Functional near-infrared spectroscopy (NIRS) is a noninvasive method for measuring changes in oxyhemoglobin and deoxyhemoglobin concentration in the brain with high temporal resolution (Villringer et al. 1993
, Gibson et al. 2005
). NIRS is a portable and relatively inexpensive technology that can be used when a subject is walking (Suzuki et al. 2008
, Miyai et al. 2001
), during rehabilitation from brain injuries (Strangman et al. 2006
), or concurrently with other brain imaging technologies (Ou et al. 2009
, Moosmann et al. 2003
). The portability and low cost of NIRS make it an attractive option for development of clinical applications, however the sensitivity of NIRS is limited to superficial regions of the brain because photons only penetrate a few centimeters into the head.
In diffuse optical tomography (DOT), the interpretation of the cortical origin of NIRS measurements is typically achieved by generating a forward model based on simulated photon propagation in the head. An image inversion is then used to recover the location of hemoglobin concentration changes. Subject-specific head geometries may be used for creating forward models, although some work has also been done using an atlas head geometry (Custo et al. 2010
). Photon propagation may be calculated with analytical methods based on the diffusion approximation for simple geometries (Boas et al. 2002
). For more complex geometries such as a human head, techniques include finite element methods (Dehghani et al. 2009
), or Monte Carlo methods (Boas et al. 2002
, Okada & Delpy 2003b
, Fang 2010
). In this work we use a mesh-based Monte Carlo method as described in Fang (2010)
to create forward models for a set of eight MRI-derived head geometries. These eight optical forward models demonstrate the anatomical variability that may be seen in an experimental population.
DOT requires the placement of source and detector optodes on the scalp. The arrangement of these optode locations is often called the head probe design. The optical probe design impacts the sensitivity to the cortex, so the probe used in this analysis is an important consideration. Currently, many investigators design DOT head probes with a fixed source-detector spacing of 2 to 3 cm, and these probes are then placed on the scalp over a region of interest such as the frontal, sensorimotor, visual, or auditory cortex (e.g. Ayaz et al. 2012
, Huppert et al. 2006
, Plichta et al. 2007
, Kennan et al. 2002
). Another approach is to use high-density probes that have many overlapping source-detector pairs with a range of source-detector spacings (Boas et al. 2004
, Zeff et al. 2007
, White et al. 2009
). The probe design involves not only the spacing of sources and detectors but also how they are positioned on the scalp. It has been proposed to standardize optode locations on the scalp using derivatives of the 10/20 scalp positioning system developed for eletroencephalography (EEG) (Jurcak et al. 2007
), such as the high-density 10/5 system (Oostenveld & Praamstra 2001
). The 10/20 and 10/5 systems use anatomical landmarks on the scalp to determine the subject-specific placement of sensors. The four landmarks identified on each subject are the nasion, inion, and left and right pre-auricular points. The distance along the scalp between these points is subdivided to determine sensor placement. In the case of the 10/20 system, the distances are divided into 10% and 20% of the total arc length between landmarks for a set of 21 sensor locations (Oostenveld & Praamstra 2001
). The high-density 10/5 system upsamples the 10/20 system and has subdivisions of 10% and 5% of the total arc length between landmarks, for a total of up to 329 sensor locations (Jurcak et al. 2007
). Using EEG 10/5 locations for optodes would allow for standardization of probe placement across subjects in a study or between laboratories. However, the EEG locations are based on anatomical landmarks on an individual basis, and this causes variation in source-detector distances between different individuals. Recent work has characterized the variability in placement of 10/5 locations (Jurcak et al. 2007
), and the nearest brain locations to the 10/20 EEG locations (Okamoto & Dan 2005
), but has not explored the effect that using these standardized optode locations would have on optical sensitivity to the cortex. A quantitative evaluation of the anatomical regions of the cortex that would be measurable using the 10/5 locations for optodes is also needed.
Prior work characterizing the sensitivity of DOT to the cortex has relied on a slab representation of head geometry (Okada & Delpy 2003b
), or a single representative three-dimensional head geometry (Boas & Dale 2005
). Variations in skull and scalp thickness over the head have been quantified, (Okamoto et al. 2004
), but the information was strictly geometrically based on anatomy and photon propagation in this complex geometry was not investigated. Prior work can therefore provide guidelines about optical sensitivity on the cortex but it does not inform an experimental investigator if a particular cortical region of interest is likely to be measurable with DOT in a population of subjects. A quantitative study is needed to understand the variability in optical sensitivity over the cortex due to anatomical variation in features such as skull and scalp thickness, sulcal and gyral geometry, and vessel position and size. In addition, blood in vessels is highly light absorbing and many large veins are located between the cortical surface and the outside of the scalp. Some work has been done modeling the effect of vessels on signal reconstruction (Dehaes et al. 2011
), but this work was limited to the occipital region of the brain and did not explore population variability in vasculature.
Our approach is to use a full head probe design based on the 10/5 system. Light propagation is simulated using these optode locations, and the optical sensitivity is calculated in terms of a contrast-to-noise ratio (CNR) for the head geometries with and without vasculature. The percentage of the cortex that is above a detection threshold for each head model is reported. Light penetration depth is quantified, as are assumptions about the optical instrument used that allow us to quantify the CNR over the cortex. Differences in sensitivity between subjects are explored in terms of their anatomical features such as skull and scalp thickness. The percentage of cortex visible and CNR are reported for anatomical regions. Correlations between the cortical coverage and anatomical metrics are also reported.
The main contributions of this work are estimating variability in DOT sensitivity over the cortex due to anatomical variations, quantifying which regions of the cortex are most amenable to measurement with DOT, and assessing the forward model variations due to the presence of large pial blood vessels. These contributions allow experimenters to understand how anatomical variation in a subject population may influence DOT or functional NIRS measurements.