Elevated Emotion Network Connectivity Is Associated with Fluctuations in Depression

Kelley, Seán; Fisher, Aaron J.; Lee, Chi Tak; Gallagher, Eoghan; Hanlon, Anna K; Robertson, Ian H.; Gillan, Claire M.

doi:10.31234/osf.io/agt5m

Cited by 3 publications

(4 citation statements)

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“…That said, it is important to recognise that variance contributes to, but is not the same as, network connectivity. For example, correlations between network connectivity and variance were moderate (r = 0.41-0.58), and recent work examining temporal, intraindividual networks found associations with symptom change over time that survived controlling for variance (Kelley et al, 2022). Moreover, networks actually became more connected following treatment despite reductions in both symptom severity and variance, a counterintuitive finding most consistent in the network literature (Beard et al, 2016;Berlim et al, 2021;Blanco et al, 2020;Bos et al, 2018;Curtiss et al, 2021).…”

Section: Discussionmentioning

confidence: 76%

“…One explanation is that increased symptom connectivity is not necessarily bad; a highly connected network should theoretically lead to a more malleable system, but not necessarily worsening mental health (Fried et al, 2016;McElroy et al, 2019), as therapeutic gains may be due to systems becoming less 'stuck' and more open to change. Recent work examining personalised network dynamics in healthy individuals supports this; those with more connected depression networks tended to have greater fluctuations in depression over 6 weeks, but these went in both positive and negative directions of change (Kelley et al, 2022).…”

Section: Discussionmentioning

confidence: 95%

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Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression

et al. 2023

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Background Tightly connected symptom networks have previously been linked to treatment resistance, but most findings come from small-sample studies comparing single responder v. non-responder networks. We aimed to estimate the association between baseline network connectivity and treatment response in a large sample and benchmark its prognostic value against baseline symptom severity and variance. Methods N = 40 518 patients receiving treatment for depression in routine care in England from 2015–2020 were analysed. Cross-sectional networks were constructed using the Patient Health Questionnaire-9 (PHQ-9) for responders and non-responders (N = 20 259 each). To conduct parametric tests investigating the contribution of PHQ-9 sum score mean and variance to connectivity differences, networks were constructed for 160 independent subsamples of responders and non-responders (80 each, n = 250 per sample). Results The baseline non-responder network was more connected than responders (3.15 v. 2.70, S = 0.44, p < 0.001), but effects were small, requiring n = 750 per group to have 85% power. Parametric analyses revealed baseline network connectivity, PHQ-9 sum score mean, and PHQ-9 sum score variance were correlated (r = 0.20–0.58, all p < 0.001). Both PHQ-9 sum score mean (β = −1.79, s.e. = 0.07, p < 0.001), and PHQ-9 sum score variance (β = −1.67, s.e. = 0.09, p < 0.001) had larger effect sizes for predicting response than connectivity (β = −1.35, s.e. = 0.12, p < 0.001). The association between connectivity and response disappeared when PHQ-9 sum score variance was accounted for (β = −0.28, s.e. = 0.19, p = 0.14). We replicated these results in patients completing longer treatment (8–12 weeks, N = 22 952) and using anxiety symptom networks (N = 70 620). Conclusions The association between baseline network connectivity and treatment response may be largely due to differences in baseline score variance.

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Section: Discussionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 95%

Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression

et al. 2023

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“…The increase in overall connectivity after treatment is in line with previous cross-sectional network studies . A strong connectivity could imply that the improvement of individual symptoms leads to the improvement of many other symptoms or that the worsening of one symptom leads to the worsening of many other symptoms . Similarly, a negative symptom interaction could imply that the occurrence of specific symptoms is associated with the absence of other symptoms or that the absence of specific symptoms is associated with the occurrence of other symptoms.…”

Section: Discussionmentioning

confidence: 99%

Implications of the Network Theory for the Treatment of Mental Disorders

Schumacher,

Klein,

Elsaesser

et al. 2023

JAMA Psychiatry

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ImportanceConceptualizing mental disorders as latent entities has been challenged by the network theory of mental disorders, which states that psychological problems are constituted by a network of mutually interacting symptoms. While the implications of the network approach for planning and evaluating treatments have been intensively discussed, empirical support for the claims of the network theory regarding treatment effects is lacking.ObjectiveTo assess the extent to which specific hypotheses derived from the network theory regarding the (interindividual) changeability of symptom dynamics in response to treatment align with empirical data.Design, Setting, and ParticipantsThis secondary analysis entails data from a multisite randomized clinical trial, in which 254 patients with chronic depression reported on their depressive symptoms at every treatment session. Data collection was conducted between March 5, 2010, and October 14, 2013, and this analysis was conducted between November 1, 2021, and May 31, 2022.InterventionThirty-two sessions of either disorder-specific or nonspecific psychotherapy for chronic depression.Main Outcomes and MeasuresLongitudinal associations of depressive symptoms with each other and change of these associations through treatment estimated by a time-varying longitudinal network model.ResultsIn a sample of 254 participants (166 [65.4%] women; mean [SD] age, 44.9 [11.9] years), symptom interactions changed through treatment, and this change varied across treatments and individuals. The mean absolute (ie, valence-ignorant) strength of symptom interactions (logarithmic odds ratio scale) increased from 0.40 (95% CI, 0.36-0.44) to 0.60 (95% CI, 0.52-0.70) during nonspecific psychotherapy and to 0.56 (95% CI, 0.48-0.64) during disorder-specific psychotherapy. In contrast, the mean raw (ie, valence-sensitive) strength of symptom interactions decreased from 0.32 (95% CI, 0.28-0.36) to 0.26 (95% CI, 0.20-0.32) and to 0.09 (95% CI, 0.02-0.16), respectively. Changing symptom severity could be explained to a large extent by symptom interactions.Conclusions and RelevanceThese findings suggest that specific treatment-related hypotheses of the network theory align well with empirical data. Conceptualizing mental disorders as symptom networks and treatments as measures that aim to change these networks is expected to give further insights into the working mechanisms of mental health treatments, leading to the improvement of current and the development of new treatments.Trial RegistrationClinicalTrials.gov Identifier: NCT00970437

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“…Data from experiment 1 and all analysis codes (experiments 1, 2, and 3) are available at https://osf.io/wz6rn/ ( 43 ). Data from experiment 2 are available at https://osf.io/5ybxt/ ( 44 ).…”

Section: Data Materials and Software Availabilitymentioning

confidence: 99%

Elevated emotion network connectivity is associated with fluctuations in depression

Kelley,

Fisher,

Lee

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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Elevated emotion network connectivity is thought to leave people vulnerable to become and stay depressed. The mechanism through which this arises is however unclear. Here, we test the idea that the connectivity of emotion networks is associated with more extreme fluctuations in depression over time, rather than necessarily more severe depression. We gathered data from two independent samples of N = 155 paid students and N = 194 citizen scientists who rated their positive and negative emotions on a smartphone app twice a day and completed a weekly depression questionnaire for 8 wk. We constructed thousands of personalized emotion networks for each participant and tested whether connectivity was associated with severity of depression or its variance over 8 wk. Network connectivity was positively associated with baseline depression severity in citizen scientists, but not paid students. In contrast, 8-wk variance of depression was correlated with network connectivity in both samples. When controlling for depression variance, the association between connectivity and baseline depression severity in citizen scientists was no longer significant. We replicated these findings in an independent community sample ( N = 519). We conclude that elevated network connectivity is associated with greater variability in depression symptoms. This variability only translates into increased severity in samples where depression is on average low and positively skewed, causing mean and variance to be more strongly correlated. These findings, although correlational, suggest that while emotional network connectivity could predispose individuals to severe depression, it could also be leveraged to bring about therapeutic improvements.

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Elevated Emotion Network Connectivity Is Associated with Fluctuations in Depression

Cited by 3 publications

References 34 publications

Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression

Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression

Implications of the Network Theory for the Treatment of Mental Disorders

Elevated emotion network connectivity is associated with fluctuations in depression

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