Disorder-specific versus transdiagnostic cognitive mechanisms in anxiety and depression: Machine-learning-based prediction of symptom severity.

INTRODUCTION: Psychiatric evaluation of anxiety and depression is currently based on self-reported symptoms and their classification into discrete disorders. Yet the substantial overlap between these disorders as well as their within-disorder heterogeneity may contribute to the mediocre success rates of treatments. The proposed research examines a new framework for diagnosis that is based on alterations in underlying cognitive mechanisms. In line with the Research Domain Criteria (RDoC) approach, the current study directly compares disorder-specific and transdiagnostic cognitive patterns in predicting the severity of anxiety and depression symptoms. METHODS: The sample included 237 individuals exhibiting differing levels of anxiety and depression symptoms, as measured by the STAI-T and BDI-II. Random Forest regressors were used to analyze their performance on a battery of six computerized cognitive-behavioral tests targeting selective and spatial attention, expectancy, interpretation, memory, and cognitive control biases. RESULTS: Unique anxiety-specific biases were found, as well as shared anxious-depressed bias patterns. These cognitive biases exhibited relatively high fitting rates when predicting symptom severity (questionnaire scores common range 0-60, MAE = 6.03, RMSE = 7.53). Interpretation and expectancy biases exhibited the highest association with symptoms, above all other individual biases. LIMITATIONS: Although internal validation methods were applied, models may suffer from potential overfitting due to sample size limitations. CONCLUSION: In the context of the ongoing dispute regarding symptom-centered versus transdiagnostic approaches, the current study provides a unique comparison of these two views, yielding a novel intermediate approach. The results support the use of mechanism-based dimensional diagnosis for adding precision and objectivity to future psychiatric evaluations.

Perception of interpersonal distance and social distancing before and during COVID-19 pandemic.

Since COVID-19 is easily transmitted among people in close physical proximity, the focus of epidemiological policy during the COVID-19 crisis included major restrictions on interpersonal distance. However, the way in which distance restrictions affected spatial perception is unclear. In the current study, we examined interpersonal distance preferences and perceptions at three time points: pre-pandemic, early post-pandemic, and late post-pandemic. The results indicate that following the pandemic outbreak, people perceived others as farther away than they actually were, suggesting that the distance restrictions were associated with an enlargement of perceived interpersonal distance. Interestingly, however, people maintained the same distance from one another as before the outbreak, indicating no change in actual distance behavior due to the risk of infection. These findings suggest that COVID-19 was associated with a change in the way distance is perceived, while in practice, people maintain the same distance as before. In contrast, COVID-related anxiety predicted both a preference for maintaining a greater distance and a bias toward underestimating perceived distance from others. Thus, individuals who were highly fearful of COVID-19 perceived other people to be closer than they actually were and preferred to maintain a larger distance from them. The results suggest that subjective risk can lead to an increased perception of danger and a subsequent change in behavior. Taken together, even when behaviors should logically change, the decision-making process can be based on distorted perceptions. This insight may be used to predict public compliance.

Expectancy and attention bias to spiders: Dissecting anticipation and allocation processes using ERPs.

The current registered report focused on the temporal dynamics of the relationship between expectancy and attention toward threat, to better understand the mechanisms underlying the prioritization of threat detection over expectancy. In the current event-related potentials experiment, a-priori expectancy was manipulated, and attention bias was measured, using a well-validated paradigm. A visual search array was presented, with one of two targets: spiders (threatening) or birds (neutral). A verbal cue stating the likelihood of encountering a target preceded the array, creating congruent and incongruent trials. Following cue presentation, preparatory processes were examined using the contingent negative variation (CNV) component. Following target presentation, two components were measured: early posterior negativity (EPN) and late positive potential (LPP), reflecting early and late stages of natural selective attention toward emotional stimuli, respectively. Behaviorally, spiders were found faster than birds, and congruency effects emerged for both targets. For the CNV, a non-significant trend of more negative amplitudes following spider cues emerged. As expected, EPN and LPP amplitudes were larger for spider targets compared to bird targets. Data-driven, exploratory, topographical analyses revealed different patterns of activation for bird cues compared to spider cues. Furthermore, 400-500 ms post-target, a congruency effect was revealed only for bird targets. Together, these results demonstrate that while expectancy for spider appearance is evident in differential neural preparation, the actual appearance of spider target overrides this expectancy effect and only in later stages of processing does the cueing effect come again into play.

Spider vs. guns: expectancy and attention biases to phylogenetic threat do not extend to ontogenetic threat.

Introduction: Attention bias plays an important role in specific fears and phobias. Previous studies revealed that a-priori expectancies affect attention toward neutral stimuli but not threatening stimuli. The aim of the current study was to test whether this selective influence of expectancies on attention is specific to phylogenetic threat (i.e., spiders) or whether it can be generalized to ontogenetic threat (i.e., guns). Correspondingly, we directly compared expectancy effects on attentional allocation to phylogenetically vs. ontogenetically threatening stimuli. Method: Expectancies were manipulated by presenting a cue indicating the likelihood of the appearance of a deviant picture in a visual search array. The array included eight distractors and one neutral (phone/bird) or threatening (gun/spider) deviant picture. In a comprehensive design, we examined the effects of stimulus type (phylogenetic/ontogenetic) and visual background (white and sterile/complex and ecological). Individual differences such as intolerance of uncertainty and spider fear were also measured. Results: Results showed that attention bias toward spiders does not extend to threatening ontogenetic stimuli (i.e., guns). Our previous findings on attention bias toward spiders were replicated and a small to medium positive correlation was found between reaction time to bird targets and pre-existing fear of spider levels. Cues were used to detect threatening as well as neutral targets on both background types, except for spider targets on a complex background, replicating previous results. A small to medium positive correlation was also found between fear of spiders and intolerance of uncertainty. Discussion: Together, these results suggest that expectancy and attentional processes may differ between ontogenetic and phylogenetic threat. Importantly, the effects of expectancy on attentional allocation depend on an interaction between the type of threat (ontogenetic/phylogenetic), visual factors, and individual differences.

Cognitive Avoidance Is Associated with Decreased Brain Responsiveness to Threat Distractors under High Perceptual Load.

Cognitive coping strategies to deal with anxiety-provoking events have an impact on mental and physical health. Dispositional vigilance is characterized by an increased analysis of the threatening environment, whereas cognitive avoidance comprises strategies to inhibit threat processing. To date, functional neuroimaging studies on the neural underpinnings of these coping styles are scarce and have revealed discrepant findings. In the present study, we examined automatic brain responsiveness as a function of coping styles using functional magnetic resonance imaging. We administered a perceptual load paradigm with contemptuous and fearful faces as distractor stimuli in a sample of N = 43 healthy participants. The Mainz Coping Inventory was used to assess cognitive avoidance and vigilance. An association of cognitive avoidance with reduced contempt and fear processing under high perceptual load was observed in a widespread network including the amygdala, thalamus, cingulate gyrus, insula, and frontal, parietal, temporal, and occipital areas. Our findings indicate that the dispositional tendency to divert one's attention away from distressing stimuli is a valuable predictor of diminished automatic neural responses to threat in several cortical and subcortical areas. A reduced processing in brain regions involved in emotion perception and attention might indicate a potential threat resilience associated with cognitive avoidance.

Using clustering algorithms to examine the association between working memory training trajectories and therapeutic outcomes among psychiatric and healthy populations.

Working memory (WM) training has gained interest due to its potential to enhance cognitive functioning and reduce symptoms of mental disorders. Nevertheless, inconsistent results suggest that individual differences may have an impact on training efficacy. This study examined whether individual differences in training performance can predict therapeutic outcomes of WM training, measured as changes in anxiety and depression symptoms in sub-clinical and healthy populations. The study also investigated the association between cognitive abilities at baseline and different training improvement trajectories. Ninety-six participants (50 females, mean age = 27.67, SD = 8.84) were trained using the same WM training task (duration ranged between 7 to 15 sessions). An algorithm was then used to cluster them based on their learning trajectories. We found three main WM training trajectories, which in turn were related to changes in anxiety symptoms following the training. Additionally, executive function abilities at baseline predicted training trajectories. These findings highlight the potential for using clustering algorithms to reveal the benefits of cognitive training to alleviate maladaptive psychological symptoms.

The attention-emotion interaction in healthy female participants on oral contraceptives during 1-week escitalopram intake.

Previous findings in healthy humans suggest that selective serotonin reuptake inhibitors (SSRIs) modulate emotional processing via earlier changes in attention. However, many previous studies have provided inconsistent findings. One possible reason for such inconsistencies is that these studies did not control for the influence of either sex or sex hormone fluctuations. To address this inconsistency, we administered 20 mg escitalopram or placebo for seven consecutive days in a randomized, double-blind, placebo-controlled design to sixty healthy female participants with a minimum of 3 months oral contraceptive (OC) intake. Participants performed a modified version of an emotional flanker task before drug administration, after a single dose, after 1 week of SSRI intake, and after a 1-month wash-out period. Supported by Bayesian analyses, our results do not suggest a modulatory effect of escitalopram on behavioral measures of early attentional-emotional interaction in female individuals with regular OC use. While the specific conditions of our task may be a contributing factor, it is also possible that a practice effect in a healthy sample may mask the effects of escitalopram on the attentional-emotional interplay. Consequently, 1 week of escitalopram administration may not modulate attention toward negative emotional distractors outside the focus of attention in healthy female participants taking OCs. While further research in naturally cycling females and patient samples is needed, our results represent a valuable contribution toward the preclinical investigation of antidepressant treatment.

The role of fear in hierarchical processing of fear-related stimuli.

According to a widely accepted definition, visual objects are composed of hierarchical structures classified as either global or local, depending on their location in the hierarchy. Previous research has demonstrated that fear and anxiety are associated with faster identification of local level stimuli, supporting the popular view that fear narrows attention. Nevertheless, the modulating role of fear in hierarchical processing has never been examined for stimuli that are personally relevant. To fill this research gap, we used emotional modification of a global-local paradigm to examine hierarchical processing of spider and snake stimuli among participants with high specific fear of spiders or snakes, respectively. The results indicate that personal fear facilitates identification of an animal's global configuration and interferes with identification of its local features (i.e., body pattern). This effect was found in comparison with participants with low fear and in comparison with other threatening but fear-irrelevant stimulus. Furthermore, a general global bias was found, highlighting the ecological validity of Navon's theory. The current findings are inconsistent with the view that anxiety narrows attention to local features. We suggest that the results can be reconciled with existing literature if we consider the narrowing of attention to the global level. The current study offers initial evidence for the role of fear in hierarchical processing of fear-relevant information, which may, in turn, play an important role in the etiology and maintenance of fear and serve as a new target for interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

How Social Experiences Affect Interpretation Bias Among Individuals With Non-clinical Depression: The Role of Ostracism.

BACKGROUND: Extensive knowledge and research indicate that interpretation bias is very common among individuals with sub-clinical and clinical levels of depression. Nevertheless, little is known about the role of social experiences in enhancing interpretation bias. Given the major relevance of social experiences in the context of depression, the present study investigated the role of potential interactions between social experiences and levels of depression symptoms in the interpretation of ambiguous information. METHOD: Seventy participants underwent a laboratory controlled manipulation either of social ostracism or of overinclusion. Participants completed a computerized task that measured both direct and indirect interpretation bias and reported their level of depression symptoms. RESULTS: The findings show that ostracism enhanced interpretation bias when symptom levels were higher, while overinclusion did not. This interaction effect between social ostracism and symptom level was found both for direct and for indirect interpretation bias. CONCLUSION: Whereas previous research showed the existence of interpretation bias among people with symptoms of depression, the present study expands previous knowledge by shedding light on the conditions under which interpretation bias emerges, suggesting that ostracism enhances negative interpretation of ambiguous information when levels of depression symptoms are higher.

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions.

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper's overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders.

In light of the need for objective mechanism-based diagnostic tools, the current research describes a novel diagnostic support system aimed to differentiate between anxiety and depression disorders in a clinical sample. Eighty-six psychiatric patients with clinical anxiety and/or depression were recruited from a public hospital and assigned to one of the experimental groups: Depression, Anxiety, or Mixed. The control group included 25 participants with no psychiatric diagnosis. Participants performed a battery of six cognitive-behavioral tasks assessing biases of attention, expectancies, memory, interpretation and executive functions. Data were analyzed with a machine-learning (ML) random forest-based algorithm and cross-validation techniques. The model assigned participants to clinical groups based solely on their aggregated cognitive performance. By detecting each group's unique performance pattern and the specific measures contributing to the prediction, the ML algorithm predicted diagnosis classification in two models: (I) anxiety/depression/mixed vs. control (76.81% specificity, 69.66% sensitivity), and (II) anxiety group vs. depression group (80.50% and 66.46% success rates in classifying anxiety and depression, respectively). The findings demonstrate that the cognitive battery can be utilized as a support system for psychiatric diagnosis alongside the clinical interview. This implicit tool, which is not based on self-report, is expected to enable the clinician to achieve increased diagnostic specificity and precision. Further, this tool may increase the confidence of both clinician and patient in the diagnosis by equipping them with an objective assessment tool. Finally, the battery provides a profile of biased cognitions that characterizes the patient, which in turn enables more fine-tuned, individually-tailored therapy.

Cognitive Biases in Blood-Injection-Injury Phobia: A Review.

Blood-injection-injury (BII) phobia can lead to avoidance of crucial medical procedures and to detrimental health consequences, even among health workers. Yet unlike other specific phobias, BII phobia has been understudied. Specifically, while cognitive biases have been extensively investigated in other anxiety disorders, little is known about the same biases in BII phobia. The current article reviews cognitive biases in BII phobia and suggest future directions for further study and treatment. The reviewed biases include attention, expectancy, memory, perception, and interpretation biases. The investigation of these biases is highly relevant, as cognitive biases have been found to interact with anxiety symptoms. Results showed that attention, expectancy, and memory biases are involved in BII phobia, while no studies were found on interpretation nor perception biases. Mixed results were found for attention bias, as different studies found different components of attention bias, while others found no attention bias at all. Similarly, some studies found a-priori/a-posteriori expectancy biases, while other studies found only one type of bias. A better understanding of the cognitive particularities of BII phobia may lead to better treatments and ultimately reduce avoidance of needles and blood-related situations, thereby enabling individuals with BII phobia to undergo potentially life-saving medical procedures.

Identifying Variables That Predict Depression Following the General Lockdown During the COVID-19 Pandemic.

This study aimed to define the psychological markers for future development of depression symptoms following the lockdown caused by the COVID-19 outbreak. Based on previous studies, we focused on loneliness, intolerance of uncertainty and emotion estimation biases as potential predictors of elevated depression levels. During the general lockdown in April 2020, 551 participants reported their psychological health by means of various online questionnaires and an implicit task. Out of these participants, 129 took part in a second phase in June 2020. Subjective loneliness during the lockdown rather than objective isolation was the strongest predictor of symptoms of depression 5 weeks later. Younger age and health related worry also predicted higher non-clinical levels of depression and emotional distress. The results support the diathesis-stress model, which posits that a combination of preexisting vulnerabilities along with stressors such as negative life events are among the factors affecting the development of psychopathology. Moreover, our results correspond with those of previous studies conducted worldwide during the COVID-19 pandemic. Taken together, these findings call for focusing on psychological factors, especially among younger people, to identify individuals at risk for future development of depression and to promote new strategies for prevention.

Abnormalities in emotional and motor reactions among young prehypertensive individuals: employing continuous blood pressure analysis.

BACKGROUND: Essential hypertension is an important risk factor for cerebrovascular diseases and a major cause of premature death in industrialized societies. A predisposing factor for essential hypertension is prehypertension: blood pressure (BP) values at rest that are at the higher end of the normal range. Abnormally enhanced cardiovascular responses to motor and emotional tasks have been found as predictors of essential hypertension. Yet, knowledge regarding the BP reaction to aversive stimuli and motor reaction in prehypertension is limited. METHODS: We compared the reaction to aversive and neutral stimuli inducing an emotional response (experiment 1) and to the isometric handgrip exercise (IHE) inducing a motor response (experiment 2), between prehypertensive and normotensive controls. BP reactions were measured and analyzed in a continuous fashion, in contrast to previous studies that averaged BP responses across blocks. We applied a multilevel B-spline model, a continuous analysis that enabled a better understanding of the BP time course and the detection of subtle differences between groups. RESULTS: In both tasks, we found that prehypertensive individuals showed enhanced DBP reactions compared with normotensive controls; prehypertensive individuals exhibited lower BP responses to aversive pictures and higher BP responses to the IHE. These results are in line with previous studies with healthy or hypertensive participants and suggest abnormalities already in the prehypertensive stage. CONCLUSION: Considering the high frequency and health risks related to prehypertension, understanding the autonomic reactions to emotional and motor stimuli in this population is of clinical and theoretical importance and could serve as a behavioural marker to identify at-risk groups.

When two hearts beat as one: Heart-rate synchrony in social anxiety disorder.

Individuals with social anxiety disorder (SAD) experience significant interpersonal impairment. However, little is known about the physiological processes that are associated with interpersonal dysfunction in the disorder. In the present study we examined heart-rate (HR) synchrony in SAD during "getting-acquainted" interactions between opposite-sex partners. Participants included 118 individuals who formed 59 dyads: 30 dyads that included one individual with SAD and one non-socially anxious (NSA) individual (SAD dyads) and 29 dyads that included two NSA individuals (control dyads). Dyads were randomly assigned to either a closeness-generating conversation or a small talk conversation. For closeness-generating conversations, we found that social anxiety was positively associated with HR synchrony in control dyads but negatively associated with HR synchrony in SAD dyads. These results remained when controlling for depressive symptoms and participants' movement. Our findings suggest that in more intimate social contexts, SAD may impair the ability to create HR synchrony between interlocuters and this can have negative relational consequences. Our findings are further discussed in the context of cognitive behavioral and interpersonal models of SAD, and clinical and research implications are delineated.

A single dose of escitalopram blunts the neural response in the thalamus and caudate during monetary loss.

Background: Selective serotonin reuptake inhibitors (SSRIs) show acute effects on the neural processes associated with negative affective bias in healthy people and people with depression. However, whether and how SSRIs also affect reward and punishment processing on a similarly rapid time scale remains unclear. Methods: We investigated the effects of an acute and clinically relevant dose (20 mg) of the SSRI escitalopram on brain response during reward and punishment processing in 19 healthy participants. In a doubleblind, placebo-controlled study using functional MRI, participants performed a well-established monetary reward task at 3 time points: at baseline; after receiving placebo or escitalopram; and after receiving placebo or escitalopram following an 8-week washout period. Results: Acute escitalopram administration reduced blood-oxygen-level-dependent (BOLD) response during punishment feedback in the right thalamus (family-wise error corrected [FWE] p = 0.013 at peak level) and the right caudate head (pFWE = 0.011 at peak level) compared to placebo. We did not detect any significant BOLD changes during reward feedback. Limitations: We included only healthy participants, so interpretation of findings are limited to the healthy human brain and require future testing in patient populations. The paradigm we used was based on monetary stimuli, and results may not be generalizable to other forms of reward. Conclusion: Our findings extend theories of rapid SSRI action on the neural processing of rewarding and aversive stimuli and suggest a specific and acute effect of escitalopram in the punishment neurocircuitry.

Personalized cognitive training: Protocol for individual-level meta-analysis implementing machine learning methods.

Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.

Biased distance estimation in social anxiety disorder: A new avenue for understanding avoidance behavior.

OBJECTIVES: People regulate their interpersonal space appropriately to obtain a comfortable distance for interacting with others. Socially anxious individuals are especially prone to discomfort from and fear of physical closeness, leading them to prefer a greater interpersonal distance from others. Previous studies also indicate that fear can enhance the threat-related elements of a threatening stimulus. For example, spider phobia is associated with estimating spiders as bigger and faster than they actually are. Nonetheless, it is still unclear whether the preference of those with social anxiety disorder (SAD) to maintain greater distance from others is associated with biased estimations of interpersonal distance. MATERIALS AND METHODS: A total of 87 participants (44 clinically diagnosed with SAD and 43 control) performed validated computerized and ecological tasks in a real-life setting while social space estimations and preferences were measured. RESULTS: Participants with SAD felt comfortable when maintaining a greater distance from unfamiliar others compared to the control group and estimated unfamiliar others to be closer to them than they actually were. Moreover, the estimation bias predicted their preferred distance from strangers, indicating a strong association between estimation bias severity and actual approach-avoidance behavior. CONCLUSION: Our findings indicate that distance estimation bias underlies avoidance behavior in SAD, suggesting the involvement of a new cognitive mechanism in personal space regulation.

Using machine learning-based analysis for behavioral differentiation between anxiety and depression.

Anxiety and depression are distinct-albeit overlapping-psychiatric diseases, currently diagnosed by self-reported-symptoms. This research presents a new diagnostic methodology, which tests rigorously for differences in cognitive biases among subclinical anxious and depressed individuals. 125 participants were divided into four groups based on the levels of their anxiety and depression symptoms. A comprehensive behavioral test battery detected and quantified various cognitive-emotional biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The prediction model for differentiating between symptomatic participants (i.e., high symptoms of depression, anxiety, or both) compared to the non-symptomatic control group revealed a 71.44% prediction accuracy for the former (sensitivity) and 70.78% for the latter (specificity). 68.07% and 74.18% prediction accuracy was obtained for a two-group model with high depression/anxiety, respectively. The analysis also disclosed which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.