Shahsavarani Somayeh, Abraham Ivan T, Zimmerman Benjamin J, Baryshnikov Yuliy M, Husain Fatima T
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Front Comput Neurosci. 2020 Jan 20;13:94. doi: 10.3389/fncom.2019.00094. eCollection 2019.
The resting state fMRI time series appears to have cyclic patterns, which indicates presence of cyclic interactions between different brain regions. Such interactions are not easily captured by pre-established resting state functional connectivity methods including zero-lag correlation, lagged correlation, and dynamic time warping distance. These methods formulate the functional interaction between different brain regions as similar temporal patterns within the time series. To use information related to temporal ordering, cyclicity analysis has been introduced to capture pairwise interactions between multiple time series. In this study, we compared the efficacy of cyclicity analysis with aforementioned similarity-based techniques in representing individual-level and group-level information. Additionally, we investigated how filtering and global signal regression interacted with these techniques. We obtained and analyzed fMRI data from patients with tinnitus and neurotypical controls at two different days, a week apart. For both patient and control groups, we found that the features generated by cyclicity and correlation (zero-lag and lagged) analyses were more reliable than the features generated by dynamic time warping distance in identifying individuals across visits. The reliability of all features, except those generated by dynamic time warping, improved as the global signal was regressed. Nevertheless, removing fluctuations >0.1 Hz deteriorated the reliability of all features. These observations underscore the importance of choosing appropriate preprocessing steps while evaluating different analytical methods in describing resting state functional interactivity. Further, using different machine learning techniques including support vector machines, discriminant analyses, and convolutional neural networks, our results revealed that the manifestation of the group-level information within all features was not sufficient enough to dissociate tinnitus patients from controls with high sensitivity and specificity. This necessitates further investigation regarding the representation of group-level information within different features to better identify tinnitus-related alternation in the functional organization of the brain. Our study adds to the growing body of research on developing diagnostic tools to identify neurological disorders, such as tinnitus, using resting state fMRI data.
静息态功能磁共振成像（fMRI）时间序列似乎具有循环模式，这表明不同脑区之间存在循环相互作用。这种相互作用不易被包括零滞后相关性、滞后相关性和动态时间规整距离在内的预先建立的静息态功能连接方法所捕捉。这些方法将不同脑区之间的功能相互作用表述为时间序列内相似的时间模式。为了利用与时间顺序相关的信息，引入了循环性分析来捕捉多个时间序列之间的成对相互作用。在本研究中，我们比较了循环性分析与上述基于相似性的技术在表示个体水平和组水平信息方面的功效。此外，我们研究了滤波和全局信号回归如何与这些技术相互作用。我们在相隔一周的两天从耳鸣患者和神经典型对照者那里获取并分析了fMRI数据。对于患者组和对照组，我们发现循环性分析和相关性（零滞后和滞后）分析所生成的特征在跨访次识别个体方面比动态时间规整距离所生成的特征更可靠。除动态时间规整所生成的特征外，所有特征的可靠性随着全局信号的回归而提高。然而，去除大于0.1赫兹的波动会降低所有特征的可靠性。这些观察结果强调了在评估描述静息态功能交互性的不同分析方法时选择合适预处理步骤的重要性。此外，使用包括支持向量机、判别分析和卷积神经网络在内的不同机器学习技术，我们的结果表明所有特征中组水平信息的表现不足以以高灵敏度和特异性将耳鸣患者与对照者区分开来。这就需要进一步研究不同特征中组水平信息的表示，以便更好地识别大脑功能组织中与耳鸣相关的改变。我们的研究为利用静息态fMRI数据开发诊断工具以识别耳鸣等神经系统疾病的研究不断增加的文献做出了贡献。
