Gray matter alterations in visual cortex of patients with loss of central vision due to hereditary retinal dystrophies.

Abstract

In patients with central visual field scotomata a large part of visual cortex is not adequately stimulated. Over time this lack of input could lead to a reduction of gray matter in the affected cortical areas. We used Voxel Based Morphometry to investigate structural brain changes in patients with central scotomata due to hereditary retinal dystrophies and compared their results to those of normal sighted subjects. Additionally we correlated clinical and demographic characteristics like duration of disease, scotoma size, visual acuity, fixation stability and reading speed to the amount of gray matter in whole brain analyses within the patient group. We found a decrease in gray matter around the lesion projection zone in visual cortex of patients in comparison to controls. Gray matter loss along the posterior and middle portions of the calcarine sulcus is also correlated with scotoma size, indicating that indeed the lack of functional input provokes the gray matter alterations. In whole brain regression analyses within the patient group we found an additional cluster in the right superior and middle frontal gyri, slightly anterior to the frontal eye fields, where gray matter correlated positively with fixation stability. This could be regarded as a consequence of oculomotor learning.

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Gray matter alterations in visual cortex of patients with loss of central vision due to
hereditary retinal dystrophies
Tina Planka,⁎, Jozef Froloa, Sabine Brandl-Rühleb, Agnes B. Rennerb, Karsten Hufendiekb,
Horst Helbigb, Mark W. Greenleea
aInstitute of Psychology, University of Regensburg, Universitätsstr. 31, 93053 Regensburg, Germany
bDepartment of Ophthalmology, University Medical Center Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Regensburg, Germany
a b s t r a c ta r t i c l e i n f o
Article history:
Received 10 December 2010
Revised 28 January 2011
Accepted 17 February 2011
Available online 23 February 2011
Keywords:
Hereditary macular dystrophy
Central visual field scotomas
Visual cortex
Voxel-based morphometry
Gray matter density
Gray matter volume
In patients with central visual field scotomata a large part of visual cortex is not adequately stimulated. Over
time this lack of input could lead to a reduction of gray matter in the affected cortical areas. We used Voxel
Based Morphometry to investigate structural brain changes in patients with central scotomata due to
hereditary retinal dystrophies and compared their results to those of normal sighted subjects. Additionally we
correlated clinical and demographic characteristics like duration of disease, scotoma size, visual acuity,
fixation stability and reading speed to the amount of gray matter in whole brain analyses within the patient
group. We found a decrease in gray matter around the lesion projection zone in visual cortex of patients in
comparison to controls. Gray matter loss along the posterior and middle portions of the calcarine sulcus is also
correlated with scotoma size, indicating that indeed the lack of functional input provokes the gray matter
alterations. In whole brain regression analyses within the patient group we found an additional cluster in the
right superior and middle frontal gyri, slightly anterior to the frontal eye fields, where gray matter correlated
positively with fixation stability. This could be regarded as a consequence of oculomotor learning.
© 2011 Elsevier Inc. All rights reserved.
Introduction
The hereditary retinal dystrophies, Stargardt's disease and cone–
rod dystrophy, usually start between the age of 10 and 30 years and
lead to a progressive loss of photoreceptors in the retina. The cones
are primarily affected resulting in a disturbed macular function.
Therefore, the patients suffer from a reduced visual acuity and
binocular central visual field scotomata (Glazer and Dryja, 2002;
Scullica andFalsini, 2001). Dueto the loss of central vision,a large part
of visual cortex is not adequately stimulated (Van Essen and
Anderson, 1995) and thus appears predestined for gray matter
reductions.
There are numerous reports pointing to cortical and subcortical
reorganization as well as structural changes due to sensory depriva-
tion. This has, in addition to those for the visual system, also been
shownfor thesomatosensorysystem(e.g. Draganskiet al., 2006a; Flor
et al., 1995; Jurkiewicz et al., 2006; Merzenich et al., 1984;
Ramachandran and Hirstein, 1998; Kaas et al., 2008), the olfactory
system (Bitter et al., 2010a, b) and the auditory system (Emmorey
et al., 2003; Landgrebe et al., 2009; Mühlau et al., 2006; Penhune et al.,
2003; Schneider et al., 2009; Shibata, 2007).
Several studies have indicated that the visual system is especially
affected by sensory deprivation. Ptito et al. (2008) found significant
atrophy along the visual pathways including primary and associative
visual cortex in congenitally blind adults. Similar results are shown for
early blind adults, who lost sight before the age of two (Noppeney
et al., 2005) or before the age of six (Pan et al., 2007). Abnormalities in
the structure of the visual cortex are also detected in children and
adults with amblyopia (Mendola et al., 2005; Xiao et al., 2007), as well
as in albinism (Hoffmann et al., 2003). Furthermore studies on albinos
found differences in the visual pathways (Schmitz et al., 2003) and a
reduction of gray matter in the foveal representation area of the visual
cortex, assumed to occur as a consequence of a decreased number of
ganglion cells in the fovea (Von dem Hagen et al., 2005).
When it comes to visual field defects acquired later in life, several
studies have found neural degeneration in glaucoma patients. Gupta
etal.(2006)reportedevidenceforneuraldegenerationalongtheoptic
nerve, the lateral geniculate nucleus (LGN) and the visual cortex in a
NeuroImage 56 (2011) 1556–1565
Abbreviations: AMD, age-related macular degeneration; ANCOVA, analysis of
covariance; ANOVA, analysis of variance; BA, Brodmann area; FEF, frontal eye field;
FOV, field of view; FWE, family wise error; FWHM, full width at half maximum; LGN,
lateral geniculate nucleus; LPZ, lesion projection zone; MNI, Montreal Neurological
Institute; MRI, magnetic resonance imaging; PALS, Population-Average, Landmark- and
Surface-based; PRL, preferred retinal locus; SPM, Statistical Parametric Mapping; VBM,
voxel-based morphometry; VOI, volume of interest.
⁎ Corresponding author at: Institut für Psychologie, Universität Regensburg,
Universitätsstr. 31, 93053 Regensburg, Germany. Fax: +49 941 943 81 3233.
E-mail addresses: tina.plank@psychologie.uni-regensburg.de (T. Plank),
jozeffrolo@hotmail.com (J. Frolo), ruehle@eye-regensburg.de (S. Brandl-Rühle),
a.renner@berlin.de (A.B. Renner), hufendiek@eye-regensburg.de (K. Hufendiek),
helbig@eye-regensburg.de (H. Helbig), mark.greenlee@psychologie.uni-regensburg.de
(M.W. Greenlee).
1053-8119/$ – see front matter © 2011 Elsevier Inc. All rights reserved.
doi:10.1016/j.neuroimage.2011.02.055
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Page 2

post-mortemcase study, and Gupta et al. (2009) also found atrophy in
theLGNin10glaucomapatientscomparedtocontrolsusingstructural
magnetic resonance imaging (MRI). In the case of glaucoma, this
neural degeneration could be a consequence of trans-synaptic
degenerationduetoelevatedintraocularpressureandinjurytoretinal
ganglion cells (Gupta and Yücel, 2007) and thus might not be a direct
consequence of visual defects in the peripheral field. Kitajima et al.
(1997) used MRI to measure the width of the left and right calcarine
sulcus in patients with different retinal degenerative diseases and
found significant calcarine atrophy in comparison to controls. But it is
unclear what kind of visual field defects those patients had.
In a recent study, Boucard et al. (2009) used voxel-based
morphometry (VBM) to compare gray matter density in the visual
cortex of glaucoma patients and patients with age-related macular
degeneration (AMD). They assessed cortical gray matter in eight
primaryopen-angleglaucomapatients with peripheral vision loss and
nine AMD patients with central vision loss persisting in all patients for
at least 3 years, as well as in an age-matched control group. For both
patient groups they found a reduction of gray matter density in the
respective lesion projection zones (LPZ) in the calcarine sulcus,
suggesting that visual field scotomata can lead to retinotopic-specific
structural changes in the visual cortex.
Table 1
Characteristics of patients (P1–P26) and controls (C1–C26) according to gender, age, duration of disease in years, diagnosis, decimal visual acuity, scotoma size (diameter in degree
visual angle), fixation stability (percentage of fixation in 2° and 4° visual angle around fixation target; patients fixated with their PRL, except P16, who fixated with his fovea, as did
the controls) and reading speed (in words per minute); m = male; f = female; Stargardt = Stargardt's disease; CACD = central areolar choroidal dystrophy; MD = unclassified
hereditary macular dystrophy; Cone D = cone dystrophy; Cone–rod D = cone–rod dystrophy; OS = oculus sinister; OD = oculus dexter; CF = counting fingers; characteristics of
the better eye of each patient are reported in bold, that are correlated with MRI data. For the controls the respective eye was chosen.
Subject
#
Gender AgeDuration
of disease
in years
Diagnosis Decimal visual
acuity
Scotoma size
(diameter in degree
visual angle)
Fixation stabilityReading speed
(words per
minute)
OD OS
OD OSOD OS2° 4°2° 4°
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
P21
P22
P23
P24
P25
P26
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
C26
m
f
f
f
m
m
f
m
m
f
f
m
m
m
m
m
f
m
m
m
f
m
m
m
m
m
m
f
m
f
m
m
f
f
f
m
f
m
m
m
f
m
f
f
f
m
m
f
f
m
f
m
12
19
24
25
25
29
35
39
43
43
43
45
55
62
66
42
44
53
50
33
41
42
44
59
65
65
13
23
23
23
26
28
34
34
37
37
37
38
40
43
44
45
51
54
55
59
60
62
62
63
68
70
2
9
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
Stargardt
CACD
CACD
MD
Cone D
Cone–rod D
Cone–rod D
Cone–rod D
Cone–rod D
Cone–rod D
Cone–rod D
Cone–rod D
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
.08
.05
.08
.08
.1
.1
.1
.05
.1
.1
.1
.1
.1
.02
.05
.5
.05
.08
.1
.08
.08
.04
.001
.1
.03
.05
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
.08
.05
.05
.05
.05
.1
.1
.067
.1
.08
.1
.2
.1
.03
.02
.1
.067
.05
.2
.08
.1
CF
.001
.1
.05
.1
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
25
n.a.
20
20
10
10
10
30
20
15
10
10
15
60
30
15
10
10
10
25
30
60
40
10
35
30
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
10
15
20
25
25
10
10
30
20
15
10
10
15
65
20
15
25
15
10
25
25
60
30
10
30
30
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
10
7
83
20
100
95
98
18
58
80
26
99
32
5
44
100
15
46
41
47
75
48
1
20
4
33
81
n.a.
100
97
100
100
n.a.
n.a.
100
89
100
100
100
100
100
96
97
89
100
100
100
n.a.
n.a.
100
98
76
46
14
100
57
100
100
100
67
74
95
28
100
59
9
72
100
37
59
69
48
96
92
4
21
9
81
86
n.a.
100
100
100
100
n.a.
n.a.
100
89
100
100
100
100
100
96
100
89
100
100
100
n.a.
n.a.
100
99
77
62
0
86
47
20
69
55
68
69
83
70
96
52
n.a.
2
100
14
59
42
100
83
n.a.
n.a.
10
78
43
99
100
n.a.
98
n.a.
100
n.a.
100
96
100
100
n.a.
n.a.
100
61
77
n.a.
100
100
100
100
70
100
91
95
97
97
2
100
64
23
99
97
93
77
99
93
100
95
n.a.
5
100
50
60
73
100
100
n.a.
n.a.
18
97
48
100
100
n.a.
100
n.a.
100
n.a.
100
96
100
100
n.a.
n.a.
100
61
77
n.a.
100
100
100
100
72
100
100
100
100
95
110
132
77
98
76
83
78
60
78
60
96
83
n.a.
22
81
57
137
56
27
19
n.a.
n.a.
80
14
31
127
112
156
160
n.a.
127
173
n.a.
n.a.
169
126
139
139
199
164
153
123
156
143
107
151
151
138
180
126
125
11
8
8
5
6
14
24
9
28
23
16
14
13
12
29
23
18
8
28
22
42
16
6
17
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
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In the present study, we used VBM to investigate structural brain
changes in 26 patients with central scotomata due to hereditary
retinal dystrophies, in comparison to normal sighted age-matched
subjects. Taking advantage of our large patient sample, we correlated
clinical and behavioral characteristics of the patients, like scotoma
size, duration of disease, eccentric fixation stability and reading speed
to the amount of gray matter in a whole brain analysis as well as in
volumes of interest along the calcarine sulcus, to determine the
retinotopic distribution of possible gray matter loss from central to
peripheral representation areas.
Materials and methods
Subjects
Twenty-six patients (P) with central scotomata due to hereditary
retinal dystrophy (cone dystrophy, cone–rod dystrophy, Stargardt's
disease and other forms of hereditary macular dystrophies; see
Table 1 for details) participated in this study (eight females, 18 males;
mean age 42 years, range 12–66 years), as well as an age-matched
controlgroupof 26 subjects withnormalor correctedto normal vision
(13 females, 13 males; mean age 43 years, range 13–70 years; see
Table 1 for details). The patient group was recruited from the patient
population of the Department of Ophthalmology, University Medical
Center Regensburg, as well as through advertisements in the Internet,
local newspapers and the magazine of the patient association “Pro
Retina”. The control group was recruited among students and staff of
the University of Regensburg and the University Medical Center
Regensburg, among the spouses and relatives of the patients, as well
as through advertisements in local newspapers and by the help of the
City of Regensburg. All patients and controls signed an informed
consent form prior to the study and received monetary compensation
for their participation. The study was approved by the Ethical
Committee of the University of Regensburg and conducted in
accordance to the ethical guidelines of the Declaration of Helsinki.
Clinical characteristics and visual field measurements
Table 1 presents details on characteristics of patients and controls,
including the diagnosis, duration of disease, visual acuity, scotoma
size, fixation stability and reading speed. All patients had absolute
central scotomata binocularly with a diameter of ten degree visual
angle or larger, except P16, who had a small sparing of residual foveal
vision of about four degree visual angle in diameter, resulting in a
best-corrected decimal visual acuity of .5. All other patients had
decimal visual acuities of .2 or less in both eyes. All controls had a
best-corrected decimal visual acuity of 1.0. Best-corrected visual
acuity was determined by using a Vision Screener (Rodenstock
Rodavist 524/S1) and Eye Charts for distant visual acuity (Oculus Nr.
4616) and near visual acuity (Zeiss/Frohnhäuser). For the patient
group clinical characteristics of one eye were chosen to correlate with
the VBM results. Those characteristics are given in Table 1, the data for
the study eye depicted in bold font. Usually the dominant eye was
chosen, whereas for six patients (P2, P5, P8, P15, P16, and P19) the
non-dominant eye was chosen, because it was the better eye and/or
the one with higher fixation stability.
Scotoma size was measured using kinetic Goldmann perimetry
withthe isoptersIII/4e, I/4e, I/3e,I/2e andI/1e. Goldmannperimetry is
a kinetic perimetry technique, where the assessor moves light points
of different intensities manually from the periphery to the center.
Patients are requested to press a button as soon as they perceive a
light stimulus. Each eye is measured separately. Participants are
instructed to fixate centrally. Defined as edges of the scotomata, those
points were marked, where isopter III/4e were no longer detected
(see Fig. 1 for examples). Scotoma size is reported in Table 1 as
scotoma diameter in degrees of visual angle as an average of vertical
and horizontal dimensions. Goldmann perimetry was primarily done
by author S B-R, who has been trained by author AB R, an experienced
assessor for several years, with the aim to minimize inter-observer
variation. Controls did not undergo Goldmann perimetry.
To measure fixation stability of the patients and controls, we used
a Nidek MP-1 microperimeter (Nidek Co, Japan). Patients had to fixate
a red cross of 4 degree visual angle in diameter with their preferred
eccentric location on the retina (PRL) for on average 30 s. Only P16
fixated the target with his spared fovea. Controls also fixated the
target with their fovea. The technique measures 25 samples per
second,so that750 samplesof fixationpointsresultover a timeperiod
of 30 s. During the measurement the camera sometimes lost track of
the subject's eye. This can be due to eye blinks or fixation instability in
the formof large saccades. The Nideksoftware records thetime period
that was measured and the proportion of the time span that was
effectively tracked, as well as the percentages of fixation points that
fell in a range of 2° or 4°diameter visualangle around the centerof the
target, based on the time spans effectively tracked. Thus fixation
Fig. 1. Examples of visual field measurements using Goldmann perimetry: (A) for the right eye of a patient with a scotoma size of about 10° visual angle in diameter (P7) and (B) for
the right eye of a patient with a scotoma size of about 60° visual angle in diameter (P14). As edges of the scotomata those points were marked, where III/4e isopter was no longer
detected.
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stability can be overestimated by long or frequent time spans where
the camera lost track of eye position due to large saccades. To
compensate for this we corrected the given fixation stability in the
following way: First we calculated the mean time span for which the
camera lost track of eye position in the normal sighted control group,
who used foveal fixation. The resulting mean value of 6 s (SE=1.7 s)
gave us the average time that could be attributed to eye blinks. In a
second step we subtracted this amount from the measured time in the
patient group. The difference between the measured time remaining
and the effectively tracked time we attribute to large saccades
(exceeding 4° visual angle around the fixation target) and added that
time span to the effectively tracked time. On this basis we recalculated
the percentages of fixation points falling in a range of 2° or 4° visual
angle around the target. These corrected values are given in Table 1.
Fig. 2 presents examples from a patient with stable fixation (A) and
one with less stable fixation (B), respectively.
The Nidek MP-1 was also used to measure a microperimetry of 30
degree diameter around the PRL of the patients. Patients fixated the
fixation cross with their PRL on intact retina and were instructed to
press a button as soon as they perceived a target. Controls also
underwent microperimetry and fixated the target with their fovea.
We used “strategy-fast” with static light points of intensity 16 and
8 dB, maximal brightness of 127 cd/m2, that were presented for
200 ms each on a grid comprising the central 30° of the visual field.
In the study of Boucard et al. (2009), visual field measurements
were done using a Humphrey Field Analyser (HFA, Carl Zeiss Meditec,
Dublin, CA, USA). This device assesses the central 30° visual angle of
the visual field and is thus comparable to the Nidek MP-1 we used for
doing the microperimetry described above. For determining scotoma
size in patients with central visual field defects, these patients have to
fixate centrally. Under these conditions it is usually extremely difficult
to keep up a stable fixation. In our study it has turnedout that this task
waseasiertoperformintheGoldmannperimetryinsteadoftheNidek-
MP1 microperimetry. Additionally, the microperimetry, as well as the
HFA, performs an automatic perimetry that takes incommensurately
long for patients with large central scotomas, who are not able to
perceive most of the light points. On the other hand, in the Goldmann
perimetry the assessor can observe and control the fixation of the
patient better and adapt the procedure accordingly. Thus the Gold-
mann perimetry delivers more reliable results here and is used as the
standarddevice for patientswith hereditary retinal dystrophies, while
the HFA is more sensitive to visual field defects in glaucoma patients,
who were measured in the study of Boucard et al. (2009). Moreover, a
fewofourpatientshadcentralscotomaslargerthan30°visualanglein
diameter, which could not be assessed using the Nidek-MP1.
For measuring reading speed patients and controls read aloud a
continuous text for 3 min, which was recorded. We then counted the
number of words read and calculated the mean of words read per
minute. These values are given in Table 1. Both patients (.73/min) and
controls (.83/min) made less than one reading error per minute on
average, leading us to exclude reading error rates in further analyses.
All participants read the same text, taken from a book (German
translation of Doris Lessing (2003): The Grandmothers), printed on a
sheet of paper (font: Arial, font size: 10 pt, single spaced). Patients
used magnification glasses customized to their needs.
Magnetic resonance imaging
A high-resolution T1-weighted image (160 slices covering the
whole brain, 1×1×1 mm3voxel size, FOV=256×256 mm2) was
obtained from each subject, using the ADNI sequence (TR=2250 ms,
TE=2.6 ms, flip angle 9°) (Laboratory for Neuro Imaging, UCLA, Los
Angeles, CA) on a 3 T Allegra Scanner (Siemens, Erlangen, Germany).
The current reported structural analysis is part of a major study
that uses functional MRI (data not yet published) to examine
differences between patients with central visual field scotomata and
normal sighted controls. Therefore, several functional images were
acquired in the same session additionally. The results from the
functional imaging will be reported elsewhere (Frolo et al., unpub-
lished results).
Voxel-based morphometry
Data preprocessing and analysis were performed using the SPM8
(Statistical Parametric Mapping) software (Wellcome Department of
Imaging Neuroscience Group, London, UK; http://www.fil.ion.ucl.ac.
uk/spm). The origin of the T1-weighted images of all subjects was set
to the anterior commissure manually. For the voxel-based morpho-
metric analysis (Ashburner and Friston, 2000) we used the VBM8
toolbox (http://dbm.neuro.uni-jena.de/vbm/) with default para-
meters (as described in the following) as implemented in SPM8.
Images were bias corrected, tissue classified and registered using
probability maps, based on the ICBM tissue probabilistic atlases as
priors, within a unified model (Ashburner and Friston, 2005). High-
dimensional Dartel normalization was chosen (Ashburner, 2007).
Further on we based the analysis mainly on the modulated
normalized gray-matter images. Modulated images allow us to test
for regional differences in the absolute amount of gray matter
(volume) (Good et al., 2001). Modulated images were corrected for
non-linear warping only, thus implementing a correction for volume
changes caused by nonlinear spatial normalization. Additionally we
tested if the results of our gray matter volume analysis also hold in a
gray matter density analysis that is based on the unmodulated
normalized gray-matter images. Unmodulated images allow us to test
Fig. 2. Examples of fixation stability measurements with the Nidek MP-1 microperimeter: (A) for the left eye of a patient exhibiting fairly stable fixation (P12) and (B) for the right
eye of a patient with less stable fixation (P17). The patients fixated with their PRL eccentrically on a red cross for 30 s. The blue dots represent 750 samples of fixation points on the
retina during that time period.
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for regional differences in concentration of gray matter (tissue density
per voxel) (Good et al., 2001). This was done to assure the
comparability of our results with the ones of Boucard et al. (2009),
who analyzed gray matter density alterations. Afterwards the
modulated and unmodulated normalized images were smoothed
with a Gaussian kernel of 8 mm FWHM. Although patients and
controls were matched on age, age was added as a covariate to
exclude any possible confounding effects.
Statistical analyses were performed on the basis of the General
Linear Model in SPM8. Differences in gray matter volume and density
between the patient and control group were analyzed with analysis of
covariance (ANCOVA), where the factor age was added as a nuisance
covariate to exclude any remaining confounds that could arise from
differences in age. To identify possible correlations between gray
matter volume or density and patients' characteristics, we applied
regression analysis in single SPM regression models with the factors
duration of disease, visual acuity, fixation stability (in 2° and 4°
around target), scotoma size and reading speed, respectively, within
the patient group. Again, the factor age was added as a nuisance
covariate to all regression models. Clusters with positive or negative
correlation to the behavioral factors were identified by applying a
one-sided t-test on the variable containing the respective behavioral
factor. Voxels with values of less than .1 were excluded (absolute
threshold masking) to account for possible edge effects around tissue
borders. Contrasts were determined by applying a threshold of
pb.005 (uncorrected for multiple comparisons on voxel level) for
whole brain analyses and we report all clusters thresholded p≤.05 on
cluster level. Results from the analysis of covariance (ANCOVA) that
tested for gray matter differences between patient and control group
were also thresholded at p (FWE)b.05 (corrected for multiple
comparisons). Additionally we applied VOI analysis as provided by
SPM8 tolook specifically at graymatterdifferencesalong thecalcarine
sulcus. We anatomically defined four spherical VOIs of 5 mm radius
each from posterior to anterior calcarine sulcus of the left and right
hemisphere respectively (centered at the MNI coordinates x, y, z=+/
−6, −100, −6; +/−6, −90, 0; +/−6, −80, 6; +/−6, −70, 10; see
also Figs. 4 (A, B and C) for their location). Another spherical VOI with
5 mm radius was centered at MNI coordinates x,y,z=27,21,58, the
peak voxel of a cluster obtained by a regression model, where gray
matter volume was correlated to fixation stability in 4° around the
target (see Results section for details). In these VOIs defined as
described above we calculated the first eigenvariate using the SPM
VOI function, to obtain relative values of gray matter volume or gray
matter density of each subject. These values were correlated to
subjects' age, duration of disease, scotoma size, visual acuity, fixation
stability (in 2° and 4° visual field area around target) and reading
speed. Pearson correlation coefficients were calculated and thre-
sholded at p=.05 (Bonferroni corrected for multiple comparisons).
To determine the relative effect of the person-specific variables (e.g.,
age, duration of disease, and scotoma size) on VOI gray matter
volume, we performed a multiple regression analysis with these
independent variables on the dependent variable gray matter volume
for VOI 1–4.
Significant clusters are visualized on a standard brain of the
surface-based PALS-B12 atlas (Van Essen, 2005), using the software
CARET (Van Essen et al., 2001; http://www.nitrc.org/projects/caret/)
or on a volume-based standard brain from a single normal subject
(ch2.nii.gz), using the software MRIcron (Rorden and Brett, 2000;
http://www.nitrc.org/projects/mricron).
Results
Behavioral results
Table 2 presents the correlation results (Pearson correlation
coefficients) of demographic and behavioral data. Scotoma size and
age (only in the patient group) are negatively correlated with reading
speed. Duration of disease is negatively correlated with fixation
stability and visual acuity is positively correlated with fixation
stability.
MRI results
Gray matter volume
Gray-matter differences between patients and controls are shown
in Fig. 3. Patients with central scotomata show a significant reduction
of gray matter volume around the calcarine sulcus of both hemi-
spheres [for the threshold pb.005 on voxel level (uncorrected for
multiple comparisons): MNI coordinates of peak voxel (x,y,z)=26,
−81,−14; z-value=5.05; cluster size=14,037 voxels, pcorrb.001 on
cluster level; for the threshold p (FWE)b.05 on voxel level (corrected
formultiple comparisons):MNIcoordinatesof peakvoxel (x,y,z)=11,
−92,6; z-value=5.0; cluster size=535 voxels].The oppositecontrast
“patientsNcontrols” revealed no significant clusters.
When we examined the results of the volume-of-interest analysis
along the calcarine sulcus, we found that in both hemispheres
normalized gray matter volume of the patients' group with central
scotomata was reduced at the posterior part of calcarine sulcus in
comparison to the more anterior part (see Fig. 4), the posterior extent
corresponding to the foveal projection zone. The normalized values
were obtained by dividing the gray matter volume values of the
patients by the mean gray matter volume value of the control group.
A repeated-measures ANOVA with the factors hemisphere (left,
right) and position of VOI (four VOIs, numbered 1 to 4 from posterior
toanterior,seeFig.4)revealedasignificantmaineffectofVOI-position
[F (1,25)=6.66; p=.016; Greenhouse–Geisser corrected]. The main
effect for the factor hemisphere was not significant [F (1,25)=.23;
p=.64], as well as the interaction between the two factors [F (1,25)=
1.05; p=.37; Greenhouse–Geisser corrected]. Pairwise comparisons
revealed that normalized gray matter volume in the two most
posterior VOIs (numbers 1–2) of both hemispheres was significantly
lower (indicating greater differences between patients and controls)
than in the most anterior VOI (number 4; pb.05; Bonferroni corrected
for multiple comparisons). Additionally we calculated Pearson's
correlation coefficients to determine whether gray matter volume in
these VOIs is correlated to the participants' behavioral characteristics
like age, duration of disease, scotoma size, visual acuity, fixation
stability and reading speed. Table 3 presents the results of this
correlation analysis. Age correlated negatively with gray matter
volume in the anterior VOIs 3 and 4 in both hemispheres and in both
patient and control groups. Scotoma size correlated negatively with
gray matter volume in VOIs 2 and 3. Duration of disease, visual acuity
Table 2
Correlation of demographic and clinical characteristics with each other. Pearson
correlation coefficients are presented. Significant correlations (pb.05) are highlighted
in bold font; P = patients, C = controls.
AgeDuration
of disease
Scotoma
size
Visual
acuity
Fixation
stability
Reading
speed
2°4°
AgeP
C
P
1
1
–
.32.38
−.03
−.25
−.21
−.42
−.27
−.20
−.44
−.49
−.02
−.14Duration of
disease
Scotoma size
Visual acuity
Fixation stability
2°
Fixation stability
4°
Reading speed
1 .22
−.11
P
P
P
C
P
C
P
C
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
1
–
–
–
–
–
–
–
−.37
1
–
–
–
–
–
–
−.29
.43
1
1
–
–
–
–
−.19
.32
.92
.99
1
1
–
–
−.54
−.14
−.03
−.17
−.08
−.17
1
1
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and fixation stability showed no significant correlations with gray
matter volume along the calcarine sulcus. On the other hand, reading
speed was positively correlated to gray matter volume in the more
anteriorVOI 3 of the left hemisphere in patients,this effect is absent in
controls, who do not have to rely on their peripheral visual field for
reading.
The results of multiple regression for the person-specific variables
on the gray matter volumes in VOI 1–4 are presented for the left and
right hemisphere separately in Supplementary Table S1. These
findings support the results of correlation analysis and point
especially to the role of scotoma size in determining the gray matter
volume (VOIs 2 and 3) in our patients.
By applying whole brain SPM regression analysis to the image data
of the patients group with the clinical characteristics duration of
disease, visual acuity, scotoma size, fixation stability and reading
speed as modulators, we found that factors duration of disease, visual
acuity and reading speed yielded no significant clusters. The factor
scotoma size was negatively correlated to gray matter volume in a
cluster around the calcarine sulcus and the effect was most
pronounced in the right hemisphere (MNI coordinates of peak voxel
(x,y,z)=6,−87,22; z-value=3.56; cluster size=1735 voxels,
pcorr=.03 on cluster level). The factor fixation stability in a range of
4° around fixation target correlated positively with gray matter
volume in a cluster in the right superior frontal gyrus (BA 6, 8; MNI
coordinates of peak voxel (x,y,z)=27,21,58; z-value=4.31; cluster
size=1515 voxels, pcorr=.05 on cluster level). Fig. 5 shows the
cluster and correlation of individual gray matter volume values and
fixation stability in 4° visual angle around the target. The rightmost
Fig. 3. Analysis of gray matter volume, results from the two-sample-t-test between patients with central scotomata and controls. The contrast “controlsNpatients” shows a
significant reduction of gray matter volume around the occipital pole of both, the left and the right hemisphere [p (FWE)b.05 on voxel level (corrected for multiple comparisons):
MNI coordinates of peak voxel (x,y,z)=11,−92,6; z-value=5.0; cluster size=535 voxels]. The significant cluster is visualized on sagittal slices of a standard brain from a single
normal subject (MRIcron: ch2.nii.gz) with MNI coordinates x=−6, −2, 2, 6, 10, 14.
Fig. 4. Location of VOIs and results of VOI-analysis along calcarine sulcus of occipital cortex in the left and right hemispheres, displayed on a standard brain from a single normal
subject (MRIcron: ch2.nii.gz). Four spherical VOIs (radius=5 mm) were defined from posterior to anterior calcarine sulcus, numbered from 1 to 4. The VOIs were centered at the
following MNI coordinates (x,y,z): VOI 1: +/−6, −100, −6; VOI 2: +/−6, −90, 0; VOI 3: +/−6, −80, 6; VOI 4: +/−6, −70, 10. A: Location of VOIs 1–4 on a sagittal slice (x=−6).
B: Location of VOIs 1–4 in the left and right hemispheres on a tilted axial slice through the calcarine sulcus in a 3D depiction. C: Location of VOIs 4 in the left and right hemispheres on
a coronal slice (y=−70). The crosshairs are set to MNI-coordinates (x,y,z)=0, −70, 0. D: Results from VOI-analysis: Mean normalized gray matter volume values and standard
errors of patients are shown for all four VOIs in the left (blue) and right (red) hemispheres. The normalized values were obtained by dividing the gray matter volume values of the
patients by the mean gray matter volume value of the control group.
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column in Table 3 gives the Pearson correlation coefficients, Table S1
(lower panel) the results of the multiple regression analysis, for the
individual gray matter volume values in the sphere with 5 mm radius
around the peak voxel with subjects' characteristics. As expected, only
fixation stability in patients, but not in controls, showed a significant
positive correlation with gray matter volume in this cluster. The mean
values of the patient and the control group do not differ significantly
from each other [t(50)=1.35; p=.18].
Table 3
Correlation of gray matter volume with demographic and clinical characteristics. Pearson correlation coefficients are presented. Significant correlations (pb.05, Bonferroni corrected
for multiple comparisons) are highlighted in bold font; P = patients, C = controls; L = left; R = right.
VOIsSphere
(r=5 mm)
Posterior Anterior
Group Hemisphere1234x,y,z=27, 21, 58
AgePL
R
L
R
L
R
L
R
L
R
L
R
L
R
L
R
L
R
L
R
L
R
.002
.003
.07
−.16
.40
.34
−.23
−.36
−.09
.15
.01
.17
−.22
.24
.09
.18
−.22
.25
−.07
−.06
.06
−.12
−.30
−.18
−.36
−.38
.06
.22
−.59
−.51
−.10
.03
.18
.20
.02
.16
.19
.24
.01
.17
.25
.11
.24
−.04
−.69
−.62
−.60
−.57
−.02
−.03
−.56
−.62
−.03
−.001
.10
.12
.04
.10
.16
.14
.03
.09
.52
.48
.09
−.03
−.85
−.71
−.65
−.55
−.16
−.15
−.35
−.47
−.06
−.09
.20
.08
.13
.01
.25
.10
.12
−.004
.47
.48
−.04
−.05
−.27
C
−.22
Duration of diseaseP
−.22
Scotoma sizeP
−.19
Visual acuityP
.22
Fixation stability
2°
P
.68
C
.04
Fixation stability
4°
P
.75
C
.04
Reading speedP
.06
C
−.002
Fig. 5. Results from whole brain regression analysis with the factor fixation stability. A: Significant cluster (pcorr=.05 on cluster level) in the superior and middle frontal gyri of the
right hemisphere, where gray matter volume correlated positively with fixation stability of the patients in a range of 4° visual angle around the target, visualized on a standard brain
from a single normal subject (MRIcron: ch2.nii.gz). B: The same cluster visualized on a standard brain of the surface-based PALS-B12 atlas (Van Essen, 2005) together with the
borders of Brodmann areas 6 and 8. The factor age was added as a nuisance covariate in the analysis. C: Results of VOI-analysis. Gray matter volume values were calculated in a sphere
of 5 mm radius around the peak voxel of the obtained cluster (x,y,z=27,21,58) and have been normalized based on the results of the control participants for that VOI.
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The regressionanalysis with the factor fixation stability in a 2° area
around the fixation target yielded a similar cluster in the right
superior frontal gyrus, but fails to reach the threshold p=.05 on
cluster level (MNI coordinates of peak voxel (x,y,z)=27,18,60; z-
value=3.96; cluster size=883 voxels, pcorr=.30 on cluster level).
Gray matter density
To providecomparability tothestudy ofBoucardet al. (2009), who
calculated gray matter density differences in their examination, we
also put the unmodulated images to test in those models that yielded
significant results in the gray matter volume analysis. We report these
results in the Supplementary material. The gray matter density
analyses yielded results comparable to those found in the gray matter
volume analyses.
Discussion
In this study we determined in a large group of patients with
hereditary retinal dystrophy that central scotomata lead to a
reduction of gray matter volume and density in the occipital cortex
of both hemispheres in and around the calcarine sulcus. Gray matter
density analysis examines regional differences in the concentration
of gray matter in voxels, while gray matter volume analysis examines
regional differences in the absolute amount of gray matter. The latter
is possible after applying modulation that corrects for volume
changes caused by nonlinear spatial normalization (Good et al.,
2001). Both kinds of analyses yielded similar clusters. When we
examined more carefully the distribution of normalized gray matter
volume between posterior and anterior parts of the occipital cortex
applying VOI-analysis, we found that the gray matter loss was most
pronounced around the occipital pole and less pronounced at
anterior parts. A similar pattern of results was found for gray matter
density (see Supplementary material). This region corresponds well
to the lesion projection zone of the patients in our study. Due to their
central scotomata the foveal representation area at the occipital pole
is deprived of input from the retina, leading, on the long term, to a
reduction in gray matter volume. We also found that gray matter
volume and density were negatively correlated to scotoma size
especially in middle and anterior portions along the calcarine sulcus,
indicating that indeed the lack of functional input is responsible for
gray matter reduction. The observation that the highest correlations
between scotoma size and local gray matter volume were found in
VOI 2 and VOI 3 probably reflect the fact that the patients tend to
exhibit the greatest amount for variance at these intermediate
locations. These findings are in line with Boucard et al. (2009), who
showed in groups of nine AMD patients and eight glaucoma patients
that the loss of gray matter density in occipital cortex corresponds to
the respective visual field defect projection zones of their patient
groups. In that study, AMD patients with central scotomata showed
reduced gray matter density in the posterior extent of the calcarine,
whereas glaucoma patients with peripheral visual field defects
showed reduced gray matter density in the anterior part of occipital
cortex. Gray matter reduction can also be a consequence of aging
(Good et al., 2001). Indeed we found that gray matter correlated
negatively with age in both patient and control group along the more
anterior part of the calcarine sulcus. To account for this effect, we had
added the factor age as a covariate to our statistical models in SPM.
The resulting clusters are free of confounding linear effects due to
age. Moreover, the most pronounced gray matter loss in patients
when compared to controls was found at the posterior part of the
calcarine sulcus, where aging appears to have little effect (see Table 3
and Tables S1, S2 and S3). The results thus confirm that gray matter
loss could occur as a consequence of visual deprivation. This could be
due to changes in synaptic density, dendritic spine numbers or
arborization of axons (Noppeney, 2007) regulated by gene expres-
sion factors in and around the LPZ (Chen et al., 2010). Reductions in
gray matter volume should also occur in extrastriate visual areas,
since they are involved in relaying information from V1 to the ventral
and dorsal pathways. The lack of significant clusters beyond V1
(Fig. 3) is probably related to our sample size and signal-to-noise
limitations.
Patients with central scotomata have to rely on their peripheral
visual field for everyday visual tasks and for reading.Although reading
speed correlatedpositivelywithgray mattervolume,the results of the
multiple regression indicated that reading speed did not contribute
significantly to the explained variance, suggesting that scotoma size
affects both gray matter volume and reading speed. We also found a
modest positive correlation between duration of disease and gray
matter volume in the more posterior VOIs at the calcarine sulcus (see
Table S1). It is unclear what the reasons for this relationship could be,
but we could speculate that mechanisms controlling neuroplasticity
might upregulate synaptogenesis at the rim of the lesion projection
zone (corresponding to the more posterior VOI locations). No
significant correlations were evident between gray matter volume
or density along the calcarine sulcus and the factors visual acuity and
fixation stability, while the multiple regression analyses (Tables S1
and S3) revealed a negative effect of visual acuity on the amount of
gray matter in left VOI 2.
In whole brain regression analyses within the patient group we
found that gray matter volume was positively correlated to the
eccentric fixation stability of the patients in a cluster in the right
superior and middle frontal gyri (see Fig. 5). The mean values for
gray matter volume in the patient and the control groups do not
differ significantly from each other, indicating that changes could not
simply be attributed to atrophy in the patient group. This cluster
appears to lie a bit anterior to the right frontal eye fields, next to the
supplementary eye fields (Amiez and Petrides, 2009; Gitelman et al.,
2002; Ioannides et al., 2010; Kastner et al., 2007; Paus, 1996).
Interestingly, Ettinger et al. (2005) found a very similar cluster
anterior to the right frontal eye fields in their study. There the
authors used VBM to correlate gray matter volume with saccadic
performance of healthy controls. They found that gray matter
volume was negatively correlated with the error rate in the
antisaccade task, where subjects had to perform a saccade in the
direction opposite to a peripheral target. Their findings indicated that
more gray matter volume was associated with fewer antisaccade
errors. The authors discuss this result in the light of functional
studies. For example, Connolly et al. (2000) found activation in
clusters also anterior to the frontal eye fields, which they called
preFEF, while the subjects performed an antisaccadic task. Lepsien
and Pollmann (2002) found this area active during covert reorienting
and inhibition of return in a spatial cueing paradigm. They discuss
this activation as supportive of the hypothesis that inhibition of
return is caused by inhibitory oculomotor processes (Taylor and
Klein, 1998). Milea et al. (2007) found clusters in dorsolateral
prefrontal cortex, including an area in the right hemisphere anterior
to the frontal eye fields, involved when participants had to make a
free decision about the direction of a saccade they should make. The
authors attribute this activation to the underlying decision making
process. As the authors point out, performing the saccade in one
direction implies the inhibition of a saccade in the opposite direction.
Additionally, this region (among others) has been found to be active
in motor learning (Bischoff-Grethe et al., 2004), including oculomo-
tor learning (Grosbras et al., 2001). We hypothesize that this area
may have developed as a result of adaptation to the need for
eccentric viewing. Patients with central scotomata have to learn to
use their peripheral visual field in an optimized way. Most of them
develop a distinct area on the healthy retina that they use preferably
for viewing tasks (PRL) (e.g. Bäckman and Inde, 1979; Fletcher and
Schuchard, 1997; Guez et al., 1993; Timberlake et al., 1987;
Whittaker et al., 1988), that can also be stabilized by training (e.g.
Gustafsson and Inde, 2004; Nilsson et al., 2003). As a consequence,
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this area becomes a kind of pseudo-fovea, and patients have to learn
to adapt their oculomotor behavior so that they fixate with their PRL
instead of their damaged fovea. This also requires them to learn to
inhibit the initial impulse to direct the fovea to the target. As
expected, patients who were able to stabilize their eccentric viewing,
as indicated by the high fixation stability we measured, also show
higher gray matter volume values in this area in superior and middle
frontal gyri. This might be related to oculomotor learning. Several
studies have shown that changes in gray matter are related to
practice or learning. For example, Gaser and Schlaug (2003) showed
increased gray matter volume in motor, auditory and visuospatial
areas of the brains of professional musicians in comparison to non-
musicians. Hyde et al. (2009) found similar results in children after a
15-month musical training. Aydin et al. (2007) showed increased
gray matter density in frontal and parietal areas of mathematicians in
comparison to controls. Cannonieri et al. (2007) found an experience
dependent increase of gray matter volume in motor areas of
experienced typists. Granert et al. (2010) showed that gray matter
density in the hand area of the motor cortex was modulated by the
level of manual activity. Similarly, Bonilha et al. (2009) showed a loss
of gray matter in the right sensorimotor area of right-handed older
adults that they attributed to atrophy as a consequence of disuse of
the left hand. Filippi et al. (2010) observed an increase in gray matter
volume in several task related areas of the brain after learning goal-
directed motor tasks. Draganski et al. (2004) showed that training in
juggling led to gray matter increase in the visual motion area V5 as
well as in the intraparietal sulcus. Driemeyer et al. (2008) confirmed
these results and showed that structural changes in gray matter
occur as early as after one week of training, if subjects engage in
learning a new task instead of continued training of an already
learned task. Boyke et al. (2008) showed similar effects of training
juggling for older subjects (mean age 60 years). Older people also
benefit from memory training and show as well structural changes as
a consequence of the intervention (Engvig et al., 2010). Gray matter
increase has also been associated with the learning of Morse code
(Schmidt-Wilcke et al., 2010), with cognitive learning (Draganski
et al., 2006b; Ceccarelli et al., 2009) and with a reading intervention
in dyslexic children (Krafnick et al., 2010).
Results from whole brain regression models regarding gray matter
density (or local concentration of gray matter) are on a whole very
similar to the ones we obtained from gray matter volume analyses.
The factors duration of disease and reading speed yielded no
significant results. The factor scotoma size yielded a similar significant
cluster along the calcarine sulcus as we found for gray matter volume.
Pearson correlation coefficients show a significant negative relation-
ship between scotoma size and gray matter density in posterior and
middle portions of the calcarine sulcus (see Table S2). For the factor
fixation stability we obtained a similar cluster as for the gray matter
volume analysis in the superior and middle frontal gyri, but this time
thecluster failed toreach statistical significance.Nevertheless Pearson
correlation coefficients show a positive relationship also between
fixation stability and gray matter density that holds for the patient
group, but not for the control group (see rightmost column of Table
S2). It should be emphasized here that correlations only point to an
association and not to any causal relationship. Further studies would
need to be performed, perhaps in healthy subjects with artificial
central scotoma, to determine whether learning or adaptation
underlie these observations.
Although not the specific focus of this study, changes in white
matter and white matter connectivity have been shown to be altered
in the resultant projection zones in patients with spinocerebellar
ataxia type 7 who have central retinal lesions (Alcauter et al., 2011).
We are currently expanding our observations on an independent
subject sample of macular degeneration patients with the aim to
determine whether white matter volume and fiber connectivity are
altered by visual deprivation resulting from retinal lesions.
Conclusions
In this study we found a decrease in gray matter around the lesion
projection zone in visual cortex of patients with central visual field
scotomata due to hereditary retinal dystrophies. These results are in
line with Boucard et al. (2009), who also found a gray matter decrease
in visual cortex of AMDandglaucomapatients aroundtheir respective
lesion projection zones. We could also show that gray matter loss
along the posterior and middle portions of calcarine sulcus in both
hemispheres correlated with scotoma size, indicating that indeed the
lack of functional input provokes the gray matter alterations. In whole
brain regression analyses within the patient group we found an
additional cluster where gray matter correlated significantly with the
clinical characteristic fixation stability. It correlated positively with
the amount of gray matter in a cluster in the right superior and middle
frontal gyri, slightly anterior to the frontal eye fields, which could be
regarded as a consequence of oculomotor learning.
Supplementary materials related to this article can be found online
at doi:10.1016/j.neuroimage.2011.02.055.
Acknowledgments
This work was supported by the Deutsche Forschungsge-
meinschaft within the framework of Research Group FOR 1075:
Regulation and pathology of homeostatic processes in visual function
(GR 988/18-1). The authors thank Herbert Jägle for his critical
comments to an earlier version of the manuscript, the Pro Retina
Foundation and the City of Regensburg (Seniorenbüro) for their
assistance in participant recruitment as well as the participants of our
study for their careful observations.
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