Westhoff Marlon, Berg Max, Reif Andreas, Rief Winfried, Hofmann Stefan G
Department of Psychology, Translational Clinical Psychology Group, Philipps-University of Marburg, Schulstraße 12, D-35032 Marburg, Germany.
Department of Psychology, Clinical Psychology and Psychotherapy Group, Philipps-University of Marburg, Gutenbergstraße 18, D-35032 Marburg, Germany.
Cognit Ther Res. 2024 Oct;48(5):791-807. doi: 10.1007/s10608-024-10487-9. Epub 2024 Jun 24.
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.
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.
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.
The proposed approach could improve psychological treatment by providing individualized models of psychopathology and by suggesting individual treatment angles.
尽管最佳实践疗法的传播项目令人印象深刻，但临床心理学在开发更有效的精神障碍治疗方法方面仍面临障碍。与其他医学学科相比，心理治疗在改善治疗效果方面进展缓慢。精神障碍分类的改进可以加强治疗的针对性以提高疗效。我们引入一种多模态动态网络方法，以应对临床研究面临的一些挑战。这些挑战包括缺乏全面的元理论、共病、大量的诊断异质性、遍历性假设的违反以及对因果过程的理解有限。
通过应用多模态动态网络分析，我们描述了如何通过解决该领域的核心问题来推进临床研究。通过利用动态网络分析技术（例如，组迭代多模型估计、多元格兰杰因果关系）、多模态测量（即心理、精神病理和神经生物学数据）、密集纵向数据收集（例如，生态瞬时评估）以及因果推理方法（例如，GIMME），我们的方法可以提高对精神障碍的理解和治疗。在系统方法的框架下，利用例如图论和控制理论，我们旨在整合来自纵向多模态测量的数据。
多模态动态网络方法能够将精神障碍全面理解为相互关联症状的动态网络。它打破了人为的诊断界限，促进了对精神病理学的跨诊断观点。纵向数据和因果推理技术的整合增强了我们识别有影响力的节点、确定干预优先级以及预测治疗策略影响的能力。
所提出的方法可以通过提供个性化的精神病理学模型并建议个性化的治疗角度来改善心理治疗。
