Zendehrouh Elaheh, Sendi Mohammad S E, Sui Jing, Fu Zening, Zhi Dongmei, Lv Luxian, Ma Xiaohong, Ke Qing, Li Xianbin, Wang Chuanyue, Abbott Christopher C, Turner Jessica A, Miller Robyn L, Calhoun Vince D
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1493-1496. doi: 10.1109/EMBC44109.2020.9175872.
Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.Index Terms- Major depressive disorder, Dynamic functional network connectivity, Machine learning, Resting- state functional magnetic resonance imaging, Hidden Markov model.
重度抑郁症（MDD）是一种常见且严重的精神障碍，其特征为持续的负面情绪和极度悲伤。近几十年来，多项研究使用从静息态功能磁共振成像（fMRI）估计的功能网络连接性（FNC）来研究MDD的生物学特征。然而，其中大多数研究都忽略了大脑交互的时间变化，专注于静态FNC（sFNC）。探索功能连接性（FC）时间模式的动态功能网络连接性（dFNC）可能会为其静态对应物提供额外信息。在当前研究中，通过对MDD患者和健康对照（HCs）的dFNC应用k均值聚类，我们估计了5种不同状态。接下来，我们使用隐马尔可夫模型作为潜在生物标志物，以区分MDD患者和HCs的dFNC模式。通过比较MDD和HC受试者的隐马尔可夫模型（HMM）特征，我们强调了状态之间的转移概率作为潜在生物标志物的作用，并确定在MDD受试者中，随着症状严重程度增加，从轻度连接状态到高度连接状态的转移概率会降低。
重度抑郁症；动态功能网络连接性；机器学习；静息态功能磁共振成像；隐马尔可夫模型
