Post-traumatic stress disorder (PTSD) is one of the common chronic diseases in psychiatry. It has a long course of disease which seriously affects patient’s life and work, and has a serious impact on social function. So far, the diagnosis of PTSD is limited to clinical symptom manifestations and physician experience, and no exact pathological substance basis is found, while the pathological substance basis is crucial for accurate diagnosis, selection of effective treatment methods, evaluation of treatment results, and assessment of disability. Head MRI is currently one of the most promising techniques to solve this problem. This paper reviews the progress in MRI research on brain structure changes in PTSD patients, in order to explore the possible future development directions.
Post-traumatic stress disorder (PTSD) presents with complex and diverse clinical manifestations, making accurate and objective diagnosis challenging when relying solely on clinical assessments. Therefore, there is an urgent need to develop reliable and objective auxiliary diagnostic models to provide effective diagnosis for PTSD patients. Currently, the application of graph neural networks for representing PTSD is limited by the expressiveness of existing models, which does not yield optimal classification results. To address this, we proposed a multi-graph multi-kernel graph convolutional network (MK-GCN) model for classifying PTSD data. First, we constructed functional connectivity matrices at different scales for the same subjects using different atlases, followed by employing the k-nearest neighbors algorithm to build the graphs. Second, we introduced the MK-GCN methodology to enhance the feature extraction capability of brain structures at different scales for the same subjects. Finally, we classified the extracted features from multiple scales and utilized graph class activation mapping to identify the top 10 brain regions contributing to classification. Experimental results on seismic-induced PTSD data demonstrated that our model achieved an accuracy of 84.75%, a specificity of 84.02%, and an AUC of 85% in the classification task distinguishing between PTSD patients and non-affected subjects. The findings provide robust evidence for the auxiliary diagnosis of PTSD following earthquakes and hold promise for reliably identifying specific brain regions in other PTSD diagnostic contexts, offering valuable references for clinicians.
目的 利用局部一致性（ReHo）方法探測創傷后應激障礙（PTSD）患者在靜息狀態下是否存在著大腦功能異常。 方法 2010年5月－7月對18例未經治療的地震PTSD患者和19例同樣經歷地震但未患PTSD的對照者進行了靜息態功能磁共振成像（Rs-fMRI） 掃描。應用ReHo方法處理Rs-fMRI數據，得出PTSD患者的異常腦區，并將患者存在組間差異的腦區ReHo值與臨床用PTSD診斷量表（CAPS）、漢密爾頓抑郁量表（HAMD）和漢密爾頓焦慮量表（HAMA）分別進行相關分析。 結果 ① PTSD組ReHo顯著增加的腦區包括右側顳下回、楔前葉、頂下葉、中扣帶回，左側枕中回以及左/右側后扣帶回；ReHo顯著降低的腦區包括左側海馬和左/右側腹側前扣帶回。② 異常腦區中后扣帶回和右側中扣帶回ReHo與HAMD呈負相關（中扣帶回r=?0.575，P=0.012；右側后扣帶回：r=?0.507，P=0.032），其余腦區ReHo與臨床指標無明顯相關性（P＞0.05），左側海馬與CAPS的相關性相對其他腦區較大（r=?0.430，P=0.075）。 結論 PTSD患者在靜息狀態下即存在著局部腦功能活動的降低和增加，ReHo方法可能有助于研究PTSD患者靜息狀態腦活動。