Supplementary Material

Network Outcome Analysis identifies difficulty initiating sleep as primary target for prevention

of depression: A six-year prospective study

Tessa F Blanken1, Denny Borsboom2, Brenda WJH Penninx3, Eus JW Van Someren1,3,4

1 Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, The Netherlands.

2 Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WS Amsterdam, The Netherlands.

3 Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience Research Institute, Amsterdam UMC, Vrije Universiteit, Oldenaller 1, 1081 HJ Amsterdam, The Netherlands.

4 Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.

Supplementary Methods and Results

1. Inclusion of sampleWe included participants from the Netherlands Study of Depression and Anxiety (NESDA)

[1] who were without a current or prior lifetime Major Depressive Disorder according to the

DSM-IV, determined using the Composite International Diagnostic Interview (CIDI) [2], and

for whom a CIDI at each of three follow-up measurements was completed. Of the N=2981

participants enrolled in the NESDA, N=1008 were without a current or lifetime diagnosis of

depression or dysthymia at baseline. Of these N=1008, N=240 missed at least one of the

follow-up measurements T1-T3, resulting in N=768 participants who met our selection

criteria. They were between 18 and 65 years of age (M=41.1 years, SD=14.4 years; see

Figure S1 for a histogram), and 482 (62.7%) were female. Most of the included participants

(N=526, 68.4%) were free from a current or lifetime diagnosis of any anxiety disorder. Of the

remaining 242 participants N=159 (20.7%) had a current diagnosis of any anxiety disorder at

baseline, and N=83 (10.8%) had ever been diagnosed with of any anxiety disorder in the

past. From the 768 selected participants, the Cox Proportional Hazard analysis included the

N=723 participants for whom baseline sleep data were complete. The Network Outcome

Analysis (NOA) included the N=743 for whom baseline depression symptoms were

complete.

2. Network Outcome Analysis

2.1. Network regularization

LASSO regularization is applied to prevent the inclusion of spurious edges due to sampling

variation [3]. The amount of regularization that is applied is controlled by a tuning parameter:

a higher tuning parameter omits more edges from the network, resulting in a more sparse

network, whereas a lower tuning parameter removes only some edges and a more dense

network will be retrieved. The tuning parameter thus controls a trade-off between the in- and

exclusion of edges and the number of false positives and false negative edges in the

network. The optimal tuning parameter can be selected using either cross-validation or by

minimizing the Extended Bayesian Information Criterion (EBIC). When using the EBIC a

hyperparameter needs to be set that controls how much the EBIC prefers simpler (i.e.,

sparser) models, with higher values corresponding to more sparse networks. Generally,

using the EBIC to select the tuning parameter results in sparser networks compared to

cross-validation.

For the network estimation reported on in the main article, we used the EBIC to select

the optimal tuning parameter. The hyperparameter is typically set between 0 and 0.5, where

an hyperparameter of 0 errs on the side of discovery and an hyperparameter of 0.5 errs on

the side of caution [3]. We adopted the default setting of 0.5 to err on the side of caution.

Using this setting NOA identified five symptoms to predict first-onset MDD directly. To

evaluate whether some direct effects might have been put to zero by regularisation, we also

estimated the networks using a hyperparameter of 0, see Figure S2. Using this setting in

which more edges are estimated, NOA again identified the same five symptoms to be

directly predictive of FO-MDD, indicting robustness of our results.

Finally, we estimated the networks using cross-validation to select the tuning parameter,

see Figure S3. As expected, using cross-validation more edges are estimated and a more

dense network is retrieved. In addition to the five symptoms that were found using the EBIC,

weight change was now also identified as a direct predictor of FO-MDD. Given that this

direct link was not found using both hyperparameter settings of the EBIC, and given that

cross-validation includes more edges, likely resulting in some spurious edges, it is plausible

that this ‘direct’ predictive effect of weight change on FO-MDD is spurious.

These analyses were performed in R (version 3.5.0) using the packages ‘qgraph’,

‘bootnet’, and ‘mgm’.

2.2. Edge weight accuracy

After estimating the network edges it is important to assess how accurately these edge-

weights are estimated and how robust they are [4]. To evaluate the precision and robustness

of the estimated network, the bootnet R-package has been created that uses bootstraps to

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estimate the accuracy of the network parameters. Specifically, we ran a hundred bootstrap

samples for which we fitted the model. For each edge, this results in a hundred estimates of

the edge-weight by which we can evaluate how accurate (i.e., what is the range of observed

values for a certain parameters?) and how robust (i.e., how often is a parameter larger than

zero and thus included into the network?) our original estimate is. It is important to note that

because we used LASSO regularization, the edge weights and their sampling distribution

are biased towards zero. As a result, plotting the 5% and 95% quantiles might include zero,

whereas the corresponding 95% confidence interval does not. Therefore we plot the 5% and

95% quantiles only for the times the parameter was not set to zero. In addition it is shown in

how many of the estimated networks the edge weight was put to zero. The resulting

sampling distribution for the associations that we have interpreted (i.e., the ones included

first-onset MDD) is shown in Figure S4. For example, the edge weight between ‘appetite

change’ and ‘weight change’ was never put to zero (indicated by the 0.0), and its 5% and

95% quantiles lie around 0.15 and 0.35. For a more detailed explanation, see Epskamp,

Borsboom, & Fried [5].

3. Ancillary analysesNetwork Outcome Analysis (NOA) including age, gender, and presence of any anxiety

disorder at baseline did not alter the results, see Figure S5. In this analysis age was entered

as a continuous variable, and sex (male vs. female) and presence of any anxiety disorder at

baseline (yes vs. no) as categorical variables. We moreover performed NOA on the

subsample of participants (N=609) that were free from any anxiety disorder at baseline. Of

these 609 participants, 87 (14.3%) developed a first-onset depression. The results are

shown in Figure S6. As can be seen from both analyses, the relationship between ‘difficulty

initiating sleep’ was robust to either including presence of any anxiety disorder at baseline

into the network or excluding the cases with any anxiety disorder at baseline. As can be

seen from Table S1 below, the estimated effect was roughly the same across analyses.

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Table S1. Edge weight of the different links between nonclinical baseline depression complaints and first-onset major depressive disorder. Prospective link Original analyses Excluding cases with

current anxietyIncluding anxiety

agi – FO-MDD 0.15 0.00 0.11con – FO-MDD 0.07 0.05 0.07dep – FO-MDD 0.14 0.14 0.14dis – FO-MDD 0.08 0.10 0.08ene – FO-MDD 0.12 0.11 0.11Abbreviations: agi = psychomotor agitation; con = concentration problems; ene = loss of energy; dis = difficulty initiating sleep; dep = depressed mood.

References1. Penninx BWJH, Beekman ATF, Smit JH, Zitman FG, Nolen WA, Spinhoven P, et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res 2008; 17: 121-140.

2. World Health Organization. Composite International Diagnostic Interview, Version 1.0. Geneva: World Health Organization, 1990.

3. Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods 2018; doi:10.1037/met0000167.

4. Borsboom D, Robinaugh DJ, The Psychosystems Group, Rhemtulla M, Cramer AOJ. Robustness and replicability of psychopathology networks. World Psychiatry 2018; 17: 143-144.

5. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods 2017; 50: 195-212.

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Figure S1

Age distribution of N=768 participants of the Netherlands Study for Depression and Anxiety that were included in the current study.

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Figure S2

Estimated regularized networks where the gamma hyperparameter was set to 0.25 (left) or 0.50 (right).

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Figure S3

Estimated regularized networks using EBIC (left) or cross-validation (right) to select the tuning parameter. Note that there is an edge between FO-MDD and ‘con’ in the EBIC network, that is partially obscured by the edge between FO-MDD and ‘dep’. See supplementary information paragraph 2.1 above for details.

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Figure S4

Bootstrapped sampling distributions zoomed in on the associations that included first-onset MDD. A PDF version containing all bootstrapped associations is available upon request.

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Figure S5: Including age, sex, and presence of any anxiety disorder diagnosis at baseline as covariates.

Network Outcome Analysis including age, sex, and presence of any anxiety disorder diagnosis at baseline. Edges represent conditional dependence relations among the variables and capture unique effects that remain after controlling for all the other variables in the network. The thickness and colour saturation of the edges corresponds to the strength of the association. In this network, all associations are positive. Abbreviations: agi = psychomotor agitation; app = appetite change; con = concentration problems; dep = depressed mood; dis = difficulty initiating sleep; dms = difficulty maintaining sleep; ene = fatigue or loss of energy; ema = early morning awakenings; FO-MDD = first-onset depression; gui = feelings of guilt or worthlessness; hyp = hypersomnia; int = loss of interest; ret = psychomotor retardation; sui = suicidal thoughts; wei = weight change.

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Figure S6: Excluding cases with presence of any anxiety disorder diagnosis at baseline.

Network Outcome Analysis on N=609 participants free from any anxiety disorder diagnosis at baseline. Edges represent conditional dependence relations among the variables and capture unique effects that remain after controlling for all the other variables in the network. The thickness and colour saturation of the edges corresponds to the strength of the association. In this network, all associations are positive. Abbreviations: agi = psychomotor agitation; app = appetite change; con = concentration problems; dep = depressed mood; dis = difficulty initiating sleep; dms = difficulty maintaining sleep; ene = fatigue or loss of energy; ema = early morning awakenings; FO-MDD = first-onset depression; gui = feelings of guilt or worthlessness; hyp = hypersomnia; int = loss of interest; ret = psychomotor retardation; sui = suicidal thoughts; wei = weight change.

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