Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.
Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.
Prog Neurobiol. 2020 Dec;195:101824. doi: 10.1016/j.pneurobio.2020.101824. Epub 2020 May 22.
Relatively little is known about how the human brain identifies movement of objects while the observer is also moving in the environment. This is, ecologically, one of the most fundamental motion processing problems, critical for survival. To study this problem, we used a task which involved nine textured spheres moving in depth, eight simulating the observer's forward motion while the ninth, the target, moved independently with a different speed towards or away from the observer. Capitalizing on the high temporal resolution of magnetoencephalography (MEG) we trained a Support Vector Classifier (SVC) using the sensor-level data to identify correct and incorrect responses. Using the same MEG data, we addressed the dynamics of cortical processes involved in the detection of the independently moving object and investigated whether we could obtain confirmatory evidence for the brain activity patterns used by the classifier. Our findings indicate that response correctness could be reliably predicted by the SVC, with the highest accuracy during the blank period after motion and preceding the response. The spatial distribution of the areas critical for the correct prediction was similar but not exclusive to areas underlying the evoked activity. Importantly, SVC identified frontal areas otherwise not detected with evoked activity that seem to be important for the successful performance in the task. Dynamic connectivity further supported the involvement of frontal and occipital-temporal areas during the task periods. This is the first study to dynamically map cortical areas using a fully data-driven approach in order to investigate the neural mechanisms involved in the detection of moving objects during observer's self-motion.
关于当观察者在环境中移动时大脑如何识别物体的运动，我们知之甚少。从生态学的角度来看，这是最基本的运动处理问题之一，对生存至关重要。为了研究这个问题，我们使用了一项涉及九个纹理球体在深度中移动的任务，其中八个模拟观察者的向前运动，而第九个目标则以不同的速度独立地向观察者或远离观察者移动。利用脑磁图（MEG）的高时间分辨率，我们使用传感器级别的数据训练了一个支持向量分类器（SVC），以识别正确和错误的响应。使用相同的 MEG 数据，我们研究了参与检测独立运动物体的皮质过程的动力学，并调查我们是否可以获得用于分类器的大脑活动模式的确认证据。我们的发现表明，SVC 可以可靠地预测响应的正确性，在运动后和响应前的空白期内准确性最高。对正确预测至关重要的区域的空间分布与诱发活动下的区域相似但不相同。重要的是，SVC 确定了额区和额颞区的活动，而这些区域在诱发活动中没有被检测到，但对于任务的成功表现似乎很重要。动态连通性进一步支持了在任务期间额区和额颞区的参与。这是第一项使用完全数据驱动的方法动态绘制皮质区域的研究，以调查观察者自身运动期间检测运动物体涉及的神经机制。
