Pediatric attention deficit/hyperactivity disorder (ADHD) is a heterogeneous condition. In particular, children with ADHD display varying profiles of dispositional traits, as assessed through ...temperament and personality questionnaires. Previous data-driven community detection analyses based on temperament dimensions identified an irritable profile of patients with ADHD, uniquely characterized by elevated emotional dysregulation symptoms. Belonging to this profile increased the risk of developing comorbid disorders. Here, we investigated whether we could replicate this profile in a sample of 178 children with ADHD, using community detection based on personality dimensions. Stability of the identified profiles, of individual classifications, and clinical prediction were longitudinally assessed over a 1-year interval. Three personality profiles were detected: The first two profiles had high levels of neuroticism, with the first displaying higher ADHD severity and lower openness to experience (profile 1;
N
= 38), and the second lower agreeableness (profile 2;
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= 73). The third profile displayed scores closer to the normative range on all five factors (profile 3;
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= 67). The identified profiles did only partially replicate the temperament-based profiles previously reported, as higher levels of neuroticism were found in two of the three detected profiles. Nonetheless, despite changes in individual classifications, the profiles themselves were highly stable over time and of clinical predictive value. Whereas children belonging to profiles 1 and 2 benefited from starting medication, children in profile 3 did not. Hence, belonging to an emotionally dysregulated profile at baseline predicted the effect of medication at follow-up over and above initial ADHD symptom severity. This finding suggests that personality profiles could play a role in predicting treatment response in ADHD.
Introduction: The global disease burden of major depressive disorder urgently requires prevention in high-risk individuals, such as recently discovered insomnia subtypes. Previous studies targeting ...insomnia with fully automated eHealth interventions to prevent depression are inconclusive: dropout was high and likely biased, and depressive symptoms in untreated participants on average improved rather than worsened. Objective: This randomized controlled trial aimed to efficiently prevent the worsening of depressive symptoms by selecting insomnia subtypes at high risk of depression for internet-based circadian rhythm support (CRS), cognitive behavioral therapy for insomnia (CBT-I), or their combination (CBT-I+CRS), with online therapist guidance to promote adherence. Methods: Participants with an insomnia disorder subtype conveying an increased risk of depression (n = 132) were randomized to no treatment (NT), CRS, CBT-I, or CBT-I+CRS. The Inventory of Depressive Symptomatology – Self Report (IDS-SR) was self-administered at baseline and at four follow-ups spanning 1 year. Results: Without treatment, depressive symptoms indeed worsened (d = 0.28, p = 0.041) in high-risk insomnia, but not in a reference group with low-risk insomnia. Therapist-guided CBT-I and CBT-I+CRS reduced IDS-SR ratings across all follow-up assessments (respectively, d = –0.80, p = 0.001; d = –0.95, p < 0.001). Only CBT-I+CRS reduced the 1-year incidence of clinically meaningful worsening (p = 0.002). Dropout during therapist-guided interventions was very low (8%) compared to previous automated interventions (57–62%). Conclusions: The findings tentatively suggest that the efficiency of population-wide preventive strategies could benefit from the possibility to select insomnia subtypes at high risk of developing depression for therapist-guided digital CBT-I+CRS. This treatment may provide effective long-term prevention of worsening of depressive symptoms. Trial registration: the Netherlands Trial Register (NL7359).
Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and ...biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample.
We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging.
The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = −0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = −0.10), and mOFC was negatively associated with anhedonia (partial cor. = −0.05).
This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression).
This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.
•We evaluated the utility of brain-symptom network models of depressive symptoms.•No brain-symptom associations in a network with depressive symptom severity score.•Individual depressive symptoms showed various connections with neural substrates.•Lower cortical thickness (insula, mOFC, cingulate) associated with symptoms.
Many studies have found that depressive complaints are associated with the regulation of affect while facing stress. Individuals inclined towards the experience of negative affect are more vulnerable ...to developing depressive complaints, while frequent experiences of positive affect buffer the development of such complaints. To better understand the dynamic mechanisms between affect and depression in detail, this paper investigates how different evaluations of depressive complaints over a prolonged period of stress relate to fluctuations in affect. We included assessments of affect (Positive and Negative Affect Scale) and depressive complaints (Patient Health Questionnaire) in 228 participants who completed at least 20 assessments spanning between 9-14 weeks. We (i) explored affect trajectories for different evolutions of depressive complaints, (ii) estimated longitudinal multilevel network models to examine the direct interplay between affect and depressive complaints in detail, and (iii) investigated how person-specific network density relates to changes in depressive complaints over time. When separating affect trajectories based on depressive complaints, we identified that individuals consistently experiencing depressive complaints (PHQ > 4) report higher negative affect levels than positive affect. Contrary, individuals consistently reporting no depressive complaints (PHQ ≤4) showed the opposite pattern. Furthermore, the longitudinal networks included many and strong relations between the affects and depressive complaints variables. Lastly, we found a strong correlation between the density of person-specific networks and their change (aggravation or alleviation) in depressive complaints. We conclude that affect fluctuations and evolutions of depressive complaints are directly related both within- and across individuals over time.
People with Insomnia Disorder tend to underestimate their sleep compared with polysomnography or actigraphy, a phenomenon known as paradoxical insomnia or sleep‐state misperception. Previous studies ...suggested that night‐to‐night variability could be an important feature differentiating subtypes of misperception. This study aimed for a data‐driven definition of misperception subtypes revealed by multiple sleep features including night‐to‐night variability. We assessed features describing the mean and dispersion of misperception and objective and subjective sleep duration from 7‐night diary and actigraphy recordings of 181 people with Insomnia Disorder and 55 people without sleep complaints. A minimally collinear subset of features was submitted to latent class analysis for data‐driven subtyping. Analysis revealed three subtypes, best discriminated by three of five selected features: an individual’s shortest reported subjective sleep duration; and the mean and standard deviation of misperception. These features were on average 5.4, −0.0 and 0.5 hr in one subtype accommodating the majority of good sleepers; 4.1, −1.4 and 1.0 hr in a second subtype representing the majority of people with Insomnia Disorder; and 1.7, −2.2 and 1.5 hr in a third subtype representing a quarter of people with Insomnia Disorder and hardly any good sleepers. Subtypes did not differ on an individual’s objective sleep duration mean (6.9, 7.2 and 6.9 hr) and standard deviation (0.8, 0.8 and 0.9 hr). Data‐driven analysis of naturalistic sleep revealed three subtypes that markedly differed in misperception features. Future studies may include misperception subtype to investigate whether it contributes to the unexplained considerable individual variability in treatment response.
In standard practice, sleep is classified into distinct stages by human observers according to specific rules as for instance specified in the AASM manual. We here show proof of principle for a ...conceptualization of sleep stages as attractor states in a nonlinear dynamical system in order to develop new empirical criteria for sleep stages.
EEG (single channel) of two healthy sleeping participants was used to demonstrate this conceptualization. Firstly, distinct EEG epochs were selected, both detected by a MLR classifier and through manual scoring. Secondly, change point analysis was used to identify abrupt changes in the EEG signal. Thirdly, these detected change points were evaluated on whether they were preceded by early warning signals.
Multiple change points were identified in the EEG signal, mostly in interplay with N2. The dynamics before these changes revealed, for a part of the change points, indicators of generic early warning signals, characteristic of complex systems (e.g., ecosystems, climate, epileptic seizures, global finance systems).
The sketched new framework for studying critical transitions in sleep EEG might benefit the understanding of individual and pathological differences in the dynamics of sleep stage transitions. Formalising sleep as a nonlinear dynamical system can be useful for definitions of sleep quality, i.e. stability and accessibility of an equilibrium state, and disrupted sleep, i.e. constant shifting between instable sleep states.
It has been difficult to find robust brain structural correlates of the overall severity of major depressive disorder (MDD). We hypothesized that specific symptoms may better reveal correlates and ...investigated this for the severity of insomnia, both a key symptom and a modifiable major risk factor of MDD. Cortical thickness, surface area and subcortical volumes were assessed from T1-weighted brain magnetic resonance imaging (MRI) scans of 1053 MDD patients (age range 13-79 years) from 15 cohorts within the ENIGMA MDD Working Group. Insomnia severity was measured by summing the insomnia items of the Hamilton Depression Rating Scale (HDRS). Symptom specificity was evaluated with correlates of overall depression severity. Disease specificity was evaluated in two independent samples comprising 2108 healthy controls, and in 260 clinical controls with bipolar disorder. Results showed that MDD patients with more severe insomnia had a smaller cortical surface area, mostly driven by the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus. Associations were specific for insomnia severity, and were not found for overall depression severity. Associations were also specific to MDD; healthy controls and clinical controls showed differential insomnia severity association profiles. The findings indicate that MDD patients with more severe insomnia show smaller surfaces in several frontoparietal cortical areas. While explained variance remains small, symptom-specific associations could bring us closer to clues on underlying biological phenomena of MDD.
In the absence of a vaccine, social distancing behaviour is pivotal to mitigate COVID-19 virus spread. In this large-scale behavioural experiment, we gathered data during Smart Distance Lab: The Art ...Fair (n = 839) between August 28 and 30, 2020 in Amsterdam, the Netherlands. We varied walking directions (bidirectional, unidirectional, and no directions) and supplementary interventions (face mask and buzzer to alert visitors of 1.5 metres distance). We captured visitors' movements using cameras, registered their contacts (defined as within 1.5 metres) using wearable sensors, and assessed their attitudes toward COVID-19 as well as their experience during the event using questionnaires. We also registered environmental measures (e.g., humidity). In this paper, we describe this unprecedented, multi-modal experimental data set on social distancing, including psychological, behavioural, and environmental measures. The data set is available on figshare and in a MySQL database. It can be used to gain insight into (attitudes toward) behavioural interventions promoting social distancing, to calibrate pedestrian models, and to inform new studies on behavioural interventions.
The principal goals of experimental psychopathology (EPP) research are to offer insights into the pathogenic mechanisms of mental disorders and to provide a stable ground for the development of ...clinical interventions. The main message of the present article is that those goals are better served by the adoption of Bayesian statistics than by the continued use of null-hypothesis significance testing (NHST). In the first part of the article we list the main disadvantages of NHST and explain why those disadvantages limit the conclusions that can be drawn from EPP research. Next, we highlight the advantages of Bayesian statistics. To illustrate, we then pit NHST and Bayesian analysis against each other using an experimental data set from our lab. Finally, we discuss some challenges when adopting Bayesian statistics. We hope that the present article will encourage experimental psychopathologists to embrace Bayesian statistics, which could strengthen the conclusions drawn from EPP research.
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