Abstract：
OBJECTIVE: This study examined the relationship between structural brain networks and long-term treatment outcomes in patients with panic disorder (PD) using machine learning methods.
METHODS: The study involved 80 participants (53 PD patients and 27 healthy controls) and included clinical assessments and MRI scans at baseline and after two years (160 MRIs). Patients were categorized based on their response to two-year pharmacotherapy. Brain networks were analyzed using white matter tractography and network-based statistics.
RESULTS: Results showed structural network changes in PD patients, particularly in the extended fear network, including frontal regions, thalamus, and cingulate gyrus. Longitudinal analysis revealed that increased connections to the amygdala, hippocampus, and insula were associated with better treatment response. Conversely, overconnectivity in the amygdala and insula at baseline was associated with poor response, and similar patterns were found in the insula and parieto-occipital cortex related to non-remission. This study found that SVM and CPM could effectively predict treatment outcomes based on network pattern changes in PD.
CONCLUSIONS: These findings suggest that monitoring structural connectome changes in limbic and paralimbic regions is critical for understanding PD and tailoring treatment. The study highlights the potential of using personalized biomarkers to develop individualized treatment strategies for PD.
METHODS: The study involved 80 participants (53 PD patients and 27 healthy controls) and included clinical assessments and MRI scans at baseline and after two years (160 MRIs). Patients were categorized based on their response to two-year pharmacotherapy. Brain networks were analyzed using white matter tractography and network-based statistics.
RESULTS: Results showed structural network changes in PD patients, particularly in the extended fear network, including frontal regions, thalamus, and cingulate gyrus. Longitudinal analysis revealed that increased connections to the amygdala, hippocampus, and insula were associated with better treatment response. Conversely, overconnectivity in the amygdala and insula at baseline was associated with poor response, and similar patterns were found in the insula and parieto-occipital cortex related to non-remission. This study found that SVM and CPM could effectively predict treatment outcomes based on network pattern changes in PD.
CONCLUSIONS: These findings suggest that monitoring structural connectome changes in limbic and paralimbic regions is critical for understanding PD and tailoring treatment. The study highlights the potential of using personalized biomarkers to develop individualized treatment strategies for PD.
摘要：
目的：本研究使用机器学习方法研究了惊恐障碍（PD）患者的结构性脑网络与长期治疗结果之间的关系。
方法：该研究涉及80名参与者（53名PD患者和27名健康对照），包括基线和两年后的临床评估和MRI扫描（160MRI）。根据患者对两年药物治疗的反应进行分类。使用白质纤维束成像和基于网络的统计学分析了大脑网络。
结果：结果显示PD患者的结构网络改变，特别是在扩展的恐惧网络中，包括额叶区域，丘脑,和扣带回.纵向分析显示与杏仁核的连接增加，海马体,和脑岛与更好的治疗反应相关。相反，基线时杏仁核和脑岛的过度连接与反应不良相关，在与非缓解相关的脑岛和顶枕皮质中发现了类似的模式。这项研究发现，SVM和CPM可以根据PD的网络模式变化有效地预测治疗结果。
结论：这些研究结果表明，监测边缘和旁缘区域的结构连接组变化对于了解PD和定制治疗至关重要。该研究强调了使用个性化生物标志物开发PD个性化治疗策略的潜力。
方法：该研究涉及80名参与者（53名PD患者和27名健康对照），包括基线和两年后的临床评估和MRI扫描（160MRI）。根据患者对两年药物治疗的反应进行分类。使用白质纤维束成像和基于网络的统计学分析了大脑网络。
结果：结果显示PD患者的结构网络改变，特别是在扩展的恐惧网络中，包括额叶区域，丘脑,和扣带回.纵向分析显示与杏仁核的连接增加，海马体,和脑岛与更好的治疗反应相关。相反，基线时杏仁核和脑岛的过度连接与反应不良相关，在与非缓解相关的脑岛和顶枕皮质中发现了类似的模式。这项研究发现，SVM和CPM可以根据PD的网络模式变化有效地预测治疗结果。
结论：这些研究结果表明，监测边缘和旁缘区域的结构连接组变化对于了解PD和定制治疗至关重要。该研究强调了使用个性化生物标志物开发PD个性化治疗策略的潜力。
