Functional Magnetic Resonance Imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and single-subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single-subject and low-signal fMRI by developing a computationally feasible and methodologically sound model-based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish voxels and regions having different intensities of activation. Our suggested approach is evaluated in realistic two- and three-dimensional simulation experiments as well as on multiple real-world datasets. Finally, the value of our suggested approach in low-signal and single-subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in persistent vegetative state (PVS). Our ability to reliably distinguish activation in this experiment potentially opens the door to the adoption of fMRI as a clinical tool for the improved treatment and therapy of PVS survivors and other patients.

翻译：功能磁共振成像（fMRI）可绘制大脑对刺激的激活反应，但这种激活往往难以检测，尤其是在低信号背景和单被试研究中。精确的激活检测可以通过以下事实来指导：实际上只有极少数体素被真正激活，且这些体素在空间上具有局部性，但将这两方面事实相结合具有挑战性。我们通过开发一种计算可行且方法学完备的基于模型的方法，来解决单被试和低信号fMRI中的这两个挑战。该方法已在R语言包MixfMRI中实现，它能限制先验预期中被激活体素的比例，同时融入空间上下文信息。我们方法的另一个优势是能够区分具有不同激活强度的体素和脑区。我们提出的方法在逼真的二维和三维模拟实验以及多个真实世界数据集上进行了评估。最后，我们通过一项常用于检测意识并改善持续性植物状态（PVS）患者治疗的运动想象实验，展示了所提方法在低信号和单被试fMRI研究中的价值。我们能够可靠地区分该实验中的激活信号，这有望为fMRI成为改善PVS幸存者及其他患者治疗的临床工具打开大门。