identified algorithm explained 5.6% of variance [95%CI: 5.1-6.1%], with baseline SBQ-R score, emotional suppression, and positive emotional experiences among the most important predictors.Limitations: External validation of the model with another independent sample is needed for further demonstrating its replicability.Conclusions: ML analyses replicated known factors contributing to STBs, and identified novel, potentially modifiable risk and resilience factors. Intervention programing on college campuses aiming to reduce depressive symptomatology, promote positive affect and social connectedness, and foster a sense of meaning and purpose, may be effective in reducing STBs.

BACKGROUND Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). METHODS A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. RESULTS The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). LIMITATIONS This study was limited by heterogeneity of the clinical sample and an inability to examine learning. CONCLUSION These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Functional magnetic resonance imaging studies frequently use emotional face processing tasks to probe neural circuitry related to psychiatric disorders and treatments with an emphasis on regions within the salience network (e.g., amygdala). Findings across previous test-retest reliability studies of emotional face processing have shown high variability, potentially due to differences in data analytic approaches. The present study comprehensively examined the test-retest reliability of an emotional faces task utilizing multiple approaches to region of interest (ROI) analysis and by examining voxel-wise reliability across the entire brain for both neural activation and functional connectivity. Analyses included 42 healthy adult participants who completed an fMRI scan concurrent with an emotional faces task on two separate days with an average of 25.52 days between scans. Intraclass correlation coefficients (ICCs) were calculated for the ‘FACES-SHAPES’ and ‘FACES’ (compared to implicit baseline) contrasts across the following: anatomical ROIs identified from a publicly available brain atlas (i.e., Brainnetome), functional ROIs consisting of 5-mm spheres centered on peak voxels from a publicly available meta-analytic database (i.e., Neurosynth), and whole-brain, voxel-wise analysis. Whole-brain, voxel-wise analyses of functional connectivity were also conducted using both anatomical and functional seed ROIs. While group-averaged neural activation maps were consistent across time, only one anatomical ROI and two functional ROIs showed good or excellent individual-level reliability for neural activation. The anatomical ROI was the right medioventral fusiform gyrus for the FACES contrast (ICC ​= ​0.60). The functional ROIs were the left and the right fusiform face area (FFA) for both FACES-SHAPES and FACES (Left FFA ICCs ​= ​0.69 & 0.79; Right FFA ICCs ​= ​0.68 & 0.66). Poor reliability (ICCs ​ ​0.75) that were primarily located in visual cortex, with several clusters in bilateral dorsal lateral prefrontal cortex (DLPFC). Whole-brain voxel-wise analyses of functional connectivity for amygdala and fusiform gyrus identified very few voxels with good to excellent reliability using both anatomical and functional seed ROIs. Exceptions included clusters in right cerebellum and right DLPFC that showed reliable connectivity with left amygdala (ICCs ​> ​0.6). In conclusion, results indicate that visual cortical regions demonstrate good reliability at the individual level for neural activation, but reliability is generally poor for salience regions often focused on within psychiatric research (e.g., amygdala). Given these findings, future clinical neuroimaging studies using emotional faces tasks to examine individual differences might instead focus on visual regions and their role in psychiatric disorders.

Background: Sacrificing rewarding aspects of one’s life due to potential aversive outcomes is an important characteristic of multiple psychiatric disorders. Such decisions occur during approach-avoidance conflict (AAC), which has become the topic of a growing number of behavioral and neuroimaging studies. Here we describe a novel computational modeling approach to studying AAC.Methods: A previously-validated AAC task was completed by 479 participants including healthy controls (HCs), and individuals with depression, anxiety, and/or substance use disorders (SUDs), as part of the Tulsa 1000 study. An active inference model was utilized to identify parameters corresponding to the subjective aversiveness of affective stimuli (VNegative), the subjective value of points that could be won (VPoints), and decision uncertainty (β). We used correlational analyses to examine relationships to self-reported experiences during the task, analyses of variance to examine diagnostic group differences (depression/anxiety, substance use, HCs), and exploratory machine learning analyses to examine the contribution of dimensional clinical and neuropsychological measures.Results: Model parameters correlated with self-reported experience and reaction times during the task in expected directions. Relatve to HCs, both clinical groups showed higher VNegative values, and the SUD group exhibited less decision uncertainty (lower β values). Machine learning analyses highlighted several clinical domains (i.e., alcohol use, personality, working memory) potentially contributing to task parameters.Conclusions: Our results suggest that avoidance behavior in individuals with depression, anxiety, and SUDs may be driven by increased sensitivity to predicted negative outcomes and that insufficient decision uncertainty (overconfidence) may also further contribute to avoidance in substance use disorder.

Background Although patient history is essential for informing mental health assessment, diagnosis, and prognosis, there is a dearth of standardized instruments measuring time-dependent factors relevant to psychiatric disorders. Previous research has demonstrated the potential utility of graphical representations, termed life charts, for depicting the complexity of the course of mental illness. However, the implementation of these assessments is limited by the exclusive focus on specific mental illnesses (ie, bipolar disorder) and the lack of intuitive graphical interfaces for data collection and visualization. Objective This study aimed to develop and test the utility of the Tulsa Life Chart (TLC) as a Web-based, structured approach for obtaining and graphically representing historical information on psychosocial and mental health events relevant across a spectrum of psychiatric disorders. Methods The TLC interview was completed at baseline by 499 participants of the Tulsa 1000, a longitudinal study of individuals with depressive, anxiety, substance use, or eating disorders and healthy comparisons (HCs). All data were entered electronically, and a 1-page electronic and interactive graphical representation was developed using the Google Visualization Application Programming Interface. For 8 distinct life epochs (periods of approximately 5-10 years), the TLC assessed the following factors: school attendance, hobbies, jobs, social support, substance use, mental health treatment, family structure changes, negative and positive events, and epoch and event-related mood ratings. We used generalized linear mixed models (GLMMs) to evaluate trajectories of each domain over time and by sex, age, and diagnosis, using case examples and Web-based interactive graphs to visualize data. Results GLMM analyses revealed main or interaction effects of epoch and diagnosis for all domains. Epoch by diagnosis interactions were identified for mood ratings and the number of negative-versus-positive events (all P values Conclusions The TLC provides a structured, Web-based transdiagnostic assessment of psychosocial history relevant for the diagnosis and treatment of psychiatric disorders. Interactive, 1-page graphical representations of the TLC allow for the efficient communication of historical life information that would be useful for clinicians, patients, and family members.