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Flexible Adaptive Paradigms for fMRI Using a Novel Software Package ‘Brain Analysis in Real-Time’ (BART)

机译:使用新型软件包实时脑部分析(BART)的功能磁共振成像的灵活自适应范例

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摘要

In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject’s compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject’s gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by the post-hoc removal of erroneous trials. Here, we propose an a priori adaptation of the paradigm during the experiment’s runtime. Our fMRI findings clearly show the benefits of an adapted paradigm in terms of statistical power and higher effect sizes in emotion-related brain regions. This can be of special interest for all experiments with low statistical power due to a limited number of subjects, a limited amount of time, costs or available data to analyze, as is the case with real-time fMRI.
机译:在这项工作中,我们提出了一个新的开源软件包,为fMRI刺激程序的实时适应提供了统一的框架。该软件提供了直接的设置和高度灵活的方法,可在实验运行时适应fMRI范例。总体框架包括受试者依从性的参数,例如将视线引导到视觉呈现的刺激和生理波动,例如血压或脉搏。此外,这种方法还提供了研究复杂科学问题的可能性,例如脑电节律或功能磁共振成像信号本身的影响。为了证明这种方法的概念,我们在fMRI实验的可用性示例中使用了我们的软件,其中情感图片的呈现取决于对象的凝视位置。这会对结果产生重大影响。到目前为止,如果在功能磁共振成像数据分析中考虑到这一点,通常可以通过事后删除错误的试验来完成。在这里,我们建议在实验运行时对范例进行先验调整。我们的fMRI研究结果清楚地表明,在情绪相关的大脑区域中,统计范式和更高的效应量方面,采用自适应范式的好处。由于受检者数量有限,时间,成本或可分析的可用数据数量有限(如实时功能磁共振成像),这对于所有统计能力低的实验都可能会引起特别关注。

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