Elsevier

NeuroImage

Volume 34, Issue 4, 15 February 2007, Pages 1506-1518
NeuroImage

Virtual spatial registration of stand-alone fNIRS data to MNI space

https://doi.org/10.1016/j.neuroimage.2006.10.043Get rights and content

Abstract

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.

Introduction

Functional near-infrared spectroscopy (fNIRS) is gaining popularity as a non-invasive tool for monitoring brain activity. fNIRS utilizes the tight coupling between neural activity and regional cerebral blood flow and monitors relative regional changes of hemoglobin concentration (reviewed in Hoshi, 2003, Koizumi et al., 2003, Obrig and Villringer, 2003, Strangman et al., 2002). Since fNIRS is a compact experimental system, is less expensive, easily portable, and relatively tolerant of body movements, it provides researchers with a means to use a wide variety of flexible experimental setups for clinical diagnosis and psychological experiments. fNIRS was only used to monitor a single or a few channels until the invention of differential illumination technology, which prevents cross-talk among closely situated illuminators, enabling up to a few dozen channels to be simultaneously monitored (Maki et al., 1995). Currently, even whole-head monitoring systems with more than a hundred channels are commercially available (Koizumi et al., 2003).

As the number of channels increases, however, it becomes more tedious to set the optodes. This is one trade off for the convenience of fNIRS. Ultimately, the region of interest (ROI) tends to be confined to a smaller number of channels. Along with this, determining fNIRS channel locations is gaining importance: to realize reproducible fNIRS measurements across subjects and studies, the channel or optode locations should be statistically defined. Nevertheless, channel locations are only vaguely described in most fNIRS studies. Considering that an increasing number of studies are being performed in stand-alone settings, there should be a standardized way of describing the fNIRS channel locations, preferably in the common language of neuroimaging.

Meanwhile, there is a strong trend in the neuroimaging community to represent different brain activation data in a common anatomical platform, which allows group analysis over multiple subjects and further comparison across different studies. In pursuit of a common arena for cross-modal assessment, there appeared a movement called “probabilistic atlas” for expressing all functional brain data as entries in a brain atlas that expands into space and time (Mazziotta et al., 2000, Mazziotta et al., 2001a, Mazziotta et al., 2001b, Toga and Thompson, 2001). Although the term itself is not usually addressed, its concept has already become widespread as a means of spatial data presentation in stereotaxic standard coordinate systems such as Talairach or MNI coordinates (Collins et al., 1994, Talairach and Tournoux, 1988). For expression of functional imaging data in these stereotaxic systems, structural brain imaging data are “normalized” or fit to the standard template brain by linear and nonlinear transformation processes (reviewed in Brett et al., 2002). The Talairach coordinate system is based on a single brain specimen with detailed descriptions of anatomical features including Brodmann estimates (Talairach and Tournoux, 1988). The MNI system is an extension of the Talairach system: the standard template of the MNI system was generated by fitting the brains of multiple subjects to the Talairach template and subsequently averaging them (Collins et al., 1994). Functional data are also registered for the normalized brain and hence to the MNI or Talairach coordinate systems. Accordingly, presenting functional mapping data on standard coordinate space has become a common practice for tomographic functional brain mapping methods, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET).

Despite the increasing importance of standard stereotaxic coordinate systems in neuroimaging studies, they have only recently been introduced to fNIRS studies (Okamoto et al., 2006a, Okamoto et al., 2006b). Our group has presented a series of papers discussing the probabilistic estimation of fNIRS channels in standard stereotaxic coordinate systems. First we created an initial reference head–brain database presenting a probabilistic correspondence between the 10–20 standard positions in the real world and the MNI standard coordinate spaces (Okamoto et al., 2004). Second, we presented an algorithm to automatically project any given head surface position onto the underlying brain surface using MRIs of the subject, thereby presenting a theoretical framework to transform fNIRS data obtained on the head surface to the cortical surface (Okamoto and Dan, 2005). Third, we developed a virtual 10–20 measurement method for already acquired MR images to extend the reference head–brain database, and we further developed this method to be applicable to 10–10 and 10–5 systems (Jurcak et al., 2005, Jurcak et al., 2007). Fourth, we established a registration method based on the reference head–brain database, which enables fNIRS data registration on the standard MNI brain without MRIs of the subject (probabilistic registration method; Singh et al., 2005). We also described the error factor associated with probabilistic registration for both single-subject and group analysis (Singh et al., 2005). However, careful measurement of 10–20 landmarks and optode positions on a subject’s head using a 3D-digitizer is necessary to reproduce the optode placements in the MR images of the reference head–brain database. This imposes a certain burden on subjects and thus limits the application of the probabilistic registration method.

Therefore, in this study, we propose a novel registration method to realize a 3D-digitizer-free registration of fNIRS data (virtual registration method). Essentially, the method simulates the placement of an optode holder on the scalp, taking into consideration its deformation and the registration of the optodes and channels onto subjects’ brains. We first describe an algorithm used to virtually place optode holders onto the head surface. Second, we examine the validity of the algorithm by comparing the predicted optode positions and the actual deformations of the optode holders on spherical phantoms and on real head surfaces. Third, we demonstrate virtual registration of an fNIRS optode holder onto MNI space with a simulated group dataset comprised of 1000 virtual subjects. We include a description of the associated error factor. Taken together, we present a virtual registration method for completely stand-alone fNIRS optodes and channels onto MNI space, based on the guidance of the 10–20 system and on prior knowledge of optode holder locations and deformations.

Section snippets

Subjects and source MRI datasets

Six healthy adult volunteers (3 males and 3 females, aged 25 to 44 years) participated in the validation study of the deformation algorithm. Written informed consent was obtained after a complete explanation of the study. The study was approved by the institutional ethics committee of the National Food Research Institute of Japan.

To simulate an fNIRS subject population, we used MRI datasets acquired from our previous study (Okamoto et al., 2004), which consist of the whole-head MRI images of 17

Examination of deformation algorithms using spherical phantoms

We examined whether holder deformation algorithms can simulate actual deformation by placing elastic and flexible holders on spherical phantoms. Prior to the physical measurements, we used virtual heads to explore the range of spheres that fit the curvature of various head regions. Fig. 10 shows the distribution of the radii of the best-fit spheres. For 3 × 5 holders, the minimum and maximum radii were 60.2 and 183.6 mm, respectively; the 95% confidence interval was 68.6 to 127.1 mm. For 3 × 3

Discussion

As presented above, our method makes the virtual spatial registration of stand-alone fNIRS optodes and channels onto the MNI space possible. Here we would like to discuss how this method can facilitate fNIRS research, especially in a more global context surrounding the whole neuroimaging community.

Neuroimaging techniques serve not only neuroimaging research, but also as important tools for other scientific disciplines. In this perspective, fNIRS has great potential. Its affordability,

Conflict of interest statement

All authors hereby declare that they have no financial and personal relationships with other people or organization that could inappropriately influence our work.

Acknowledgments

We thank the subjects who participated in this study. We are grateful to Dr. Haruka Dan for her helpful advice. We thank Ms Akiko Oishi for preparation of the manuscript and data, and Ms Melissa Nuytten for examination of the manuscript. This work is partly supported by the Industrial Technology Research Grant Program in 03A47022 from the New Energy and Industrial Technology Development Organization (NEDO) of Japan, and Grant-in-Aid for Scientific Research 18390404 from the Japan Society for

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