Emotional Biases and Recurrence in Major Depressive Disorder. Results of 2.5 Years Follow-Up of Drug-Free Cohort Vulnerable for Recurrence

An interesting factor explaining recurrence risk in Major Depressive Disorder (MDD) may be neuropsychological functioning, i.e., processing of emotional stimuli/information. Negatively biased processing of emotional stimuli/information has been found in both acute and (inconclusively) remitted states of MDD, and may be causally related to recurrence of depression. We aimed to investigate self-referent, memory and interpretation biases in recurrently depressed patients in remission and relate these biases to recurrence. We included 69 remitted recurrent MDD-patients (rrMDD-patients), 35–65 years, with ≥2 episodes, voluntarily free of antidepressant maintenance therapy for at least 4 weeks. We tested self-referent biases with an emotional categorization task, bias in emotional memory by free recall of the emotion categorization task 15 min after completing it, and interpretation bias with a facial expression recognition task. We compared these participants with 43 never-depressed controls matched for age, sex and intelligence. We followed the rrMDD-patients for 2.5 years and assessed recurrent depressive episodes by structured interview. The rrMDD-patients showed biases toward emotionally negative stimuli, faster responses to negative self-relevant characteristics in the emotional categorization, better recognition of sad faces, worse recognition of neutral faces with more misclassifications as angry or disgusting faces and less misclassifications as neutral faces (0.001 < p < 0.05). Of these, the number of misclassifications as angry and the overall performance in the emotional memory task were significantly associated with the time to recurrence (p ≤ 0.04), independent of residual symptoms and number of previous episodes. In a support vector machine data-driven model, prediction of recurrence-status could best be achieved (relative to observed recurrence-rate) with demographic and childhood adversity parameters (accuracy 78.1%; 1-sided p = 0.002); neuropsychological tests could not improve this prediction. Our data suggests a persisting (mood-incongruent) emotional bias when patients with recurrent depression are in remission. Moreover, these persisting biases might be mechanistically important for recurrence and prevention thereof

Depression recurrence is accompanied by longer periods in default mode and more frequent attentional and reward processing dynamic brain-states during resting-state activity

Recurrence in major depressive disorder (MDD) is common, but neurobiological models capturing vulnerability for recurrences are scarce. Disturbances in multiple resting-state networks have been linked to MDD, but most approaches focus on stable (vs. dynamic) network characteristics. We investigated how the brain's dynamical repertoire changes after patients transition from remission to recurrence of a new depressive episode. Sixty two drug-free, MDD-patients with ≥2 episodes underwent a baseline resting-state fMRI scan when in remission. Over 30-months follow-up, 11 patients with a recurrence and 17 matched-remitted MDD-patients without a recurrence underwent a second fMRI scan. Recurrent patterns of functional connectivity were characterized by applying Leading Eigenvector Dynamics Analysis (LEiDA). Differences between baseline and follow-up were identified for the 11 non-remitted patients, while data from the 17 matched-remitted patients was used as a validation dataset. After the transition into a depressive state, basal ganglia-anterior cingulate cortex (ACC) and visuo-attentional networks were detected significantly more often, whereas default mode network activity was found to have a longer duration. Additionally, the fMRI signal in the basal ganglia-ACC areas underlying the reward network, were significantly less synchronized with the rest of the brain after recurrence (compared to a state of remission). No significant changes were observed in the matched-remitted patients who were scanned twice while in remission. These findings characterize changes that may be associated with the transition from remission to recurrence and provide initial evidence of altered dynamical exploration of the brain's repertoire of functional networks when a recurrent depressive episode occurs.</p

Depression recurrence is accompanied by longer periods in default mode and more frequent attentional and reward processing dynamic brain-states during resting-state activity

Recurrence in major depressive disorder (MDD) is common, but neurobiological models capturing vulnerability for recurrences are scarce. Disturbances in multiple resting-state networks have been linked to MDD, but most approaches focus on stable (vs. dynamic) network characteristics. We investigated how the brain's dynamical repertoire changes after patients transition from remission to recurrence of a new depressive episode. Sixty two drug-free, MDD-patients with ≥2 episodes underwent a baseline resting-state fMRI scan when in remission. Over 30-months follow-up, 11 patients with a recurrence and 17 matched-remitted MDD-patients without a recurrence underwent a second fMRI scan. Recurrent patterns of functional connectivity were characterized by applying Leading Eigenvector Dynamics Analysis (LEiDA). Differences between baseline and follow-up were identified for the 11 non-remitted patients, while data from the 17 matched-remitted patients was used as a validation dataset. After the transition into a depressive state, basal ganglia-anterior cingulate cortex (ACC) and visuo-attentional networks were detected significantly more often, whereas default mode network activity was found to have a longer duration. Additionally, the fMRI signal in the basal ganglia-ACC areas underlying the reward network, were significantly less synchronized with the rest of the brain after recurrence (compared to a state of remission). No significant changes were observed in the matched-remitted patients who were scanned twice while in remission. These findings characterize changes that may be associated with the transition from remission to recurrence and provide initial evidence of altered dynamical exploration of the brain's repertoire of functional networks when a recurrent depressive episode occurs.</p

Digital phenotyping and the COVID-19 pandemic:Capturing behavioral change in patients with psychiatric disorders

Contains fulltext : 227418.pdf (publisher's version ) (Closed access)The COVID-19 pandemic has led to unprecedented societal changes limiting us in our mobility and our ability to connect with others in person. These unusual but widespread changes provide a unique opportunity for studies using digital phenotyping tools. Digital phenotyping tools, such as mobile passive monitoring platforms (MPM), provide a new perspective on human behavior and hold promise to improve human behavioral research. However, there is currently little evidence that these tools can reliably detect changes in behavior. Considering the Considering the COVID-19 pandemic as a high impact common environmental factor we studied potential impact on behavior of participants using our mobile passive monitoring platform BEHAPP that was ambulatory tracking them during the COVID-19 pandemic. We pooled data from three MPM studies involving Schizophrenia (SZ), Major Depressive Disorder (MDD) and Bipolar Disorder (BD) patients (N = 12). We compared the data collected on weekdays during three weeks prior and three weeks subsequent to the start of the quarantine. We hypothesized an increase in communication and a decrease in mobility. We observed a significant increase in the total time spent on communication applications (median 179 and 243 min per week respectively, p = 0.005), and a significant decrease in the number of unique places visited (median 6 and 3 visits per week respectively, p = 0.007), while the total time spent at home did not change significantly (median 64 and 77 h per week, respectively, p = 0.594). The data provides a proof of principle that digital phenotyping tools can identify changes in human behavior incited by a common external environmental factor.6 p

Coping with Covid-19

A large research project aiming to assess severeal research questions, regarding the interplay of Covid-19 related stress, anxiety and depressive symptoms

Age related differences in symptom networks of overall psychological functioning in a sample of patients diagnosed with anxiety, obsessive compulsive disorder, or posttraumatic stress disorder

Anxiety disorders, obsessive compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) are among the most prevalent mental disorders across the lifespan. Yet, it has been suggested that there are phenomenological differences and differences in treatment outcomes between younger and older adults. There is, however, no consensus about the age that differentiates younger adults from older adults. As such, studies use different cut-off ages that are not well founded theoretically nor empirically. Network tree analysis was used to identify at what age adults differed in their symptom network of psychological functioning in a sample of Dutch patients diagnosed with anxiety disorders, OCD, or PTSD (N = 27,386). The networktree algorithm found a first optimal split at age 30 and a second split at age 50. Results suggest that differences in symptom networks emerge around 30 and 50 years of age, but that the core symptoms related to anxiety remain stable across age. If our results will be replicated in future studies, our study may suggest using the age split of 30 or 50 years in studies that aim to investigate differences across the lifespan. In addition, our study may suggest that age-related central symptoms are an important focus during treatment monitoring.</p

Age related differences in symptom networks of overall psychological functioning in a sample of patients diagnosed with anxiety, obsessive compulsive disorder, or posttraumatic stress disorder

Anxiety disorders, obsessive compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) are among the most prevalent mental disorders across the lifespan. Yet, it has been suggested that there are phenomenological differences and differences in treatment outcomes between younger and older adults. There is, however, no consensus about the age that differentiates younger adults from older adults. As such, studies use different cut-off ages that are not well founded theoretically nor empirically. Network tree analysis was used to identify at what age adults differed in their symptom network of psychological functioning in a sample of Dutch patients diagnosed with anxiety disorders, OCD, or PTSD (N = 27,386). The networktree algorithm found a first optimal split at age 30 and a second split at age 50. Results suggest that differences in symptom networks emerge around 30 and 50 years of age, but that the core symptoms related to anxiety remain stable across age. If our results will be replicated in future studies, our study may suggest using the age split of 30 or 50 years in studies that aim to investigate differences across the lifespan. In addition, our study may suggest that age-related central symptoms are an important focus during treatment monitoring.</p