Brain Structural Network Connectivity of Formal Thought Disorder Dimensions in Affective and Psychotic Disorders

Stein, Frederike; Gruber, Marius; Mauritz, Marco; Pfarr, Julia‐Katharina; Ringwald, Kai G.; Thomas-Odenthal, Florian; Wroblewski, Adrian; Evermann, Ulrika; Steinsträter, Olaf; Grumbach, Pascal; Thiel, Katharina; Winter, Alexandra; Bonnekoh, Linda M.; Flinkenflügel, Kira; Goltermann, Janik; Meinert, Susanne; Grotegerd, Dominik; Bauer, Jochen; Hahn, Tim; Leehr, Elisabeth J.; Jansen, Andreas; Lange, Siemon C. de; Heuvel, Martijn van den; Nenadić, Igor; Krug, Axel; Lemke, Hannah; Repple, Jonathan; Kircher, Tilo

doi:10.1016/j.biopsych.2023.05.010

Cited by 4 publications

(2 citation statements)

References 78 publications

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“…External factors (e.g., critical life events or experiential manipulations) can influence the network, activating symptoms within the network that subsequently lead to changes in downstream, adjacent symptoms (Borsboom, 2017). Considering symptoms and therefore data from different domains may be advantageous due to the biopsychosocial nature of mental disorders (for recent examples of multimodal integration, see: e.g., Astill Wright et al, 2021;Stein et al, 2023;Tao et al, 2022). Given the multimodal nature of mental disorders, a unified theoretical framework seems to be required to understand mental disorders comprehensively.…”

Section: Addressing Problem One the Systems Approach As A Meta-theorymentioning

confidence: 99%

“…Longitudinal measurements are essential, for capturing the evolution of network states over time. By employing MMDNs (e.g., Morales et al, 2018;Stein et al, 2023;Wang et al, 2014;Yu et al, 2021), such as time-series or panel data analysis, we enable monitoring, prediction, and intervention planning for mental disorders. Data collection methods like Ecological Momentary Assessment (EMA; Myin-Germeys & Kuppens, 2021;Shiffman et al, 2008) and passive data collection techniques (e.g., smartphones, wearable sensors, electronic recordings, social media, and Internet data; Harari et al, 2016;Mohr et al, 2017) allow for data acquisition in natural environments, providing valuable insights into symptomatology development and dynamical interactions.…”

Section: Addressing Problem One the Systems Approach As A Meta-theorymentioning

confidence: 99%

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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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Section: Addressing Problem One the Systems Approach As A Meta-theorymentioning

confidence: 99%

Section: Addressing Problem One the Systems Approach As A Meta-theorymentioning

confidence: 99%

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

View full text Add to dashboard Cite

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Different structural connectivity patterns in the subregions of the thalamus, hippocampus, and cingulate cortex between schizophrenia and psychotic bipolar disorder

Cao,

Li,

Dong

et al. 2024

Journal of Affective Disorders

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High state boredom vastly affects psychiatric inpatients and predicts their treatment duration

Seiler,

Zerr,

Rumpel

et al. 2023

Transl Psychiatry

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Boredom is a ubiquitous, aversive human experience typically elicited by low information and monotony. Boredom can occur either as a transient mental state that prompts individuals to adapt their behavior to avoid monotony or as a temporally stable trait, describing a chronic susceptibility to feeling bored. Increased trait boredom was found to correlate with various psychopathologies and indicators of mental burden. However, the role of state boredom in psychopathological conditions and its implications for psychiatric treatment remain elusive. Here, we address this issue by investigating state boredom and trait boredom in a cohort of psychiatric inpatients and a healthy control cohort. We find that in both groups, state boredom, even more than trait boredom, shows remarkable associations with psychopathology. In the inpatient group, state boredom is implicated broadly in multiple mental disorders and shows an association with treatment in closed psychiatric wards. Furthermore, through statistical modeling, we find that high-state boredom during inpatient therapy is predictive of a longer therapy duration. Thus, we show that state boredom constitutes an indicator of mild and severe psychopathology in different mental disorders, affecting the outcome of psychiatric patients. Potential therapeutic interventions are discussed, aiming to enhance information flow in the brain in order to alleviate boredom in clinical settings.

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Brain Structural Network Connectivity of Formal Thought Disorder Dimensions in Affective and Psychotic Disorders

Cited by 4 publications

References 78 publications

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

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

Different structural connectivity patterns in the subregions of the thalamus, hippocampus, and cingulate cortex between schizophrenia and psychotic bipolar disorder

High state boredom vastly affects psychiatric inpatients and predicts their treatment duration

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