Mathematical Neuroscience Team, CIRB - Collège de France (CNRS UMR 7241, INSERM U1050, UPMC ED 158, MEMOLIFE PSL), 11 Place Marcelin Berthelot, 75005 Paris, France.
Mathematical Neuroscience Team, CIRB - Collège de France (CNRS UMR 7241, INSERM U1050, UPMC ED 158, MEMOLIFE PSL), 11 Place Marcelin Berthelot, 75005 Paris, France; INRIA Mycenae Team, Paris-Rocquencourt, France.
Neuroimage. 2017 Jul 15;155:394-405. doi: 10.1016/j.neuroimage.2017.03.053. Epub 2017 Mar 24.
Neuronal activation triggers local changes in blood flow and hemoglobin oxygenation. These hemodynamic signals can be recorded through functional magnetic resonance imaging or intrinsic optical imaging, and allows inferring neural activity in response to stimuli. These techniques are widely used to uncover functional brain architectures. However, their accuracy suffers from distortions inherent to hemodynamic responses and noise. The analysis of these signals currently relies on models of impulse hemodynamic responses to brief stimuli. Here, in order to infer precise functional architectures, we focused on integrated signals associated to the dynamic response of functional maps. To this end, we recorded orientation and direction maps in cat primary visual cortex and compared two protocols: the conventional episodic stimulation technique and a continuous, periodic stimulation paradigm. Conventional methods show that the dynamics of activation and deactivation of the functional maps follows a linear first-order differential equation representing a low-pass filter. Comparison with the periodic stimulation methods confirmed this observation: the phase shifts and magnitude attenuations extracted at various frequencies were consistent with a low-pass filter with a 5s time constant. This dynamics presumably reflects the variations in deoxyhemoglobin mediated by arterial dilations. This dynamics open new avenues in the analysis of neuroimaging data that differs from common methods based on the hemodynamic response function. In particular, we demonstrate that inverting this first-order low-pass filter minimized the distortions of the signal and enabled a much faster and accurate reconstruction of functional maps.
神经元激活会引发局部血液流动和血红蛋白氧合的变化。这些血液动力学信号可以通过功能磁共振成像或固有光学成像进行记录，并允许推断对刺激的神经活动。这些技术广泛用于揭示功能大脑结构。然而，它们的准确性受到血液动力学反应和噪声固有的失真的影响。这些信号的分析目前依赖于对短暂刺激的脉冲血液动力学反应的模型。在这里，为了推断精确的功能架构，我们专注于与功能图谱的动态响应相关的综合信号。为此，我们在猫的初级视觉皮层中记录了方位和方向图谱，并比较了两种方案：传统的间歇性刺激技术和连续的周期性刺激范式。传统方法表明，功能图谱的激活和失活的动力学遵循代表低通滤波器的线性一阶微分方程。与周期性刺激方法的比较证实了这一观察结果：在各种频率下提取的相位偏移和幅度衰减与具有 5s 时间常数的低通滤波器一致。这种动力学可能反映了动脉扩张介导的脱氧血红蛋白的变化。这种动力学在神经影像学数据分析中开辟了新的途径，与基于血液动力学反应函数的常见方法不同。特别是，我们证明了反转这个一阶低通滤波器最小化了信号的失真，并使功能图谱的重建更快、更准确。
