Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Department of Research and Development, Epilepsy Center Kempenhaeghe, Heeze, The Netherlands.
J Neuroimaging. 2023 May-Jun;33(3):404-414. doi: 10.1111/jon.13085. Epub 2023 Jan 29.
The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging.
In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects.
We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis.
Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.
由于缺乏稳健的诊断生物标志物，从神经生物学角度理解抑郁症成为一个重要目标，尤其是在脑成像的背景下。
本研究旨在创建新的基于图像的特征，以实现抑郁症的客观诊断。利用小波相干和格兰杰因果关系（G-因果关系）研究静息态网络时间序列的神经动力学。引入了三个新特征：总小波相干、小波领先相干和小波相干斑分析。第四个特征，成对条件格兰杰因果关系，用于建立静息态网络之间的因果关系。我们使用提出的特征对成年患者进行抑郁分类。
在小波领先相干、格兰杰因果关系和小波相干斑分析中，我们分别获得了 86%、80%和 86%的准确率。患有抑郁症的患者表现出背侧注意网络和听觉网络之间以及后默认模式网络和背侧注意网络之间的超连接。前默认模式网络和听觉网络以及右侧额顶网络和外侧视觉网络之间存在连接不足。根据小波相干斑分析，还发现小脑与外侧运动网络之间存在异常的协同激活模式。
基于脑网络之间异常的功能动力学，我们能够以较高的准确率识别出患有抑郁症的患者。这项研究的结果有助于理解与抑郁症相关的情绪和注意力处理受损以及运动活动减少。
