BackgroundMost network analyses on central symptoms in eating disorders (EDs) have been cross-sectional. Longitudinal within-person analyses of therapy processes are scarce. Our aim was to... Show moreBackgroundMost network analyses on central symptoms in eating disorders (EDs) have been cross-sectional. Longitudinal within-person analyses of therapy processes are scarce. Our aim was to investigate central change processes in therapy in a transdiagnostic sample, considering the influence of childhood maltreatment.MethodWe employed dynamic time warping analyses to identify clusters of symptoms that tended to change similarly across therapy on a within-person level. Symptoms were measured by a 28-item Eating Disorder Examination Questionnaire (EDE-Q). Furthermore, we examined the temporal direction of symptom change to identify symptoms that tended to precede and predict other symptoms. Finally, we estimated two directed, temporal networks in patients with and without a history of childhood maltreatment.ResultsOur analysis included 122 ED patients (mean age = 30.9, SD = 9.7; illness duration = 14.2 years, SD = 8.9; prior treatment = 5.6 years, SD = 5.1). The initial network revealed three robust clusters of symptoms over time: (1) ED behavior, (2) inhibition, and (3) cognitions and feelings about body and weight. Overvaluation of shape had the highest out-strength preceding and predicting other symptoms. Dissatisfaction with weight preceded and predicted other symptoms in the maltreatment network. The non-maltreatment network showed a similar structure to the transdiagnostic network.ConclusionTargeting and monitoring feelings and cognitions related to shape may be crucial for achieving lasting symptom improvement in a transdiagnostic sample. Furthermore, our findings highlight the need for further investigation into the different processes driving EDs based on maltreatment status. Show less
BackgroundManic and depressive mood states in bipolar disorder (BD) may emerge from the non-linear relations between constantly changing mood symptoms exhibited as a complex dynamic system. Dynamic... Show moreBackgroundManic and depressive mood states in bipolar disorder (BD) may emerge from the non-linear relations between constantly changing mood symptoms exhibited as a complex dynamic system. Dynamic Time Warp (DTW) is an algorithm that may capture symptom interactions from panel data with sparse observations over time.MethodsThe Young Mania Rating Scale and Quick Inventory of Depressive Symptomatology were repeatedly assessed in 141 individuals with BD, with on average 5.5 assessments per subject every 3–6 months. Dynamic Time Warp calculated the distance between each of the 27 × 27 pairs of standardized symptom scores. The changing profile of standardized symptom scores of BD participants was analyzed in individual subjects, yielding symptom dimensions in aggregated group-level analyses. Using an asymmetric time-window, symptom changes that preceded other symptom changes (i.e., Granger causality) yielded a directed network.ResultsThe mean age of the BD participants was 40.1 (SD 13.5) years old, and 60% were female participants. Idiographic symptom networks were highly variable between subjects. Yet, nomothetic analyses showed five symptom dimensions: core (hypo)mania (6 items), dysphoric mania (5 items), lethargy (7 items), somatic/suicidality (6 items), and sleep (3 items). Symptoms of the “Lethargy” dimension showed the highest out-strength, and its changes preceded those of “somatic/suicidality,” while changes in “core (hypo)mania” preceded those of “dysphoric mania.”ConclusionDynamic Time Warp may help to capture meaningful BD symptom interactions from panel data with sparse observations. It may increase insight into the temporal dynamics of symptoms, as those with high out-strength (rather than high in-strength) could be promising targets for intervention. Show less
BackgroundDepression is associated with metabolic alterations including lipid dysregulation, whereby associations may vary across individual symptoms. Evaluating these associations using a network... Show moreBackgroundDepression is associated with metabolic alterations including lipid dysregulation, whereby associations may vary across individual symptoms. Evaluating these associations using a network perspective yields a more complete insight than single outcome-single predictor models.MethodsWe used data from the Netherlands Study of Depression and Anxiety (N = 2498) and leveraged networks capturing associations between 30 depressive symptoms (Inventory of Depressive Symptomatology) and 46 metabolites. Analyses involved 4 steps: creating a network with Mixed Graphical Models; calculating centrality measures; bootstrapping for stability testing; validating central, stable associations by extra covariate-adjustment; and validation using another data wave collected 6 years later.ResultsThe network yielded 28 symptom-metabolite associations. There were 15 highly-central variables (8 symptoms, 7 metabolites), and 3 stable links involving the symptoms Low energy (fatigue), and Hypersomnia. Specifically, fatigue showed consistent associations with higher mean diameter for VLDL particles and lower estimated degree of (fatty acid) unsaturation. These remained present after adjustment for lifestyle and health-related factors and using another data wave.ConclusionsThe somatic symptoms Fatigue and Hypersomnia and cholesterol and fatty acid measures showed central, stable, and consistent relationships in our network. The present analyses showed how metabolic alterations are more consistently linked to specific symptom profiles. Show less
Objectives: Late-life major depressive disorder (MDD) can be conceptualized as a complex dynamic system. However, it is not straightforward how to analyze the covarying depressive symptoms over... Show moreObjectives: Late-life major depressive disorder (MDD) can be conceptualized as a complex dynamic system. However, it is not straightforward how to analyze the covarying depressive symptoms over time in case of sparse panel data. Dynamic time warping (DTW) analysis may yield symptom networks and dimensions both at the patient and group level. Methods: In the Netherlands Study of Depression in Older People (NESDO) depressive symptoms were assessed every 6 months using the 30-item Inventory of Depressive Symptomatology (IDS) with up to 13 assessments per participant. Our sample consisted of 182 persons, aged >= 60 years, with an IDS total score of 26 or higher at baseline. Symptom networks dimensions, and centrality metrics were analyzed using DTW and Distatis analyses. Results: The mean age was 69.8 years (SD 7.1), with 69.0% females, and a mean IDS score of 38.0 (SD = 8.7). DTW enabled visualization of an idiographic symptom network in a single NESDO participant. In the group-level nomothetic approach, four depressive symptom dimensions were identified: "core symptoms", "lethargy/somatic", "sleep", and "appetite/atypical". Items of the "internalizing symptoms" dimension had the highest centrality, whose symptom changes over time were most similar to those changes of other symptoms. Conclusions: DTW revealed symptom networks and dimensions based on the within-person symptom changes in older MDD patients. Its centrality metrics signal the most influential symptoms, which may aid personalized care. Show less
Background: Though mediotemporal lobe volume changes are well-known features of Alzheimer's disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed... Show moreBackground: Though mediotemporal lobe volume changes are well-known features of Alzheimer's disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed changes are not independent due to the underlying network structure and can be described in terms of a structural covariance network (SCN).Objective: To investigate how the cortical brain organization is altered in AD we studied the mutual connectivity of hubs in the SCN, i.e., the rich-club.Methods: To construct the SCNs, cortical thickness was obtained from structural MRI for 97 participants (normal cognition, n = 37; mild cognitive impairment, n = 41; Alzheimer-type dementia, n = 19). Subsequently, rich-club coefficients were calculated from the SCN, and related to memory performance and hippocampal volume using linear regression.Results: Lower rich-club connectivity was related to lower memory performance as well as lower hippocampal volume.Conclusion: Therefore, this study provides novel evidence of reduced connectivity in hub areas in relation to AD-related cognitive impairments and atrophy. Show less
De Weerdt, H.G.D.G.; Ho, B.; Wagner, A.; Qiao, J.; Chu, M. 2020
Many plant genes are known to be involved in the development of cambium and wood, but how the expres- sion and functional interaction of these genes determine the unique biology of wood remains... Show moreMany plant genes are known to be involved in the development of cambium and wood, but how the expres- sion and functional interaction of these genes determine the unique biology of wood remains largely unknown. We used the soc1ful loss of function mutant – the woodiest genotype known in the otherwise herbaceous model plant Arabidopsis – to investigate the expression and interactions of genes involved in secondary growth (wood formation). Detailed anatomical observations of the stem in combination with mRNA sequencing were used to assess transcriptome remodeling during xylogenesis in wild-type and woody soc1ful plants. To interpret the transcriptome changes, we constructed functional gene association networks of differentially expressed genes using the STRING database. This analysis revealed functionally enriched gene association hubs that are differentially expressed in herbaceous and woody tissues. In particular, we observed the differential expression of genes related to mechanical stress and jasmonate biosynthesis/signaling during wood formation in soc1ful plants that may be an effect of greater tension within woody tissues. Our results suggest that habit shifts from herbaceous to woody life forms observed in many angiosperm lineages could have evolved convergently by genetic changes that modulate the gene expres- sion and interaction network, and thereby redeploy the conserved wood developmental program. Show less
In digital China, networked actors ranging from state agencies to private Internet users engage in highly active online discourse. Yet as diverse as this discourse may be, political content on... Show moreIn digital China, networked actors ranging from state agencies to private Internet users engage in highly active online discourse. Yet as diverse as this discourse may be, political content on China’s web remains highly regulated, particularly on issues affecting the legitimacy of the ruling party. A prominent issue in this regard has been the conflict-laden relationship with Japan. This article asks how Chinese websites shape online discourse on two Japan issues (the Nanjing Massacre and the East China Sea conflict), and what these sites can tell us about the leadership’s strategy for managing digital communication. Combining content analysis and digital tools, the article shows how the authorities apply a Leninist mass-communication logic to the web, treating websites not as spaces for networked social interaction but as authoritative information sources that broadcast approved content to a mass audience, which effectively brings digital media into the fold of China’s ‘traditional’ mass-media system. Show less
The aim of this paper is to examine the relation between monumentality and connectivity of the cities on the Iberian Peninsula during the High Empire, using spatial and social network analyses.... Show moreThe aim of this paper is to examine the relation between monumentality and connectivity of the cities on the Iberian Peninsula during the High Empire, using spatial and social network analyses. Firstly, the presence of the monuments under scrutiny (amphitheatre, circus and theatre) will be treated by a critical analysis of the different sources. Secondly, a social network analysis will be used to illuminate the role of Centrality in relation to the monumentality of cities. Naturally, the history of specific cities can explain their individual situation. However, large patterns cannot be understood by individual study of cities. Show less
