von Schwanenflug Nina, Krohn Stephan, Heine Josephine, Paul Friedemann, Prüss Harald, Finke Carsten
Department of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Brain Commun. 2022 Feb 1;4(1):fcab298. doi: 10.1093/braincomms/fcab298. eCollection 2022.
Traditional static functional connectivity analyses have shown distinct functional network alterations in patients with anti--methyl-d-aspartate receptor encephalitis. Here, we use a dynamic functional connectivity approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in anti--methyl-d-aspartate receptor encephalitis and assess the discriminatory power of functional brain states in a supervised classification approach. We included resting-state functional magnetic resonance imaging data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in functional connectivity, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. Compared to controls, patients exhibited diverging dynamic functional connectivity patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic functional connectivity measures were associated with disease severity and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic functional connectivity models and outperformed static analyses, reaching up to 78.6% classification accuracy. By applying time-resolved analyses, we disentangle state-specific functional connectivity impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on the functional reorganization underlying anti-N-methyl-d-aspartate receptor encephalitis. Finally, the correlation of dynamic functional connectivity measures with disease symptoms and severity demonstrates a clinical relevance of spatiotemporal connectivity dynamics in anti--methyl-d-aspartate receptor encephalitis.
传统的静态功能连接分析已显示，抗N-甲基-D-天冬氨酸受体脑炎患者存在明显的功能网络改变。在此，我们采用一种动态功能连接方法，将连接分析的时间分辨率从分钟提高到秒。我们借此探索抗N-甲基-D-天冬氨酸受体脑炎中大规模脑网络活动的时空变异性，并在监督分类方法中评估功能性脑状态的辨别能力。我们纳入了57例患者和61例对照的静息态功能磁共振成像数据，以提取四种离散的连接状态，并评估功能连接、停留时间、转换频率、分数时间和发生率方面的状态组差异。此外，对于每种状态，训练具有嵌入式特征选择的逻辑回归模型，以在留一法交叉验证方案中预测组状态。与对照组相比，患者在四种状态中的三种状态下表现出不同的动态功能连接模式，主要包括默认模式网络和额叶区域。这伴随着患者停留时间模式的特征性变化和状态转换的更高波动性。此外，动态功能连接测量与疾病严重程度以及阳性和阴性精神分裂症样症状相关。动态功能连接模型的预测能力最高，优于静态分析，分类准确率高达78.6%。通过应用时间分辨分析，我们解开了静态分析中未检测到的状态特异性功能连接损伤和时间动态的特征性变化，为抗N-甲基-D-天冬氨酸受体脑炎潜在的功能重组提供了新的视角。最后，动态功能连接测量与疾病症状和严重程度的相关性证明了抗N-甲基-D-天冬氨酸受体脑炎中时空连接动力学的临床相关性。
