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.

Spatial attention impairments are characterized by specific electro-encephalographic correlates and partially mediate the association between early life stress and anxiety

Although impaired attention is a diagnostic feature of anxiety disorders, we lack an understanding of which aspects of attention are impaired, the neurobiological basis of these impairments, and the contribution of stressors. To address these gaps in knowledge, we developed and tested behavioral tasks designed to parse the subdomains of attention impairments associated with anxiety symptoms and used electro-encephalographic (EEG) recordings to probe the neural basis of attentional performance. Participants were n = 55 individuals aged 18-35 with mild-to-moderate mood and anxiety symptoms. We also assessed stressful life events that may impact mental health and attention abilities, including stressors that occurred in early life before age 18 years. Severity of anxiety was found to be specifically associated with impairments in spatial attention but not feature-based attention. These impairments in spatial attention also partially mediated the association between early-life stressors and anxiety symptoms. Impairments in spatial selective attention were associated with decreased posterior alpha oscillations in EEG recordings in a subsample of participants, whereas spatial divided attention impairments were associated with decreased frontocentral theta oscillations. Our results provide a thorough characterization of attention impairments associated with anxiety, their EEG correlates, and the impact of stressors both in early life and adulthood.

Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks

We describe the design and evaluation of a sub-clinical digital assessment tool that integrates digital biomarkers of depression. Based on three standard cognitive tasks (D2 Test of Attention, Delayed Matching to Sample Task, Spatial Working Memory Task) on which people with depression have been known to perform differently than a control group, we iteratively designed a digital assessment tool that could be deployed outside of laboratory contexts, in uncontrolled home environments on computer systems with widely varying system characteristics (e.g., displays resolution, input devices). We conducted two online studies, in which participants used the assessment tool in their own homes, and completed subjective questionnaires including the Patient Health Questionnaire (PHQ-9)-a standard self-report tool for assessing depression in clinical contexts. In a first study (n = 269), we demonstrate that each task can be used in isolation to significantly predict PHQ-9 scores. In a second study (n = 90), we replicate these results and further demonstrate that when used in combination, behavioral metrics from the three tasks significantly predicted PHQ-9 scores, even when taking into account demographic factors known to influence depression such as age and gender. A multiple regression model explained 34.4% of variance in PHQ-9 scores with behavioral metrics from each task providing unique and significant contributions to the prediction.

Recall and Self-Relevance of Emotional Words Predict Subjective Self-Evaluation of Cognition in Patients with MTLE with or without Depressive Symptoms

We examined whether word processing is associated with subjective self-evaluation of cognition in patients with mesial temporal lobe epilepsy (MTLE) as a function of their depressive symptoms. MTLE patients with (MTLE +d, N = 28) or without (MTLE -d, N = 11) depression were compared to pair-matched healthy control participants on free recall and self-relevance ratings of emotionally valenced words. Correlation and hierarchical analyses were conducted to investigate whether the subjective self-evaluation of cognition in MTLE patients is predicted by the negative emotional bias reflected in task performance. MTLE +d patients endorsed as self-relevant fewer positive words and more negative words than the MTLE -d patients and healthy participants. They also self-evaluated their cognition poorer than the MTLE -d patients. Analyses indicated that recall and self-endorsement of emotional words predicted both self-evaluation of cognition as well as epilepsy duration. Our findings indicate that negative self-relevance emotional bias is observed in MTLE patients and is predictive of subjective self-evaluation of cognition. Application of brief behavioral tasks probing emotional functions could be valuable for clinical research and practice in the patients with MTLE.

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.

Attentional bias in individuals with depression and adverse childhood experiences: influence of the noradrenergic system?

RATIONALE

Major depressive disorder (MDD) is a severe mental disorder with affective, cognitive, and somatic symptoms. Mood congruent cognitive biases, including a negative attentional bias, are important for development, maintenance, and recurrence of depressive symptoms. MDD is associated with maladaptive changes in the biological stress systems such as dysregulations of central noradrenergic alpha2-receptors in the locus coeruleus-noradrenergic system, which can affect cognitive processes including attention. Patients with adverse childhood experiences (ACE), representing severe stress experiences in early life, might be particularly affected.

OBJECTIVES

With an experimental design, we aimed to gain further knowledge about the role of noradrenergic activity for attentional bias in MDD patients with and without ACE.

METHODS

We tested the effect of increased noradrenergic activity induced by the alpha2-receptor blocker yohimbine on attentional bias in a placebo-controlled repeated measures design. Four groups were included as follows: MDD patients with and without ACE, and healthy participants with and without ACE (total N = 128, all without antidepressant medication).

RESULTS

A significant effect of MDD on attentional bias scores of sad face pictures (p = .037) indicated a facilitated attentional processing of sad face pictures in MDD patients (compared to non-MDD individuals). However, we found no such effect of ACE. For attentional bias of happy face pictures, we found no significant effects of MDD and ACE. Even though a higher increase of blood pressure and salivary alpha-amylase following yohimbine compared to placebo indicated successful noradrenergic stimulation, we found no significant effects of yohimbine on attentional bias of happy or sad face pictures.

CONCLUSIONS

Our results are consistent with the hypothesis of a negative attentional bias in MDD patients. However, as we found no effect of ACE or yohimbine, further research is needed to understand the mechanisms by which ACE increases the risk of MDD and to understand the biological basis of the MDD-related negative attentional bias.

Affective Processing Biases in Relation to Past, Current, and Future Depression in Children and Adolescents

BACKGROUND

The affective go/no-go (AGN) task has been used to assess affective biases in attention set-shifting and deficits in inhibitory control of emotional information among depressed youth, but results have been inconsistent. We aimed to test AGN robustness and clarify temporal relationships between depressive symptoms and affective processing in youth.

METHODS

We evaluated AGN performance twice (Time 1 N = 306; Time 2 N = 238) in relation to current, previous, and future depression in the same children/adolescents with depression and those without diagnoses who participated in the Longitudinal Assessment of Manic Symptoms (LAMS) study. Mixed repeated ANCOVAs were powered to detect small-medium group by valence interactions in response latency and errors. Supplemental regression analyses examined depressive symptoms as a continuous variable in relation to AGN performance.

RESULTS

No clear pattern emerged, mirroring the broader AGN literature. In primary analyses, group by valence interactions were only observed at one AGN administration; none replicated across administrations. Similarly, in regression analyses depressive symptoms had no relation to affective processing biases/deficits at AGN Time 1, though some relationships were detected between symptoms and AGN Time 2.

LIMITATIONS

Relatively few youth met criteria for a depressive disorder, though analyses were appropriately powered and supplemental analyses examined depressive symptoms continuously. Comparison groups were not healthy controls at recruitment but were free from any Axis I disorder at AGN administration.

CONCLUSIONS

Given the inconsistency of AGN findings, attention should be focused on tasks that provide more sensitive, robust measures of emotional information processing in depressed youth.

Paying attention to attention in depression

Attention is the gate through which sensory information enters our conscious experiences. Oftentimes, patients with major depressive disorder (MDD) complain of concentration difficulties that negatively impact their day-to-day function, and these attention problems are not alleviated by current first-line treatments. In spite of attention's influence on many aspects of cognitive and emotional functioning, and the inclusion of concentration difficulties in the diagnostic criteria for MDD, the focus of depression as a disease is typically on mood features, with attentional features considered less of an imperative for investigation. Here, we summarize the breadth and depth of findings from the cognitive neurosciences regarding the neural mechanisms supporting goal-directed attention in order to better understand how these might go awry in depression. First, we characterize behavioral impairments in selective, sustained, and divided attention in depressed individuals. We then discuss interactions between goal-directed attention and other aspects of cognition (cognitive control, perception, and decision-making) and emotional functioning (negative biases, internally-focused attention, and interactions of mood and attention). We then review evidence for neurobiological mechanisms supporting attention, including the organization of large-scale neural networks and electrophysiological synchrony. Finally, we discuss the failure of current first-line treatments to alleviate attention impairments in MDD and review evidence for more targeted pharmacological, brain stimulation, and behavioral interventions. By synthesizing findings across disciplines and delineating avenues for future research, we aim to provide a clearer outline of how attention impairments may arise in the context of MDD and how, mechanistically, they may negatively impact daily functioning across various domains.

Positive cognitive behavior therapy in the treatment of depression: A randomized order within-subject comparison with traditional cognitive behavior therapy

Previous research suggests that a stronger focus on positive emotions and positive mental health may improve efficacy of Cognitive Behavior Therapy (CBT). Objectives were to compare differential improvement of depressive symptoms (primary outcome), positive affect, and positive mental health indices during positive CBT (P-CBT; CBT in a solution-focused framework, amplified with optional positive psychology exercises) versus traditional, problem-focused CBT (T-CBT). Forty-nine patients with major depressive disorder (recruited in an outpatient mental health care facility specialized in mood disorders) received two treatment blocks of eight sessions each (cross-over design, order randomized). Intention-To-Treat mixed regression modelling indicated that depressive symptoms improved similarly during the first, but significantly more in P-CBT compared to T-CBT during the second treatment block. Rate of improvement on the less-frequently measured secondary outcomes was not significantly different. However, P-CBT was associated with significantly higher rates of clinically significant or reliable change for depression, negative affect, and happiness. Effect sizes for the combined treatment were large (pre-post Cohen's d = 2.71 for participants ending with P-CBT, and 1.85 for participants ending with T-CBT). Positive affect, optimism, subjective happiness and mental health reached normative population averages after treatment. Overall, findings suggest that explicitly focusing on positive emotions efficiently counters depressive symptoms.

Neural architecture supporting active emotion processing in children: A multivariate approach

BACKGROUND

Adaptive emotion processing is critical for nearly all aspects of social and emotional functioning. There are distinct developmental trajectories associated with improved emotion processing, with a protracted developmental course for negative or complex emotions. The specific changes in neural circuitry that underlie this development, however are still scarcely understood. We employed a multivariate approach in order to elucidate distinctions in complex, naturalistic emotion processing between childhood and adulthood.

METHOD

Twenty-one adults (M±SD age = 26.57 ± 5.08 years) and thirty children (age = 7.75 ± 1.80 years) completed a free-viewing movie task during BOLD fMRI scanning. This task was designed to assess naturalistic processing of movie clips portraying positive, negative, and neutral emotions. Multivariate support vector machines (SVM) were trained to classify age groups based on neural activation during the task.

RESULTS

SVMs were able to successfully classify condition (positive, negative, and neutral) across all participants with high accuracy (61.44%). SVMs could successfully distinguish adults and children within each condition (ps < 0.05). Regions that informed the age group SVMs were associated with sensory and socio-emotional processing (inferior parietal lobule), emotion regulation (inferior frontal gyrus), and sensory regions of the temporal and occipital lobes.

CONCLUSIONS

These results point to distributed differences in activation between childhood and adulthood unique to each emotional condition. In the negative condition specifically, there is evidence for a shift in engagement from regions of sensory and socio-emotional integration to emotion regulation regions between children and adults. These results provide insight into circuitry contributing to maturation of emotional processing across development.

Increased neural sensitivity to self-relevant stimuli in major depressive disorder

The current research examined how individuals with depression process emotional, self-relevant stimuli. Across two studies, individuals with depression and healthy controls read stimuli that varied in self-relevance while EEG data were recorded. We examined the late positive potential (LPP), an ERP component that captures the dynamic allocation of attention to motivationally salient stimuli. In Study 1, participants read single words in a passive-viewing task. Participants viewed negative, positive, or neutral words that were either normative or self-generated. Exploratory analyses indicated that participants with depression exhibited affective modulation of the LPP for self-generated stimuli only (both positive and negative) and not for normative stimuli; healthy controls exhibited similar affective modulation of the LPP for both self-relevant and normative stimuli. In Study 2, using a separate sample and a different task, stimuli were provided within the context of sentence stems referring to the self or other people. Participants with depression were more likely to endorse negative self-referent sentences and reject positive ones compared to healthy controls. Depressed participants also exhibited an increased LPP to negative stimuli compared to positive or neutral stimuli. Together, these two studies suggest that depression is characterized by relatively increased sensitivity to affective self-relevant stimuli, perhaps in the context of a broader reduction in emotional reactivity to stimuli that are not self-relevant. Thus, depression may be characterized by a more nuanced pattern based on the degree of stimulus self-relevance than either a global decrease or increase in reactivity to affective stimuli.

Attention Bias in Rumination and Depression: Cognitive Mechanisms and Brain Networks

Depressed individuals exhibit biased attention to negative emotional information. However, much remains unknown about (1) the neurocognitive mechanisms of attention bias (e.g., qualities of negative information that evoke attention bias, or functional brain network dynamics that may reflect a propensity for biased attention) and (2) distinctions in the types of attention bias related to different dimensions of depression (e.g., ruminative depression). Here, in 50 women, clinical depression was associated with facilitated processing of negative information only when such information was self-descriptive and task-relevant. However, among depressed individuals, trait rumination was associated with biases towards negative self-descriptive information regardless of task goals, especially when negative self-descriptive material was paired with self-referential images that should be ignored. Attention biases in ruminative depression were mediated by dynamic variability in frontoinsular resting-state functional connectivity. These findings highlight potential cognitive and functional network mechanisms of attention bias specifically related to the ruminative dimension of depression.

Differences in emotional stimuli processing in subjects with MTLE with and without depression

In healthy people, a preference in attention maintenance and memory for words with emotional valence comparing to neutral words has been shown. The pattern of emotional stimuli processing may be different in people with mesial temporal lobe epilepsy (MTLE) and it may be sensitive to the presence of depressive symptoms. In order to explore these possibilities, we applied the emotional spatial cueing attentional task and the free recall memory task to participants (N=39) with MTLE and compared them with healthy controls. We hypothesized that the pattern of maintaining attention and remembering emotional words is different in people with MTLE. Current literature indicates that this pattern will change from positive bias in the controls, though no emotional bias in the participants with MTLE without depression (MTLE-d), and in this work we examined this pattern in the participants with MTLE with depressive symptoms (MTLE+d). Our results show that in both attention and memory, control subjects exhibit positive emotional bias, the subjects with MTLE-d show nonemotional bias and the subjects with MTLE+d have bias away from positive words. Participants with MTLE+d maintained attention for positive words shorter than others. Participants with MTLE+d had worse recall for positive words than the participants with MTLE-d and for all words when compared to controls. We found that faster attention disengagement from positive words and worse memory for positive words is associated with elevated levels of depressive symptoms.

Facial and prosodic emotion recognition in social anxiety disorder

INTRODUCTION

Patients with social anxiety disorder (SAD) have a cognitive preference to negatively evaluate emotional information. In particular, the preferential biases in prosodic emotion recognition in SAD have been much less explored. The present study aims to investigate whether SAD patients retain negative evaluation biases across visual and auditory modalities when given sufficient response time to recognise emotions.

METHODS

Thirty-one SAD patients and 31 age- and gender-matched healthy participants completed a culturally suitable non-verbal emotion recognition task and received clinical assessments for social anxiety and depressive symptoms. A repeated measures analysis of variance was conducted to examine group differences in emotion recognition.

RESULTS

Compared to healthy participants, SAD patients were significantly less accurate at recognising facial and prosodic emotions, and spent more time on emotion recognition. The differences were mainly driven by the lower accuracy and longer reaction times for recognising fearful emotions in SAD patients. Within the SAD patients, lower accuracy of sad face recognition was associated with higher severity of depressive and social anxiety symptoms, particularly with avoidance symptoms.

CONCLUSION

These findings may represent a cross-modality pattern of avoidance in the later stage of identifying negative emotions in SAD. This pattern may be linked to clinical symptom severity.

Anxiety and depression: toward overlapping and distinctive features

This Special Issue of Cognition and Emotion addresses one of the cardinal concerns of affective science, which is overlapping and distinctive features of anxiety and depression. A central finding in the study of anxiety and depression is that they are moderately highly correlated with each other. This leads us to the question: What is behind this co-occurrence? Possible explanations relate to poor discriminant validity of measures; both emotional states are associated with negative affect; stressful life events; impaired cognitive processes; they share a common biological/genetic diathesis. However, despite a set of common (nonspecific) features, anxiety and depression are clearly not identical emotional states. Differences between them might be best viewed, for example, through their heterogeneous and multi-layered nature, adaptive functions and relations with regulatory processes, positive affect, and motivation or complex cognitive processes. In this introduction we consider several approaches (e.g. functional approach; tripartite model and content-specificity hypothesis) to which most research in this Special Issue is relevant. In addition, we have asked contributors to this Special Issue to indicate how their own studies on comparisons between anxiety and depression and models on anxiety and depression move this area of research to more mature science with applicability.

Combined behavioural markers of cognitive biases are associated with anhedonia

Biases towards negative information, as well as away from positive information, are associated with psychopathology. Examining biases in multiple processes has been theorised to be more predictive than examining bias in any process alone. Anhedonia is a core symptom of psychopathology and predictive of future psychopathological symptoms. Finding that combined biases are associated with anhedonia would advance knowledge of the nature of emotional processing biases and the value of objective performance-based measures for identifying early risk markers. Participants (N = 139) completed tasks that assess latency bias (dot probe) and biased recognition (two-alternative forced-choice) of emotional information, as well as an anhedonia measure. An index was computed for each task's performance reflecting biased processing of positive and negative words. Only combined biases on both tasks were associated with anhedonia. Attentional bias was positively associated with anhedonia, but only when recognition bias for emotional words was high. Thus, assessing biases in multiple domains increased sensitivity to uncover relationships between emotional processing biases and anhedonic symptoms.

Cognitive-Behavioral Therapy: Nature and Relation to Non-Cognitive Behavioral Therapy

Since the introduction of Beck's cognitive theory of emotional disorders, and their treatment with psychotherapy, cognitive-behavioral approaches have become the most extensively researched psychological treatment for a wide variety of disorders. Despite this, the relative contribution of cognitive to behavioral approaches to treatment are poorly understood and the mechanistic role of cognitive change in therapy is widely debated. We critically review this literature, focusing on the mechanistic role of cognitive change across cognitive and behavioral therapies for depressive and anxiety disorders.