Das Soukhin, Yi Weigang, Ding Mingzhou, Mangun George R
Center for Mind and Brain, University of California, Davis, Davis, CA, United States.
Department of Psychology, University of California, Davis, Davis, CA, United States.
Front Neuroimaging. 2023 Apr 17;2:1068616. doi: 10.3389/fnimg.2023.1068616. eCollection 2023.
Functional magnetic resonance imaging (fMRI) has revolutionized human brain research. But there exists a fundamental mismatch between the rapid time course of neural events and the sluggish nature of the fMRI blood oxygen level-dependent (BOLD) signal, which presents special challenges for cognitive neuroscience research. This limitation in the temporal resolution of fMRI puts constraints on the information about brain function that can be obtained with fMRI and also presents methodological challenges. Most notably, when using fMRI to measure neural events occurring closely in time, the BOLD signals may temporally overlap one another. This overlap problem may be exacerbated in complex experimental paradigms (stimuli and tasks) that are designed to manipulate and isolate specific cognitive-neural processes involved in perception, cognition, and action. Optimization strategies to deconvolve overlapping BOLD signals have proven effective in providing separate estimates of BOLD signals from temporally overlapping brain activity, but there remains reduced efficacy of such approaches in many cases. For example, when stimulus events necessarily follow a non-random order, like in trial-by-trial cued attention or working memory paradigms. Our goal is to provide guidance to improve the efficiency with which the underlying responses evoked by one event type can be detected, estimated, and distinguished from other events in designs common in cognitive neuroscience research. We pursue this goal using simulations that model the nonlinear and transient properties of fMRI signals, and which use more realistic models of noise. Our simulations manipulated: (i) Inter-Stimulus-Interval (ISI), (ii) proportion of so-called null events, and (iii) nonlinearities in the BOLD signal due to both cognitive and design parameters. We offer a theoretical framework along with a python toolbox called deconvolve to provide guidance on the optimal design parameters that will be of particular utility when using non-random, alternating event sequences in experimental designs. In addition, though, we also highlight the challenges and limitations in simultaneously optimizing both detection and estimation efficiency of BOLD signals in these common, but complex, cognitive neuroscience designs.
功能磁共振成像（fMRI）彻底改变了人类大脑研究。但是，神经事件的快速时间进程与fMRI血氧水平依赖（BOLD）信号的迟缓特性之间存在根本不匹配，这给认知神经科学研究带来了特殊挑战。fMRI时间分辨率的这种限制对通过fMRI可获得的大脑功能信息施加了约束，也带来了方法学上的挑战。最值得注意的是，当使用fMRI测量在时间上紧密发生的神经事件时，BOLD信号可能在时间上相互重叠。在旨在操纵和分离参与感知、认知和行动的特定认知-神经过程的复杂实验范式（刺激和任务）中，这种重叠问题可能会加剧。事实证明，对重叠的BOLD信号进行反卷积的优化策略在提供来自时间上重叠的大脑活动的BOLD信号的单独估计方面是有效的，但在许多情况下，这种方法的效果仍然会降低。例如，当刺激事件必然遵循非随机顺序时，如在逐次试验提示注意或工作记忆范式中。我们的目标是提供指导，以提高在认知神经科学研究中常见的设计中，一种事件类型诱发的潜在反应能够被检测、估计并与其他事件区分开来的效率。我们通过模拟来实现这一目标，这些模拟对fMRI信号的非线性和瞬态特性进行建模，并使用更逼真的噪声模型。我们的模拟操纵了：（i）刺激间隔（ISI），（ii）所谓零事件的比例，以及（iii）由于认知和设计参数导致的BOLD信号中的非线性。我们提供了一个理论框架以及一个名为deconvolve的Python工具箱，以提供关于最佳设计参数的指导，这些参数在实验设计中使用非随机、交替事件序列时将特别有用。此外，我们还强调了在这些常见但复杂的认知神经科学设计中，同时优化BOLD信号的检测和估计效率时所面临的挑战和局限性。
