Virtual spatial registration of stand-alone fNIRS data to MNI space
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
References (46)
- et al.
Topographic mapping of the human motor cortex with magnetic stimulation: factors affecting accuracy and reproducibility
Electroencephalogr. Clin. Neurophysiol.
(1992) - et al.
Multichannel near-infrared spectroscopy detects specific inferior-frontal activation during incongruent Stroop trials
Biol. Psychol.
(2005) - et al.
Right frontal activation during the continuous performance test assessed with near-infrared spectroscopy in healthy subjects
Neurosci. Lett.
(1997) - et al.
Evoked-cerebral blood oxygenation changes in false-negative activations in BOLD contrast functional MRI of patients with brain tumors
NeuroImage
(2004) - et al.
Virtual 10–20 measurement on MR images for inter-modal linking of transcranial and tomographic neuroimaging methods
NeuroImage
(2005) - et al.
10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems
NeuroImage
(2007) - et al.
Frontal lobe function in bipolar disorder: a multichannel near-infrared spectroscopy study
NeuroImage
(2006) - et al.
A probabilistic approach for mapping the human brain
- et al.
Cortical mapping of gait in humans: a near-infrared spectroscopic topography study
NeuroImage
(2001) - et al.
The Cerefy Neuroradiology Atlas: a Talairach–Tournoux atlas-based tool for analysis of neuroimages available over the Internet
NeuroImage
(2003)
IFCN standards for digital recording of clinical EEG. International Federation of Clinical Neurophysiology
Electroencephalogr. Clin. Neurophysiol.
Automated cortical projection of head-surface locations for transcranial functional brain mapping
NeuroImage
Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping
NeuroImage
Prefrontal activity during taste encoding: an fNIRS study
NeuroImage
Prefrontal activity during flavor difference test: application of functional near-infrared spectroscopy to sensory evaluation studies
Appetite
The five percent electrode system for high-resolution EEG and ERP measurements
Clin. Neurophysiol.
Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI
NeuroImage
Non-invasive neuroimaging using near-infrared light
Biol. Psychiatry
Multichannel near-infrared spectroscopy in depression and schizophrenia: cognitive brain activation study
Biol. Psychiatry
Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
NeuroImage
Noninvasive mapping of muscle representations in human motor cortex
Electroencephalogr. Clin. Neurophysiol.
Non-invasive assessment of language dominance with near-infrared spectroscopic mapping
Neurosci. Lett.
The problem of functional localization in the human brain
Nat. Rev., Neurosci.
Cited by (460)
Shared and distinct prefrontal cortex alterations of implicit emotion regulation in depression and anxiety: An fNIRS investigation
2024, Journal of Affective DisordersObese people are more likely to exhibit unhealthy food decisions when sated
2023, Food Quality and PreferenceNIRS-aided differential diagnosis among patients with major depressive disorder, bipolar disorder, and schizophrenia
2023, Journal of Affective DisordersEffects of different exercise patterns on drug craving in female methamphetamine-dependent patients: Evidence from behavior and fNIRS
2023, Mental Health and Physical Activity
- 1
The two authors contributed equally to this work.