Vriend Chris, Fitzsimmons Sophie M D D, Aarts Inga, Broekhuizen Aniek, van der Werf Ysbrand D, Douw Linda, Visser Henny A D, Thomaes Kathleen, van den Heuvel Odile A
Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, de Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, de Boelelaan 1117, Amsterdam, the Netherlands; Compulsivity, Impulsivity and Attention, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands.
Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, de Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, de Boelelaan 1117, Amsterdam, the Netherlands; Compulsivity, Impulsivity and Attention, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands.
Neuroimage Clin. 2025 Aug 18;48:103870. doi: 10.1016/j.nicl.2025.103870.
Predicting treatment efficacy in psychiatric disorders remains challenging, despite the availability of effective interventions. Previous studies suggest a link between pre-treatment brain network characteristics and treatment efficacy in individual disorders, but cross-disorder investigations are lacking. We analyzed pre-treatment MRI data from 177 individuals (113 females) with either obsessive-compulsive disorder (OCD) or post-traumatic stress disorder with comorbid personality disorders (PTSD) that received different non-pharmacological treatments. Using diffusion and resting-state MRI, we constructed structural, functional, and multilayer connectomes and calculated network measures for network integration (e.g. global efficiency, eccentricity), segregation (modularity) and their balance (small-worldness). We assessed the relationship between these pre-treatment network measures, and treatment improvement using mixed-model and Bayesian analyses. We also compared psychiatric cases with healthy controls and investigated associations between clinical response and treatment-induced changes in network measures. Across disorders and treatments, psychiatric cases showed a 41.6 ± 29.6 % symptom improvement (62 % response rate) after treatment. They also showed pre-treatment differences in functional and multilayer network topology compared to healthy controls. Symptom improvement was associated with pre-treatment functional (P = 0.04) and structural small-worldness (P = 0.01), and multilayer eccentricity (P = 0.01), while responders had higher functional modularity (P = 0.02). Results were robust across trials and treatments, when adjusting for medication status and showed high credibility in Bayesian analyses. Network change associations with treatment response were only modest. These results show that pre-treatment connectome characteristics are related to treatment response, regardless of treatment and psychiatric disorder, and suggest that individual differences in intrinsic features of the human connectome underlie amenability to treatment.
尽管有有效的干预措施,但预测精神疾病的治疗效果仍然具有挑战性。先前的研究表明,个体疾病的治疗前脑网络特征与治疗效果之间存在联系,但跨疾病的调查尚缺乏。我们分析了177名个体(113名女性)的治疗前MRI数据,这些个体患有强迫症(OCD)或患有共病性人格障碍的创伤后应激障碍(PTSD),他们接受了不同的非药物治疗。使用扩散和静息态MRI,我们构建了结构、功能和多层连接组,并计算了网络整合(例如全局效率、偏心率)、分离(模块化)及其平衡(小世界性质)的网络指标。我们使用混合模型和贝叶斯分析评估了这些治疗前网络指标与治疗改善之间的关系。我们还将精神病例与健康对照进行了比较,并研究了临床反应与治疗引起的网络指标变化之间的关联。在所有疾病和治疗中,精神病例在治疗后症状改善了41.6±29.6%(反应率为62%)。与健康对照相比,他们在治疗前的功能和多层网络拓扑结构上也存在差异。症状改善与治疗前的功能(P = 0.04)、结构小世界性质(P = 0.01)和多层偏心率(P = 0.01)相关,而反应者具有更高的功能模块化(P = 0.02)。在调整药物状态后,结果在所有试验和治疗中都很稳健,并且在贝叶斯分析中具有很高的可信度。网络变化与治疗反应的关联仅为中等程度。这些结果表明,无论治疗方法和精神疾病如何,治疗前连接组特征都与治疗反应相关,并表明人类连接组内在特征的个体差异是治疗易感性的基础。