Bos, Fionneke M.

de Jonge, Peter

de Vos, Stijn

Hartmann, Jessica A.

Krieke, van der, L.

Simons, Claudia J. P.

Snippe, Evelien

Wichers, Marieke

English

van der Krieke, Lian

NARCIS 

Can We Jump from Cross-Sectional to Dynamic Interpretations of Networks? Implications for the Network Perspective in Psychiatry

Dissertations of the University of Groningen

   University of GroningenCan We Jump from Cross-Sectional to Dynamic Interpretations of Networks? Implications forthe Network Perspective in PsychiatryBos, Fionneke M.; Snippe, Evelien; de Vos, Stijn; Hartmann, Jessica A.; Simons, Claudia J.P.; Krieke, van der, L.; de Jonge, Peter; Wichers, MariekePublished in:Psychotherapy and psychosomaticsDOI:10.1159/000453583IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2017Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Bos, F. M., Snippe, E., de Vos, S., Hartmann, J. A., Simons, C. J. P., Krieke, van der, L., de Jonge, P., &Wichers, M. (2017). Can We Jump from Cross-Sectional to Dynamic Interpretations of Networks?Implications for the Network Perspective in Psychiatry. Psychotherapy and psychosomatics, 86(3), 175-177. https://doi.org/10.1159/000453583CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.E-Mail karger@karger.com Letter to the Editor symptoms (i.e., dynamic networks)  [7] . If both approaches result in similar conclusions, this would greatly facilitate future research and clinical applications because cross-sectional data can be more easily obtained. If not, then cross-sectional symptom networks are unlikely to reflect causal symptom dynamics, as postulated by this network theory. To this end, we used the same experience sampling methodol-ogy (ESM) data on mood states to compare the 2 network ap-proaches. Data came from the baseline ESM measurements of an interventional trial including 104 patients with a DSM-IV-TR di-agnosis of a major depressive episode  [8] . Patients rated their mo-mentary mental states on 7-point Likert scales 10 times a day for 5 days, resulting in a maximum of 50 measurement points per indi-vidual. Here, we focus on the 7 mood items that reflect symptoms of depression and that showed sufficient within-person variance over time, namely, sadness, irritation, loneliness, restlessness, worry, self-doubt, and anhedonia (i.e., cheerfulness reverse coded).  Given that traditional cross-sectional networks are based on(1) a single measurement point per individual, and (2) perceived symptoms that are not momentary but estimated over a somewhat longer period, we estimated 2 cross-sectional networks, each re-flecting 1 of the above situations. First, we estimated partial cor-relations between the first observation of the first day of each par-ticipant for all 7 symptoms ( Fig. 1 a). Second, we estimated partial correlations between the person-means of the symptoms ( Fig. 1 b), i.e., the mean of all 50 measurement points of a symptom provided by 1 individual. Third, a dynamic network was estimated by ex-amining the within-person time-lagged associations among the symptoms in 7 multilevel vector autoregressive models ( Fig. 1 c), including 1 of the symptoms as the dependent variable and all symptoms at a previous moment in time ( t – 1) as fixed and ran-dom effects  [7] . Variables were detrended to remove time trends (not detrending the variables yielded similar results) and person-means were centered to disaggregate within-person effects from between-person effects  [9] . The analyses were conducted inSTATA (v14.1) and the network graphs were made using the igraph package in R. For clarity, only significant associations are shown in the figures of each network ( Fig. 1 ).  For each network, we computed node strength centrality, which is the sum of the absolute value of the strength of all asso-ciations of a given symptom with all other symptoms (also nonsig-nificant ones)  [10] . In the dynamic network, strength can be split into ‘‘instrength’’ (the total weight of incoming arrows, not includ-ing self-loops) and ‘‘outstrength’’ (the total weight of outgoing ar-rows). As betweenness and closeness centrality correlated strong-ly with strength centrality, we decided to not additionally report these measures here.  The results showed that the network approaches not only iden-tified different associations between symptoms, they also yielded different conclusions with regards to which symptom is the most  Interest in the network perspective of psychopathology is rap-idly growing. This theory conceptualizes mental disorders as net-works in which symptoms can trigger the presence of other symp-toms  [1] . It thus theorizes that symptoms actively generate other symptoms, and that this process can eventually lead to a full-blown mental disorder. If this is true, then symptom networks may be informative for clinical practice; symptoms that are more central in the network, and are thus assumed to influence many other symptoms, seem to be a logical starting point for intervention. However, in order to know how to optimally investigate the net-work perspective empirically, it is crucial to first establish whether different network approaches (cross-sectional vs. dynamic) result in similar conclusions. Although the network theory described above proposes that symptoms are causally related to one other, most of the published studies use cross-sectional data on symptoms to find empirical support for this  [2, 3] . These studies thereby assume, implicitly or explicitly, that group-level contemporaneous associations between symptoms reflect causal influences between symptoms over time  [1, 3] . To acquire support for causality, it is important to establish a timeline, i.e., that some symptoms temporally precede other symptoms  [4] . It has been questioned, however, whether concur-rent, group-level associations among symptoms inform us on how symptoms follow each other over time within individuals  [5, 6] . We therefore aimed to investigate to what extent a cross-sec-tional network yields the same conclusion as a network on the same data that includes dynamic (temporal) associations between  Received: July 9, 2016  Accepted after revision: November 17, 2016  Published online: May 11, 2017  © 2017 S. Karger AG, Basel  www.karger.com/pps  Psychother Psychosom 2017;86:175–177  DOI: 10.1159/000453583  Can We Jump from Cross-Sectional to Dynamic Interpretations of Networks? Implications for the Network Perspective in Psychiatry  Fionneke M. Bos a, b , Evelien Snippe b , Stijn de Vos b , Jessica A. Hartmann d, f , Claudia J.P. Simons d, e , Lian van der Krieke a, b , Peter de Jonge b, c , Marieke Wichers b  a  University of Groningen, University Medical Center Groningen, Rob Giel Research Center, and  b  University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), and  c  Department of Developmental Psychology, University of Groningen,  Groningen ,  d  Department of Psychiatry and Psychology, Maastricht University Medical Center,  Maastricht , and  e  GGzE, Institute of Mental Health Care Eindhoven and De Kempen,  Eindhoven , The Netherlands;  f  Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne,  Melbourne, VIC , Australia  Fionneke M. Bos, MSc University of Groningen, University Medical Center GroningenRob Giel Research Center PO Box 30.001 ,  NL–9700 RB Groningen (The Netherlands)  E-Mail f.m.bos01   @   umcg.nl Downloaded by: University of Groningen                                129.125.166.142 - 5/24/2017 2:34:52 PM Bos/Snippe/de Vos/Hartmann/Simons/van der Krieke/de Jonge/Wichers Psychother Psychosom 2017;86:175–177DOI: 10.1159/000453583176central ( Fig. 1 ). This would have clinical implications if centrality is used to guide targets for treatment. Whereas the first observa-tions network finds self-doubt to be the most central symptom, the person-means network indicates that sadness is the most central symptom. The dynamic network also points towards sadness, but sadness only has the highest instrength, indicating that it is mostly influenced by other symptoms. It does not have the highest out-strength, suggesting that it does not have the strongest impact on other symptoms. Anhedonia has the highest outstrength in the dynamic network, suggesting that any change here would strongly influence the occurrence of other symptoms. Thus, the cross-sec-tional and dynamic networks would all indicate different targets for intervention. In terms of order of rank, the node strengths of the different networks only correlated modestly or even negatively with each other. The node strengths of the first observations network corre-lated negatively with the node instrengths ( r = –0.24) and nega-tively with the node outstrengths ( r = –0.33) of the dynamic net-work. The person-mean node strengths correlated negatively with the node outstrengths ( r = –0.43) and positively with the node instrenghts ( r = 0.62) of the dynamic network.  Our results strongly suggest that cross-sectional networks do not reflect how symptoms trigger each other over time, and there-fore may not be interpreted as such. Cross-sectional networks may, however, be useful to examine the co-occurrence of symptoms, for example, to offer an insight into patterns of current symptom co-morbidity across individuals  [1] . Thus, if we would like to empiri-cally test whether causal symptom dynamics are responsible for the development of mental disorders, the use of dynamic network analysis is advised. If future studies yield support for the network theory, then dynamic network techniques may also have relevance for clinical practice in pointing towards promising targets for in-tervention. Acknowledgement  We acknowledge an ERC consolidator grant to M. Wichers (project No. 681466; TRANS-ID, 2015), a Rubicon Fellowship from the Netherlands Organisation for Scientific Research to J.A. Hartmann (NWO 825.15.015), and a VICI grant from the Nether-lands Organisation for Scientific Research (NWO/ZonMW No. 91812607) to P. de Jonge. a b cSymptom NodestrengthcentralitySAD 0.69IRR 0.89LON 0.79RES 0.89WOR 1.13DOU 1.15ANH 0.61Symptom NodeinstrengthcentralityNodeoutstrengthcentralitySAD 0.33 0.18IRR 0.21 0.16LON 0.14 0.21RES 0.21 0.10WOR 0.18 0.29DOU 0.14 0.13ANH 0.20 0.34Symptom NodestrengthSADANHDOUWOR RESLONIRRSADANHDOUWOR RESLONIRRSADANHDOUWOR RESLONIRRcentralitySAD 1.44IRR 1.08LON 0.74RES 1.16WOR 0.97DOU 0.91ANH 0.74 Fig. 1. The first observation network ( n = 104) ( a ), the person-mean network ( n = 104) ( b ), and the dynamic network ( n = 104 × 50) ( c ), including measures of node strength centrality for each approach. SAD, sadness; IRR, irritation; LON, loneliness; RES, restlessness; WOR, worry; DOU, self-doubt; ANH, anhedonia. Values in bold type denote the centrality estimates that are the most central symptoms within each network approach. Color version available onlineDownloaded by: University of Groningen                                129.125.166.142 - 5/24/2017 2:34:52 PM Cross-Sectional versus Dynamic Interpretations of Networks Psychother Psychosom 2017;86:175–177DOI: 10.1159/000453583177 References  1 Cramer AOJ, Waldorp LJ, van der Maas HLJ, Borsboom D: Comorbid-ity: a network perspective. Behav Brain Sci 2010; 33: 137–150.  2 van Borkulo C, Boschloo L, Borsboom D, Penninx BWJH, Waldorp LJ, Schoevers RA: Association of symptom network structure with the course of longitudinal depression. JAMA Psychiatry 2015; 12: 1219–1226.  3 McNally RJ, Robinaugh DJ, Wu GWY, Wang L, Deserno MK, Borsboom D: Mental disorders as causal systems: a network approach to posttrau-matic stress disorder. Clin Psychol Sci 2015; 3: 836–849.  4 Bolger N, Laurenceau J: Intensive Longitudinal Methods: An Introduc-tion to Diary and Experience Sampling Research.  New York, Guilford Press, 2013.  5 Bos EH, Wanders RBK: Group-level symptom networks in depression. JAMA Psychiatry 2016; 73: 411–411.  6 Molenaar PCM: A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Measurement 2004; 2: 201–218.  7 Bringmann LF, Vissers N, Wichers M, et al: A network approach to psy-chopathology: new insights into clinical longitudinal data. PLoS One 2013; 8:e60188.  8 Kramer I, Simons CJP, Hartmann JA, et al: A therapeutic application of the experience sampling method in the treatment of depression: a ran-domized controlled trial. World Psychiatry 2014; 13: 68–77.  9 Curran PJ, Bauer DJ:The disaggregation of within-person and between-person effects in longitudinal models of change. Annu Rev Psychol 2011;  62: 583–619.  10 Opsahl T, Agneessens F, Skvoretz J: Node centrality in weighted net-works: generalizing degree and shortest paths. Soc Netw 2010; 32: 245–251.  Downloaded by: University of Groningen                                129.125.166.142 - 5/24/2017 2:34:52 PM

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 University of Groningen
Can We Jump from Cross-Sectional to Dynamic Interpretations of Networks? Implications for
the Network Perspective in Psychiatry
Bos, Fiona; Snippe, Evelien; de Vos, Stijn; Hartmann, Jessica A.; Simons, Claudia J. P.; van
der Krieke, Lian; de Jonge, Peter; Wichers, Maria
Published in:
Psychotherapy and psychosomatics
DOI:
10.1159/000453583
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from
it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date:
2017
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Bos, F. M., Snippe, E., de Vos, S., Hartmann, J. A., Simons, C. J. P., Krieke, van der, L., ... Wichers, M.
(2017). Can We Jump from Cross-Sectional to Dynamic Interpretations of Networks? Implications for the
Network Perspective in Psychiatry. Psychotherapy and psychosomatics, 86(3), 175-177. DOI:
10.1159/000453583
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the
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number of authors shown on this cover page is limited to 10 maximum.
Download date: 11-02-2018
E-Mail karger@karger.com
 Letter to the Editor 
symptoms (i.e., dynamic networks)  [7] . If both approaches result 
in similar conclusions, this would greatly facilitate future research 
and clinical applications because cross-sectional data can be more 
easily obtained. If not, then cross-sectional symptom networks are 
unlikely to reflect causal symptom dynamics, as postulated by this 
network theory.
 To this end, we used the same experience sampling methodol-
ogy (ESM) data on mood states to compare the 2 network ap-
proaches. Data came from the baseline ESM measurements of an 
interventional trial including 104 patients with a DSM-IV-TR di-
agnosis of a major depressive episode  [8] . Patients rated their mo-
mentary mental states on 7-point Likert scales 10 times a day for 5 
days, resulting in a maximum of 50 measurement points per indi-
vidual.
 Here, we focus on the 7 mood items that reflect symptoms of 
depression and that showed sufficient within-person variance over 
time, namely, sadness, irritation, loneliness, restlessness, worry, 
self-doubt, and anhedonia (i.e., cheerfulness reverse coded). 
 Given that traditional cross-sectional networks are based on
(1) a single measurement point per individual, and (2) perceived 
symptoms that are not momentary but estimated over a somewhat 
longer period, we estimated 2 cross-sectional networks, each re-
flecting 1 of the above situations. First, we estimated partial cor-
relations between the first observation of the first day of each par-
ticipant for all 7 symptoms ( Fig. 1 a). Second, we estimated partial 
correlations between the person-means of the symptoms ( Fig. 1 b), 
i.e., the mean of all 50 measurement points of a symptom provided 
by 1 individual. Third, a dynamic network was estimated by ex-
amining the within-person time-lagged associations among the 
symptoms in 7 multilevel vector autoregressive models ( Fig. 1 c), 
including 1 of the symptoms as the dependent variable and all 
symptoms at a previous moment in time ( t – 1) as fixed and ran-
dom effects  [7] . Variables were detrended to remove time trends 
(not detrending the variables yielded similar results) and person-
means were centered to disaggregate within-person effects from 
between-person effects  [9] . The analyses were conducted in
STATA (v14.1) and the network graphs were made using the 
igraph package in R. For clarity, only significant associations are 
shown in the figures of each network ( Fig. 1 ). 
 For each network, we computed node strength centrality, 
which is the sum of the absolute value of the strength of all asso-
ciations of a given symptom with all other symptoms (also nonsig-
nificant ones)  [10] . In the dynamic network, strength can be split 
into ‘‘instrength’’ (the total weight of incoming arrows, not includ-
ing self-loops) and ‘‘outstrength’’ (the total weight of outgoing ar-
rows). As betweenness and closeness centrality correlated strong-
ly with strength centrality, we decided to not additionally report 
these measures here. 
 The results showed that the network approaches not only iden-
tified different associations between symptoms, they also yielded 
different conclusions with regards to which symptom is the most 
 Interest in the network perspective of psychopathology is rap-
idly growing. This theory conceptualizes mental disorders as net-
works in which symptoms can trigger the presence of other symp-
toms  [1] . It thus theorizes that symptoms actively generate other 
symptoms, and that this process can eventually lead to a full-blown 
mental disorder. If this is true, then symptom networks may be 
informative for clinical practice; symptoms that are more central 
in the network, and are thus assumed to influence many other 
symptoms, seem to be a logical starting point for intervention. 
However, in order to know how to optimally investigate the net-
work perspective empirically, it is crucial to first establish whether 
different network approaches (cross-sectional vs. dynamic) result 
in similar conclusions.
 Although the network theory described above proposes that 
symptoms are causally related to one other, most of the published 
studies use cross-sectional data on symptoms to find empirical 
support for this  [2, 3] . These studies thereby assume, implicitly or 
explicitly, that group-level contemporaneous associations between 
symptoms reflect causal influences between symptoms over time 
 [1, 3] . To acquire support for causality, it is important to establish 
a timeline, i.e., that some symptoms temporally precede other 
symptoms  [4] . It has been questioned, however, whether concur-
rent, group-level associations among symptoms inform us on how 
symptoms follow each other over time within individuals  [5, 6] .
 We therefore aimed to investigate to what extent a cross-sec-
tional network yields the same conclusion as a network on the 
same data that includes dynamic (temporal) associations between 
 Received: July 9, 2016 
 Accepted after revision: November 17, 2016 
 Published online: May 11, 2017 
 © 2017 S. Karger AG, Basel 
 www.karger.com/pps 
 Psychother Psychosom 2017;86:175–177 
 DOI: 10.1159/000453583 
 Can We Jump from Cross-Sectional to Dynamic 
Interpretations of Networks? Implications for the 
Network Perspective in Psychiatry 
 Fionneke M. Bos a, b , Evelien Snippe b , Stijn de Vos b , 
Jessica A. Hartmann d, f , Claudia J.P. Simons d, e , 
Lian van der Krieke a, b , Peter de Jonge b, c , Marieke Wichers b 
 a  University of Groningen, University Medical Center Groningen, 
Rob Giel Research Center, and  b  University of Groningen, 
University Medical Center Groningen, Interdisciplinary 
Center Psychopathology and Emotion Regulation (ICPE), 
and  c  Department of Developmental Psychology, University 
of Groningen,  Groningen ,  d  Department of Psychiatry and 
Psychology, Maastricht University Medical Center,  Maastricht , 
and  e  GGzE, Institute of Mental Health Care Eindhoven and De 
Kempen,  Eindhoven , The Netherlands;  f  Orygen, The National 
Centre of Excellence in Youth Mental Health, University of 
Melbourne,  Melbourne, VIC , Australia 
 Fionneke M. Bos, MSc 
University of Groningen, University Medical Center Groningen
Rob Giel Research Center
 PO Box 30.001 ,  NL–9700 RB Groningen (The Netherlands) 
 E-Mail f.m.bos01   @   umcg.nl 
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van der Krieke/de Jonge/Wichers
 
Psychother Psychosom 2017;86:175–177
DOI: 10.1159/000453583
176
central ( Fig. 1 ). This would have clinical implications if centrality 
is used to guide targets for treatment. Whereas the first observa-
tions network finds self-doubt to be the most central symptom, the 
person-means network indicates that sadness is the most central 
symptom. The dynamic network also points towards sadness, but 
sadness only has the highest instrength, indicating that it is mostly 
influenced by other symptoms. It does not have the highest out-
strength, suggesting that it does not have the strongest impact on 
other symptoms. Anhedonia has the highest outstrength in the 
dynamic network, suggesting that any change here would strongly 
influence the occurrence of other symptoms. Thus, the cross-sec-
tional and dynamic networks would all indicate different targets 
for intervention.
 In terms of order of rank, the node strengths of the different 
networks only correlated modestly or even negatively with each 
other. The node strengths of the first observations network corre-
lated negatively with the node instrengths ( r = –0.24) and nega-
tively with the node outstrengths ( r = –0.33) of the dynamic net-
work. The person-mean node strengths correlated negatively with 
the node outstrengths ( r = –0.43) and positively with the node 
instrenghts ( r = 0.62) of the dynamic network. 
 Our results strongly suggest that cross-sectional networks do 
not reflect how symptoms trigger each other over time, and there-
fore may not be interpreted as such. Cross-sectional networks may, 
however, be useful to examine the co-occurrence of symptoms, for 
example, to offer an insight into patterns of current symptom co-
morbidity across individuals  [1] . Thus, if we would like to empiri-
cally test whether causal symptom dynamics are responsible for the 
development of mental disorders, the use of dynamic network 
analysis is advised. If future studies yield support for the network 
theory, then dynamic network techniques may also have relevance 
for clinical practice in pointing towards promising targets for in-
tervention.
 Acknowledgement 
 We acknowledge an ERC consolidator grant to M. Wichers 
(project No. 681466; TRANS-ID, 2015), a Rubicon Fellowship 
from the Netherlands Organisation for Scientific Research to J.A. 
Hartmann (NWO 825.15.015), and a VICI grant from the Nether-
lands Organisation for Scientific Research (NWO/ZonMW No. 
91812607) to P. de Jonge.
 
a b c
Symptom Node
strength
centrality
SAD 0.69
IRR 0.89
LON 0.79
RES 0.89
WOR 1.13
DOU 1.15
ANH 0.61
Symptom Node
instrength
centrality
Node
outstrength
centrality
SAD 0.33 0.18
IRR 0.21 0.16
LON 0.14 0.21
RES 0.21 0.10
WOR 0.18 0.29
DOU 0.14 0.13
ANH 0.20 0.34
Symptom Node
strength
SAD
ANH
DOU
WOR RES
LON
IRR
SAD
ANH
DOU
WOR RES
LON
IRR
SAD
ANH
DOU
WOR RES
LON
IRR
centrality
SAD 1.44
IRR 1.08
LON 0.74
RES 1.16
WOR 0.97
DOU 0.91
ANH 0.74
 Fig. 1. The first observation network ( n = 104) ( a ), the person-mean network ( n = 104) ( b ), and the dynamic 
network ( n = 104 × 50) ( c ), including measures of node strength centrality for each approach. SAD, sadness; IRR, 
irritation; LON, loneliness; RES, restlessness; WOR, worry; DOU, self-doubt; ANH, anhedonia. Values in bold 
type denote the centrality estimates that are the most central symptoms within each network approach. 
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 Cross-Sectional versus Dynamic 
Interpretations of Networks 
Psychother Psychosom 2017;86:175–177
DOI: 10.1159/000453583
177
 References 
 1 Cramer AOJ, Waldorp LJ, van der Maas HLJ, Borsboom D: Comorbid-
ity: a network perspective. Behav Brain Sci 2010; 33: 137–150. 
 2 van Borkulo C, Boschloo L, Borsboom D, Penninx BWJH, Waldorp LJ, 
Schoevers RA: Association of symptom network structure with the 
course of longitudinal depression. JAMA Psychiatry 2015; 12: 1219–1226. 
 3 McNally RJ, Robinaugh DJ, Wu GWY, Wang L, Deserno MK, Borsboom 
D: Mental disorders as causal systems: a network approach to posttrau-
matic stress disorder. Clin Psychol Sci 2015; 3: 836–849. 
 4 Bolger N, Laurenceau J: Intensive Longitudinal Methods: An Introduc-
tion to Diary and Experience Sampling Research.  New York, Guilford 
Press, 2013. 
 5 Bos EH, Wanders RBK: Group-level symptom networks in depression. 
JAMA Psychiatry 2016; 73: 411–411. 
 6 Molenaar PCM: A manifesto on psychology as idiographic science: 
bringing the person back into scientific psychology, this time forever. 
Measurement 2004; 2: 201–218. 
 7 Bringmann LF, Vissers N, Wichers M, et al: A network approach to psy-
chopathology: new insights into clinical longitudinal data. PLoS One 
2013; 8:e60188. 
 8 Kramer I, Simons CJP, Hartmann JA, et al: A therapeutic application of 
the experience sampling method in the treatment of depression: a ran-
domized controlled trial. World Psychiatry 2014; 13: 68–77. 
 9 Curran PJ, Bauer DJ:The disaggregation of within-person and between-
person effects in longitudinal models of change. Annu Rev Psychol 2011; 
 62: 583–619. 
 10 Opsahl T, Agneessens F, Skvoretz J: Node centrality in weighted net-
works: generalizing degree and shortest paths. Soc Netw 2010; 32: 245–
251. 
 
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Can We Jump from Cross-Sectional to Dynamic Interpretations of Networks? Implications for the Network Perspective in Psychiatry

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