Content uploaded by Abraham Z. Snyder
Author content
All content in this area was uploaded by Abraham Z. Snyder on Mar 21, 2023
Content may be subject to copyright.
A quantitative spatial comparison of high-density diffuse optical tomography and
fMRI cortical mapping
Adam T. Eggebrecht
a
, Brian R. White
a,b
, Silvina L. Ferradal
a,c
, Chunxiao Chen
d
, Yuxuan Zhan
e
,
Abraham Z. Snyder
a,f
, Hamid Dehghani
e
, Joseph P. Culver
a,b,c,
⁎
a
Department of Radiology, Washington University School of Medicine, 4525 Scott Ave, East Bldg. CB 8225, St Louis, MO, 63110, USA
b
Department of Physics, Washington University in St. Louis, One Brookings Dr., St Louis, MO, 63110, USA
c
Department of Biomedical Engineering, Washington University School of Engineering and Applied Science, Whittaker Hall, One Brookings Dr., St Louis, MO, 63130, USA
d
Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Jie, Nanjing, Jiangsu 210016, China
e
School of Computer Science, University of Birmingham, B15 2TT, UK
f
Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St Louis, MO, 63110, USA
abstractarticle info
Article history:
Received 16 September 2011
Revised 26 January 2012
Accepted 28 January 2012
Available online 10 February 2012
Keywords:
Optical tomography
Functional neuroimaging
Cortex
Mapping
Human
Functional neuroimaging commands a dominant role in current neuroscience research. However its use in
bedside clinical and certain neuro-scientific studies has been limited because the current tools lack the
combination of being non-invasive, non-ionizing and portable while maintaining moderate resolution and
localization accuracy. Optical neuroimaging satisfies many of these requirements, but, until recent advances
in high-density diffuse optical tomography (HD-DOT), has been hampered by limited resolution. While early
results of HD-DOT have been promising, a quantitative voxel-wise comparison and validation of HD-DOT
against the gold standard of functional magnetic resonance imaging (fMRI) has been lacking. Herein, we
provide such an analysis within the visual cortex using matched visual stimulation protocols in a single
group of subjects (n =5) during separate HD-DOT and fMRI scanning sessions. To attain the needed voxel-
to-voxel co-registration between HD-DOT and fMRI image spaces, we implemented subject-specific head
modeling that incorporated MRI anatomy, detailed segmentation, and alignment of source and detector
positions. Comparisons of the visual responses found an average localization error between HD-DOT and
fMRI of 4.4+/−1 mm, significantly less than the average distance between cortical gyri. This specificity
demonstrates that HD-DOT has sufficient image quality to be useful as a surrogate for fMRI.
© 2012 Elsevier Inc. All rights reserved.
Introduction
Functional brain mapping has revolutionized neuroscience re-
search, by providing noninvasive investigations into human brain ac-
tivity. However, functional imaging of the brain has, so far, found only
limited clinical application with early uses in pre-operative planning
(Nelles et al., 2009; Shimony et al., 2009; Wengenroth et al., 2011;
Zhang et al., 2009). Functional imaging has the potential to play a
larger clinical role in diagnosis, prognosis and monitoring due to its
ability to find subtle changes in function before disease progresses
to large-scale structural change. However, traditional functional
brain mapping methods, including functional MRI (fMRI) and
positron emission tomography (PET) are limited in many settings
by immobility, expense, and constraints on subjects. Additionally,
they have contraindications for metallic implants (fMRI) or use
ionizing radiation (PET), limiting the number of repeated studies. In
contrast, optical methods provide non-ionizing functional neuroim-
aging with potentially portable and wearable technology that is
well-suited for many of the subjects inaccessible by fMRI or PET.
Early diffuse optical imaging (DOI) methods used sparse sets of
source-detector pairs to generate two-dimensional, low-resolution
images of cerebral hemodynamics (Maki et al., 1995; Obrig and
Villringer, 2003; Villringer et al., 1993). A more advanced method,
diffuse optical tomography (DOT), relies on a variety of measurement
strategies to improve lateral and depth resolution. Time-resolved
(TR) measurements use time-gating (Benaron et al., 2000; Gibson et
al., 2006; Hebden et al., 2002; Kohl-Bareis et al., 2002; Selb et al.,
2005; Steinbrink et al., 2001) or frequency-domain phase data
(Kohl-Bareis et al., 2002) to profile different tissue depths. However,
the complexity and cost of TR systems impose practical limits and re-
quire tradeoffs between channel count, source and detector density,
coverage (field-of-view), and frame rate. Another strategy uses
NeuroImage 61 (2012) 1120–1128
Abbreviations: HD-DOT, high-density diffuse optical tomography; DOI, diffuse opti-
cal imaging; HbO
2
, oxyhemoglobin; HbR, deoxyhemoglobin; HbT, total hemoglobin;
FOV, field of view.
⁎Corresponding author at: Washington University School of Medicine, Department
of Radiology, 4525 Scott Avenue, Room 1137, Saint Louis, MO, 63110, USA. Fax: +1
314 747 5191.
E-mail address: culverj@wustl.edu (J.P. Culver).
1053-8119/$ –see front matter © 2012 Elsevier Inc. All rights reserved.
doi:10.1016/j.neuroimage.2012.01.124
Contents lists available at SciVerse ScienceDirect
NeuroImage
journal homepage: www.elsevier.com/locate/ynimg
high-density DOT grids with overlapping continuous wave measure-
ments at multiple SD-pair separations (Bluestone et al., 2001; Boas
et al., 2004a; Joseph et al., 2006; Zeff et al., 2007). Relative to DOI,
the newer HD-DOT methods achieve higher resolution and improved
localization accuracy (Gibson and Dehghani, 2009; Gibson et al.,
2005; Habermehl et al., 2011; Koch et al., 2010; White and Culver,
2010b; Zeff et al., 2007). While HD-DOT's ability to decipher detail
has been established in studies of retinotopy in visual cortex (White
and Culver, 2010a; Zeff et al., 2007) and finger-topy in the motor cor-
tex (Custo et al., 2009; Koch et al., 2010; White et al., 2009), the image
quality of HD-DOT at the voxel level has not been compared directly
to fMRI, the current gold standard in hemodynamic-based functional
neuroimaging. Establishing the relationship between HD-DOT and
fMRI functional maps could significantly strengthen the impact that
HD-DOT might have when used as a surrogate for fMRI. The purpose
of this study is to validate HD-DOT functional mapping accuracy
through a quantitative voxel-wise comparison to fMRI in subject-
matched datasets of visual cortex activity.
Previous comparative studies of diffuse optical and fMRI signals
have used either non-imaging systems or sparse measurement data-
sets and performed comparisons unrelated to image quality. For ex-
ample, thorough comparisons have been made in the measurement
space of the DOI instrument (Cui et al., 2011; Huppert et al., 2006a,
b; Sassaroli et al., 2006; Strangman et al., 2002; Toronov et al.,
2001). Additionally, there have been detailed temporal evaluations
(e.g., comparing the time course of the DOI response within a similar
volume as that displaying an MRI response (Okamoto et al., 2004;
Sakatani et al., 2007)). Throughout these studies, correlations were
found between the time courses of BOLD and optical data. These find-
ings along with parallel studies in rodent models (Bouchard et al.,
2009; Culver et al., 2003; Custo et al., 2009; Devor et al., 2003;
Dunn et al., 2005; Siegel et al., 2003) and human neonates
(Villringer and Chance, 1997) lay the foundation for optical measure-
ments to be used in calculations of metabolic markers such as CMRO
2
at the bedside. Additionally, image- and time-domain comparisons
have been made with simultaneously acquired MRI and NIRS
(Toronov et al., 2007; Zhang et al., 2005). However, these studies
used point-like activations and have not investigated image quality
throughout an extended cortical region.
In this study we perform spatial voxel-wise comparisons between
HD-DOT and fMRI data sets for cortical responses to visual stimulations
throughout the visual field of view. The HD-DOT and fMRI datasets
were co-registered on a subject specific basis by segmenting anatomi-
cal MRIs for each subject, locating and co-registering the HD-DOT cap
placement on each subject's head, and solving the forward light
model within the subject-specific space. Visual activations were used
because they have served extensively as a substrate for validation by
other neuroimaging methodologies (Belliveau et al., 1991; Engel et
al., 1997; Fox et al., 1986; Fox et al., 1985) and because the structure
and function of the visual cortex has been comprehensively mapped
via invasive anatomical and electrical studies in mammals (Felleman
and Van Essen, 1991; Gilbert and Wiesel, 1979; Rosa et al., 1993)and
humans (Harding et al., 1991; Spalding, 1952). The comparison be-
tween HD-DOT and fMRI was quantified by calculating the center-of-
mass of the imaged hemodynamic response to matched visual activa-
tions, and by a complete phase analysis of the responses to stimulations
throughout the full visual field. The resulting HD-DOT image quality
evaluation serves as a strong foundation and validation enabling
further adoption of HD-DOT by both neuroscientists and clinicians.
Methods
Subjects and stimulus protocol
Five healthy adult right-handed subjects (aged 21–30) were
recruited for this study. All subjects passed MR screening to ensure
their safe participation. Informed consent was obtained for all
subjects. The research was approved by the Human Research
Protection Office at Washington University School of Medicine. All
stimuli are angularly sweeping reversing black-and-white logarith-
mic checkerboard wedges (10 Hz reversal) on a 50% gray background
(Engel et al., 1994; Warnking et al., 2002). The grids rotate around a
white cross located at the center of the visual field, step 10° each
second, and complete a full sweep every 36 s. For the HD-DOT stimuli,
subjects are seated in an adjustable chair facing a 19" LCD display at a
90 cm viewing distance. For the fMRI stimuli, the stimulus is pre-
sented via a projector onto a screen that the subject could visualize
from their position within the MR tube with a mirror attached to
the head coil. The stimulus size is calibrated to be the same size on
the retina as when presented in the HD-DOT setting: each wedge sub-
tends a radial angle of 2.5°–10.5° and a polar angle of 60°. A set of
stimuli consists of 10 repetitions of either clockwise- or counter-
clockwise-rotating flickering wedges. Subjects are instructed to fixate
on the central crosshair. Gray screens are presented for 30 s before
and after each stimuli set.
HD-DOT imaging system and acquisition
The high-density imaging system has been described previously
(Zeff et al., 2007). Briefly, our high-density DOT instrument uses
light-emitting diode (LED) sources at 750 nm and 850 nm (750-
03 AU and OPE5T85, Roithner Lasertechnik) and avalanche photo
diode (APD, Hamamatsu C5460-01) detectors (Zeff et al., 2007).
Each detector has a dedicated 24-bit analog-to-digital converter
(MOTU HD-192). Sources and detectors are coupled with fiber optic
bundles to a flexible imaging cap held on to the back of the
head with hook-and-loop strapping. After digitization, the APD
measurements are written directly to hard-disk at 96 kHz. With tem-
poral, frequency, and spatial encoding, the system works with a
frame rate of 11 Hz in continuous wave mode (the time and fre-
quency encoding is at ~ 10 kHz, much slower than the > 10 MHz
needed for measuring the time of flight for light propagation
through tissue). The array has 24 source and 28 detector positions
placed in two interlaced rectangular arrays with first- through
fourth- nearest neighbor separations as follows: 1.3, 3.0, 3.9, and
4.7 cm. In order to ensure consistent pad placement between ses-
sions, measurements of the distance between the optode array
and the nasion and eyes are taken and recorded. Once the cap is
placed comfortably with good signal-to-noise, the exact placement
of the pad is found by measuring the locations of the outer four cor-
ner positions of the optode array using a commercially available 3D
digitizer (Fastrak, Polhemus). Concurrently, we also measure the lo-
cations of anatomical landmarks on the head and face of the subject
(e.g., the nasion) (Klem et al., 1999)tolocatethepadrelativetothe
subject's head.
fMRI acquisition
MRI scans are collected on a Siemens Trio (Erlagen, Germany) 3T
scanner. Anatomical T1-weighted MPRAGE (echo time (TE)= 3.13 ms,
repetition time (TR)=2400 ms, flip angle =8°, 1 ×1 ×1 mm isotropic
voxels) and T2-weighted (TE= 84 ms, flip angle= 120°, 1 ×1× 4 mm
voxels) scans are taken at each session (for simplicity, we will subse-
quently refer to these scans as T1 and T2). Functional images are collect-
ed using a series of asymmetric gradient spin-echo echo-planar (EPI)
sequences (each brain volume had a TE= 27 ms, TR=2000 ms, flip
angle= 90°, 4×4 × 4 mm voxels) to measure the blood-oxygenation-
level-dependent (BOLD) contrast. In keeping with standard methods
for performing BOLD analysis, we transform the BOLD data to a 3 mm
isotropic voxelated space.
1121A.T. Eggebrecht et al. / NeuroImage 61 (2012) 1120–1128
Head modeling
To place the HD-DOT image space into the correct subject specific
anatomic location, a full head model must be constructed. The head
model incorporates the surface head shape, assumed optical proper-
ties of each voxel, and locations of the sources and detectors of the
HD-DOT system. The basis for the head model is the subject-specific
anatomical T1 (Fig. 1A) and T2. These volumes contribute comple-
mentary information that provides characteristic information for
each tissue type. An iterative series of thresholding, region growing,
and masking techniques are used to segment the head tissue into
scalp, skull, cerebral spinal fluid (CSF), gray matter, and white matter
regions (Fig. 1B).
To generate a space for the numerical light modeling, a high-
density volumetric tetrahedral head mesh (Fig. 1C) is created from
the segmented head using the Mimics software package (Materialize,
Belgium). Each subject's head mesh has approximately 5 × 10
5
nodes and 3×10
6
tetrahedral volume elements. To ensure proper
resolution, the maximum inter-node distance both on the surface
and within the mesh volume is set to 3 mm. Tissue-type-specific
labeling of optical properties generates a more accurate light model
than assuming homogeneous optical properties (Custo et al., 2006;
Dehaes et al., 2011; Dehghani et al., 2000; Heiskala et al., 2009;
Ripoll et al., 2000). Thus, each node is labeled by tissue type and
assigned optical properties from the literature as summarized in
Table 1 (Bevilacqua et al., 1999; Custo et al., 2006; Strangman et al.,
2003). While errors in the assumed baseline optical properties will
propagate through to the magnitude of the differential concentration
changes (Strangman et al., 2003), this treatment of baseline optical
properties is consistent with standard DOT processing (Blasi et al.,
2007; Bluestone et al., 2001; Boas et al., 2001; Custo et al., 2009;
Dehghani et al., 2009). Interpretation of concentration magnitudes
should account for these model assumptions. The image quality as-
sessments at the focus of this paper use normalized differential
magnitudes.
To place the optode locations on the head model, the anatomical
landmarks on the subject's head (measured during data collection)
are then identified on the subject specific mesh generated from the
anatomical MRIs. An affine transform aligns the coordinate space
from the 3D digitizer in real space to the coordinate space in the
MRI space (Benaron et al., 2000; Custo et al., 2009; Gibson et al.,
2003). This is necessary because the MRI volume has small inherent
artifacts and distortions relative to real space. Thus, the digitizer
points are translated, rotated, and skewed to match the MRI space.
Using this same transform, the four measured points on the optode
cap are transformed to their locations on the head mesh. These four
optode locations are then used as anchors to fit the entire cap onto
the head mesh using a spring-relaxation energy-minimization
algorithm (Joseph et al., 2006). Source and detector locations on the
head obtained during the HD-DOT session are thus placed at the
relevant loci on the mesh (Fig. 1D). Similar methods of optode
placement have been described previously (Custo et al., 2009; Fuchs
et al., 2002).
A sensitivity matrix for the subject-specific head model is calculat-
ed using a finite-element forward light model based on the diffusion
approximation to the radiative transport equation using the NIRFAST
software package (Dehghani et al., 2003). The sensitivity matrix pro-
vides a mapping function that transforms the measured HD-DOT data
to optical parameter changes within the model. The original tetrahe-
dral finite-element based sensitivity function is then transformed into
the 3 mm isotropic voxel space (standard in current BOLD processing
practices) through a weighted spatial average to create the sensitivity
matrix for the subject-specific space. As a result of these procedures
the image spaces for the two modalities are co-aligned.
Functional data analysis
HD-DOT light measurement data were converted to log-ratio
and high-pass filtered (0.02 Hz cutoff) to remove long term drift.
An average of all 1st-nearest-neighbor measurements (sampling
predominantly scalp and skull) was constructed as an estimate of sys-
temic signals (Obrig and Villringer, 2003; White et al., 2009; Zeff et
al., 2007). This signal was then regressed from all measurements.
After a low-pass filter (0.5 Hz cutoff) removed residual pulse signals,
the time traces were used for image reconstruction. Note that while a
time trace of the average across channels of all 1st nearest-neighbor
pairs was removed during the global signal regression, the individual
1st-nearest-neighbor channels retain variance after this regression
and are used during the reconstruction. To manage system noise
during cap fit, real-time data displays are used to adjust the cap (at
the individual optode level if needed) to optimize the highest possi-
ble light level and lowest possible noise level. Further, the data is
de-noised by removing noisy source-detector pair measurement
channels that have signal standard deviations (across time) greater
than 7.5% of their mean signal magnitude. Using this threshold, the
following percentages of nearest neighbor (nn) measurements are
typically retained: 100% 1st nn, 95% 2nd nn, 65% 3 rd nn, and 19%
Table 1
Optical properties of segmented head tissue for forward light model.
750 nm 850 nm Index of
refraction
μ
a
[mm
−1
]μ
s'
[mm
−1
]μ
a
[mm
−1
]μ
s'
[mm
−1
]n
Scalp⁎0.0170 0.74 0.0190 0.64 1.4
Skull⁎0.0116 0.94 0.0139 0.84 1.4
CSF⁎⁎ 0.004 0.3 0.004 0.3 1.4
Gray matter⁎⁎⁎ 0.0180 0.8359 0.0192 0.6726 1.4
White matter⁎⁎⁎ 0.0167 1.1908 0.0208 1.0107 1.4
⁎Strangman et al., 2002.
⁎⁎ Custo et al., 2006.
⁎⁎⁎ Bevilacqua et al., 1999.
Fig. 1. Subject-specific head modeling. (A) T1-weighted MRI volume. (B) Segmented head volume displaying scalp, skull, CSF, gray matter and white matter. (C) High-density tet-
rahedral mesh for finite element forward light model. (D) Optode positions localized on mesh. The high-density optode grid is composed of 24 sources (red) and 28 detectors
(blue). (E) Surface rendering of cortex. Yellow coloring denotes FOV of HD-DOT imaging pad derived from top 50% of the summed sensitivity matrix (see Methods) on the cortical
ribbon of Subject 2.
1122 A.T. Eggebrecht et al. / NeuroImage 61 (2012) 1120–1128
4th nn. The numerous overlapping measurements provided by the
HD-DOT arrays minimize the effect of a single noisy channel on a
particular tissue voxel. Thus the reconstruction of data sampling
brain tissue is driven by the 1st and 2nd nearest-neighbor pairs
with some additional depth information provided by 3rd nearest-
neighbor and 4th nearest-neighbor pairs where possible. An aver-
age total of 257 independent measurements were used in each
reconstruction.
The sensitivity matrix is inverted as described previously
(Dehghani et al., 2009; Zeff et al., 2007). Following the notation of
Dehghani et al., 2009, we set the Tikhonov regularization constant
λ=0.01, and the spatially variant regularization parameter
β=0.01. Our reconstruction utilizes a spatial constraint to aid in
separating out superficial surface noise from deeper measurements.
While previous studies have constrained their reconstructions to the
cortical ribbon (Boas and Dale, 2005), we used a less restrictive
spatial prior. The reconstructions were constrained to all voxels in
either scalp or brain tissue. The scalp voxels capture potential optode
coupling noise, as well as potential superficial scalp signals. The brain
tissue voxels allow brain activations to reconstruct freely within any
brain tissue (gray or white matter). The volumetric constraint is
applied prior to inversion and is enabled by the use of an anatomical
head model. The reconstructed hemodynamic responses still reside
within a three-dimensional voxelated space, but the constraint ex-
cludes them from the skull and CSF which is a reasonable assumption
for images of neural activity. The voxelated hemodynamic response
within the brain tissue volume is used for the entire comparative
DOT-fMRI quantitative analysis. In keeping with standard methods
for performing BOLD analysis, we use a 3 mm isotropic voxelated
space.
Relative changes in oxygenated (HbO
2
), deoxygenated (HbR), and
total hemoglobin (HbT) concentrations are obtained from the absorp-
tion coefficients by spectral decomposition of the extinction coeffi-
cients of HbO
2
and HbR at the two wavelengths (Bluestone et al.,
2001). Additionally, HD-DOT data is down-sampled to 1 Hz.
MRI data analysis was performed using standard pre-processing of
anatomical and functional images as described previously (Shulman
et al., 2010), except that while the MRIs are rotated and translated
to a standard imaging coordinate system, they are not transformed
(stretched) into an atlas space. Each subject's fMRI data are aligned
to their own subject-specific MRPAGE (T1) volume and functional
data is re-sampled to a 3 × 3 × 3 mm voxelated space, standard for
BOLD analysis. Additionally, the fMRI data is spatially smoothed to
match the point-spread function of the HD-DOT system, 1.3 cm
(White and Culver, 2010b), and interpolated to 1 Hz, to match data
rates for subsequent comparison.
For visualization of the data only, the data is mapped onto the
mesh that represents the complex two-dimensional cortical mid-
thickness sheet. Each subject's cortical pial surface and white matter
outer surface were extracted using FreeSurfer (Dale et al., 1999;
Fischl et al., 2001; Segonne et al., 2004)(http://surfer.nmr.mgh.
harvard.edu). These surfaces were converted to a cortical mid-
thickness using the Caret 5.62 software package (Van Essen et al.,
2001)(http://brainmap.wustl.edu/caret/). For visualization only, all
volumetric activation data is then mapped onto the cortical surface
in Caret.
Comparison analysis
All subsequent processing was performed within the field of view
(FOV) of the HD-DOT system, as defined by the region of the imaging
domain with high sensitivity (>50% of max sensitivity) determined
through the forward light model (Fig. 1E). Sensitivity within a voxel
is given by the sum over all measurements within the sensitivity ma-
trix. To compare the image-volumes produced by the two modalities,
we utilized two metrics. First, we quantified the localization error
within four visual quadrants, areas within the FOV of the HD-DOT
system that have high SNR. The center-of-mass (CofM) of an activa-
tion within a quadrant is calculated for each of the hemoglobin spe-
cies concentrations as well as the BOLD data. The CofM error in
location of an activation was computed as the Euclidian distance
from the center-of-mass of the fMRI response to the center-of-mass
of the HD-DOT response separately for each frame.
To quantify the localization error at every voxel within the FOV,
we used phase analysis. Rather than using data from a small subset
of the FOV as in the center-of-mass analysis, phase maps provide
access to an error metric for every voxel within the FOV. We first
define a physiologically-based constraint to the FOV for the phase
analysis by calculating the spatially variant signal to noise ratio in
the response defined as the ratio of the power of the Fourier trans-
form at the stimulus frequency to the power at all other frequencies
(excluding very low frequencies and harmonics of the stimulus fre-
quency) (Saygin and Sereno, 2008; Sereno et al., 1995). In effect,
this models the signal to noise as a ratio of two χ
2
statistics, each
with degrees of freedom equal to the number of time points. This
ratio has an F-distribution from which we obtained p-values. Voxels
with a p-value less than 0.001 were included in the analysis. Note
that because the noise is not evenly distributed across frequencies,
this is a conservative threshold estimate. The phase of the response
at each voxel is calculated using methods described previously
(White and Culver, 2010a). The hemodynamic delay is removed from
the analysis by taking the vector average of the phase of the response
to the counter-clockwise-moving stimuli with the response to the
clockwise-moving stimuli after reflecting the imaginary part of the re-
sponse to the clockwise stimuli about zero (Sereno et al., 1995). This
way the stimulus phase is directly related to the response phase at
each time point in the data. Each voxel's phase error Δθ
fMRI-Hd-DOT
=
θ
fMRI
−θ
Hd-DOT
is calculated as the difference between the phase mea-
sured with HD-DOT to that measured via fMRI. The norm of the gradient
of the phase map ‖∇θ‖at a given voxel is themagnitude of the change in
retinal polar phase angle given a unit change in distance in the imaged
volume. Therefore, dividing an error in phase Δθ
fMRI-Hd-DOT
by this
norm provides us with an estimate of the distance error for each voxel
(Δl
e
=Δθ
fMRI-Hd-DOT
/‖∇θ‖). All analyses for each subject are carried
out within the subject-specific space as defined by the T1-weighted
MRI.
Results
In response to flickering checkerboard wedges (Fig. 2) the location
and spatial extent of the HD-DOT and fMRI activations within an
individual subject are qualitatively similar (Fig. 3). All activations
displayed are block averaged from a set of ten repetitions. For visual
comparison, both the voxel and cortical surface representations
where cropped to a threshold of 50% maximum response. In the para-
sagittal slice view of Fig. 3A, it can be seen that the responses in the
Fig. 2. Experimental design. Subject head model fixating on a stimulus screen placed
90 cm away. The visual stimulus is an angularly sweeping black and white reversing ra-
dial grid (10 Hz reversal) on a 50% gray background. The wedge extends over a polar
angle of 60° and a radial angle of 2.5°–10.5° and was rotated in steps of 10° each
second.
1123A.T. Eggebrecht et al. / NeuroImage 61 (2012) 1120–1128
right cortex to a stimulation in the left visual field are detected in
qualitatively similar locations. The activations lie within the opposite
quadrant as the visual stimulus. While the HD-DOT signals lie on the
same gyri as the fMRI signals, it is apparent that the activation is
reconstructed towards the surface. Responses to the ventral stimuli
are displayed in a dorsally located axial slice in Fig. 3B (Neurological
orientation of the figures: subject left is figure left). Again, there is
strong agreement between the fMRI BOLD signal and HD-DOT recon-
structed hemoglobin concentrations. In the volumetric slice view, it
can be challenging to visualize the full three-dimensional nature of
the activation, especially the intersection with the convoluted cortical
surface. Thus, Fig. 3C displays the response to stimulation within each
of the four quadrants overlaid on the cortical surface as seen from be-
hind the head. It is apparent that the spatial extents of the HD-DOT
and fMRI activations are also qualitatively similar on the cortical
surface. The spatial correspondence of HD-DOT with fMRI is more
fully demonstrated in a movie of all the phase positions of the
block-averaged periodic stimuli (Supplemental Movie 1). Qualitative-
ly, the topography of the responses along the cortical anatomy agrees
strongly throughout the entirety of the stimulus presentation; both
modalities continually exhibit a response in the opposite quadrant
of the visual cortex from the quadrant of the visual field.
With the HD-DOT and fMRI data sets co-registered we can exam-
ine the temporal hemodynamics within a single voxel of visual cortex.
Both modalities present signals with a strong periodicity and high
contrast to noise ratio (CNR) (Fig. 4A; red: oxygenated hemoglobin
(ΔHbO
2
), blue: deoxygenated hemoglobin (ΔHbR), and green: total
hemoglobin (ΔHbT), black: fMRI-BOLD). The high contrast to noise
ratio is also evident in the Fourier transform of hemodynamic time
course with a strong peak at the stimulation frequency, a smaller
peak at the first harmonic and lower relative power at other frequen-
cies (Fig. 4b). Even though the HD-DOT and fMRI recordings are taken
during different sessions, the responses within the same single voxel
for each modality, are highly correlated (Fig. 2b).
Similar spatial results were observed for all subjects recorded
(Fig. 5, for clarity, only the HbR reconstructed response is shown for
HD-DOT). For a comparison of image quality on every subject for
each HD-DOT contrast, see Supplemental Fig. 1. In Subject 5, the
lower quadrants are nearly missing in the HD-DOT image. This is
due to low SNR on the ventral part of the pad for that subject. In gen-
eral, dorsally located activations for HD-DOT had better contrast-to-
noise and better agreement with the BOLD response than ventral
activations.
To quantify the localization error of HD-DOT reconstructions
relative to the fMRI BOLD signal, we calculated the CofM error as
the Euclidian distance between the CofM of the activations. The aver-
age CofM errors across all subjects are: ΔHbO2 4.9 +/−1 mm, ΔHbR
5.4+/−2 mm, ΔHbT 4.7 +/−1 mm, with an average across all he-
moglobin contrasts of: 5.0 +/−1 mm. The errors for the dorsal
quadrants (4.2+/−1 mm) are lower than for the ventral quadrants
(5.9+/−1 mm) (Fig. 3, Fig. 5, Supplementary Fig. 1).
Qualitatively, closer inspection shows that the HD-DOT signal di-
minishes with depth into the sulcal folds as compared to fMRI
(Fig. 6); the HD-DOT response is contained almost entirely within
Fig. 3. Quadrant activations of BOLD and each hemoglobin concentration species. (A) Parasagittal slice through the T1-weighted MRI of Subject 1. Activations are shown in response
to a wedge within the opposite visual quadrant (see visual stimulus key in (C)). Activations for each contrast are thresholded at 50% maximum for that specific activation. (B) Axial
slice with activations. Slices are shown in Neurological space (left is left). (C) Visual stimulus key and surface rendering of cortex with activations overlaid for each quadrant. Note
there is qualitative agreement between the measured BOLD activation and each of the HD-DOT reconstructed hemoglobin concentrations both in activation location and spatial
extent. All activations are the block-averaged responses (10 repetitions) from Subject 1.
Fig. 4. Time course of activations in a single voxel in Subject 1. (A) Time trace of hemo-
globin concentrations (measured with HD-DOT) in response to eight repetitions of the
wedge stimulus. (ΔHbO
2
, red; ΔHbR, blue; ΔHbT, green). The time trace of BOLD signal
is shown in black. (B) Fourier transforms of the time traces in (A). Note the strong peak
at the rotation frequency of the stimulus wedge, with little background noise in the
signal.
1124 A.T. Eggebrecht et al. / NeuroImage 61 (2012) 1120–1128
the fMRI response area, and co-localized at the surface ridges of the
cortical gyri.
To determine the image quality throughout the field-of-view of
the imaging pad, we applied a more continuous metric of response
activation using Fourier phase analysis. We calculated phase maps
for both fMRI (Fig. 7a) and HD-DOT (Fig. 7c) that revealed the
pinwheel structure of the stimulus (Fig. 7b). For clarity, only the
HbO
2
contrast data is shown. The circular correlation coefficient
(Lee and Fisher, 1983) CCC of the phases of the different modalities
shows a strong correlation of 0.50 (Fig. 7d). This provides an assess-
ment of how correlated HD-DOT and fMRI responses are for stimuli
throughout the entire visual field of view.
We also used the phase analysis to evaluate the localization error
between HD-DOT and fMRI. The phase error between the HD-DOT
and fMRI was calculated for each voxel and converted into a distance
error. After converting to a distance error, the map of errors of the
imaging pad on Subject 2 is smooth across the field of view and not de-
pendent on the location of the voxel relative to the stimulus (Fig. 7f). A
map of the localization error across the whole field of view was created
for each subject (Supplementary Fig. 2). After removing outliers and aver-
aging over all voxels for all subjects, the average localization error for the
HD-DOT imaging pad is ΔHbO
2
4.2+/−1 mm, ΔHbR 4.2+/−2 mm,
ΔHbT 4.8+/−1 mm, giving an overall average of 4.4 +/−1 mm. A
summary of all localization errors is provided in Table 2.
Discussion
Our results provide an image quality benchmark test of HD-DOT
via a voxel-to-voxel comparison with fMRI. We demonstrate that
the location and spatial extent HD-DOT activations are qualitatively
similar to fMRI activations throughout the accessible portions of the
visual cortex (Fig. 3 and Fig. 5). Since the data sets were co-
registered time traces of responses within individual voxels could
be compared. Consistent with previous fMRI and NIRS studies, the
signals are highly correlated between the two modalities (Fig. 4).
Qualitatively, the cortical topography of the responses agree strongly
throughout the phase cycle of the stimulus presentation (Supplemental
Movie 1). Quantitatively, we find the correspondence of quadrant loca-
tions between the two modalities to have an average center-of-mass
error of 5.0 +/−1 mm. Using phase maps to calculate localization error
in every voxel across the field of view we measured an average localiza-
tion error in the HD-DOT of 4.4+/−1 mm. Through examination of the
voxel overlap between HD-DOT and fMRI, we find excellent agreement
along the cortical ridges (Fig. 6), while the concurrence falls off as the
cortex folds away from the scalp surface and the sensitivity of the HD-
DOT system diminishes.
In this study a high density of overlapping measurements (Zeff et
al., 2007) enabled a quantitative voxel-wise comparison of image
quality of co-registered HD-DOT and fMRI over an extended region
of cortex. Previous comparisons between fMRI and DOT have exam-
ined the co-localization between the two modalities in visual
(Zhang et al., 2005) and motor (Joseph et al., 2006; Koch et al.,
2010) cortices. However, these studies did not perform quantitative
voxel-by-voxel comparisons. The motor papers used qualitative
means to infer image agreement by projecting the fMRI data to
individual DOI measurements at the scalp surface (Joseph et al.,
2006), or used a generic head model for the forward light modeling
rather than using the subject-specific anatomy to generate a realistic
photon-propagation model (Koch et al., 2010) and then projected the
data from the ‘model head’onto the subject specific MRI anatomy. In
contrast this visual study compared fMRI activations to DOT recon-
structions in a subject-specific brain volume. The use of the subject-
specific anatomy guarantees true alignment with the fMRI data set
leading to the gyral specificity apparent in the reconstructions. The
use of phase encoded retinotopy establishes the co-localization of
HD-DOT and fMRI over contiguous regions of the visual cortex.
While topographic DOI methods remain in wide use (Cui et al.,
2011; Franceschini et al., 2006; Khan et al., 2010; Lindauer et al.,
2001; Ou et al., 2009; Sakatani et al., 2007; Tian et al., 2010), the
image quality improvements of HD-DOT methods have been estab-
lished by several research groups (Joseph et al., 2006; Koch et al.,
Fig. 5. Quadrant activations for all five subjects. All HD-DOT quadrants are ΔHbR contrast. Note that for all subjects, there is qualitative agreement between HD-DOT and fMRI ac-
tivations both in location as well as extent. The blue quadrant is nearly absent in Subject 4's BOLD response because the actual recorded activation is deeper in the cortical folds than
can be seen within this view. Note that the activation to that quadrant is detected with HD-DOT, but part of the activation is localized on the gyri superficial to the BOLD-measured
location.
Fig. 6. Overlay of HD-DOT (ΔHbO
2
) and fMRI activation in Subject 1. Visual stimulus in
the lower right visual field gives rise to a strong response in the upper left visual cortex.
Red: Overlap of HD-DOT and fMRI measured response. Yellow: HD-DOT but not fMRI.
Green: fMRI but not HD-DOT. Note that the majority of the HD-DOT signal is co-
localized with the fMRI response along the top 10 mm of the gyral ridges of the cortical
surface. Activations are thresholded at 50% maximum for each modality. D: dorsal. A:
anterior. L: left.
1125A.T. Eggebrecht et al. / NeuroImage 61 (2012) 1120–1128
2010; Zeff et al., 2007). Quantitative comparative studies, using both
simulated and in vivo data, have demonstrated clear improvements
in resolution of the tomography approach (Boas et al., 2001, 2004a,
b; Koch et al., 2010; White and Culver, 2010b). While elegant depth
compensation algorithms have been proposed to increase the quality
of depth localization of DOT (Niu et al., 2010), a full comparison of
such methods is beyond the scope of the present work. The
spatially-dependent regularization techniques used herein for
human DOT have been detailed in references: (Dehghani et al.,
2009; White and Culver, 2010a,b; White et al., 2009; Zeff et al.,
2007). Incorporation of additional overlapping measurements pro-
vides the most straightforward approach to further increases in
image quality and extension of the imaging domain. Specifically, it
has been shown (Dehghani et al., 2009) that the inclusion of larger
source-detector pair separations leads to improved depth profiling
and sensitivity. As the measurement pair distances increase, the
SNR falls off exponentionally (Boas et al., 2001). Thus, if DOT systems
are developed with better sensitivity, they might sample deeper and
possibly improve upon the resolution and localization accuracy of
current systems. In these five subjects, the current system is
capable of maintaining good localization (b5 mm error) down to a
cortical depth of 0.75 cm, or about 1.75 cm below the surface of the
scalp.
In this study, we have used NIRFAST, a finite-element solver of the
diffusion equation (Dehghani et al., 2003) to construct the model so-
lution to the forward light scattering problem. Alternatively, a solu-
tion to the full radiative transport equation (RTE) can be obtained
via Monte Carlo (MC) methods. Recent comparative studies between
RTE and diffusion modeling have shown that the diffusion equation is
sufficient for general considerations of DOT in healthy adults (Custo
et al., 2006). The favorable comparative fMRI-DOT results of this
paper using diffusion modeling further support the use of diffusion
modeling. However, there may be clinical situations, for example
patients with cerebral edema, in which MC modeling will have signif-
icant advantages. While historically MC modeling methods were very
time consuming, recent advances in GPU technologies hold promise
for more time efficient implementation of forward solvers (Fang
and Boas, 2009; Ren et al., 2010). It will be important for future
studies to compare the image quality of reconstructions obtained
via both RTE and diffusion modeling.
General Linear Models (GLMs), while standard in the fMRI litera-
ture, are not yet standard with DOT. The localization error of the
centers of mass of the quadrants was calculated using the recon-
structed hemoglobin concentrations for HD-DOT and the raw BOLD
signal for fMRI. No noise-weighting correction was made for either
modality. However, in the phase-map analysis, we did calculate the
statistical significance for the measured response within each voxel
with a conservative weighting by the noise (see Methods). The
phase map analysis conducted herein provided a similar analysis as
GLM by calculating both the correlation magnitude and phase of the
response to a periodic stimuli. More generalized statistical parametric
mapping (SPM) methods have been developed for NIRS data
(Villringer et al., 1993; Ye et al., 2009), that address the uneven spa-
tial sampling of NIRS topography. In principal, due to the improved
spatial sampling of DOT compared to NIRS (White and Culver,
2010b), the extension of SPM methods used in fMRI for calculating
roughness and addressing the multiple comparisons problem should
be more straight forward for DOT than NIRS.
In our analysis we smoothed (Gaussian kernel with FWHM =13 mm)
the fMRI data to match the DOT imaging point spread function based on
the rationale that we wanted to compare the differences in the modali-
ties, not differences in the point spread functions. However an alternate
approach would be to use standard fMRI processing which typically
uses 8 mm Gaussian smoothing (Wenger et al., 2004). To evaluate the lo-
calization errors with normal fMRI processing we re-ran the quadrant lo-
calization error analysis with a set of fMRI data smoothed to 8 mm. The
resulting average localization error of the quadrants was 4.5 +/−2mm,
(within the error of the results from 13 mm smoothing). Thus the small
localization errors between fMRI and HD-DOT hold even with standard
fMRI processing.
Fig. 7. Phase map error analysis in Subject 2. (A) A map of phase is defined throughout the visual cortex for the BOLD response, shown here for Subject 2. (B) Visual stimulus key for
the phase maps. (C) Phase map for HD-DOT (ΔHbO
2
contrast). Note spatial correspondence with (A). (D) Scatter plot of circular correlation coefficient of BOLD and ΔHbO
2
phases in
each voxel within the field of view. Black line is the zero-error line. (E) A phase error plot is created by subtracting the HD-DOT phase in each voxel from the fMRI phase. All phase
errors greater than +/−πare corrected via phase wrapping. (F) An estimate of the localization error between fMRI and DOT derived from the phase maps.
Table 2
Summary of HD-DOT imaging errors across subjects and contrasts.
Subject Center of mass error
(mm, mean+/−SD)
Phase map error
(mm, mean+/−SD)
HbO2 HbR HbT HbO2 HbR HbT
1 5.6+/−2 5.7+/−1 5.6 +/−2 4.6 +/−4 4.7 +/−4 4.8+/−4
2 3.4+/−2 4.4+/−2 3.6 +/−1 3.2 +/−3 4.4 +/−3 4.0+/−4
3 3.9+/−3 3.9+/−2 4.1 +/−4 6.5 +/−5 5.3 +/−4 6.0+/−5
4 6.7+/−1 7.8+/−1 5.4 +/−2 3.4 +/−3 3.5 +/−2 6.0+/−3
5 4.9+/−2 5.2+/−2 4.9 +/−2 3.4 +/−3 3.2 +/−3 3.2+/−3
Average 4.9 +/−1 5.4+/−2 4.7 +/−1 4.2 +/−1 4.2 +/−1 4.8+/−1
Average 5.0 +/−1 4.4+/−1
1126 A.T. Eggebrecht et al. / NeuroImage 61 (2012) 1120–1128
The fMRI and HD-DOT data reside within the same model space.
Thus, the magnitude of an error in the location of activation or a
phase map value can be directly compared. The purpose of the
current paper is a within-subject voxel-to-voxel comparison of HD-
DOT and fMRI. We used five subjects to obtain a sampling across
head types. We provide group averaging of the within subject errors
between fMRI and DOT (errors in CofM and in the phase plots).
Group averaging of the DOT data on a voxel by voxel basis requires
spatial normalization that, while standardized for fMRI, has not yet
been established in the literature for DOT. However, spatial
normalization of DOT data will be an important area of future work
to enable voxel-wise group level comparisons, data averaging, and
better alignment with fMRI processing.
A general limitation of DOT systems is that while the upper
portions of the cortical hull can be sampled, deep brain structures
and deep cortical tissue along midline (e.g. area V1 of the visual
cortex) cannot be sampled. To address this issue we constrained our
comparison of fMRI and DOT to the regions of the tissue that were
well sampled by the DOT imaging array. This limited the potential
confound of having displaced or mismatched sampling volumes.
Retinotopic stimuli, even at a single focal location in the visual field
of view, lead to multiple activations within the visual cortex due to
the presence of multiple representations (i.e. V1, V2, V3 etc…).
Qualitatively, the cortical topography of the responses are in agree-
ment throughout the accessible visual areas (Supplemental Movie
1). The phase analysis confirms this quantitatively.
Conclusion
These co-registered retinotopic results establish that HD-DOT
methods can map brain function with good (b5 mm localization
error) voxel-to-voxel correspondence with fMRI. By using a phase-
encoded visual paradigm this study not only compared point-
activations but also full maps of visual cortex, a standard analysis in
fMRI literature (Warnking et al., 2002). The HD-DOT maps were
verified to be not only aligned with the fMRI but also possessing the
same qualitative contours and structure along the cortical ribbon.
Limiting aspects of previous HD-DOT studies (Custo et al., 2009; Zeff
et al., 2007) were directly addressed. Specifically, we used anatomical
head modeling with accurate optode placement, co-registration of
the data set to not only the specific subject anatomy but also to an
additional neuroimaging modality, and contiguous mapping of an
extend region of the cortex. The resulting voxel-wise comparison
reveals that the localization errors between HD-DOT and fMRI in
functional maps of visual cortex are on average within the size of a
gyral ridge. This gyral specificity demonstrates that HD-DOT methods
show great promise and have sufficient image quality to be useful as a
surrogate for fMRI in both clinical and basic neuroscience.
Supplementary materials related to this article can be found
online at doi:10.1016/j.neuroimage.2012.01.124
Role of the funding source
This work was supported in part by NIH grants R01-EB009233
(J.P.C), T90-DA022871 (Imaging Science Fellowship, B.R.W.) and a
Fulbright Science and Technology Ph.D. Award (S.L.F.). The funding
source had no involvement in the study design, collection, analysis,
interpretation of the data, writing of the paper, or decision to submit
the paper for publication. J.P.C and Washington University have finan-
cial interests in Cephalogics LLC based on a license of related optical
imaging technology by the University to Cephalogics LLC.
Acknowledgments
We thank Gavin Perry and Martin Olevitch for help with HD-DOT
instrumentation and software; Fran Miezin for developing the BOLD
sequence we used; Donna Dierker for help and patience with Caret
and FreeSurfer software; and Tracy Nolan and Linda Larson-Prior
with some MRI data acquisition.
References
Belliveau, J.W., Kennedy Jr., D.N., McKinstry, R.C., Buchbinder, B.R., Weisskoff, R.M.,
Cohen, M.S., Vevea, J.M., Brady, T.J., Rosen, B.R., 1991. Functional mapping of the
human visual cortex by magnetic resonance imaging. Science 254, 716–719.
Benaron, D.A., Hintz, S.R., Villringer, A., Boas, D., Kleinschmidt, A., Frahm, J., Hirth, C.,
Obrig, H., van Houten, J.C., Kermit, E.L., Cheong, W.F., Stevenson, D.K., 2000.
Noninvasive functional imaging of human brain using light. J. Cereb. Blood Flow.
Metab. 20, 469–477.
Bevilacqua, F., Piguet, D., Marquet, P., Gross, J.D., Tromberg, B.J., Depeursinge, C., 1999.
In vivo local determination of tissue optical properties: applications to human
brain. Appl. Opt. 38, 4939–4950.
Blasi, A., Fox, S., Everdell, N., Volein, A., Tucker, L., Csibra, G., Gibson, A.P., Hebden, J.C.,
Johnson, M.H., Elwell, C.E., 2007. Investigation of depth dependent changes in
cerebral haemodynamics during face perception in infants. Phys. Med. Biol. 52,
6849–6864.
Bluestone, A., Abdoulaev, G., Schmitz, C., Barbour, R., Hielscher, A., 2001. Three-dimensional
optical tomography of hemodynamics in the human head. Opt. Express 9, 272–286.
Boas, D.A., Chen, K., Grebert, D., Franceschini, M.A., 2004a. Improving the diffuse optical
imaging spatial resolution of the cerebral hemodynamic response to brain
activation in humans. Opt. Lett. 29, 1506–1508.
Boas, D.A., Dale, A.M., 2005. Simulation study of magnetic resonance imaging-guided
cortically constrained diffuse optical tomography of human brain function. Appl.
Opt. 44, 1957–1968.
Boas, D.A., Dale, A.M., Franceschini, M. A., 2004b. Diffuse optical imaging of brain activation:
approaches to optimizing image sensitivity, resolution, and accuracy. Neuroimage 23
(Suppl. 1), S275–S288.
Boas, D.A., Gaudette, T., Strangman, G., Cheng, X., Marota, J.J., Mandeville, J.B., 2001. The
accuracy of near infrared spectroscopy and imaging during focal changes in
cerebral hemodynamics. Neuroimage 13, 76–90.
Bouchard, M.B., Chen, B.R., Burgess, S.A., Hillman, E.M., 2009. Ultra-fast multispectral
optical imaging of cortical oxygenation, blood flow, and intracellular calcium
dynamics. Opt. Express 17, 15670–15678.
Cui, X., Bray, S., Bryant, D.M., Glover, G.H., Reiss, A.L., 2011. A quantitative comparison
of NIRS and fMRI across multiple cognitive tasks. Neuroimage 54, 2808–2821.
Culver, J.P., Durduran, T., Furuya, D., Cheung, C., Greenberg, J.H., Yodh, A.G., 2003.
Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism
in rat during focal ischemia. J. Cereb. Blood Flow. Metab. 23, 911–924.
Custo, A., Boas, D.A., Tsuzuki, D., Dan, I., Mesquita, R., Fischl, B., Grimson, W.E., Wells III,
W., 2009. Anatomical atlas-guided diffuse optical tomography of brain activation.
Neuroimage 49, 561–567.
Custo, A., Wells III, W.M., Barnett, A.H., Hillman, E.M., Boas, D.A., 2006. Effective scatter-
ing coefficient of the cerebral spinal fluid in adult head models for diffuse optical
imaging. Appl. Opt. 45, 4747–4755.
Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. Segmentation
and surface reconstruction. Neuroimage 9, 179–194.
Dehaes, M., Grant, P.E., Sliva, D.D., Roche-Labarbe, N., Pienaar, R., Boas, D.A.,
Franceschini, M.A., Selb, J., 2011. Assessment of the frequency-domain multi-
distance method to evaluate the brain optical properties: Monte Carlo simulations
from neonate to adult. Biomed. Opt. Express 2, 552–567.
Dehghani, H., Arridge, S.R., Schweiger, M., Delpy, D.T., 2000. Optical tomography in the
presence of void regions. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 17, 1659–1670.
Dehghani, H., Pogue, B.W., Poplack, S.P., Paulsen, K.D., 2003. Multiwavelength three-
dimensional near-infrared tomography of the breast: initial simulation, phantom,
and clinical results. Appl. Opt. 42, 135–145.
Dehghani, H., White, B.R., Zeff, B.W., Tizzard, A., Culver, J.P., 2009. Depth sensitivity and
image reconstruction analysis of dense imaging arrays for mapping brain function
with diffuse optical tomography. Appl. Opt. 48, D137–D143.
Devor, A., Dunn, A.K., Andermann, M.L., Ulbert, I., Boas, D.A., Dale, A.M., 2003. Coupling
of total hemoglobin concentration, oxygenation, and neural activity in rat somato-
sensory cortex. Neuron 39, 353–359.
Dunn, A.K., Devor, A., Dale, A.M., Boas, D.A., 2005. Spatial extent of oxygen metabolism
and hemodynamic changes during functional activation of the rat somatosensory
cortex. Neuroimage 27, 279–290.
Engel, S.A., Glover, G.H., Wandell, B.A., 1997. Retinotopic organization in human visual
cortex and the spatial precision of functional MRI. Cereb. Cortex 7, 181–192.
Engel, S.A., Rumelhart, D.E., Wandell, B.A., Lee, A.T., Glover, G.H., Chichilnisky, E.J.,
Shadlen, M.N., 1994. fMRI of human visual cortex. Nature 369, 525.
Fang, Q., Boas, D.A., 2009. Monte Carlo simulation of photon migration in 3D turbid
media accelerated by graphics processing units. Opt. Express 17, 20178–20190.
Felleman, D.J., Van Essen, D.C., 1991. Distributed hierarchical processing in the primate
cerebral cortex. Cereb. Cortex 1, 1–47.
Fischl, B., Liu, A., Dale, A.M., 2001. Automated manifold surgery: constructing geomet-
rically accurate and topologically correct models of the human cerebral cortex.
IEEE Trans. Med. Imaging 20, 70–80.
Fox, P.T., Mintun, M.A., Raichle, M.E., Miezin, F.M., Allman, J.M., Van Essen, D.C., 1986.
Mapping human visual cortex with positron emission tomography. Nature 323,
806–809.
Fox, P.T., Perlmutter, J.S., Raichle, M.E., 1985. A stereotactic method of anatomical local-
ization for positron emission tomography. J. Comput. Assist. Tomogr. 9, 141–153.
1127A.T. Eggebrecht et al. / NeuroImage 61 (2012) 1120–1128
Franceschini, M.A., Joseph, D.K., Huppert, T.J., Diamond, S.G., Boas, D.A., 2006. Diffuse
optical imaging of the whole head. J. Biomed. Opt. 11, 054007.
Fuchs, M., Kastner, J., Wagner, M., Hawes, S., Ebersole, J.S., 2002. A standardized bound-
ary element method volume conductor model. Clin. Neurophysiol. 113, 702–712.
Gibson, A., Dehghani, H., 2009. Diffuse optical imaging. Philosophical transactions. A
Math Phys. Eng. Sci. 367, 3055–3072.
Gibson, A., Yusof, R.M., Dehghani, H., Riley, J., Everdell, N., Richards, R., Hebden, J.C.,
Schweiger, M., Arridge, S.R., Delpy, D.T., 2003. Optical tomography of a realistic
neonatal head phantom. Appl. Opt. 42, 3109–3116.
Gibson, A.P., Austin, T., Everdell, N.L., Schweiger, M., Arridge, S.R., Meek, J.H., Wyatt, J.S.,
Delpy, D.T., Hebden, J.C., 2006. Three-dimensional whole-head optical tomography
of passive motor evoked responses in the neonate. Neuroimage 30, 521–528.
Gibson, A.P., Hebden, J.C., Arridge, S.R., 2005. Recent advances in diffuse optical imaging.
Phys. Med. Biol. 50, R1–R43.
Gilbert, C.D., Wiesel, T.N., 1979. Morphology and intracortical projections of functionally
characterised neurones in the cat visual cortex. Nature 280, 120–125.
Habermehl, C., Holtze, S., Steinbrink, J., Koch, S.P., Obrig, H., Mehnert, J., Schmitz, C.H.,
2011. Somatosensory activation of two fingers can be discriminated with
ultrahigh-density diffuse optical tomography. Neuroimage 59 (4), 3201–3211.
Harding, G.F., Janday, B., Armstrong, R.A., 1991. Topographic mapping and source local-
ization of the pattern reversal visual evoked magnetic response. Brain Topogr. 4,
47–55.
Hebden, J.C., Gibson, A., Yusof, R.M., Everdell, N., Hillman, E.M., Delpy, D.T., Arridge, S.R.,
Austin, T., Meek, J.H., Wyatt, J.S., 2002. Three-dimensional optical tomography of
the premature infant brain. Phys. Med. Biol. 47, 4155–4166.
Heiskala, J., Pollari, M., Metsaranta, M., Grant, P.E., Nissila, I., 2009. Probabilistic atlas
can improve reconstruction from optical imaging of the neonatal brain. Opt.
Express 17, 14977–14992.
Huppert, T.J., Hoge, R.D., Dale, A.M., Franceschini, M.A., Boas, D.A., 2006a. Quantitative
spatial comparison of diffuse optical imaging with blood oxygen level-dependent
and arterial spin labeling-based functional magnetic resonance imaging. J. Biomed.
Opt. 11, 064018.
Huppert, T.J., Hoge, R.D., Diamond, S.G., Franceschini, M.A., Boas, D.A., 2006b. A temporal
comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in
adult humans. Neuroimage 29, 368–382.
Joseph, D.K., Huppert, T.J., Franceschini, M.A., Boas, D.A., 2006. Diffuse optical tomography
system to image brain activation with improved spatial resolution and validation
with functional magnetic resonance imaging. Appl. Opt. 45, 8142–8151.
Khan, B., Tian, F., Behbehani, K., Romero, M.I., Delgado, M.R., Clegg, N.J., Smith, L., Reid, D.,
Liu, H., Alexandrakis,G., 2010. Identification of abnormal motor cortex activation pat-
terns in children with cerebral palsy by functional near-infrared spectroscopy. J.
Biomed. Opt. 15, 036008.
Klem, G.H., Luders, H.O., Jasper, H.H., Elger, C., 1999. The ten-twenty electrode system
of the International Federation. The International Federation of Clinical Neuro-
physiology. Electroencephalogr. Clin. Neurophysiol. (Suppl. 52), 3–6.
Koch, S.P., Habermehl, C., Mehnert, J., Schmitz, C.H., Holtze, S., Villringer, A., Steinbrink,
J., Obrig, H., 2010. High-resolution optical functional mapping of the human
somatosensory cortex. Front. Neuroenerg. 2, 12.
Kohl-Bareis, M., Obrig, H., Steinbrink, J., Malak, J., Uludag, K., Villringer, A., 2002. Non-
invasive monitoring of cerebral blood flow by a dye bolus method: separation of
brain from skin and skull signals. J. Biomed. Opt. 7, 464–470.
Lee, W.H., Fisher, J., 1983. Right ventricular diastolic disorders. Arch. Intern. Med. 143,
332–337.
Lindauer, U., Royl, G., Leithner, C., Kuhl, M., Gold, L., Gethmann, J., Kohl-Bareis, M.,
Villringer, A., Dirnagl, U., 2001. No evidence for early decrease in blood oxygenation
in rat whisker cortexin response to functional activation. Neuroimage 13, 988–1001.
Maki, A., Yamashita, Y., Ito, Y., Watanabe, E., Mayanagi, Y., Koizumi, H., 1995. Spatial
and temporal analysis of human motor activity using noninvasive NIR topography.
Med. Phys. 22, 1997–2005.
Nelles, M., Gieseke, J., Urbach, H., Semrau, R., Bystrov, D., Schild, H.H., Willinek, W.A.,
2009. Pre- and postoperative MR brain imaging with automatic planning and
scanning software in tumor patients: an intraindividual comparative study at 3
Tesla. J. Magn. Reson. Imaging 30, 672–677.
Niu, H., Lin, Z.J., Tian, F., Dhamne, S., Liu, H., 2010. Comprehensive investigation of
three-dimensional diffuse optical tomography with depth compensation algo-
rithm. J. Biomed. Opt. 15, 046005.
Obrig, H., Villringer, A., 2003. Beyond the visible–imaging the human brain with light. J.
Cereb. Blood Flow. Metab. 23, 1–18.
Okamoto, M., Dan, H., Shimizu, K., Takeo, K., Amita, T., Oda, I., Konishi, I., Sakamoto, K.,
Isobe, S., Suzuki, T., Kohyama, K., Dan, I., 2004. Multimodal assessment of cortical
activation during apple peeling by NIRS and fMRI. Neuroimage 21, 1275–1288.
Ou, W., Nissila, I., Radhakrishnan, H., Boas, D.A., Hamalainen, M.S., Franceschini, M.A.,
2009. Study of neurovascular coupling in humans via simultaneous magnetoen-
cephalography and diffuse optical imaging acquisition. Neuroimage 46, 624–632.
Ren, N., Liang, J., Qu, X., Li, J., Lu, B., Tian, J., 2010. GPU-based Monte Carlo simulation for
light propagation in complex heterogeneous tissues. Opt. Express 18, 6811–6823.
Ripoll, J., Nieto-Vesperinas, M., Arridge, S.R., Dehghani, H., 2000. Boundary conditions
for light propagation in diffusive media with nonscattering regions. J. Opt. Soc.
Am. A Opt. Image Sci. Vis. 17, 1671–1681.
Rosa, M.G., Soares, J.G., Fiorani Jr., M., Gattass, R., 1993. Cortical afferents of visual area
MT in the Cebus monkey: possible homologies between New and Old World mon-
keys. Vis. Neurosci. 10, 827–855.
Sakatani, K., Murata, Y., Fujiwara, N., Hoshino, T., Nakamura, S., Kano, T., Katayama, Y.,
2007. Comparison of blood-oxygen-level-dependent functional magnetic reso-
nance imaging and near-infrared spectroscopy recording during functional brain
activation in patients with stroke and brain tumors. J. Biomed. Opt. 12, 062110.
Sassaroli, A., de, B.F.B., Tong, Y., Renshaw, P.F., Fantini, S., 2006. Spatially weighted
BOLD signal for comparison of functional magnetic resonance imaging and near-
infrared imaging of the brain. Neuroimage 33, 505–514.
Saygin, A.P., Sereno, M.I., 2008. Retinotopy and attention in human occipital, temporal,
parietal, and frontal cortex. Cereb. Cortex 18, 2158–2168.
Segonne, F., Dale, A.M., Busa, E., Glessner, M., Salat, D., Hahn, H.K., Fischl, B., 2004. A hy-
brid approach to the skull stripping problem in MRI. Neuroimage 22, 1060–1075.
Selb, J., Stott, J.J., Franceschini, M.A., Sorensen, A.G., Boas, D.A., 2005. Improved sensitiv-
ity to cerebral hemodynamics during brain activation with a time-gated optical
system: analytical model and experimental validation. J. Biomed. Opt. 10, 11013.
Sereno, M.I., Dale, A.M., Reppas, J.B., Kwong, K.K., Belliveau, J.W., Brady, T.J., Rosen, B.R.,
Tootell, R.B., 1995. Borders of multiple visual areas in humans revealed by func-
tional magnetic resonance imaging. Science 268, 889–893.
Shimony, J.S., Zhang, D., Johnston, J.M., Fox, M.D., Roy, A., Leuthardt, E.C., 2009. Resting-
state spontaneous fluctuations in brain activity: a new paradigm for presurgical
planning using fMRI. Acad. Radiol. 16, 578–583.
Shulman, G.L., Pope, D.L., Astafiev, S.V., McAvoy, M.P., Snyder, A.Z., Corbetta, M., 2010.
Right hemisphere dominance during spatial selective attention and target detec-
tion occurs outside the dorsal frontoparietal network. J. Neurosci. 30, 3640–3651.
Siegel, A.M., Culver, J.P., Mandeville, J.B., Boas, D.A., 2003. Temporal comparison of
functional brain imaging with diffuse optical tomography and fMRI during rat
forepaw stimulation. Phys. Med. Biol. 48, 1391–1403.
Spalding, J.M., 1952. Wounds of the visual pathway. Part II. The striate cortex. J. Neurol.
Neurosurg. Psychiatry 15, 169–183.
Steinbrink, J., Wabnitz, H., Obrig, H., Villringer, A., Rinneberg, H., 2001. Determining
changes in NIR absorption using a layered model of the human head. Phys. Med.
Biol. 46, 879–896.
Strangman, G., Culver, J.P., Thompson, J.H., Boas, D.A., 2002. A quantitative comparison
of simultaneous BOLD fMRI and NIRS recordings during functional brain activation.
Neuroimage 17, 719–731.
Strangman, G., Franceschini, M.A., Boas, D.A., 2003. Factors affecting the accuracy of
near-infrared spectroscopy concentration calculations for focal changes in oxygen-
ation parameters. Neuroimage 18, 865–879.
Tian, F., Delgado, M.R., Dhamne, S.C., Khan, B., Alexandrakis, G., Romero, M.I., Smith, L.,
Reid, D., Clegg, N.J., Liu, H., 2010. Quantification of functional near infrared
spectroscopy to assess cortical reorganization in children with cerebral palsy.
Opt. Express 18, 25973–25986.
Toronov, V., Webb, A., Choi, J.H., Wolf, M., Michalos, A., Gratton, E., Hueber, D., 2001.
Investigation of human brain hemodynamics by simultaneous near-infrared
spectroscopy and functional magnetic resonance imaging. Med. Phys. 28, 521–527.
Toronov, V.Y., Zhang, X., Webb, A.G., 2007. A spatial and temporal comparison of
hemodynamic signals measured using optical and functional magnetic resonance
imaging during activation in the human primary visual cortex. Neuroimage 34,
1136–1148.
Van Essen, D.C., Drury, H.A., Dickson, J., Harwell, J., Hanlon, D., Anderson, C.H., 2001. An
integrated software suite for surface-based analyses of cerebral cortex. J. Am. Med.
Inform. Assoc. 8, 443–459.
Villringer, A., Chance, B., 1997. Non-invasive optical spectroscopy and imaging of
human brain function. Trends Neurosci. 20, 435–442.
Villringer, A., Planck, J., Hock, C., Schleinkofer, L., Dirnagl, U., 1993. Near infrared
spectroscopy (NIRS): a new tool to study hemodynamic changes during activation
of brain function in human adults. Neurosci. Lett. 154, 101–104.
Warnking, J., Dojat, M., Guerin-Dugue, A., Delon-Martin, C., Olympieff, S., Richard, N.,
Chehikian, A., Segebarth, C., 2002. fMRI retinotopic mapping—step by step. Neuro-
image 17, 1665–1683.
Wengenroth, M., Blatow, M., Guenther, J., Akbar, M., Tronnier, V.M., Stippich, C., 2011.
Diagnostic benefits of presurgical fMRI in patients with brain tumours in the
primary sensorimotor cortex. Eur. Radiol. 21, 1517–1525.
Wenger, K.K., Visscher,K.M., Miezin, F.M., Petersen, S.E.,Schlaggar, B.L., 2004.Comparison
of sustained and transient activity in children and adults using a mixed blocked/
event-related fMRI design. Neuroimage 22, 975–985.
White, B.R., Culver, J.P., 2010a. Phase-encoded retinotopy as an evaluation of diffuse
optical neuroimaging. Neuroimage 49, 568–577.
White, B.R., Culver, J.P., 2010b. Quantitative evaluation of high-density diffuse optical
tomography: in vivo resolution and mapping performance. J. Biomed. Opt. 15,
026006.
White, B.R., Snyder, A.Z., Cohen, A.L., Petersen, S.E., Raichle, M.E., Schlaggar, B.L., Culver,
J.P., 2009. Resting-state functional connectivity in the human brain revealed with
diffuse optical tomography. Neuroimage 47, 148–156.
Ye, J.C., Tak, S., Jang, K.E., Jung, J., Jang, J., 2009. NIRS-SPM: statistical parametric
mapping for near-infrared spectroscopy. Neuroimage 44, 428–447.
Zeff, B.W., White, B.R., Dehghani, H., Schlaggar, B.L., Culver, J.P., 2007. Retinotopic map-
ping of adult human visual cortex with high-density diffuse optical tomography.
Proc. Natl. Acad. Sci. U. S. A. 104, 12169–12174.
Zhang, D., Johnston, J.M., Fox, M.D., Leuthardt, E.C., Grubb, R.L., Chicoine, M.R., Smyth,
M.D., Snyder, A.Z., Raichle, M.E., Shimony, J.S., 2009. Preoperative sensorimotor
mapping in brain tumor patients using spontaneous fluctuations in neuronal
activity imaged with functional magnetic resonance imaging: initial experience.
Neurosurgery 65, 226–236.
Zhang, X., Toronov, V., Webb, A., 2005. Simultaneous integrated diffuse optical
tomography and functional magnetic resonance imaging of the human brain.
Opt. Express 13, 5513–5521.
1128 A.T. Eggebrecht et al. / NeuroImage 61 (2012) 1120–1128
