A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder

Hahn, Tim; Jamalabadi, Hamidreza; Emden, Daniel; Goltermann, Janik; Ernsting, Jan; Winter, Nils R.; Fisch, L.; Leenings, Ramona; Sarink, Kelvin; Holstein, Vincent; Gruber, Marius; Grotegerd, Dominik; Meinert, Susanne; Dohm, Katharina; Leehr, Elisabeth J.; Richter, Maike; Sindermann, Lisa; Enneking, Verena; Lemke, Hannah; Witt, Stephanie H.; Rietschel, Marcella; Pfarr, Julia‐Katharina; Meller, Tina; Ringwald, Kai G.; Schmitt, Simon; Stein, Frederike; Nenadić, Igor; Kircher, Tilo; Müller-Myhsok, Bertram; Andlauer, Till F. M.; Repple, Jonathan; Dannlowski, Udo

doi:10.48550/arxiv.2107.10178

Cited by 4 publications

(8 citation statements)

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“…Within clinical psychology and the closely related psychiatric community, an increasing number of scientists are calling to use the dynamical systems approach to better understand the course of mental disorders (Borsboom, 2017;S.-M. Chow et al, 2022;Durstewitz et al, 2021;Friston, 2022;Jamalabadi et al, 2022) (see Robinaugh et al(D. J. Robinaugh et al, 2020) for a recent review that includes many examples on momentary experiences, cognitive behavior therapy, and the structure of mental disorders). More recently, applied to longitudinal measurement enabled by smartphones, Hahn et al (Hahn, Jamalabadi, Emden, et al, 2021) and Jamalabadi et al (Jamalabadi et al, 2022) showed that the fluctuations of depressive symptoms follow the path predicted by dynamical systems theory (Hahn, Jamalabadi, Emden, et al, 2021;Jamalabadi et al, 2022). Further, simulation studies using this approach in studying complicated grief (Henry et al, 2022), Post-Traumatic Stress Disorder (PTSD) (Lunansky et al, 2022), and panic disorder (D. Robinaugh et al, 2019) yield strongly consistent results with what is known from the literature.…”

Section: Introductionmentioning

confidence: 54%

“…where 𝑥(𝑡) ∈ 𝐷 11 , 𝐷 = ሼ0,1,2,3,4,5,6ሽ defines the attitude towards meat at time 𝑡 (also called the state), 𝐴 represents the interaction matrix (i.e., the networks, see section 5.4 for the estimation procedure), 𝐵 (the canonical vector) is the input matrix and specifies the item that is perturbed, and 𝑢(𝑡) corresponds to the input signal (here the applied scenarios) 40 . Importantly, following previous work (Hahn, Jamalabadi, Emden, et al, 2021;Jamalabadi et al, 2022), we assume that A remains constant after perturbation. Based on this equation, we can compute the following metrics:…”

Section: Network Control Theory and The Effect Of Psychological Pertu...mentioning

confidence: 99%

“…where 𝑢(𝑡) is the solution to the optimal control problem as in (Gu et al, 2017;Hahn, Jamalabadi, Emden, et al, 2021). where 𝑇 and 𝜌 are free parameters quantifying the time to reach from 𝑥 0 to 𝑥 𝑇 and the relative importance of cost terms in equation ( 5).…”

Section: Control Energymentioning

confidence: 99%

“…Following (Gu et al, 2015;Hahn, Jamalabadi, Nozari, et al, 2021), we define the step size to 0.001, 𝑇 = 1, and 𝜌 = 1. To solve the optimal control equations ( 5) and ( 6), we use a customized version of the code that is used elsewhere to study the brain as well as psychological dynamics (Gu et al, 2017;Hahn, Jamalabadi, Emden, et al, 2021;Jamalabadi et al, 2022).…”

Section: Control Energymentioning

confidence: 99%

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Formalizing psychological interventions through network control theory

Stocker¹,

Koppe²,

Reich³

et al. 2022

Preprint

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Despite the growing deployment of network representation throughout psychological sciences, the question of whether and how networks can systematically describe the effects of psychological interventions remains elusive. Towards this end, we capitalize on recent breakthrough in network control theory, the engineering study of networked interventions, to investigate a representative psychological attitude modification experiment. This study examined 30 healthy participants who answered 11 questions about their attitude toward eating meat. They then received 11 arguments to challenge their attitude on the questions, after which they were asked again the same set of questions. Using this data, we constructed networks that quantify the connections between the responses and tested: 1) if the observed psychological effect, in terms of sensitivity and specificity, relates to the regional network topology as described by control theory, 2) if the size of change in responses relates to whole-network topology that quantifies the “ease” of change as described by control theory, and 3) if responses after intervention could be predicted based on formal results from control theory. We found that 1) the interventions that had higher regional topological relevance (the so-called controllability scores) had stronger effect (r > 0.5), the intervention sensitivities were systematically lower for the interventions that were “easier to control” (r = -0.49), and that the model offered substantial prediction accuracy (r = 0.36). Finally, we compared results based on subjective as well as data-driven models and found that, across all tests, the results from the data-driven approach explained a larger size of effect. Taken together, our results suggest that psychological interventions can be studied using network control theory and that data-driven models provide a reliable medium.

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Section: Introductionmentioning

confidence: 54%

Section: Network Control Theory and The Effect Of Psychological Pertu...mentioning

confidence: 99%

Section: Control Energymentioning

confidence: 99%

Section: Control Energymentioning

confidence: 99%

See 2 more Smart Citations

Formalizing psychological interventions through network control theory

Stocker¹,

Koppe²,

Reich³

et al. 2022

Preprint

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show abstract

“…In neuroscience, network control theory is increasingly used [10,18,19,21,39], for good reasons, to study neural, biological, and psychological constructs. It relates fundamental theory-driven results from controls literature to the study of networks that are the natural points of interest in neurosciences [14].…”

Section: Discussionmentioning

confidence: 99%

Network controllability measures of subnetworks: implications for neurosciences

et al. 2023

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Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially constrained by different optimality criteria, result in complex network behavior. A typical example is the quantification of the functional role certain brain regions or symptoms play in shaping the temporal dynamics of brain activity or the clinical course of a disease, a property that is quantified in terms of the so-called controllability metrics. Critically though, contrary to the engineering context in which control theory was originally developed, a mathematical understanding of the network nodes and connections in neurosciences cannot be assumed. For instance, in the case of psychological systems such as those studied to understand psychiatric disorders, a potentially large set of related variables are unknown. As such, while the measures offered by network control theory would be mathematically correct, in that they can be calculated with high precision, they could have little translational values with respect to their putative role suggested by controllability metrics. It is therefore critical to understand if and how the controllability metrics estimated over subnetworks would deviate, if access to the complete set of variables, as is common in neurosciences, cannot be taken for granted. In this paper, we use a host of simulations based on synthetic as well as structural MRI data to study the potential deviation of controllability metrics in sub- compared to the full networks. Specifically, we estimate average- and modal-controllability, two of the most widely used controllability measures in neurosciences, in a large number of settings where we systematically vary network type, network size, and edge density. We find out, across all network types we test, that average and modal controllability are systematically, over- or underestimated depending on the number of nodes in the sub- and full network and the edge density. Finally, we provide formal theoretical proof that our observations generalize to any network type and discuss the ramifications of this systematic bias and potential solutions to alleviate the problem.

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Major Problems in Clinical Psychological Science and How to Address them. Introducing a Multimodal Dynamical Network Approach

Westhoff,

Berg,

Reif

et al. 2024

Cogn Ther Res

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Background Despite impressive dissemination programs of best-practice therapies, clinical psychology faces obstacles in developing more efficacious treatments for mental disorders. In contrast to other medical disciplines, psychotherapy has made only slow progress in improving treatment outcomes. Improvements in the classification of mental disorders could enhance the tailoring of treatments to improve effectiveness. We introduce a multimodal dynamical network approach, to address some of the challenges faced by clinical research. These challenges include the absence of a comprehensive meta-theory, comorbidity, substantial diagnostic heterogeneity, violations of ergodicity assumptions, and a limited understanding of causal processes. Methods Through the application of multimodal dynamical network analysis, we describe how to advance clinical research by addressing central problems in the field. By utilizing dynamic network analysis techniques (e.g., Group Iterative Multiple Model Estimation, multivariate Granger causality), multimodal measurements (i.e., psychological, psychopathological, and neurobiological data), intensive longitudinal data collection (e.g., Ecological Momentary Assessment), and causal inference methods (e.g., GIMME), our approach could improve the comprehension and treatment of mental disorders. Under the umbrella of the systems approach and utilizing e.g., graph theory and control theory, we aim to integrate data from longitudinal, multimodal measurements. Results The multimodal dynamical network approach enables a comprehensive understanding of mental disorders as dynamic networks of interconnected symptoms. It dismantles artificial diagnostic boundaries, facilitating a transdiagnostic view of psychopathology. The integration of longitudinal data and causal inference techniques enhances our ability to identify influential nodes, prioritize interventions, and predict the impact of therapeutic strategies. Conclusion The proposed approach could improve psychological treatment by providing individualized models of psychopathology and by suggesting individual treatment angles.

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A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder

Cited by 4 publications

References 0 publications

Formalizing psychological interventions through network control theory

Formalizing psychological interventions through network control theory

Network controllability measures of subnetworks: implications for neurosciences

Major Problems in Clinical Psychological Science and How to Address them. Introducing a Multimodal Dynamical Network Approach

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