Retinotopic Organization of Primary Visual Cortex in Glaucoma: Comparing fMRI Measurements of Cortical Function with Visual Field Loss

Abstract

Primary open angle glaucoma (POAG) is a progressive optic neuropathy characterized by retinal ganglion cell loss. Experimental primate glaucoma indicates neuronal degeneration of the lateral geniculate nucleus (LGN) and activity changes in the visual cortex (V1). Neuronal degeneration has also been shown in a post-mortem human study of the optic nerve, LGN and visual cortex. Functional magnetic resonance imaging (fMRI), a non-invasive means of inferring function-specific neuronal activity, provides an opportunity to evaluate glaucomatous changes in neuronal activity throughout the visual pathway in vivo.

Full text

Progress in Retinal and Eye Research 26 (2007) 38– 56
Retinotopic organization of primary visual cortex in glaucoma:
Comparing fMRI measurements of cortical function with
visual field loss
Robert O. Duncan

, Pamela A. Sample, Robert N. Weinreb,
Christopher Bowd, Linda M. Zangwill
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, CA 92093-0946, USA
Abstract
Primary open angle glaucoma (POAG) is a progressive optic neuropathy characterized by retinal ganglion cell loss. Experimental
primate glaucoma indicates neuronal degeneration of the lateral geniculate nucleus (LGN) and activity changes in the visual cortex (V1).
Neuronal degeneration has also been shown in a post-mortem human study of the optic nerve, LGN and visual cortex. Functional
magnetic resonance imaging (fMRI), a non-invasive means of inferring function-specific neuronal activity,provides an opportunity to
evaluate glaucomatous changes in neuronal activity throughout the visual pathway in vivo.
The purpose of this study is to demonstrate that the relationship between visual field loss in human POAG and the functional
organization of V1 can be measured using novel fMRI analysis methods. Visual field defects were measured using standard automated
perimetry (SAP). A retinotopic map of visual space was obtained for V1, and the retinotopy data was fit with a template. The template
was used to project regions within the visual field onto a flattened representation of V1. Viewing through the glaucomatous vs. fellow eye
was compared by alternately presenting each eye with a scotoma-mapping stimulus. The resulting blood oxygen level dependent (BOLD)
fMRI response was compared to interocular differences in thresholds for corresponding regions of the visual field.
The spatial pattern of activity observed in the flattened representation agreed with the pattern of visual field loss. Furthermore, the
amplitude of the BOLD response was correlated on a pointwise basis with the difference in sensitivity thresholds between the
glaucomatous and fellow eyes (r¼0.53, po0.0001).
The BOLD signal in human V1 is altered for POAG patients in a manner consistent with the loss of visual function. FMRI of visual
brain areas is a potential means for quantifying glaucomatous changes in neuronal activity. This should enhance our understanding of
glaucoma, and could lead to new diagnostic techniques and therapies.
Published by Elsevier Ltd.
Contents
1. Glaucomatous neuronal degeneration in the central nervous system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2. Retinotopic mapping in humans using fMRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3. Prior investigations of cortical activity in human glaucoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4. Comparing fMRI responses to visual function in glaucoma. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1. Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.1. Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.2. Criteria for inclusion and exclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.3. Visual function testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.4. Evaluation of stereophotographs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
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Corresponding author. Tel.: +1 619 302 2931.
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4.1.5. General fMRI methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1.6. FMRI stimuli. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1.7. Projecting patterns of visual field loss onto the cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.1.8. Comparing visual fields to fMRI data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.1.9. Pointwise comparison of fMRI responses and visual fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.1.10. Control experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2. Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.1. FMRI data from a single patient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.2. FMRI responses correlate with visual function thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.3. Pointwise comparison of fMRI responses and visual function thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.4. Control experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5. Sources of potential error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.1. Sources of potential error in template fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.1.1. The visual stimulus and the reliability of the fitting procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.1.2. Distortions in the cortical flattening technique. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.1.3. Fixation losses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2. Sources of error in perimetric testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.3. Variability related to small sample sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
6. Visual sensitivity predicts cortical responses in a retinotopic fashion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
7. The role of fMRI in glaucoma research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
8. Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
9. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
1. Glaucomatous neuronal degeneration in the central
nervous system
Glaucoma is a group of progressive optic neuropathies
that share a common feature, a gradual loss of retinal
ganglion cells accompanied by a progressive degeneration
of the optic nerve. Left untreated, glaucoma results in
irreversible vision loss or blindness. Glaucoma is the
second leading cause of blindness worldwide, and will
affect 79.6 million people worldwide (Quigley and Broman,
2006).
While intraocular pressure is a leading risk factor for
glaucoma, the pathophysiology of neuronal degeneration
in glaucoma is unknown. Primary open angle glaucoma
(POAG) often causes vision loss in subjects with normal
intraocular pressure, demonstrating that there are addi-
tional factors contributing to the disease (Weinreb and
Khaw, 2004).
In addition to damaging retinal ganglion cells, POAG
also damages post-retinal mechanisms, including the lateral
geniculate nucleus of the thalamus (LGN) and the primary
visual cortex (V1). Experimental primate glaucoma in-
dicates neuronal degeneration of the LGN and activity
changes in the visual cortex (Vickers et al., 1997;Crawford
et al., 2000;Weber et al., 2000;Yucel et al., 2000;Crawford
et al., 2001;Yucel et al., 2001, 2003). Human LGN changes
in glaucoma have been described (Chaturvedi et al., 1993),
but the reported cell counts did not take changes in LGN
volume into account (Weinreb et al., 1994). Neuronal
degeneration has also been shown in a post-mortem human
study of the optic nerve, LGN and visual cortex (Gupta
et al., 2006). POAG can affect several structures along the
visual pathway before behavioral deficits are noticed. For
example, by the time visual field defects are detected using
perimetry, 50–60% of the ganglion cells may already be
dead (Harwerth et al., 1999;Harwerth and Quigley, 2006).
Structural changes in the retina and optic nerve head
observed during ophthalmologic examination may also be
detected before functional changes (Sommer et al., 1991;
Johnson et al., 2003).
A greater knowledge of post-retinal mechanisms in
POAG might provide clinicians with better tools for
diagnosis and treatment. Animal models of glaucoma are
informative, but the experimental glaucomas may stem from
different pathologies than POAG. Experimental non-human
animals primates are typically young, and glaucomatous
progression occurs more rapidly in experimentally induced
glaucoma relative to that in human POAG. Additionally,
the experimental glaucomas are primarily caused by elevated
intraocular pressure. For these reasons, human studies are
preferred for understanding human disease. However,
human histology studies have required post-mortem analy-
sis, and thus disease-related changes cannot be monitored
over time. Functional magnetic resonance imaging (fMRI),
on the other hand, is a non-invasive means of inferring
neuronal activity that can measure brain function over time
in vivo. To date, only one fMRI study has investigated the
effects of optic neuropathy on the occipital cortex of
humans, and the techniques used were not optimal (Miki
et al., 1996). Unfortunately, the methodology used in that
study resulted in poor response localization, and neuronal
and behavioral responses were not compared. A precise
method for comparing visual field thresholds to activity in
visual cortex is still needed. In the current study, novel fMRI
ARTICLE IN PRESS
R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–56 39

techniques for measuring neuronal activity in the central
nervous system of glaucoma patients have been presented.
Unlike previous fMRI studies of optic neuropathy, this
approach can accurately localize the representation of visual
scotomas within the flattened representation of V1. The
improved methodology will make it easier to compare
glaucomatous changes in neuronal activity measured from
human V1 to changes in visual field thresholds, thus
providing clinically relevant information that may lead to
new diagnostic techniques and therapies.
2. Retinotopic mapping in humans using fMRI
FMRI has rapidly become the standard for inferring
neuronal activity in human subjects because (1) it is non-
invasive, (2) it has reliable response localization, and (3) it
has superior spatial resolution compared to previously
developed technologies like positron emission tomography
(PET). To acquaint the non-specialist, the basics of fMRI
will be briefly reviewed as they relate to the study of visual
function in glaucoma.
Increases in neuronal activity are accompanied by
changes in blood oxygenation that give rise to changes in
the MR signal. This blood oxygenation level dependent
(BOLD) signal serves as the basis for a majority of studies
that measure brain function in vivo. Changes in blood
oxygenation were originally found to affect the MR signal in
rat during experiments where the oxygen content of inspired
air was manipulated (Ogawa et al., 1990). Soon thereafter,
several studies utilized the BOLD effect for functional brain
mapping in humans (Bandettini et al., 1992;Frahm et al.,
1992;Ogawa et al., 1992), including the first study of
visually evoked BOLD responses (Kwong et al., 1992). The
BOLD effect occurs for two reasons (Buxton, 2002). First,
local concentrations of deoxyhemoglobin generate magnetic
field gradients along the blood vessels that reduces the MR
signal. Second, increases in neuronal activity result in
decreases in the local oxygen extraction fraction in the
blood that, in turn, causes a corresponding drop in the local
concentration of deoxyhemoglobin. The net reduction of
deoxyhemoglobin during brain activity manifests in an
increase in the MR signal.
BOLD fMRI is the preferred method for retinotopy,
which is the process of determining the correspondence
between a visually selective neuron and its receptive field in
visual space. In the case of V1, mapping the representation
of visual space is straightforward because the cortical
topography is such that adjacent regions along the cortical
surface correspond to adjacent regions in visual space.
Even though the first visual fMRI experiment did not
attempt retinotopic mapping (Kwong et al., 1992), another
early experiment mapped the visually evoked fMRI
response along a cortical ribbon with four topologically
distinct regions (Schneider et al., 1993). Refined retinotopic
mapping was not possible until Engel and colleagues
(Engel et al., 1994) developed phase-encoded retinal
stimulation. In their experiment, an annular contrast-
reversing (8 Hz) checkerboard pattern was presented at the
center of gaze (Fig. 1(A)). This ring expanded in size until
the beginning of the next cycle, when the stimulus returned
to its original size and the period of expansion repeated.
Expanding rings elicit a traveling wave of activity in V1
ARTICLE IN PRESS
Fig. 1. Visual stimuli for FMRI experiments. Several stimuli (A–F) were
used to obtain retinotopic maps of the visual world on the flattened cortex
of the patients. Patients fixated on a stationary target while contrast-
reversing checkerboard patterns (100% contrast; 8 Hz flicker) were
presented in the periphery. All stimuli were presented for 6 cycles of 40 s
each. (A) Expanding rings continually expanded outward from the
fixation point each cycle. (B) Rotating wedges revolved 3601clockwise
around the fixation point each cycle. (C–D) Meridian-mapping stimuli.
The horizontal and vertical meridians were stimulated using ‘‘hourglass’’
and ‘‘bow tie’’ shaped checkerboard patterns that were alternated every
1/2 cycle. (E–F) 161isopter stimuli. A 161arc subtended the superior or
inferior quadrant of the hemifield containing the scotoma. Each arc was
presented alternately with a period of no stimulation every 1/2 cycle. (G)
Scotoma-mapping stimulus. A contrast-reversing checkerboard pattern
was presented to the quadrant of visual space with the scotoma. Patients
viewed the stimulus through the right or left eye in alternating 1/2 cycles.
The fixation target in the corner also served as a cue as to which eye should
be open.
R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–5640

that is phase-locked with the period of the stimulus. As a
result of phase encoding, Engel and colleagues could
distinguish between the cortical representation of the fovea
and that of the periphery.
However, the notion of retinotopic mapping did not fully
mature until visual field sign (VFS) mapping was developed
(Sereno et al., 1995). VFS mapping is typically used to define
the borders of V1, V2, V3, V3a and hV4 on a computa-
tionally flattened representation of the cortical surface (Dale
and Sereno, 1993). In addition to the expanding ring, VFS
mapping employs a rotating wedge stimulus, which is
composed of a contrast-reversing checkerboard pattern that
revolves clockwise around a fixation target for several cycles
(Fig. 1(B)). The borders of visual areas can be distinguished
by reversals in the sign of the phase of the fMRI response to
the rotating wedge. While the size and location of visual
areas can vary greatly between individuals (DeYoe et al.,
1996), experiments using VFS mapping have determined
that fMRI responses to visual stimuli are spatially invariant
within 1.1 mm of cortex (Engel et al., 1997).
VFS mapping methods are not particularly suited for
glaucoma research. Glaucoma patients typically have
localized visual field defects. To measure the extent/
existence of glaucomatous changes in neuronal activity in
V1, the borders of these defects must be mapped on the
cortical surface as a region of interest (ROI). However, the
sluggish hemodynamic response creates a delay between
the instance of visual stimulation for phase-encoded stimuli
and the BOLD response, and thus it is difficult to precisely
determine which region of V1 corresponds to the scotoma.
One possible solution to this problem would be to map the
visual field using static stimuli presented throughout the
visual field. However, this process is too time-consuming
and expensive to be practical. Furthermore, hemodynamic
responses to small visual stimuli are spatially blurred,
making it difficult to localize the borders of the ROI
representing the scotoma.
One alternative to VFS mapping is based on a template-
fitting approach (Duncan and Boynton, 2003). Static
stimuli are first used to map landmarks in V1. Then,
templates are fit to the resulting activity. Finally, the
templates are used to project the borders of the scotoma
onto the flattened cortical surface as a ROI. The technique
takes advantage of the fact that there is a logarithmic
relationship between each hemifield of visual space and its
corresponding cortical representation in V1 (Schwartz,
1980). Template fitting is superior to VFS mapping because
static stimuli do not introduce the temporal delay that is
inherent to phase-encoded stimuli. Moreover, ROIs can be
defined robustly for sub-regions of V1 that do not receive
direct retinal input.
3. Prior investigations of cortical activity in human
glaucoma
A number of studies have demonstrated an ability to
detect glaucomatous damage using multifocal visual
evoked potentials (mfVEP) (Klistorner et al., 1998;
Graham et al., 2000;Hood et al., 2000;Hasegawa et al.,
2001;Goldberg et al., 2002;Hood and Greenstein, 2003;
Thienprasiddhi et al., 2003;Hood et al., 2004;Graham
et al., 2005), PET (Kiyosawa et al., 1989), and single
photon emission computed tomography (SPECT)
(Sugiyama et al., 2006;Yoshida et al., 2006). Each
technique has its limitations. Although mfVEP has been
used successfully to objectively measure neural activity in
vivo, the technique is restricted by the fact that signals
cannot be accurately localized to specific brain regions
(Fortune and Hood, 2003). Furthermore, because the
effective spatial resolution of mfVEP is approximate 71in
the periphery, relatively small peripheral scotomas might
not be detected (Hood and Greenstein, 2003). PET and
SPECT are not practical for repeatedly monitoring
glaucomatous progression because they require radio-
isotopes. Ultimately, the spatial resolution of these
techniques limits their ability to compare cortical activity
with measurements of visual function in a retinotopic
manner.
The spatial resolution of fMRI affords the opportunity
to scrutinize restricted regions of cortex, enabling compar-
isons of diseased and fellow tissue receiving input from the
same eye. Only one other fMRI study of optic neuropathy
compared visual field defects to fMRI responses in V1
(Miki et al., 1996). In that report, a heterogeneous
population of optic neuropathies was studied in patients
ranging from 19 to 70 years of age using traditional
retinotopic techniques. The authors found that there was a
general correspondence between the pattern of visual field
defect and the pattern of the fMRI response. Pathologies
included traumatic optic neuropathy, compressive optic
neuropathy, developmental glaucoma, capsular glaucoma,
secondary open-angle glaucoma, and optic atrophy cause
by sellar tuberculoma or pituitary adenoma. As a
consequence, it is difficult to generalize to POAG. In
addition, the investigators did not make quantitative
comparisons between visual fields and brain responses.
Traditional MRI methods (e.g., T1-weighted imaging)
can already be used to measure the volume of anatomically
distinct visual areas like the LGN (Brodsky et al.,
1993;Fujita et al., 2001). However, fMRI has two key
advantages over traditional MRI for measuring glaucoma-
tous changes in neuronal activity. First, fMRI can measure
degeneration from visual areas that can only be defined
using functional criteria, which is important considering
the variability of visual areas between individuals (Duncan
and Boynton, 2003). Second, fMRI can measure functional
activity related to specific visual pathways (Kleinschmidt
et al., 1996;Demb et al., 1998;Wandell et al., 1999;
Bedwell et al., 2004;Schneider et al., 2004;Liu and
Wandell, 2005). For example, anatomical MRI methods
can measure the total volume of LGN, but only fMRI
can distinguish between the functional activity associated
with specific visual pathways (Schneider et al., 2004).
Recent developments in fMRI analysis allow for the
ARTICLE IN PRESS
R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–56 41

computational projection of visual scotomas onto the
flattened representation of V1 (Duncan and Boynton,
2003). These analysis tools were incorporated in the
following study, which was designed to quantify the
correlation between fMRI responses and several visual
function tests on a much finer spatial scale than studies
using other brain imaging analysis techniques. Future
fMRI studies will quantify glaucomatous changes in the
central nervous system and function-specific neuronal
activity associated with the three primary visual pathways.
4. Comparing fMRI responses to visual function in
glaucoma
4.1. Methods
4.1.1. Subjects
Six asymmetric POAG patients with one glaucomatous
eye and a less affected ‘‘fellow’’ eye were included. All
patients presented with abnormal visual field results and
glaucomatous appearance of the optic disk based on
stereophoto review in at least one eye. Abnormal visual
fields were defined as a repeatable defect in at least two
consecutive visits. Glaucomatous optic disks were defined
as having either asymmetric vertical cup-to-disk ratio40.2,
rim thinning, notching, excavation, disk hemorrhages, or
nerve fiber layer defects based on masked analysis of
stereoscopic photographs and consensus of two reviewers.
Visual fields were judged on the number of pattern
deviation points (PD) that were significantly different from
the normative database at po0.05 or worse. Patient fellow
eyes had markedly fewer visual field abnormalities relative
to the glaucomatous eye (w
2
,po0.0001). Patients were
evaluated at the Hamilton Glaucoma Center at the
University of California San Diego (UCSD) between July
of 2004 and August of 2005. The patients in this cross-
sectional study were recruited from a longitudinal study
designed to evaluate the optic nerve structure and visual
function in glaucoma (Diagnostic Innovations in Glauco-
ma Study—DIGS). A summary of relevant patient data
appears in Table 1.
4.1.2. Criteria for inclusion and exclusion
All patients underwent complete ophthalmologic exam-
ination including slitlamp biomicroscopy, intraocular
pressure measurement, dilated stereoscopic fundus exam-
ination, and stereophotography of the optic nerve heads.
Good quality simultaneous stereoscopic photographs were
obtained for all patients.
Informed consent was obtained from all patients after
the nature and possible consequences of the study were
explained, and the UCSD Internal Review Board approved
all methods pertaining to the use of human subjects. The
study adhered to the declaration of Helsinki for research
involving human subjects.
A neuroradiologist, reviewed the anatomical reference
volumes and found no evidence of non-glaucomatous
pathology that could present as glaucoma. Patients were
also screened for standard MRI exclusion criteria: no
conditions/medications known to affect cerebral metabo-
lism, no metal in the body that could not be removed, and
no history of claustrophobia.
4.1.3. Visual function testing
Standard automated perimetry (SAP) was conducted in
each patient using the 24–2 Swedish Interactive Threshold
Algorithm (SITA) (Carl Zeiss Meditec Inc., Dublin, CA) of
the Humphrey Visual Field Analyzer (HFA). The program
uses 52 test locations presented in a grid, and 61stimuli are
generated using conventional parameters (Goldmann
size III stimulus, 10 cd/m
2
white background). An optimal
lens correction was placed before the tested eye, and the
fellow eye was occluded with an eye patch. Exclusion
criteria for visual fields included unreliable visual fields
(defined as fixation loss, false negative, and false positive
errors X25%, unless false negatives could be explained by
ARTICLE IN PRESS
Table 1
Summary of patient data
Patient 1 2 3 4 5 6
Age 58 76 68 77 74 63
Sex Male Female Female Female Male Male
Eye
a
OD
F
OS
G
OD
G
OS
F
OD
F
OS
G
OD
G
OS
F
OD
G
OS
F
OD
G
OS
F
Acuity
g
20/20 20/25 20/40 20/20 20/30 20/20 20/30 20/30 20/20 20/20 20/30 20/20
Iop
g
17 16 14 14 15 14 14 15 18 18 12 18
Sph. 1.25 3.0 1.75 0.25 6.25 6.5 1.5 1.75 1.5 0.75 1.5 0.75
Cyl. — 0.75 0.25 0.5 — — 1.25 0.75 — 0.75 0.75 —
Surgery
b
TP, L TP TP TP — TP CE CE CE — TP, TE, CE —
SAP
c
MD
d
(dB) 0.3 6.23 17.04 4.78 4.91 8.69 6.43 2.77 2.41 0.66 14.51 0.80
PSD(dB) 1.38 8.57 12.89 6.10 1.97 6.24 3.83 1.30 1.94 1.55 10.83 1.36
Bold p-value

o0.0001 0.005 0.03 0.044 o0.0001 o0.0001
a
G¼Glaucomatous eye, F ¼Fellow eye.
b
TE ¼Trabeculectomy, TP ¼Trabeculoplasty, CE ¼Cataract extraction with intraocular lens implant, L ¼Lasic.
c
SAP ¼Standard automated perimetry.
d
MD ¼Mean deviation scores, PSD ¼Pattern standard deviations scores.

p-values indicate the significance of the BOLD signal for voxels within the cortical representation of the scotoma.
R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–5642

significant field loss). All patients in this study had five or
more standard visual fields. Results were compared to the
HFA normative database.
4.1.4. Evaluation of stereophotographs
Evaluation of glaucomatous structural damage to the
optic disk was based on assessment of simultaneous
stereoscopic optic disk photographs (Nidek Stereo Camera
Model 3-DX, Nidek Inc, Palo Alto, CA). Two experienced
graders evaluated the photographs, and each grader was
masked to the patient’s identity, the other test results, and
the other grade. Discrepancies between the two graders
were resolved either by consensus or by adjudication by a
third experienced grader.
4.1.5. General fMRI methodology
BOLD fMRI was used to infer neuronal activity in the
contralateral hemisphere of these patients. FMRI images
were acquired at the Center for Functional Magnetic
Resonance Imaging at UCSD using a General Electric 3.0
Tesla HD Signa Excite scanner with an 8-channel brain
coil. Visual stimuli were presented through a pair of fiber
optic Silent Vision goggles (Avotec Inc., Stuart, FL). The
general specifications of the visual presentation system
follow: field of view ¼30 H 23 V degrees; focus76D;
maximum luminance ¼28.9 cd/m
2
; resolution ¼1024 H 
768 V, 60 Hz refresh rate. Visual stimuli were generated
using the Psychophysics Toolbox (Brainard, 1997;Pelli,
1997) for Matlab (Mathworks, Natick, MA) on a Power-
Book G4 computer (Apple, Cupertino, CA).
Each patient participated in up to three 1-h scanning
sessions that included both functional and anatomical
scans. A high-resolution anatomical scan was obtained
using a standard T1-weighted gradient echo pulse sequence
(FSPGR, 1 11 mm resolution). This anatomical scan
provided a reference volume anatomy for each patient.
Up to eight functional scans were acquired from each
patient during each session. For each functional scan, 130
temporal frames were acquired using a low-bandwidth
echo planar imaging (EPI) pulse sequence lasting 260 s
(TR ¼2s, TE¼30 ms, flip angle ¼901, 24 coronal slices
of 3 mm thickness and 3 3 mm resolution, FOV ¼20 cm).
The first 10 temporal frames (20 s) were discarded to avoid
magnetic saturation effects. Scanning sessions ended with
an anatomical scan that was used to align functional data
across sessions to a patient’s reference volume. Cortical
flattening techniques and methods for projecting functional
data onto the flattened representation have been described
in detail elsewhere (Duncan and Boynton, 2003). The
occipital pole was flattened initially, and V1 was re-
flattened after the borders of visual areas (V1, V2, V3) were
defined using traditional retinotopy (Sereno et al., 1995).
4.1.6. FMRI stimuli
In the first scanning session, standard binocular stimuli
were used to project the visual world onto a flattened
representation of the cortex in retinotopic coordinates.
During a given scan, patients viewed either an expanding
ring (Fig. 1(A)) or a rotating wedge (Fig. 1(B)) made from
a contrast-reversing black and white checkerboard pattern
(100% contrast; 8 Hz flicker). Stimuli were presented at the
center of the screen on a mean gray background, and
patients fixated on a target (0.2510.251) at the center of
the screen. The width of the expanding rings was roughly
1/6 of the eccentricity, and the polar angle of the wedges
was 451. The rings expanded at 0.21/s and the rotating
wedges moved at an angular velocity of 91/s. Expanding
rings and rotating wedges were presented for 6 and 1/2
cycles of 40 s each. Data from the first 1/2 cycle was
discarded to avoid magnetic saturation effects. In addition
to the rings and wedges, the horizontal and vertical
meridians were mapped using alternating ‘‘hourglass’’
and ‘‘bow tie’’ shaped checkerboard patterns (Figs. 1(C)
and (D), respectively). Each square was 1 11of visual
angle. Meridian-mapping stimuli were composed of two
mirror-symmetric, triangular regions spanning 901of polar
angle about the meridian. During a given scan, the two
meridian-mapping stimuli were alternated every 20 s for 6
and 1/2 40 s cycles (including the discarded 1/2 cycle). Each
of three retinotopy stimuli was repeated twice for a total of
six functional scans during the first session. The stimulus
period (20 s on/off) and the temporal frequency of the
contrast-reversing checkerboard (8 Hz) were selected from
values known to elicit a maximum BOLD response from
V1 (DeYoe et al., 1994;Engel et al., 1994;Sereno et al.,
1995;Engel et al., 1997;Tootell et al., 1998).
Visual areas in occipital cortex (V1, V2, V3, V3a, and
hV4) represent either one quadrant or one hemifield of
visual space. Thus, the meridian-mapping stimulus evokes
a pattern of BOLD activity in the flattened cortical
representation that defines the borders of these visual
areas. Additionally, BOLD responses to the rotating wedge
reverse temporal phase at the borders of these visual areas.
The borders of visual areas were defined by the experi-
menter based on the pattern of BOLD activity to these
stimuli.
In the second scanning session, the cortical representa-
tion of a 161isopter in the affected left or right visual
hemifield was measured. Binocular stimuli were made from
contrast-reversing arcs that extended through the superior
and inferior quadrants of the glaucomatous visual field
(Figs. 1(E) and (F), respectively). Arcs were composed of
contrast-reversing checkerboard patterns with a radius of
161of visual angle and a width of 2.71(determined as the
average of outer and inner radii). Each square subtended
7.51of polar angle. Patients were instructed to fixate on a
target (0.2510.251) positioned at one corner of the screen
while one arc was presented in the periphery. For each
scan, one arc was presented alternately with a period of no
stimulation (mean gray screen) every 1/2 cycle (20 s). Four
scans were conducted measuring responses to the 161arc in
both the superior and inferior quadrants, yielding a total of
8 scans. Responses to the two arcs were projected onto the
flattened representation of V1, and the responses were
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R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–56 43

averaged to obtain the representation of the 161isopter for
the hemifield of interest.
In the third scanning session, patients fixated a target in
one corner of the screen while a ‘‘full-field’’ contrast-
reversing checkerboard pattern was presented to the
quadrant of visual space with the most visual loss
(Fig. 1(G)). Each square spanned 1 11of visual angle.
However, unlike sessions one and two, where all viewing
was binocular, patients viewed this scotoma-mapping
stimulus monocularly through their right or left eye in
alternating epochs of 20 s (equivalent to one half-cycle).
The shape of the fixation target served to direct patients as
to which eye should be open. Patients controlled mono-
cular viewing by winking. Non-compliance could poten-
tially introduce random noise in the fMRI responses to the
stimuli. Such noise, however, would not seriously affect
the results unless the patient opened the incorrect eye
perfectly out of phase with the stimulus. To insure patient
compliance, the experimenter monitored the patient’s eye
movements via an infrared camera installed in the video
presentation system. Eye position was recorded using iView
dark-pupil eye tracking software (SMI, Teltow, Germany).
Eye traces were processed in accordance to protocols
developed previously (Krauzlis and Miles, 1996). Velocity
and acceleration were computed by applying a 29-point
finite impulse response (FIR) filter (3 dB at 54 Hz).
Acceleration and velocity thresholds were used to identify
the onset of saccades and eye blinks. Deviations in eye
position beyond a certain distance (31) were labeled as
‘‘breaks of fixation’’ and their position was recorded. There
was no change in the stimulus presentation if there was a
break in fixation. The total number of fixation breaks was
determined, and chi-squared tests were used to determine if
there was a difference associated with different monocular
viewing conditions or, if the number of eye movements
varied between the four quadrants of visual space. These
analyses revealed that the direction of fixation breaks was
spatially distributed and not associated with viewing
through the glaucomatous or fellow eye (w
2
, all p40.10).
Furthermore, the number of fixation breaks did not differ
between glaucoma and control subjects (w
2
,p40.10). Thus,
patients were able to fixate well. Patients fully closed each
eye according to directions in the third session, where
monocular viewing alternated between eyes.
4.1.7. Projecting patterns of visual field loss onto the cortex
The SAP visual fields for both eyes of each patient are
presented in Fig. 2. For each glaucomatous eye, corre-
sponding visual fields for SAP were scrutinized, and the
quadrant with the most damage was selected based on the
number of test locations that deviated statistically from
the normative database. The area of the visual field
with the most extensive loss was manually defined
(locations with PD greater than 95% confidence limits).
The integrity of the scotoma was verified for each patient
by looking at visual fields obtained during prior or
subsequent research visits.
Responses to the retinotopy stimuli were fit with
templates, which were then used to project visual scotomas
onto the flattened representation of cortex. The fMRI
responses from the first and second scanning sessions were
fit using conformal mapping techniques to obtain a
template for each patient (Duncan and Boynton, 2003).
Templates were then used to project the portion of the
visual field with the most extensive loss (Fig. 3(A), red line),
onto the flattened representation of V1 (Fig. 3(D), black
line). The projected ROI was later used to restrict the
analysis to cortical regions corresponding to regions of
visual space with most extensive loss.
The template fitting technique was originally developed
to compare cortical magnification to spatial acuity thresh-
olds in normal observers (Duncan and Boynton, 2003). In
that study, the template fitting technique was sensitive
enough to find a within-subject correlation between spatial
acuity at any location in the visual field and the size of the
corresponding cortical representation in cortex. Here, the
technique was modified slightly for scotoma mapping in
glaucoma patients. Templates were derived from a
conformal mapping method developed by Schwartz
(1980). In this approach, two-dimensional visual space is
projected onto the two-dimensional flattened cortex using
the formula w ¼klog(z+a), where zis a complex
number representing a point in visual space, and w
represents the corresponding point on the flattened cortex.
The parameter arepresents the proportion of V1 devoted
to the foveal representation, and the parameter kis an
overall scaling factor. An additional parameter, b, was
added to scale the width of the map. To achieve this, the
real and imaginary components of the projected positions,
w, were separated and the real component was scaled by
parameter b. This modification affords better fits by
sacrificing the preservation of local isotropy. The templates
in this study were composed of four components represent-
ing the 161isopter, the horizontal meridian, the superior
vertical meridian, and the inferior vertical meridian
(Fig. 3). Six parameters describe the template; the overall
size (k), the position (dx, dy), the rotation (da), the foveal
representation (a), and the width (b).
Templates were fit to the fMRI activity map by adjusting
the parameters to maximize the image intensity (i.e., the
line-integral) under the projected curves. Parameter values
from the best-fitting template were obtained using a
nonlinear optimization technique in Matlab. First, straight
lines that roughly fit fMRI responses to the superior and
inferior vertical meridians were superimposed onto the
retinotopy data (Fig. 3(B), yellow lines). Second, the
intersection of these lines was computed and that point
was designated as the fovea (Fig. 3(B), red dot). Third, a
point corresponding to the peak activity for the horizontal
meridian-mapping stimulus was selected (Fig. 3(B), blue
dot). Then, the software automatically generated a generic
template that was rotated and shifted to fit the data based
on the parameters selected (Fig. 3(C)). All the other
parameters in the template (a,b, and k) were set to default
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R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–5644

values that roughly corresponded to the average V1 size
(775 mm
2
), which was determined by averaging the area
from 10 healthy control subjects in a previous experiment
(Duncan and Boynton, 2003). Note that varying the size of
the initial template does not have an affect on the final fit
(Duncan and Boynton, 2003). The template was then fit to
the data using a two-stage optimization routine (Fig. 4). In
the first stage, each individual model parameter was
optimized to fit the template to all activity maps simulta-
neously. In the second stage, the best-fitting template was
generated by simultaneously fitting all parameters to the
activity maps. Note that the parameter for the overall size
of the template, k, was excluded from the final optimization
because, without a constraint on the size of the template,
the fit would converge to the trivial condition of a single
point over a location of maximum amplitude.
The optimized fits for the patient 6 in Fig. 2 are
superimposed upon the grayscale activity maps in Fig. 4.
The colored lined superimposed upon the patterns of
activity show the locations, projected using parameters
from the best-fitting template, of the 161isopter and
meridian-mapping stimuli. Each component is color coded
to match the scheme outlined in the inset. Fig. 4(A)
displays fits to the 161isopter stimuli, and Figs. 6(B–D)
display fits to the meridian-mapping stimulus. Fig. 4(E)
shows the best-fitting template for all stimuli superimposed
upon the responses to the meridian-mapping stimulus.
Once the best-fitting template was generated for a
given patient, the visual scotoma could be projected
onto the flattened cortex (Fig. 4(D)). Further analysis of
the BOLD signal was restricted to voxels within the
projected ROI.
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Fig. 2. Visual field maps for SAP. Visual field defects were measured using standard automated perimetry. Each panel depicts the pattern deviation plot of
each eye for one patient. The deviation from the age-corrected normal values is measured in decibels (dB), and adjusted for any shifts in overall sensitivity.
Pattern deviation (PD) symbols indicate the statistical significance of the deviation at each point. Darker symbols represent more significant deviations
from the normal thresholds. The experimenter identified a contiguous portion of the visual quadrant with the greatest loss (bold lines). Patients are listed in
order from the strongest to the weakest asymmetry as determined by the difference in pattern standard deviation scores (A–E, respectively). Asterisks
mark the quadrant with the least amount of visual loss in the glaucomatous eye, which served as a control. The pattern standard deviation (PSD) global
score is also given below each graph.
R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–56 45

ARTICLE IN PRESS
Fig. 3. Projecting regions of visual space onto V1. (A) Pattern deviation plot of SAP thresholds for the glaucomatous eye of the Patient 6 from Fig. 2.A
region of visual space in the upper left quadrant was selected with the intent of projecting that region onto the flattened cortex (bold line). (B) The initial
stages of fitting retinotopic fMRI data. FMRI responses to the meridian mapping stimuli are pictured in gray. White pixels denote responses that were in
phase with stimulation of the vertical meridian. Black pixels denote responses that were in phase with stimulation of the horizontal meridian. The
experimenter selected two points along the activity patterns for each vertical meridian (yellow dots). The software automatically fit lines (yellow lines) to
the points selected and then computed the intersection of those points (red dot), which was assumed to be near the representation of the fovea. The
experimenter then selected a point along the activity pattern of the horizontal meridian (blue dot). The software automatically connected the foveal
representation with the point along the horizontal meridian (green line). A generic template was positioned and rotated according to the values computed
during this stage of the fitting process. (C) Template superimposed upon fMRI responses to the meridian-mapping stimuli. After the generic template was
generated, each component of the template was fit using an iterative least-squares method (see Fig. 3). The template was then used to project regions of
visual space onto the flattened cortex. (D) The projected scotoma as a ROI. The best-fitting template is presented along with the projected scotoma (black
lines) atop the fMRI responses to the scotoma-mapping stimulus (see Fig. 5).
Fig. 4. Cortical responses to retinotopic mapping stimuli. Templates derived from conformal mapping techniques were fit to the fMRI activity maps
generated by retinotopy stimuli. Grayscale images show BOLD activity maps on the flattened representation of cortex. Templates were fit to maximize the
image intensity under the projected curves. Colored lines are components of the best-fitting template. Components are color coded to match the schematic
of visual space in the inset. (A) Response to the 161isopter stimuli presented in the left visual hemifield. (B) Responses to stimulation of the superior
vertical meridian. (C) Responses to stimulation of the inferior vertical meridian. (D) Responses to stimulation of the horizontal meridian. (E) The best-
fitting template is superimposed upon responses to the vertical meridian stimulus. Ventral and dorsal regions of occipital cortex are labeled. Note that
(B–D) depicts three different fits to fMRI responses to the same visual stimulus. The sign of the phase in (D) is flipped relative to (B) and (C).
R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–5646

4.1.8. Comparing visual fields to fMRI data
The pattern standard deviation (PSD) scores on the SAP
printout provides a global index that indicates a localized
component in the deviation in decibels from the age-
corrected normal values somewhere within the visual field.
A comparison was made between the BOLD responses
to the scotoma-mapping stimulus and the PSD from
visual field testing for each patient. Increasingly positive
PSD scores indicate a greater deviation from normal
vision due to glaucoma. The PSD from both eyes of each
patient was subtracted to yield a difference score (i.e.,
PSD
DIF
¼PSD
GLAUCOMATOUS
—PSD
FELLOW EYE
). The
PSD
DIF
scores were compared with the mean projected
amplitudes from the scotoma-mapping experiments for all
patients. Note that the amplitude of the BOLD response is
a difference score itself, namely, the difference between
viewing through the glaucomatous and fellow eyes. The
BOLD response is not an absolute measure of neuronal
activity for viewing through either eye, but rather a relative
measure of the difference in neuronal activity associated
with viewing through the glaucomatous vs. fellow eye.
4.1.9. Pointwise comparison of fMRI responses and
visual fields
In addition to the analysis using global PSD
DIF
scores, a
‘‘pointwise’’ comparison of thresholds throughout the visual
field to fMRI responses in corresponding locations of V1
was also conducted. Apart from improving the statistical
power by increasing the number of data points, this analysis
also allows the relationship between visual function and
brain responses to be assessed at retinotopically specific
locations in the visual field rather than at the global level.
The pointwise analysis was restricted to the visual
quadrant with the most damage as defined by the SAP
visual field results. For each patient, 12 test locations from
the quadrant of interest were projected onto the flattened
representation of V1 via the best-fitting template (excluding
the blindspot and the two most temporal test locations).
The diameter of the projected test locations was 61. The
fMRI data was compared to visual function thresholds at
each of the 12 test locations for all patients. The PD value
at a given test location for the fellow eye was subtracted
from that for the glaucomatous eye. The resulting PD
DIF
score was then compared to the amplitude of the BOLD
response for the voxels within the corresponding ROI. The
total possible number of comparisons was 72 (6 patients x
12 test locations). However, five data points were omitted
from the final analysis because reliable projected ampli-
tudes from the fMRI data could not be obtained (an effect
of having less than one voxel in a given ROI). Additionally,
two outliers with low statistical leverage were identified and
removed by plotting confidence intervals around the
regression residuals (Osborne and Overbay, 2004).
4.1.10. Control experiments
Three control experiments were performed to determine
whether any correlations could have occurred by chance.
All three control experiments used data from the main
experiments. However, the analysis for each control
experiment was different.
In the first control experiment, the objective was to
determine whether a correlation existed between the
fMRI data and visual function data from the quadrant
with the least amount of vision loss (as determined by the
number of significant PD values). These quadrants are
marked with asterisks in Fig. 2. A correlation was not
expected because the PD
DIF
values for the relatively
unaffected quadrant should not predict the observed
BOLD responses. The analysis was conducted using the
same BOLD data from the pointwise ROIs. However, for
each patient, the BOLD data was randomly paired to the
PD
DIF
value from a random location in the less affected
quadrant.
In the second control experiment, a statistical boot-
strapping method was used to compute the probability
of observing a correlation in the original pointwise
comparison by chance (Henderson, 2005;Hesterberg
et al., 2005). The population of PD
DIF
values was
randomly paired with the BOLD data from the pointwise
ROIs. The correlation for this random sample was
computed, and the process was repeated 10,000 times.
The odds of getting the observed correlation were
computed as follows. First, the number of random
correlations that exceeded the observed correlation was
counted. The total number of sample correlations then
divided that number. The resulting p-value indicates the
odds of getting the correlation by chance given the sample
population.
The third control experiment was conducted to deter-
mine whether between-patient variability or within-patient
variability could account for any correlations observed in
the original pointwise comparison. A statistical boot-
strapping approach similar to that of the second control
experiment was used. For each patient, the BOLD data
from the pointwise ROIs was randomly paired with the
PD
DIF
values from the quadrant with the greatest visual
loss. Unlike the second control experiment, random
pairings between the visual function and fMRI data
occurred within patients. Correlations were computed
for 10,000 random pairings, and the probability of
getting the observed correlations was computed. If the
majority of the total variability in the observed correla-
tions could be attributed to between-patient variability,
then randomizing the data within patients should have no
affect on the correlation values. By contrast, if within-
subject variability accounts for a significant portion of
the total variability in the observed correlations, then
the correlation value, r, should be greatly affected by
randomization. Within-patient variability was expected to
account for a significant portion of the variability in the
observed. Thus, compared to the random correlations
generated by the bootstrapping process, it was predicted
that getting the observed correlations by chance was
unlikely.
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4.2. Results
4.2.1. FMRI data from a single patient
The glaucoma patients in this study had varying degrees
of asymmetry between the glaucomatous and fellow eyes.
This variability allowed us to make comparisons between
visual field defects and brain activity across patients. The
visual fields for each eye of the glaucoma patients are
presented in Fig. 2. To better illustrate the methods and
results, the data from the patient 6 in Fig. 2 is used as an
example. This patient had severe visual loss in the right eye,
particularly in the superior-nasal visual field. The bold line
superimposed upon the data for the right eye describes the
scotoma selected by the experimenter, which includes PD
points that were statistically different from normal (95%
confidence level). The complementary visual quadrant for
the left eye is relatively normal. The experimenter restricted
visual scotomas to one visual quadrant because the field of
view available during the MRI experiments was limited.
FMRI responses to retinotopic mapping stimuli for this
patient appear in Fig. 4. The grayscale images in each panel
show BOLD activity maps (i.e., projected amplitudes) on
the flattened representation of the right hemisphere. The
flattening in each panel is identical, but the pattern of
BOLD activity depends upon which visual stimulus was
presented. Bright regions correspond to locations where
changes in BOLD signal correlate positively in time with
the stimulus phase (e.g., ‘‘on’’ vs. ‘‘off’’). The ring-shaped
activity (Fig. 4(A)) indicates the BOLD response to the 16
%
o
isopter stimuli presented in the left visual hemifield.
Responses to the arc presented in the left-superior visual
field were averaged with responses to the arc presented in
the left-inferior visual field. This ring-shaped pattern of
activity extends beyond V1 into dorsal and ventral
extrastriate cortex (V2, V3, V4, and V3a). The red line
represents the corresponding component of the best-fitting
template for that hemisphere. Similarly, responses to the
meridian-mapping stimuli are depicted in Fig. 4(B–D). The
‘‘bow tie’’ and ‘‘hour glass’’ checkerboard patterns that
make up the meridian-mapping stimulus were presented in
alternation every 20 s, resulting in one BOLD activity map.
The superior (Fig. 4(B)) and inferior (Fig. 4(C)) vertical
meridians were fit independently and the best-fitting
components from the template are superimposed upon
the data (light and dark green, respectively). The sign of the
BOLD responses were reversed to fit responses to stimula-
tion of the horizontal meridian (Fig. 4(D)). The final best-
fitting template for this patient is superimposed upon the
BOLD responses to the vertical meridian (Fig. 4(E)).
Once the best-fitting template was computed, viewing
through the glaucomatous and fellow eye could be
compared for this patient (Fig. 5, patient 6). The average
BOLD responses to the scotoma-mapping stimulus in the
third session are projected onto the flattened representation.
The patient alternately viewed the stimulus through the
glaucomatous or fellow eye in 20s intervals. The phase of
the BOLD response in relation to the temporal phase of
monocular viewing is indicated by the color of the pixels. In
this example, yellowish pixels correspond to voxels where
viewing through the glaucomatous eye resulted in a larger
amplitude signal than viewing through the fellow eye.
That is to say, fMRI responses were ‘‘in phase’’ with the
glaucomatous eye. Bluish pixels correspond to voxels that
were in phase with the fellow eye. The best-fitting template
for this patient is superimposed upon the BOLD responses
along with a projection of the visual scotoma (black line).
Most of the voxels within this ROI are blue, which indicates
that BOLD responses in the ROI were in phase with viewing
through the fellow eye. The mean projected amplitude of
these voxels indicates the strength of the response. Viewing
through the fellow eye was predicted to generate larger
responses than viewing through the glaucomatous eye. For
this patient, the mean projected amplitude across eight scans
was significantly different from zero in the direction
predicted by the hypothesis (t-test, po0.0001). Hence, the
pattern of visual field loss observed using SAP is reflected by
the pattern of BOLD activity in V1.
4.2.2. FMRI responses correlate with visual function
thresholds
FMRI responses to the scotoma-mapping stimulus
are plotted as the percent change in BOLD amplitude
for all six patients (Fig. 6(A)). FMRI responses from Fig. 5
appear here with the same patient numbering scheme.
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Fig. 5. Cortical responses to scotoma-mapping stimuli. Average BOLD
response to the scotoma-mapping stimulus is projected onto the flattened
representation. Pixel color indicates the phase of the BOLD response
relative to the phase of monocular viewing. For patients where greater
vision loss occurs in the right eye (patients 2,4,5 and 6), bluish pixels
correspond to voxels that were in phase with viewing through the fellow
eye. Yellowish pixels correspond to voxels that were in phase with viewing
through the glaucomatous eye. For patients where greater vision loss
occurs in the left eye (patients 1 and 3), yellowish pixels denote voxels that
were in phase with viewing through the fellow eye, and bluish pixels
represent voxels in phase with the viewing through the glaucomatous eye.
The best-fitting template (colored lines) and the ROI (black line) are
superimposed on the data. Voxels within the ROI indicate a greater
BOLD response when viewing through the fellow eye.
R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–5648

Visual scotomas for each patient were projected onto the
flattened representations of their corresponding hemi-
spheres, and the resulting projections were defined as
ROIs. The mean phase and amplitude of the voxels within
the ROIs were correlated with the stimulus time course to
yield projected amplitudes. Mean amplitudes were ob-
tained by averaging projected amplitudes across eight scans
per patient. Because visual field loss occurs in different eyes
for each patient, the sign of the BOLD response was
normalized (multiplied by 1 or –1) across patients. In Fig.
6(A), positive numbers indicate that the BOLD signal was
in the predicted direction and viewing through the fellow
eye evoked a larger cortical response than viewing through
the glaucomatous eye. Negative numbers indicate that
viewing through the glaucomatous eye resulted in larger
mean projected amplitudes. All six patients demonstrated a
significantly positive change in the BOLD amplitude,
which denotes that viewing through the fellow eye elicited
a greater fMRI response in V1 (t-test, all po0.05). Thus, in
addition to the agreement between the pattern of visual
field loss and the pattern of BOLD activity in cortex, there
is also a significantly greater response in the ROI for
viewing through the fellow eye.
The BOLD responses to the scotoma-mapping stimulus
and the PSD
DIF
scores from visual field testing were
compared for each patient (Fig. 6(B)). There was a positive
correlation between the magnitude of a patient’s PSD
DIF
score from SAP and the change in their BOLD response
(r¼0.91; p¼0.01). Thus, patients with a large difference
in visual sensitivity between the glaucomatous eye and the
fellow eye also demonstrated a greater difference between
BOLD responses for viewing the scotoma-mapping stimu-
lus through glaucomatous and fellow eyes.
4.2.3. Pointwise comparison of fMRI responses and visual
function thresholds
The results of the pointwise analysis are presented in
Fig. 7. The fMRI data for patient 6 in Fig. 5 is presented
along with the best-fitting template for this patient
(Fig. 7(A)). The location of each projected ROI (black
circles) corresponds to one of 12 test locations in SAP.
Because cortical magnification decreases with increasing
eccentricity from the fovea, ROIs corresponding to test
locations in the periphery are smaller than foveal ROIs.
However, this difference does not present a problem for the
analysis because differences in cortical magnification be-
tween individuals are accounted for by each patient’s
template. PD
DIF
scores from all 12 test locations in the
visual field were each compared to the amplitude of the
BOLD response for the voxels within the corresponding
ROI (Fig. 7(B)). The pointwise analysis revealed a correla-
tion between the change in amplitude of the BOLD response
and PD
DIF
scores derived from SAP (r¼0.53, po0.0001).
This result remained significant when the analysis was
completed with the two outliers (r¼0.34, p¼0.0043).
4.2.4. Control experiments
(1) When the BOLD responses from the pointwise ROIs
were randomly paired with the PD
DIF
scores from the
quadrant with the least amount of vision loss in the
glaucomatous eye, there was no evidence of a correla-
tion between the BOLD responses and the PD
DIF
scores for SAP (r¼0.19, p¼0.13).
(2) Additionally, when the pairing between the BOLD
responses and the PD
DIF
scores from the quadrant with
ARTICLE IN PRESS
Fig. 6. Cortical responses to scotoma-mapping stimuli for all patients. (A)
FMRI responses are plotted as the percent change in BOLD amplitude for
all six patients. The percent change in BOLD amplitude for each patient
was averaged across eight scans. The sign of the fMRI response was
multiplied by 1 or –1 depending on which eye was affected for each
patient. Positive numbers indicate viewing through the fellow eye evokes a
larger response than viewing through the glaucomatous eye. All six
patients demonstrated significantly positive change in BOLD amplitudes.
(B) Comparison between SAP visual fields and cortical responses. FMRI
responses to the scotoma-mapping stimulus were compared to the PSD
scores from visual field testing. The PSD from the fellow eye was
subtracted from the glaucomatous eye to yield a difference score for each
patient (PSD
DIF
). PSD
DIF
scores for SAP were correlated with the
difference in fMRI for viewing through the fellow vs. glaucomatous eyes.
R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–56 49

the greatest loss was randomized, the probability of
getting the observed correlations by chance was
extremely rare (all po0.0001).
(3) BOLD responses and PD
DIF
scores for the quadrant
with the greatest visual loss were also randomly paired
within each patient. Relative to the population of
random correlations, the probability of getting the
observed correlation for SAP (po0.0001) was rare.
Hence, there is evidence to suggest a significant
portion of the total variability in the observed
pointwise correlations is due to the variability within-
patients.
5. Sources of potential error
5.1. Sources of potential error in template fitting
There are potential sources of error in the template
fitting approach that have been discussed in detail else-
where (Duncan and Boynton, 2003). In brief, the template
is a simplified summary of the fMRI data, which is
necessarily imperfect. Templates describe the basic features
of the retinotopic map by minimizing the many sources of
noise in the signal. It has been previously shown that these
sources of noise do not introduce systematic biases into the
mapping procedure (Duncan and Boynton, 2003). Some of
these issues are discussed below.
5.1.1. The visual stimulus and the reliability of the fitting
procedure
In theory, cortical magnification may have affected
estimates of the representation of the 161isopter. Increas-
ing the width of the 161isopter stimulus expands the region
of activation in V1. Due to the increased representation of
the fovea in V1, this expansion of activity would be larger
near the foveal representation, which could bias the
estimates of the representation of the 161isopter toward
the fovea. A previous study using healthy observers
demonstrated that the width of such stimuli does not have
a significant impact on the template fitting approach
(Duncan and Boynton, 2003). In that study, fits to the
activity patterns elicited by rings with very different widths
were nearly identical, indicating that estimates of peak
activity correspond to the actual stimulus representation
on the cortex.
In general, the fMRI response is reliable and repeatable
within a 3 33 mm voxel (Garcia-Alvarez et al., 2006).
Unfortunately, there were not enough subjects or repeats in
the current study to do a split-half reliability test. Never-
theless, a previous study comparing cortical magnification
in V1 to visual acuity determined that the template fitting
method was reliable and repeatable using nearly identical
stimuli (Duncan and Boynton, 2003). In that study, the
correlation between acuity and cortical magnification was
significant for both hemispheres/hemifields when analyzed
independently. The correlation was also significant using
both the first and last half of the fMRI sessions. These two
split-half reliability tests indicate that the location of the
fMRI response to the visual stimuli and the template fitting
method are repeatable and reliable.
A majority of the activity visible in the flattened
representation could be attributed to hemodynamic blur-
ring, a spatially diffuse response that is roughly Gaussian
and is not spatially biased in a manner that could affect the
fits. Hemodynamic blurring originates from multiple
sources. First, the BOLD signal is known to extend beyond
the focus of neuronal activation (Grinvald et al., 1994).
ARTICLE IN PRESS
Fig. 7. Pointwise comparison of fMRI data and visual thresholds. (A) ROIs for the pointwise comparison. Twelve individual ROIs for each patient were
derived from twelve test locations from the automated perimetry. For each test location, a 61-diameter region of visual space was projected onto the
flattened representation of cortex using the best-fitting template for each patient. The amplitude of the BOLD signal for voxels within the ROI was
compared to corresponding points from the automated perimetry. (B) Pointwise correlation between fMRI data and SAP. The difference between PD
values for the glaucomatous and fellow eyes were computed for 12 test locations within the damaged visual quadrant. These difference scores were
compared to the percent change in BOLD amplitude of the voxels within the corresponding ROI. Each point represents a comparison between the change
in BOLD amplitude within a single ROI to the PD difference score for a single test location in one patient. There was a pointwise correlation between the
fMRI data and SAP data. The color of each point indicates the responses from each patient.
R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–5650

Second, the spatial sampling of the fMR images
(3 33 mm resolution) exaggerates the extent of the
signal via partial volume effects. Finally, to smooth
the response on the flattened cortex, parameter maps on
the flattened representation of the cortex were blurred with
a Gaussian filter (width at half-height ¼1/e). These sources
of spatial blurring only affect the extent of measured
activity, but not the location of the peak. Accordingly, the
error associated with estimating a point in visual space
from a point on the flattened representation has been
shown to be less than 74.6 min of visual angle at 1.51
eccentricity and 727 min at 121(Duncan and Boynton,
2003).
5.1.2. Distortions in the cortical flattening technique
An iterative method was used to project locations from
the 3D anatomical reference volume of each patient’s brain
onto a 2D surface (Wandell et al., 2000). Minor distortions
are inherent in this process because the surface of the brain
is not topologically equivalent to a plane. A plane better
approximates smaller areas of a smooth curved surface
than it does larger areas. Therefore, flattening the smallest
region possible minimized distortions. The flattening
technique results in approximately 710% distortion with
a roughly equal amount of compression and expansion
(Duncan and Boynton, 2003). Hence, it is unlikely that this
method of cortical flattening would introduce a systematic
bias in the estimates of V1 topography in glaucoma
patients.
5.1.3. Fixation losses
Several steps were taken to ensure that patients were
fixating appropriately. Although patients did not report
any difficulty with fixation using either eye, the fixation
target in the fMRI experiments was larger than used
typically with standard diagnostic tests. Moreover, there
was no indication with eye tracking that the subjects had
any difficulty performing the fixation task as instructed.
Finally, acuity in all six glaucoma subjects was 20/40 or
better in both eyes.
It is highly unlikely that fixation losses had a negative
impact on the results. Failure to fixate properly during the
fMRI portion of the experiment theoretically could result
in a lack of clear retinotopy and a weaker correlation
between fMRI and structure/function. Moreover, fixation
losses add noise to the fMRI responses, reducing the
strength of the correlation. Hence, it is highly likely that
the correlation between fMRI response and structure/
function is greater than observed.
5.2. Sources of error in perimetric testing
The scotomas in all of the patients were repeatable
across multiple visits. However, the first patient recruited
(patient 1, Fig. 2) was originally tested using kinetic
perimetry with the Octopus 101 (Haag Streit International,
Koeniz-Berne, Switzerland). Kinetic perimetry indicated a
scotoma in the inferior-nasal quadrant of the right eye.
After kinetic perimetry, the patient participated in fMRI
scanning, but further testing with SAP revealed a more
prominent scotoma in the inferior-temporal field of the
same eye (no visual field defect was detected at this location
using kinetic perimetry). Because the patient was unavail-
able for further fMRI scanning, the original scotoma was
used in the analysis. The visual field results showing
inferior temporal and inferior nasal damage for this patient
were also repeatable in subsequent visits with SAP. Due to
the potential discrepancy between kinetic perimetry and
SAP visual fields were evaluated with SAP.
To avoid confusion with the blindspot, visual scotomas
were either located in the hemifield opposite the blindspot,
or at least 61away from the horizontal meridian. The
blindspot in normal observers is located about 151medial
to the fovea and roughly 51in diameter. When measured
using fMRI, the size of the blindspot on the flattened
representation of visual cortex depends on the correlation
threshold (Tootell et al., 1998). The template mapping
techniques were used to determine the area of the region of
cortex dedicated to the blindspot. The area of cortex
dedicated to the blindspot for the patients in this study was
3.271.2 mm
2
. For the patient with the smallest scotoma,
the area of the representation of the blindspot was only 6%
of that for the scotoma. For the patient with the largest
scotoma, the area of the representation of the blindspot
was only 1% of that for the scotoma. Note that the
representation of the blindspot did not intersect the
representation of the scotoma, and therefore voxels within
the blindspot representation were not included in the
analysis. Therefore, the cortical representation of the
blindspot contributes little to the fMRI response compared
to the representation of the visual scotomas.
5.3. Variability related to small sample sizes
The population of patients in this study had varying
degrees of visual field loss. Originally, patients with
extreme asymmetries were to be recruited (i.e., completely
blind in one eye and no visual loss in the fellow eye), but
such patients proved to be less common than anticipated.
Nevertheless, the gradual continuum of asymmetries across
patients proved to be an advantage. A lack of variability in
the sample population would have prohibited us from
detecting evidence of a correlation between visual fields
and cortical responses in the between-subjects analysis.
Still, the small number of strongly asymmetric patients
encountered ultimately limited the sample size.
The increased sample size of the pointwise analysis
resulted in significant correlations between cortical re-
sponses and results from visual function testing. However,
the Pearson’s correlation statistic appeared to be dimin-
ished relative to the between-subjects test, even though this
difference was not statistically significant (p¼0.113).
The apparent difference may have occurred because the
two analyses used (1) a different number of ROIs and
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R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–56 51

(2) a different number of test locations in the visual
function tests. The between-subjects analysis used one large
ROI whereas the pointwise analysis used 12 ROIs.
Additionally, the between-subjects analysis compared the
difference between global PSD scores for the whole visual
field, whereas the pointwise analysis compared the differ-
ence between multiple PD scores within one quadrant. The
global PSD score is not as spatially precise as the individual
PD values. However, the lack of spatial resolution of the
global PSD may be countermanded by the greater visual
area contributing to the score.
While a larger number of subjects would improve
quantitative estimates of the correlation between glauco-
matous optic neuropathy and cortical damage in the
current study, it is unlikely that an increase in subjects
would change the results or the means by which the
scotoma-mapping technique will be applied in future
studies. The objective of this study was to demonstrate
that the scotoma-mapping technique can be used to
evaluate the extent of cortical damage in human glaucoma.
More subjects are needed to provide better estimates of the
extent of the damage at various stages of glaucoma.
6. Visual sensitivity predicts cortical responses in a
retinotopic fashion
A ROI-based approach was used to compare fMRI
responses and visual sensitivity thresholds at different
locations in visual space. The difference in visual sensitivity
between eyes was correlated with the difference in fMRI
responses to visual stimulation of each eye. This study is
the first to compare visual function to cortical function at
specific and multiple locations throughout the visual field.
The results from this study suggest that the ROI template-
fitting technique can be used to quantify the effects of
POAG on post-retinal mechanisms.
The correlation in the pointwise analysis supports the
prediction that visual field thresholds in POAG are related
to cortical responses in a retinotopic fashion. Three control
experiments were conducted to test whether the correla-
tions in the pointwise analysis could have arisen by chance.
In one control experiment, the BOLD responses for all of
the patients were randomly paired with the PD
DIF
scores
from a different patient. A statistical bootstrapping
procedure indicated that the probability of getting the
observed correlations by chance was unlikely. While this
result assured us that the observed correlations were not
spurious, the experiment has little to say about the
retinotopic organization of the cortical responses. The
results of the remaining control experiments bear directly
on this hypothesis.
A second control experiment randomly paired BOLD
responses from the pointwise ROIs to PD
DIF
scores from
the quadrant with the least amount of visual loss. There
was no correlation between the BOLD responses and
PD
DIF
scores for SAP. These results support the notion
that the relationship between cortical responses and visual
function is retinotopic, and not merely a function of the
overall visual ability of the patient.
In the third control experiment, the pairing between the
PD
DIF
scores and the BOLD responses from the pointwise
ROIs was randomized within patients. Statistical boot-
strapping revealed that within-subjects randomization had
a significant effect on the correlation value. Hence, a
significant proportion of the variability for this correlation
can be attributed to within-patient variability. Together
with the results of the main pointwise analysis and the first
control experiment, these results support the notion that
visual sensitivity in POAG is precisely correlated with
cortical responses in a retinotopic fashion.
The third control experiment also provides information
about the independence of the samples in the pointwise
analysis. Two measures are generally considered indepen-
dent if the probability of observing one does not depend on
the presence of the other (Zar, 1999). The data points in the
pointwise correlations (Fig. 7(B)) might be considered
independent observations because the ROI (specifically, the
BOLD response from the voxels therein) is the unit being
correlated with the PD
DIF
values. On the other hand,
several ROIs are correlated for each observer, which might
be considered a violation of statistical independence. The
third control experiment was designed to determine
whether between-patient variability or within-patient
variability could account for any correlations observed in
the original pointwise comparison, which also addresses
whether the data points in Fig. 7 are independent. If
randomizing the data within-subjects does not affect the
probability of observing a significant correlation, then
there is evidence to suggest that inter-observer variability
accounts for most of the variability in the observed
correlations and the data points should not be considered
independent. However, within-subject variability was
determined to account for the variability in the observed
correlations, and thus there is evidence to suggest that the
data points in Fig. 7(B) should be regarded as independent
observations.
7. The role of fMRI in glaucoma research
FMRI may be a useful tool for glaucoma research as
glaucoma is an optic neuropathy that affects the central
nervous system as well as the eye.
Understanding changes in human glaucoma may
provide insights into the pathobiology of the disease.
Specifically, fMRI may enhance our understanding of the
process of transsynaptic degeneration, which occurs when
injured neurons have unfavorable effects on pre- or post-
synaptic neurons. Recently, transsynaptic degeneration has
been implicated in animal models of glaucoma (Gupta and
Yucel, 2001, 2003). The death of retinal ganglion cells may
prompt a cascade of events along the retino-cortical
pathway that has neurochemical (Vickers et al., 1997),
metabolic (Crawford et al., 2000;Crawford et al., 2001),
functional (Smith et al., 1993), and neuropathological
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R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–5652

consequences for LGN and V1 such as neuron loss and
shrinkage (Weber et al., 2000;Yucel et al., 2000, 2001,
2003).
It should be noted that fMRI measurements of changes
in neuronal activity are not direct evidence for glaucoma-
tous neurodegeneration in LGN or V1. It is possible to
have functional changes in neuronal activity that are
independent of structural changes to neurons/axons in the
central nervous system and vice versa. For example, recent
fMRI studies have found neuronal activity in the cortical
representation of the fovea persists despite obvious
macular pathology resulting in extensive losses to foveal
vision (Nguyen et al., 2004a, b;Baker et al., 2005).
Nevertheless, fMRI offers a great advantage over anato-
mical and electrophysiological methods that can directly
measure neurodegeneration. Specifically, fMRI can mea-
sure glaucomatous changes to function-specific neuronal
activity at several stages along the visual pathway in vivo.
Such fMRI measurements may constrain future anatomical
or electrophysiological studies.
Cell death in glaucoma is not necessarily limited to
transsynaptic degeneration associated with the glaucoma-
tous eye. There is also a decrease in the number and size of
M and P cells receiving input from the non-glaucomatous
eye in primate models (Yucel et al., 2003). ‘‘Sympathetic
cell death’’ may occur at locations where neurons receiving
inputs from both eyes lie adjacent to each other. The
current study paves the way for a ROI-based approach to
investigating sympathetic activity decreases in human
glaucoma.
8. Future directions
Animal models of glaucoma suggest that retinal ganglion
cell death adversely affects cells in V1 via transsynaptic
degeneration. Yet, there is a paucity of evidence regarding
the existence of such mechanisms in humans. To explore
this possibility, fMRI can be used measure the influence of
the glaucomatous eye on the processing of visual input
from the relatively unaffected fellow eye. Cortical re-
sponses to visual stimulation in control subjects can be
compared to responses driven by the fellow eye of
asymmetric glaucoma subjects. Visual fields and structural
measurements can be conducted to confirm that there is no
detectible difference between normal eyes and the fellow
eye of glaucoma subjects. According to predictions from
animal models of glaucoma, visual stimulation of the
unaffected fellow eye of glaucoma subjects should elicit V1
responses that are reduced relative to those of control
subjects. Furthermore, fMRI measurements of neuronal
activity in V1 will depend upon the severity of vision loss,
changes to optic disk topography, and thinning of the
retinal nerve fiber layer in glaucoma subjects. FMRI
measurements of neuronal activity in V1 should also
correlate with the loss of visual function at multiple
locations in visual space.
Neurons within the layers of LGN receiving input from
the unaffected fellow eye also show signs of degeneration,
which implies that the death of LGN neurons connected to
the glaucomatous eye creates a toxic environment affecting
all LGN cells (Luthra et al., 2005). Detailed fMR imaging
in the LGN has been achieved recently at 3.0 T (O’Connor
et al., 2002;Kastner et al., 2004;Schneider et al., 2004). By
measuring fMRI activity in the LGN, the presence of
‘‘sympathetic’’ cell death in human glaucoma may be
verified. Such experiments may determine whether trans-
synaptic and sympathetic degeneration occur in LGN, V1,
or both areas in human glaucoma. As such, visual
stimulation of the unaffected fellow eye of glaucoma
subjects should elicit LGN responses that are reduced
relative to those of control subjects. Furthermore, fMRI
measurements of neuronal activity in LGN should depend
upon the severity of vision loss, changes to optic disk
topography, and thinning of the retinal nerve fiber layer in
glaucoma.
The combined outcome of experiments in LGN and V1
may enhance our understanding of the nature of neuronal
activity in glaucoma. Given evidence of transsynaptic
degeneration in V1, a lack of degeneration in LGN would
imply that transsynaptic degeneration occurs in V1 in the
absence of sympathetic cell death in LGN. Because the
optic nerve fibers from each eye are functionally and
anatomically segregated in LGN (Wiesel and Hubel, 1966;
Dreher et al., 1976;Creutzfeldt et al., 1979;Shapley et al.,
1981;Merigan and Maunsell, 1993), it is possible that
selective cell death could occur in one layer of LGN
without affecting the others. This pattern of results would
also rule out the possibility that retinal ganglion cell loss in
the fellow eye could account for transsynaptic degeneration
in V1. By contrast, evidence of degeneration in LGN and
V1 suggests that each mechanism occurs at both stages of
visual processing. However, it would not be possible to
determine which mechanism was responsible for cell death
in LGN. Finally, evidence of degeneration in LGN alone
would indicate that both mechanisms may be present but
the effects of transsynaptic degeneration are undetectable
in V1.
Using the template-fitting approach, the current experi-
ment establishes a strong correlation between the size and
severity of a given scotoma and the pattern and amplitude
of the BOLD response within its corresponding ROI.
Despite these encouraging results, template-fitting techni-
ques can be improved upon. First, cortical flattening
introduces small, unbiased errors that add noise to the
correlation. To overcome this problem, a recently devel-
oped template-fitting method measures cortical distance
along the 3D cortical manifold (Dougherty et al., 2003).
Second, the series of static stimuli used in the current study
require nearly one hour to define the landmarks in V1
necessary for template-fitting. Advances in multifocal
fMRI (Vanni et al., 2005) utilize multiple static stimuli
and m-sequences (pseudorandom sequences designed
to maximize stimulus presentation efficiency) to create a
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R.O. Duncan et al. / Progress in Retinal and Eye Research 26 (2007) 38–56 53

60-piece grid in V1. While current multifocal fMRI
methods have a relatively poor signal-to-noise ratio, the
time saved using these techniques may prove beneficial for
scanning elderly glaucoma patients.
Similar to recent reports of functional reorganization
in patients with macular degeneration (Nguyen et al.,
2004a, b;Baker et al., 2005), functional reorganization may
occur within V1 in response to glaucoma, and the cortical
representation of the fellow eye could theoretically grow to
include the cortical representation of the glaucomatous eye.
As a result, neuronal activity in V1 might be increased for
viewing through the fellow eye relative to normal control
subjects. This report has assumed that cortical responses to
viewing through the fellow eye are relatively unchanged
compared to healthy control subjects. Because the BOLD
response is a relative measure of viewing through the
glaucomatous vs. the fellow eye, it is not possible to
determine how much of the BOLD response can be
attributed to changes in neuronal activity associated with
viewing through the fellow eye. Future studies are planned
to compare the BOLD response to normal controls, which
will directly address the issue of cortical plasticity.
9. Conclusion
As experimental models of glaucoma do not fully mimic
human POAG, careful study of human patients is needed.
The fMRI techniques developed in this report can be used
to study human glaucoma in vivo. Such endeavors may
lead to new approaches for diagnosing and treating this
neurodegenerative disease.
Acknowledgments
The authors would like to thank Neeru Gupta, M.D.,
PhD, FRCSC, DABO, and Thomas T. Liu, Ph.D., for
their insightful comments on this manuscript.
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