Tsuzuki Daisuke, Jurcak Valer, Singh Archana K, Okamoto Masako, Watanabe Eiju, Dan Ippeita
Sensory and Cognitive Food Science Laboratory, National Food Research Institute, 2-1-12 Kannondai, Tsukuba 305-8642, Japan.
Neuroimage. 2007 Feb 15;34(4):1506-18. doi: 10.1016/j.neuroimage.2006.10.043. Epub 2007 Jan 3.
The registration of functional brain data to common stereotaxic brain space facilitates data sharing and integration across different subjects, studies, and even imaging modalities. Thus, we previously described a method for the probabilistic registration of functional near-infrared spectroscopy (fNIRS) data onto Montreal Neurological Institute (MNI) coordinate space that can be used even when magnetic resonance images of the subjects are not available. This method, however, requires the careful measurement of scalp landmarks and fNIRS optode positions using a 3D-digitizer. Here we present a novel registration method, based on simulations in place of physical measurements for optode positioning. First, we constructed a holder deformation algorithm and examined its validity by comparing virtual and actual deformation of holders on spherical phantoms and real head surfaces. The discrepancies were negligible. Next, we registered virtual holders on synthetic heads and brains that represent size and shape variations among the population. The registered positions were normalized to MNI space. By repeating this process across synthetic heads and brains, we statistically estimated the most probable MNI coordinate values, and clarified errors, which were in the order of several millimeters across the scalp, associated with this estimation. In essence, the current method allowed the spatial registration of completely stand-alone fNIRS data onto MNI space without the use of supplementary measurements. This method will not only provide a practical solution to the spatial registration issues in fNIRS studies, but will also enhance cross-modal communications within the neuroimaging community.
将功能性脑数据注册到通用立体定向脑空间有助于跨不同受试者、研究甚至成像模态的数据共享和整合。因此,我们之前描述了一种将功能性近红外光谱(fNIRS)数据概率性注册到蒙特利尔神经学研究所(MNI)坐标空间的方法,即使在没有受试者的磁共振图像时也可使用。然而,该方法需要使用三维数字化仪仔细测量头皮标志点和fNIRS光极位置。在此,我们提出一种新颖的注册方法,基于模拟来代替光极定位的物理测量。首先,我们构建了一个固定器变形算法,并通过比较球形模型和真实头部表面上固定器的虚拟变形与实际变形来检验其有效性。差异可忽略不计。接下来,我们将虚拟固定器注册到代表人群中大小和形状变化的合成头部和大脑上。注册位置被归一化到MNI空间。通过在合成头部和大脑上重复此过程,我们统计估计了最可能的MNI坐标值,并明确了与该估计相关的误差,其在头皮上的量级为几毫米。本质上,当前方法允许将完全独立的fNIRS数据空间注册到MNI空间,而无需使用补充测量。该方法不仅将为fNIRS研究中的空间注册问题提供一个实际解决方案,还将增强神经成像领域内的跨模态通信。