Paunova Rositsa, Ramponi Cristina, Kandilarova Sevdalina, Todeva-Radneva Anna, Latypova Adeliya, Stoyanov Drozdstoy, Kherif Ferath
Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria.
Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria.
Front Psychiatry. 2023 Oct 13;14:1272933. doi: 10.3389/fpsyt.2023.1272933. eCollection 2023.
In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia ( = 17), bipolar disorder ( = 25), major depressive disorder ( = 68) and a healthy control group ( = 54).
We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.
As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.
Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.
在本研究中，我们应用多变量方法来识别在塑造与主要精神疾病诊断相关的网络连接模式中起关键作用的脑区，这些精神疾病包括精神分裂症（SCH）、重度抑郁症（MDD）、双相情感障碍（BD）以及健康对照（HC）。我们使用了164名受试者的T1加权图像，其中精神分裂症患者17例，双相情感障碍患者25例，重度抑郁症患者68例，健康对照组54例。
我们使用基于SPM12工具箱的SHOOT算法的方法提取感兴趣区域（ROI）。然后我们在各群组之间进行多变量结构协方差分析。对于那些协方差值在t检验中被确定为显著的区域，我们计算其特征中心性，作为衡量脑区在网络内影响力的指标。我们应用p = 0.001的显著性阈值。最后，我们进行聚类分析，以确定在观察组的不同两两比较网络中具有相似特征中心性分布的区域组。
结果，我们获得了4个包含不同脑区的聚类，这些聚类具有诊断特异性。聚类1在精神分裂症与健康对照以及精神分裂症与双相情感障碍之间显示出最强的判别值。聚类2对重度抑郁症患者具有最强的判别值，聚类3对双相情感障碍患者具有最强的判别值。聚类4似乎对四组之间的区分贡献几乎相同。
我们的结果表明，我们可以使用多变量结构协方差方法来识别对特定精神疾病诊断具有更高预测价值的特定区域。在我们的研究中，我们已经确定了脑特征，这表明简并性以不同方式塑造了主要精神疾病内部和之间的脑网络。
