April 2011
Volume 52, Issue 5
Free
Visual Neuroscience  |   April 2011
Functional Neuroimaging to Characterize Visual System Development in Children with Retinoblastoma
Author Affiliations & Notes
  • Scott M. Barb
    From the Departments of Radiological Sciences,
    the Departments of Ophthalmology and
    the Department of Chemistry, Rhodes College, Memphis, Tennessee; and
  • Carlos Rodriguez-Galindo
    Oncology,
    Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee;
  • Matthew W. Wilson
    Surgery,
    the Departments of Ophthalmology and
  • Nicholas S. Phillips
    From the Departments of Radiological Sciences,
    the Joint Program for Biomedical Engineering, University of Memphis and University of Tennessee Health Science Center, Memphis, Tennessee.
  • Ping Zou
    From the Departments of Radiological Sciences,
  • Matthew A. Scoggins
    From the Departments of Radiological Sciences,
    the Joint Program for Biomedical Engineering, University of Memphis and University of Tennessee Health Science Center, Memphis, Tennessee.
  • Yimei Li
    Biostatistics, and
  • Ibrahim Qaddoumi
    Oncology,
  • Kathleen J. Helton
    From the Departments of Radiological Sciences,
  • George Bikhazi
    Anesthesia, St. Jude Children's Research Hospital, Memphis, Tennessee;
  • Barrett G. Haik
    Surgery,
    the Departments of Ophthalmology and
  • Robert J. Ogg
    From the Departments of Radiological Sciences,
    the Joint Program for Biomedical Engineering, University of Memphis and University of Tennessee Health Science Center, Memphis, Tennessee.
  • Corresponding author: Robert J. Ogg, Radiological Sciences, MS 220, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105-2794; robert.ogg@stjude.org
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 2619-2626. doi:10.1167/iovs.10-5600
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      Scott M. Barb, Carlos Rodriguez-Galindo, Matthew W. Wilson, Nicholas S. Phillips, Ping Zou, Matthew A. Scoggins, Yimei Li, Ibrahim Qaddoumi, Kathleen J. Helton, George Bikhazi, Barrett G. Haik, Robert J. Ogg; Functional Neuroimaging to Characterize Visual System Development in Children with Retinoblastoma. Invest. Ophthalmol. Vis. Sci. 2011;52(5):2619-2626. doi: 10.1167/iovs.10-5600.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: To use functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) to investigate visual system development in children being treated for retinoblastoma.

Methods.: Informed consent was obtained for all participants (N = 42) in this institutional review board–approved study. Participants were imaged with a 1.5-T scanner while under propofol sedation. Diagnostic brain and orbital imaging was followed by investigational functional neuroimaging, which included fMRI during photic stimulation through closed eyelids, to measure functional activation in the visual cortex, and DTI, to evaluate diffusion parameters of white matter tracts in the corpus callosum and the periventricular optic radiations. Analysis included 115 examinations of 39 patients with a median age of 16.4 months and age range from 1.5 to 101.5 months at first evaluation.

Results.: The blood oxygen level–dependent signal was predominantly negative and located in the anterior visual cortex. Activation was affected by tumor lateralization (unilateral or bilateral), macular involvement, and retinal detachment. Patients who had undergone unilateral enucleation showed cortical dominance corresponding to the projection from the nasal hemiretina in the unaffected eye. Diffusion parameters followed a normal developmental trajectory in the optic radiations and corpus callosum, but variability was greater in the splenium than in the genu of the corpus callosum.

Conclusions.: Longitudinal functional neuroimaging demonstrated important effects of disease and treatment. Therefore, fMRI and DTI may be useful for characterizing the impact of retinoblastoma on the developing visual system and improving the prediction of visual outcome in survivors.

Retinoblastoma, a childhood cancer of the eye, typically affects children during the first 3 years of life, a time of rapid development in the central nervous system. 1,2 Overall survival at 5 years after diagnosis of retinoblastoma exceeds 90% with recent treatment protocols 3,4 and there is increased interest in factors such as visual outcome that influence quality of life in this population. Because it tends to arise during a period of rapid and crucial development of the eye and brain, retinoblastoma has a profound effect on long-term visual function in survivors. Visual outcome is influenced by tumor characteristics (i.e., tumor location, macular involvement, retinal detachment, and vitreous seeding), 5,6 but it remains largely unpredictable on the basis of initial presentation of disease within the eye. 7,8 This fact suggests that visual outcome also depends on how retinoblastoma and its treatment affect the more distal elements of the visual system, such as the visual cortex and associated white matter pathways. 
Sensory processing and signaling downstream from the retina during early development play an important part in the patterning of the visual cortex. 9 11 Monocular deprivation of normal visual input during early development disrupts the normal formation of visual fields and ocular dominance columns in the primary visual cortex and leads to visual deficit. 12 15 Other diseases of the visual system that occur early in life affect development in portions of the brain associated with vision. 16 18 Therefore, altered visual input caused by the presence of tumor or enucleation in patients with retinoblastoma most likely disrupts visual system development. However, little is known about the direct effects of retinoblastoma on this process. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) have been used successfully to investigate the visual system in healthy and diseased brains. 19 25 We used fMRI during photic stimulation and DTI to investigate basic elements of the developing visual system in patients being treated for retinoblastoma. 
Methods
Subjects
Participants were enrolled in an ongoing prospective clinical protocol designed to improve the treatment and understanding of retinoblastoma. Participation in the investigational functional neuroimaging examination was optional, and written informed consent was obtained for all subjects as approved by the St. Jude Children's Research Hospital Institutional Review Board in compliance with the guidelines in the Declaration of Helsinki. Ophthalmologists performed primary enucleation (after first performing an MRI) in all patients with either advanced unilateral disease or bilateral disease and unsalvageable eye(s). Treatment for early unilateral or advanced bilateral disease consisted of different chemotherapy regimens, focal treatments, and, in some cases, radiation therapy. Laterality of disease, date of enucleation, macular involvement, and retinal detachment were determined by chart review. 
Magnetic Resonance Imaging
Imaging examinations were conducted at the time of diagnosis, after therapeutic enucleation if performed, and then at approximately 6-month intervals. Images were obtained with a 1.5-T MRI scanner (Symphony; Siemens, New York, NY), equipped with the standard receive-only head coil. All patients were sedated with propofol (250 μg/kg/min) during imaging and were carefully monitored by anesthesiologists. Each imaging examination included diagnostic brain and orbital MRIs for management of retinoblastoma. Investigational functional neuroimaging data were acquired after completion of the diagnostics scans, beginning approximately 40 minutes after induction of sedation. 
fMRI Paradigm.
Visual stimuli were projected onto a screen at the rear of the magnet and reflected onto the subjects' closed eyelids via a mirror attached to the head coil. The stimulus paradigm consisted of an alternating (8-Hz) black/white screen in block design with 20.6 seconds on/20.6 seconds off, 10 images per 20.6-second epoch. The stimulus was a square spanning a visual angle 30° × 30°, and the illumination intensity at the approximate location of the eye was 120 lux. The fMRI paradigm was implemented with experimental control software (Presentation; Neurobehavioral Systems, Davis, CA), and trigger pulses from the MRI scanner coordinated the stimulus presentation with image acquisition. Blood oxygenation level dependent (BOLD) functional images were acquired in an oblique axial plane, parallel to the plane containing the anterior–posterior commissure line. A 23-slice volume (slice thickness, 5 mm) provided full brain coverage and was obtained every 2.06 seconds. The T2*-weighted echo planar image pulse sequence used for the fMRI scans had the following parameters: field of view, 192 × 192 mm; image matrix, 64 × 64; echo time (TE), 50 ms; flip angle, 90°; and readout bandwidth, 1954 Hz/pixel. 26  
DTI Paradigm.
Diffusion-weighted images were obtained with b = 0 and either 6 or 12 diffusion encoding gradient directions with b = 1000 seconds/mm2. These images were acquired in four repetitions and were averaged after realignment to maximize the signal-to-noise ratio in the final product images. The double-spin-echo, echo planar image pulse sequence parameters were as follows: repetition time (TR), 7 seconds; echo time (TE), 101 ms; field of view 192 × 192 mm; and image matrix, 128 × 128. 
Data Analysis
fMRI Data Analysis.
Functional images were preprocessed and analyzed by using Statistical Parametric Mapping software (SPM2, http://www.fil.ion.ucl.ac.uk/spm/ provided in the pubic domain by members and collaborators of the Wellcome Trust Centre for Neuroimaging, University College London, London, UK). Preprocessing included standard realignment, normalization, and smoothing. 27 Activation maps were generated from statistical parametric maps with threshold values of P = 0.001 (uncorrected at voxel level) and 5-voxel threshold. An occipital lobe mask 28 was applied to the activation map for each subject to evaluate both positive and negative BOLD signal changes during stimulation. The maximum t-statistic and total number of activated volume elements (voxels) were evaluated in the visual cortex. The activated volume was analyzed to determine the effects of age, laterality of disease, and enucleation. We assessed hemispheric asymmetries of activation volume in patients who had undergone unilateral enucleation to detect cortical dominance ipsilateral or contralateral to the remaining eye. Activation volume was analyzed in patients with bilateral disease to determine the impact of macular disease involvement and retinal detachment. 
DTI Analysis.
Diffusion tensor images were processed with the DTI Toolkit (http://sourceforge.net/projects/spmtools) for the Statistical Parametric Mapping software. Regions of interest were manually drawn (ImageJ, http://rsb.info.nih.gov/ij/; developed by Wayne Rasband, National Institutes of Health, Bethesda MD) to evaluate fractional anisotropy (FA) and apparent diffusion coefficient (ADC) in the genu and splenium of the corpus callosum at the midline 29 and in the periventricular optic radiations (ORs). 30 The ADC is a measure of the magnitude of water diffusion and is approximately equal in gray matter and white matter. FA measures directional anisotropy of water diffusion; FA values are much higher in white matter than gray matter, because water diffuses more easily along than across white matter tracts. 
Nonlinear regressions of FA and ADC versus age at examination were calculated for each brain region with the exponential models in equations 1 and 2, respectively, to characterize the developmental trends in these diffusion properties. Nonlinear mixed-effect analysis was used to account for the repeated measurements in the same individuals.    The parameter a corresponds to the asymptotic value of the diffusion measurement at older age, the parameter b scales the developmental change in the measurement, and the parameter c corresponds to the time constant for the developmental change of the measurement. The units of a and b are the same as the modeled quantity and the unit of c is months. The regression models were fit to the data with commercial software (SAS, ver. 9.2; SAS Institute Inc., Cary, NC). 
Statistical Analyses
Generalized estimating equations (GEEs) were used for comparisons among variables in this longitudinal dataset. 31 For comparison between two measurements from a single scan (e.g., positive versus negative and contralateral versus ipsilateral), the difference between two measurements was first calculated and then was used as the response variable in the GEE. The intercept-only model was fit to test for differences. For comparison between two measurements from different scans (e.g., unilateral versus bilateral, nonenucleated versus enucleated, macular involvement of one eye versus two eyes, or retinal detachment in one eye versus two eyes), the measurement was used as the response variable, and the group indicator (e.g., unilateral versus bilateral) was fit as a covariate in the GEE, to investigate the difference of the two measurements. The GEE analysis was also used to determine the association between activation and age. The threshold for statistical inference was P < 0.05 for all tests. 
Results
Subjects
Clinical information about the subjects is summarized in Table 1. The first 42 patients enrolled in this ongoing study were analyzed for this report. This group included 24 patients with unilateral disease and 18 patients with bilateral disease. Three patients (two with unilateral disease and one with bilateral disease) had no fMRI or DTI scans. Thus, 106 fMRI scans (n = 57 unilateral, n = 49 bilateral) and 115 DTI scans (n = 62 unilateral, n = 53 bilateral) from 39 patients were included in our analyses. The mean age at the time of diagnosis of evaluable patients with unilateral retinoblastoma (31.9 months; range, 1.5–101.5 months) was significantly older than that of patients with bilateral disease (9.2 months; range, 2.6–22.2; P = 0.0001). The discrepancy between the total number of fMRI scans and DTI scans was due to technical failures of fMRI examinations. 
Table 1.
 
Clinical Information on Patients with Retinoblastoma Who Participated in Functional Neuroimaging Examination
Table 1.
 
Clinical Information on Patients with Retinoblastoma Who Participated in Functional Neuroimaging Examination
Laterality of Disease n Age at Diagnosis (mo ± SD) Patients with Enucleated Eyes (n Eyes) Patients with Macular Involvement (n Eyes) Patients with Retinal Detachment (n Eyes)
Unilateral 24 31.9 ± 24.8 23 (23) 22 (22) 13 (13)
Bilateral 18 9.2 ± 6.5 8 (9) 16 (22) 17 (22)
Functional MRI
Functional activation was detected in all but six completed scans (three scans of patients with unilateral and three with bilateral disease). The primary locus of activation was generally located in the anterior portion of the visual cortex (Fig. 1), corresponding to the peripheral visual field. The sign of the BOLD signal change during visual stimulation was predominately negative in 87 of 100 examinations (Fig. 2). As exemplified in Figure 1, the clusters of positive signal change were generally small and scattered in the striate and extrastriate visual areas. The median value of the maximum t-statistic was greater (GEE, P < 0.001) for the negative signal changes (8.5) than for the positive signal changes (4.5), and the number of activated voxels (volume) with a negative signal (1196 voxels) was 13.4 times greater (P < 0.001) than the number with a positive signal (89 voxels). The activated volume was associated with the peak signal change for both positive (GEE, P < 0.001) and negative (GEE, P < 0.001) changes during visual stimulation. The maximum t-statistic was associated with age (Fig. 3) for positive signal changes (GEE, P = 0.03), but not for negative responses. Visual inspection of Figure 3 shows that there was a similar increasing trend in the magnitude of both positive and negative signal changes with age for the low t-statistic values at the threshold of detection. There was no association between the volume of visual cortex activated and age at examination. Subsequent analysis was based on the regions with negative BOLD signal. 
Figure 1.
 
Representative fMRIs of a pediatric patient with retinoblastoma. Orthogonal views show activation with a negative BOLD signal (blue) in the anterior portion of the visual cortex and a small cluster of activation with a positive signal (red) at the occipital pole. The coronal slices in the lower right quadrant demonstrate the localization of the activation to the gray matter along the calcarine sulcus. This examination was after enucleation in a 40-month-old patient with unilateral disease. The activation parameters for this patient were approximately equal to the cohort median values for both positive (maximum t, 5.1; activated voxels, 117) and negative (maximum t, −7.5; activated voxels, 1072) signal changes (see also Figs. 2 and 3).
Figure 1.
 
Representative fMRIs of a pediatric patient with retinoblastoma. Orthogonal views show activation with a negative BOLD signal (blue) in the anterior portion of the visual cortex and a small cluster of activation with a positive signal (red) at the occipital pole. The coronal slices in the lower right quadrant demonstrate the localization of the activation to the gray matter along the calcarine sulcus. This examination was after enucleation in a 40-month-old patient with unilateral disease. The activation parameters for this patient were approximately equal to the cohort median values for both positive (maximum t, 5.1; activated voxels, 117) and negative (maximum t, −7.5; activated voxels, 1072) signal changes (see also Figs. 2 and 3).
Figure 2.
 
Characteristics of visual cortex activation in patients with retinoblastoma. The main graph shows the relationship between the number of activated voxels and the maximum t-statistic for both positive (white) and negative (gray) BOLD signal changes. The positive and negative activation for each patient are connected by lines. Inset: an expanded view of a smaller number of activated voxels. The box-and-whisker plots at the top and right of the figure summarize the percentile distribution of the activation data (circles: 5th and 95th, whiskers: 10 and 90th, box: 25th and 75th, line within box: 50th). Activation was predominately negative in 87 of 100 examinations.
Figure 2.
 
Characteristics of visual cortex activation in patients with retinoblastoma. The main graph shows the relationship between the number of activated voxels and the maximum t-statistic for both positive (white) and negative (gray) BOLD signal changes. The positive and negative activation for each patient are connected by lines. Inset: an expanded view of a smaller number of activated voxels. The box-and-whisker plots at the top and right of the figure summarize the percentile distribution of the activation data (circles: 5th and 95th, whiskers: 10 and 90th, box: 25th and 75th, line within box: 50th). Activation was predominately negative in 87 of 100 examinations.
Figure 3.
 
Maximum t-statistic for BOLD signal changes versus age at the time of the fMRI examination. There was a significant association for the positive signal changes (white) and the age, but not for the negative signal changes (gray). Note that there appears to be a similar increasing trend in the magnitude of the signal for both positive and negative responses near the detection threshold.
Figure 3.
 
Maximum t-statistic for BOLD signal changes versus age at the time of the fMRI examination. There was a significant association for the positive signal changes (white) and the age, but not for the negative signal changes (gray). Note that there appears to be a similar increasing trend in the magnitude of the signal for both positive and negative responses near the detection threshold.
The volume of activation was greater in patients with unilateral disease than in those with bilateral disease (1876 vs. 751 voxels/scan; GEE, P = 0.04). There was no difference between activation detected before enucleation and that detected afterward in patients with unilateral or bilateral disease or in all patients analyzed together. This comparison included all scans obtained before and after enucleation within each group. In addition, we analyzed scans from a group of nine patients with unilateral disease by focusing on the volume of activation in the last scan before enucleation and comparing the data with those from the first scan after enucleation in each patient. Again, there was no detectable effect of enucleation (data not shown). 
We then evaluated the difference in the volumes of activation between hemispheres in patients with unilateral disease who underwent enucleation. Activation was significantly greater in the hemisphere contralateral to the remaining eye (1221 vs. 657 voxels/scan; GEE, P < 0.002). To demonstrate that there was no left or right hemispheric bias in this finding, we compared left and right hemisphere activation in this same group of patients and found no significant difference (P = 0.113). In patients with bilateral disease who had not undergone enucleation, the volume of activation in those with macular involvement in one eye was significantly greater than that in patients with macular involvement in both eyes (1672 vs. 426 voxels/scan; GEE, P = 0.003). Finally, the volume of activation in patients with bilateral disease was greater in those with retinal detachment in one eye than in those with retinal detachment in both eyes (990 vs. 199 voxels/scan; GEE, P = 0.03). Images of retinoblastoma involving the macula, retinoblastoma involving the retinal periphery, retinoblastoma with retinal detachment, and normal retina, are shown in Figure 4
Figure 4.
 
Funduscopic images showing retinoblastoma tumors involving (A) the macula, (B) the peripheral retina, and (C) retinal detachment. (D) Image of a normal retina.
Figure 4.
 
Funduscopic images showing retinoblastoma tumors involving (A) the macula, (B) the peripheral retina, and (C) retinal detachment. (D) Image of a normal retina.
Diffusion Tensor Imaging
There was no difference between the mean FA (P = 0.20) and ADC (P = 0.83) values measured with 6 (90 scans) gradient directions compared with those measured with 12 (25 scans); therefore, we analyzed all the DTI data together. The color map images demonstrated qualitative developmental changes in the brain structure of children with retinoblastoma (Fig. 5). FA in the genu and splenium increased to comparable values with age (Fig. 6A). However, the variability of FA was greater in the splenium than in the genu. Similarly, ADC in the genu and splenium decreased to comparable values (7.6 × 10−4 and 7.8 × 10−4 mm2/s, respectively) with age (Fig. 6A) and was also more variable in the splenium than in the genu. The FA in the left and right hemisphere ORs increased to comparable values and had similar variability (Fig. 6B). Similarly, ADC in the left and right hemisphere ORs decreased to the same value (8.8 × 10−4 mm2/s) and had similar variability. After appropriate adjustments of the diffusion parameters for age according to the regression equations from all the regions of interest (Table 2), FA was greater in patients with unilateral disease than in those with bilateral disease (+0.01; P = 0.013), and ADC was lower in patients with unilateral disease than in those with bilateral disease (−1.8 × 10−5mm2/s; P = 0.008). 
Figure 5.
 
Developmental changes in DTI color maps of one pediatric patient with retinoblastoma. The transverse slice (top row) is approximately 1 cm above the AC–PC line, and the coronal slice passes through the splenium of the corpus callosum. The orientation of white matter fibers at each location is indicated by color: red, left-right; green, anterior–posterior; blue, superior–inferior (see also Fig. 6).
Figure 5.
 
Developmental changes in DTI color maps of one pediatric patient with retinoblastoma. The transverse slice (top row) is approximately 1 cm above the AC–PC line, and the coronal slice passes through the splenium of the corpus callosum. The orientation of white matter fibers at each location is indicated by color: red, left-right; green, anterior–posterior; blue, superior–inferior (see also Fig. 6).
Figure 6.
 
Developmental changes in diffusion parameters for patients with bilateral (black) and unilateral (white) retinoblastoma. (A) FA and ADC showed similar age-related changes in the genu (left) and splenium (right) of the corpus callosum, but the variability of the diffusion parameters was greater in the splenium. (B) The same DTI parameters were assessed in the left and right ORs. The curves are fits of the regression models in equations 1 and 2; insets: the regression lines from the left and right panels together for comparison.
Figure 6.
 
Developmental changes in diffusion parameters for patients with bilateral (black) and unilateral (white) retinoblastoma. (A) FA and ADC showed similar age-related changes in the genu (left) and splenium (right) of the corpus callosum, but the variability of the diffusion parameters was greater in the splenium. (B) The same DTI parameters were assessed in the left and right ORs. The curves are fits of the regression models in equations 1 and 2; insets: the regression lines from the left and right panels together for comparison.
Table 2.
 
Regression Parameters for Developmental Changes in Fractional Anisotropy and Apparent Diffusion Coefficient in the Genu and Splenium of the Corpus Callosum and in the Optic Radiations
Table 2.
 
Regression Parameters for Developmental Changes in Fractional Anisotropy and Apparent Diffusion Coefficient in the Genu and Splenium of the Corpus Callosum and in the Optic Radiations
a b c
FA
    Genu 0.87 (0.01) 0.39 (0.02) 15.66 (1.71)
    Splenium 0.87 (0.01) 0.33 (0.03) 10.75 (1.58)
    OR left 0.58 (0.04) 0.18 (0.03) 32.71 (16.03)
    OR right 0.57 (0.02) 0.19 (0.02) 19.53 (4.52)
ADC
    Genu 0.77 (0.01) 0.75 (0.04) 8.67 (0.81)
    Splenium 0.78 (0.02) 0.71 (0.05) 10.10 (1.37)
    OR left 0.88 (0.03) 0.29 (0.03) 25.31 (6.97)
    OR right 0.92 (0.03) 0.25 (0.03) 21.10 (7.43)
Discussion
Our results demonstrate the feasibility of fMRI and DTI analyses in pediatric patients with retinoblastoma. Our functional neuroimaging findings in this patient population were generally consistent with previous reports describing specific developmental features of both the BOLD signal in the visual cortex 22,32 and the diffusion characteristics of major white matter tracts 21,33 in infants and young children. However, the patterns of activation in the visual cortex during photic stimulation and diffusion parameters in the associated white matter pathways reflected important effects of disease and treatment. Therefore, fMRI and DTI may be useful for characterizing the impact of retinoblastoma on the developing visual system and improving the prediction of visual outcome in survivors. 
Functional MRI produces distinctive patterns of activation in young subjects who are sedated with eyelids closed during visual stimulation. 22,34 36 These patterns differ from those seen in awake adult subjects 37,38 ; however, sedation, sleep, closed eyelids, and rapid brain development may contribute to these differences. 35,39 44 The most notable differences are the sign of the BOLD signal change during stimulation and the location of the activation in the visual cortex. Sedated, asleep children have more negative signal during activation, and awake adults have more positive signal. Both positive and negative signal changes represent local changes in neural activity, but it is not fully understood why certain regions of the brain are more negatively activated. Our study confirmed the dominance of negative BOLD signal during visual stimulation through closed eyelids in sedated young patients with retinoblastoma. 
Activation detected during photic stimulation was located in the anterior visual cortex corresponding to the peripheral visual field. 45,46 The paucity of foveal visual cortex activation suggests that the stimulation on closed eyelids after approximately 40 minutes of anesthesia yielded dark adaptation of the eyes and scotopic stimulation conditions. Transmission through the eyelid is 5.6% for red light and 0.3% for green light, to which the rod receptors are most sensitive. 47 Thus, the illumination intensity through the eyelid was approximately 0.4 lux for our stimulus, which is at least an order of magnitude higher than the scotopic threshold reported for BOLD fMRI activation. 48 The visual response may also have been affected by the relatively low-frequency response of the rod receptors. 49 Much work is needed to determine the basic nature of BOLD signal changes in these developing young children, but despite these issues, the activation that we measured with fMRI was associated with specific features of retinoblastoma that affect vision. 
Patients with unilateral retinoblastoma had more functional activation in the visual cortex than did those with bilateral disease. This difference in activation cannot be accounted for by the difference in the ages of the two patient groups or by the effect of enucleation, because neither of these variables had a significant effect on the volume of cortex activated. In fact, Figure 3 shows that the strength of activation was greatest in young patients. Therefore, it is likely that the difference was caused by greater tumor burden in the patients with bilateral disease, involving more of the retina and decreasing the volume of primary visual cortex activated. It is remarkable that there was little immediate effect of enucleation on the activation in the visual cortex. However, enucleated eyes most likely had little to no useful vision for a period before enucleation. These findings suggest that evaluation of visual cortical function with fMRI can help predict useful vision in affected eyes and help clinicians make informed decisions about administering conservative therapy. 
Our fMRI results showed asymmetry of cortical activation in monocular children. It is well known that each eye contributes visual input to both hemispheres and that the nasal retina of each eye has a greater cortical representation than does the temporal retina. 50,51 Previous fMRI studies have found that monocular eye stimulation of awake adults and sedated children yields greater activation of the brain hemisphere contralateral to the stimulated eye. 50,52 We replicated this finding in young, sedated patients with unilateral retinoblastoma who had undergone enucleation. It will be interesting to see whether this difference in hemispheric activation persists into adulthood in subjects with early monocular vision. The plasticity of the early visual system may affect patterns of functional activation in enucleated subjects. 53 57  
We found a significant effect of macular involvement and retinal detachment on the volume of functional activation in the visual cortex of patients with bilateral retinoblastoma. The macula mediates central vision in humans via its large density of cones and large representation in the cortical visual field relative to the rest of the retina. 58,59 When the tumor involved the macula, it disrupted the synaptic network between the photoreceptors and the ganglion cells, thereby interfering with downstream visual signal processing. 59,60 However, during retinal detachment, the synaptic activity of photoreceptors and ganglion cells often persists. 61 63 Therefore, the potential for visual return is most likely greater in an eye in which the retina reattaches to the retinal pigment epithelium. 
DTI provided a measure of development in important white matter tracts associated with vision. Ongoing functional and structural development of white matter fibers causes increased anisotropy and decreased magnitude of water diffusion in the brain. 21,64,65 Therefore, the diffusion parameters FA and ADC are reliable indicators of normal or abnormal development in the white matter tracts. Color maps of white matter tracts identified in the developing brain of children with retinoblastoma appeared to be qualitatively comparable to similar images in healthy young subjects. 66,67 However, quantitative evaluation of diffusion in the corpus callosum showed evidence of disease- or treatment-related disruption of white matter maturation. FA was systematically lower and ADC was higher in patients with bilateral retinoblastoma compared with those measures in patients with unilateral disease. Diffusion parameters in both parts of the corpus callosum showed the dramatic developmental changes associated with myelination that were comparable, both qualitatively and quantitatively, to those observed in otherwise healthy children. 21,68 However, the variability of the diffusion measurements with respect to the developmental trajectory was much greater in the splenium than in the genu. This variability in the splenium may reflect an interaction between disease and treatment and the complex patterning of the visual system that develops in young children. Despite the transient variability of diffusion parameters in the splenium, the FA and ADC converged toward normal values as the patients aged. 
DTI measurements in the ORs showed no evidence of altered development in patients with retinoblastoma compared with similar measurements in the peripheral white matter of healthy children. 21,69 Developmental trajectories of those values were comparable in the left and right brain hemispheres of our patients. However, no published studies to date have reported longitudinal FA and ADC values in the ORs of healthy children throughout development. Furthermore, the placement of a single planar region of interest in the ORs is difficult. Future analysis with fiber tracking may help improve the precision of the region of interest placement for parameter evaluation in the ORs and allow evaluation of other relevant white matter characteristics. 70 72 For example, DTI with fiber tracking in patients with amblyopia has shown that, although FA and ADC were normal, the apparent length of the optic radiation fibers was abnormal. 73  
There are two important issues related to the young age at which retinoblastoma develops that affect the interpretation of our functional neuroimaging results: Patients must be sedated for MRI examination, and the brains of these infants and young children are developing rapidly in structure and function. Sedation affects neural responsiveness and activity directly and may change the hemodynamic response to neural activity that is the basis of BOLD fMRI. 74,75 Because of the requirement for sedation during MRI, there is limited normative functional imaging data of children during this period of rapid brain development. We note that we detected predominately positive sensorimotor cortex responses in young children evaluated for neurosurgical planning under the same anesthesia protocol that was used for the retinoblastoma patients reported herein. 76 Therefore, it is important to investigate the overlapping effects of disease traits, treatment, sedation, and normal development in the functional imaging findings of patients with retinoblastoma. Despite these challenges, we have shown that fMRI and DTI are promising methods to investigate visual system development in pediatric patients treated for retinoblastoma. Analysis of functional neuroimaging data in relation to ultimate visual outcome is necessary to determine the utility of these imaging methods in the clinical management of retinoblastoma. 
Footnotes
 Supported by National Institutes of Health Grants P30CA21765 and R01HD049888 and by ALSAC.
Footnotes
 Disclosure: S.M. Barb, None; C. Rodriguez-Galindo, None; M.W. Wilson, None; N.S. Phillips, None; P. Zou, None; M.A. Scoggins, None; Y. Li, None; I. Qaddoumi, None; K.J. Helton, None; G. Bikhazi, None; B.G. Haik, None; R.J. Ogg, None
References
Ries LAG Smith MA Gurney JG . Cancer Incidence and Survival among Children and Adolescents: United States SEER Program 1975–1995 Retinoblastoma. Bethesda, MD: National Cancer Institute, SEER Program; 1999: publication 99-4649.
Huttenlocher PR de Court Garey LJ Van der LH . Synaptogenesis in human visual cortex: evidence for synapse elimination during normal development. Neurosci Lett. 1982;33:247–252. [CrossRef] [PubMed]
Rodriguez-Galindo C Chantada GL Haik BG Wilson MW . Treatment of retinoblastoma: current status and future perspectives. Curr Treat Options Neurol. 2007;9:294–307. [CrossRef] [PubMed]
Rodriguez-Galindo C Wilson MW Haik BG . Treatment of intraocular retinoblastoma with vincristine and carboplatin. J Clin Oncol. 2003;21:2019–2025. [CrossRef] [PubMed]
Demirci H Shields CL Meadows AT Shields JA . Long-term visual outcome following chemoreduction for retinoblastoma. Arch Ophthalmol. 2005;123:1525–1530. [CrossRef] [PubMed]
Desjardins L Chefchaouni MC Lumbroso L . Functional results after treatment of retinoblastoma. J AAPOS. 2002;6:108–111. [CrossRef] [PubMed]
Abramson DH Melson MR Servodidio C . Visual fields in retinoblastoma survivors. Arch Ophthalmol. 2004;122:1324–1330. [CrossRef] [PubMed]
Hall LS Ceisler E Abramson DH . Visual outcomes in children with bilateral retinoblastoma. J AAPOS. 1999;3:138–142. [CrossRef] [PubMed]
Feller MB Scanziani M . A precritical period for plasticity in visual cortex. Curr Opin Neurobiol. 2005;15:94–100. [CrossRef] [PubMed]
Lewis TL Maurer D . Multiple sensitive periods in human visual development: evidence from visually deprived children. Dev Psychobiol. 2005;46:163–183. [CrossRef] [PubMed]
Hooks BM Chen C . Critical periods in the visual system: changing views for a model of experience-dependent plasticity. Neuron. 2007;56:312–326. [CrossRef] [PubMed]
Farley BJ Yu H Jin DZ Sur M . Alteration of visual input results in a coordinated reorganization of multiple visual cortex maps. J Neurosci. 2007;27:10299–10310. [CrossRef] [PubMed]
Horton JC Hocking DR . Effect of early monocular enucleation upon ocular dominance columns and cytochrome oxidase activity in monkey and human visual cortex. Vis Neurosci. 1998;15:289–303. [CrossRef] [PubMed]
Bowering ER Maurer D Lewis TL Brent HP . Constriction of the visual field of children after early visual deprivation. J Pediatr Ophthalmol Strabismus. 1997;34:347–356. [PubMed]
Maurer D Lewis TL Brent HP Levin AV . Rapid improvement in the acuity of infants after visual input. Science. 1999;286:108–110. [CrossRef] [PubMed]
Mendola JD Conner IP Roy A . Voxel-based analysis of MRI detects abnormal visual cortex in children and adults with amblyopia. Hum Brain Mapp. 2005;25:222–236. [CrossRef] [PubMed]
Crawford ML Harwerth RS . Ocular dominance column width and contrast sensitivity in monkeys reared with strabismus or anisometropia. Invest Ophthalmol Vis Sci. 2004;45:3036–3042. [CrossRef] [PubMed]
Park HJ Jeong SO Kim EY . Reorganization of neural circuits in the blind on diffusion direction analysis. Neuroreport. 2007;18:1757–1760. [CrossRef] [PubMed]
Duncan RO Sample PA Weinreb RN Bowd C Zangwill LM . Retinotopic organization of primary visual cortex in glaucoma: comparing fMRI measurements of cortical function with visual field loss. Prog Retin Eye Res. 2007;26:38–56. [CrossRef] [PubMed]
Conner IP Odom JV Schwartz TL Mendola JD . Monocular activation of V1 and V2 in amblyopic adults measured with functional magnetic resonance imaging. J AAPOS. 2007;11:341–350. [CrossRef] [PubMed]
Hermoye L Saint-Martin C Cosnard G . Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood. Neuroimage. 2006;29:493–504. [CrossRef] [PubMed]
Born P Leth H Miranda MJ . Visual activation in infants and young children studied by functional magnetic resonance imaging. Pediatr Res. 1998;44:578–583. [CrossRef] [PubMed]
Rombouts SA Lazeron RH Scheltens P . Visual activation patterns in patients with optic neuritis: an fMRI pilot study. Neurology. 1998;50:1896–1899. [CrossRef] [PubMed]
Werring DJ Clark CA Parker GJ Miller DH Thompson AJ Barker GJ . A direct demonstration of both structure and function in the visual system: combining diffusion tensor imaging with functional magnetic resonance imaging. Neuroimage. 1999;9:352–361. [CrossRef] [PubMed]
Sener RN . Diffusion MRI in neurofibromatosis type 1: ADC evaluations of the optic pathways, and a comparison with normal individuals. Comput Med Imaging Graph. 2002;26:59–64. [CrossRef] [PubMed]
Zou P Mulhern RK Butler RW Li CS Langston JW Ogg RJ . BOLD responses to visual stimulation in survivors of childhood cancer. Neuroimage. 2005;24:61–69. [CrossRef] [PubMed]
Ogg RJ Zou P Allen DN Hutchins SB Dutkiewicz RM Mulhern RK . Neural correlates of a clinical continuous performance test. Magn Reson Imaging. 2008;26:504–512. [CrossRef] [PubMed]
Maldjian JA Laurienti PJ Kraft RA Burdette JH . An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage. 2003;19:1233–1239. [CrossRef] [PubMed]
Thurnher MM Castillo M Stadler A Rieger A Schmid B Sundgren PC . Diffusion-tensor MR imaging of the brain in human immunodeficiency virus-positive patients. AJNR Am J Neuroradiol. 2005;26:2275–2281. [PubMed]
Ueki S Fujii Y Matsuzawa H . Assessment of axonal degeneration along the human visual pathway using diffusion trace analysis. Am J Ophthalmol. 2006;142:591–596. [CrossRef] [PubMed]
Liang KY Zeger SL . Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22. [CrossRef]
Yamada H Sadato N Konishi Y . A milestone for normal development of the infantile brain detected by functional MRI. Neurology. 2000;55:218–223. [CrossRef] [PubMed]
Ben BD Ben SL Graif M . Normal white matter development from infancy to adulthood: comparing diffusion tensor and high b value diffusion weighted MR images. J Magn Reson Imaging. 2005;21:503–511. [CrossRef] [PubMed]
Muramoto S Yamada H Sadato N . Age-dependent change in metabolic response to photic stimulation of the primary visual cortex in infants: functional magnetic resonance imaging study. J Comput Assist Tomogr. 2002;26:894–901. [CrossRef] [PubMed]
Yamada H Sadato N Konishi Y . A rapid brain metabolic change in infants detected by fMRI. Neuroreport. 1997;8:3775–3778. [CrossRef] [PubMed]
Born AP Miranda MJ Rostrup E . Functional magnetic resonance imaging of the normal and abnormal visual system in early life. Neuropediatrics. 2000;31:24–32. [CrossRef] [PubMed]
Born AP Rostrup E Miranda MJ Larsson HB Lou HC . Visual cortex reactivity in sedated children examined with perfusion MRI (FAIR). Magn Reson Imaging. 2002;20:199–205. [CrossRef] [PubMed]
Martin E Marcar VL . Functional MR imaging in pediatrics. Magn Reson Imaging Clin N Am. 2001;9:231–223. [PubMed]
Czisch M Wehrle R Kaufmann C . Functional MRI during sleep: BOLD signal decreases and their electrophysiological correlates. Eur J Neurosci. 2004;20:566–574. [CrossRef] [PubMed]
Martin E Thiel T Joeri P . Effect of pentobarbital on visual processing in man. Hum Brain Mapp. 2000;10:132–139. [CrossRef] [PubMed]
Marcar VL Schwarz U Martin E Loenneker T . How depth of anesthesia influences the blood oxygenation level-dependent signal from the visual cortex of children. AJNR Am J Neuroradiol. 2006;27:799–805. [PubMed]
Born AP Law I Lund TE . Cortical deactivation induced by visual stimulation in human slow-wave sleep. Neuroimage. 2002;17:1325–1335. [CrossRef] [PubMed]
Redcay E Kennedy DP Courchesne E . fMRI during natural sleep as a method to study brain function during early childhood. Neuroimage. 2007;38:696–707. [CrossRef] [PubMed]
Martin E Joeri P Loenneker T . Visual processing in infants and children studied using functional MRI. Pediatr Res. 1999;46:135–140. [CrossRef] [PubMed]
Miki A Liu GT Fletcher DW Hunter JV Haselgrove JC . Ocular dominance in anterior visual cortex in a child demonstrated by the use of fMRI. Pediatr Neurol. 2001;24:232–234. [CrossRef] [PubMed]
Stenbacka L Vanni S . fMRI of peripheral visual field representation. Clin Neurophysiol. 2007;118:1303–1314. [CrossRef] [PubMed]
Ando K Kripke DF . Light attenuation by the human eyelid. Biol Psychiatry. 1996;39:22–25. [CrossRef] [PubMed]
Hadjikhani N Tootell RB . Projection of rods and cones within human visual cortex. Hum Brain Mapp. 2000;9:55–63. [CrossRef] [PubMed]
Schneeweis DM Schnapf JL . Photovoltage of rods and cones in the macaque retina. Science. 1995;268:1053–1056. [CrossRef] [PubMed]
Toosy AT Werring DJ Plant GT Bullmore ET Miller DH Thompson AJ . Asymmetrical activation of human visual cortex demonstrated by functional MRI with monocular stimulation. Neuroimage. 2001;14:632–641. [CrossRef] [PubMed]
Tychsen L Burkhalter A . Nasotemporal asymmetries in V1: ocular dominance columns of infant, adult, and strabismic macaque monkeys. J Comp Neurol. 1997;388:32–46. [CrossRef] [PubMed]
Miki A Liu GT Raz J . Contralateral monocular dominance in anterior visual cortex confirmed by functional magnetic resonance imaging. Am J Ophthalmol. 2000;130:821–824. [CrossRef] [PubMed]
Baseler HA Brewer AA Sharpe LT Morland AB Jagle H Wandell BA . Reorganization of human cortical maps caused by inherited photoreceptor abnormalities. Nat Neurosci. 2002;5:364–370. [CrossRef] [PubMed]
Baker CI Dilks DD Peli E Kanwisher N . Reorganization of visual processing in macular degeneration: replication and clues about the role of foveal loss. Vision Res. 2008;48:1910–1919. [CrossRef] [PubMed]
Shimony JS Burton H Epstein AA McLaren DG Sun SW Snyder AZ . Diffusion tensor imaging reveals white matter reorganization in early blind humans. Cereb Cortex. 2006;16:1653–1661. [CrossRef] [PubMed]
Weiss AH Kelly JP . Spatial-frequency-dependent changes in cortical activation before and after patching in amblyopic children. Invest Ophthalmol Vis Sci. 2004;45:3531–3537. [CrossRef] [PubMed]
Sengelaub DR Windrem MS Finlay BL . Increased cell number in the adult hamster retinal ganglion cell layer after early removal of one eye. Exp Brain Res. 1983;52:269–276. [CrossRef] [PubMed]
Curcio CA Sloan KR Kalina RE Hendrickson AE . Human photoreceptor topography. J Comp Neurol. 1990;292:497–523. [CrossRef] [PubMed]
Neveu MM von dem HE Morland AB Jeffery G . The fovea regulates symmetrical development of the visual cortex. J Comp Neurol. 2008;506:791–800. [CrossRef] [PubMed]
Watts P Westall C Colpa L . Visual results in children treated for macular retinoblastoma. Eye. 2002;16:75–80. [CrossRef] [PubMed]
Sethi CS Lewis GP Fisher SK . Glial remodeling and neural plasticity in human retinal detachment with proliferative vitreoretinopathy. Invest Ophthalmol Vis Sci. 2005;46:329–342. [CrossRef] [PubMed]
Coblentz FE Radeke MJ Lewis GP Fisher SK . Evidence that ganglion cells react to retinal detachment. Exp Eye Res. 2003;76:333–342. [CrossRef] [PubMed]
Kaneko H Nishiguchi KM Nakamura M Kachi S Terasaki H . Retardation of photoreceptor degeneration in the detached retina of rd1 mouse. Invest Ophthalmol Vis Sci. 2008;49:781–787. [CrossRef] [PubMed]
Barnea-Goraly N Menon V Eckert M . White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study. Cereb Cortex. 2005;15:1848–1854. [CrossRef] [PubMed]
Neil JJ Shiran SI McKinstry RC . Normal brain in human newborns: apparent diffusion coefficient and diffusion anisotropy measured by using diffusion tensor MR imaging. Radiology. 1998;209:57–66. [CrossRef] [PubMed]
Miller JH McKinstry RC Philip JV Mukherjee P Neil JJ . Diffusion-tensor MR imaging of normal brain maturation: a guide to structural development and myelination. AJR Am J Roentgenol. 2003;180:851–859. [CrossRef] [PubMed]
Mukherjee P Miller JH Shimony JS . Normal brain maturation during childhood: developmental trends characterized with diffusion-tensor MR imaging. Radiology. 2001;221:349–358. [CrossRef] [PubMed]
Schneider JF Il'yasov KA Hennig J Martin E . Fast quantitative diffusion-tensor imaging of cerebral white matter from the neonatal period to adolescence. Neuroradiology. 2004;46:258–266. [CrossRef] [PubMed]
Dubois J Haene-Lambertz G Perrin M . Asynchrony of the early maturation of white matter bundles in healthy infants: quantitative landmarks revealed noninvasively by diffusion tensor imaging. Hum Brain Mapp. 2008;29:14–27. [CrossRef] [PubMed]
Partridge SC Mukherjee P Berman JI . Tractography-based quantitation of diffusion tensor imaging parameters in white matter tracts of preterm newborns. J Magn Reson Imaging. 2005;22:467–474. [CrossRef] [PubMed]
Yamamoto A Miki Y Urayama S . Diffusion tensor fiber tractography of the optic radiation: analysis with 6-, 12-, 40-, and 81-directional motion-probing gradients, a preliminary study. AJNR Am J Neuroradiol. 2007;28:92–96. [PubMed]
Yamamoto T Yamada K Nishimura T Kinoshita S . Tractography to depict three layers of visual field trajectories to the calcarine gyri. Am J Ophthalmol. 2005;140:781–785. [CrossRef] [PubMed]
Xie S Gong GL Xiao JX . Underdevelopment of optic radiation in children with amblyopia: a tractography study. Am J Ophthalmol. 2007;143:642–646. [CrossRef] [PubMed]
Heinke W Fiebach CJ Schwarzbauer C Meyer M Olthoff D Alter K . Sequential effects of propofol on functional brain activation induced by auditory language processing: an event-related functional magnetic resonance imaging study. Br J Anaesth. 2004;92:641–650. [CrossRef] [PubMed]
Maandag NJ Coman D Sanganahalli BG . Energetics of neuronal signaling and fMRI activity. Proc Natl Acad Sci U S A. 2007;104:20546–20551. [CrossRef] [PubMed]
Ogg RJ Laningham FH Clarke D . Passive range of motion functional magnetic resonance imaging localizing sensorimotor cortex in sedated children. J Neurosurg Pediatr. 2009;4:317–322. [CrossRef] [PubMed]
Figure 1.
 
Representative fMRIs of a pediatric patient with retinoblastoma. Orthogonal views show activation with a negative BOLD signal (blue) in the anterior portion of the visual cortex and a small cluster of activation with a positive signal (red) at the occipital pole. The coronal slices in the lower right quadrant demonstrate the localization of the activation to the gray matter along the calcarine sulcus. This examination was after enucleation in a 40-month-old patient with unilateral disease. The activation parameters for this patient were approximately equal to the cohort median values for both positive (maximum t, 5.1; activated voxels, 117) and negative (maximum t, −7.5; activated voxels, 1072) signal changes (see also Figs. 2 and 3).
Figure 1.
 
Representative fMRIs of a pediatric patient with retinoblastoma. Orthogonal views show activation with a negative BOLD signal (blue) in the anterior portion of the visual cortex and a small cluster of activation with a positive signal (red) at the occipital pole. The coronal slices in the lower right quadrant demonstrate the localization of the activation to the gray matter along the calcarine sulcus. This examination was after enucleation in a 40-month-old patient with unilateral disease. The activation parameters for this patient were approximately equal to the cohort median values for both positive (maximum t, 5.1; activated voxels, 117) and negative (maximum t, −7.5; activated voxels, 1072) signal changes (see also Figs. 2 and 3).
Figure 2.
 
Characteristics of visual cortex activation in patients with retinoblastoma. The main graph shows the relationship between the number of activated voxels and the maximum t-statistic for both positive (white) and negative (gray) BOLD signal changes. The positive and negative activation for each patient are connected by lines. Inset: an expanded view of a smaller number of activated voxels. The box-and-whisker plots at the top and right of the figure summarize the percentile distribution of the activation data (circles: 5th and 95th, whiskers: 10 and 90th, box: 25th and 75th, line within box: 50th). Activation was predominately negative in 87 of 100 examinations.
Figure 2.
 
Characteristics of visual cortex activation in patients with retinoblastoma. The main graph shows the relationship between the number of activated voxels and the maximum t-statistic for both positive (white) and negative (gray) BOLD signal changes. The positive and negative activation for each patient are connected by lines. Inset: an expanded view of a smaller number of activated voxels. The box-and-whisker plots at the top and right of the figure summarize the percentile distribution of the activation data (circles: 5th and 95th, whiskers: 10 and 90th, box: 25th and 75th, line within box: 50th). Activation was predominately negative in 87 of 100 examinations.
Figure 3.
 
Maximum t-statistic for BOLD signal changes versus age at the time of the fMRI examination. There was a significant association for the positive signal changes (white) and the age, but not for the negative signal changes (gray). Note that there appears to be a similar increasing trend in the magnitude of the signal for both positive and negative responses near the detection threshold.
Figure 3.
 
Maximum t-statistic for BOLD signal changes versus age at the time of the fMRI examination. There was a significant association for the positive signal changes (white) and the age, but not for the negative signal changes (gray). Note that there appears to be a similar increasing trend in the magnitude of the signal for both positive and negative responses near the detection threshold.
Figure 4.
 
Funduscopic images showing retinoblastoma tumors involving (A) the macula, (B) the peripheral retina, and (C) retinal detachment. (D) Image of a normal retina.
Figure 4.
 
Funduscopic images showing retinoblastoma tumors involving (A) the macula, (B) the peripheral retina, and (C) retinal detachment. (D) Image of a normal retina.
Figure 5.
 
Developmental changes in DTI color maps of one pediatric patient with retinoblastoma. The transverse slice (top row) is approximately 1 cm above the AC–PC line, and the coronal slice passes through the splenium of the corpus callosum. The orientation of white matter fibers at each location is indicated by color: red, left-right; green, anterior–posterior; blue, superior–inferior (see also Fig. 6).
Figure 5.
 
Developmental changes in DTI color maps of one pediatric patient with retinoblastoma. The transverse slice (top row) is approximately 1 cm above the AC–PC line, and the coronal slice passes through the splenium of the corpus callosum. The orientation of white matter fibers at each location is indicated by color: red, left-right; green, anterior–posterior; blue, superior–inferior (see also Fig. 6).
Figure 6.
 
Developmental changes in diffusion parameters for patients with bilateral (black) and unilateral (white) retinoblastoma. (A) FA and ADC showed similar age-related changes in the genu (left) and splenium (right) of the corpus callosum, but the variability of the diffusion parameters was greater in the splenium. (B) The same DTI parameters were assessed in the left and right ORs. The curves are fits of the regression models in equations 1 and 2; insets: the regression lines from the left and right panels together for comparison.
Figure 6.
 
Developmental changes in diffusion parameters for patients with bilateral (black) and unilateral (white) retinoblastoma. (A) FA and ADC showed similar age-related changes in the genu (left) and splenium (right) of the corpus callosum, but the variability of the diffusion parameters was greater in the splenium. (B) The same DTI parameters were assessed in the left and right ORs. The curves are fits of the regression models in equations 1 and 2; insets: the regression lines from the left and right panels together for comparison.
Table 1.
 
Clinical Information on Patients with Retinoblastoma Who Participated in Functional Neuroimaging Examination
Table 1.
 
Clinical Information on Patients with Retinoblastoma Who Participated in Functional Neuroimaging Examination
Laterality of Disease n Age at Diagnosis (mo ± SD) Patients with Enucleated Eyes (n Eyes) Patients with Macular Involvement (n Eyes) Patients with Retinal Detachment (n Eyes)
Unilateral 24 31.9 ± 24.8 23 (23) 22 (22) 13 (13)
Bilateral 18 9.2 ± 6.5 8 (9) 16 (22) 17 (22)
Table 2.
 
Regression Parameters for Developmental Changes in Fractional Anisotropy and Apparent Diffusion Coefficient in the Genu and Splenium of the Corpus Callosum and in the Optic Radiations
Table 2.
 
Regression Parameters for Developmental Changes in Fractional Anisotropy and Apparent Diffusion Coefficient in the Genu and Splenium of the Corpus Callosum and in the Optic Radiations
a b c
FA
    Genu 0.87 (0.01) 0.39 (0.02) 15.66 (1.71)
    Splenium 0.87 (0.01) 0.33 (0.03) 10.75 (1.58)
    OR left 0.58 (0.04) 0.18 (0.03) 32.71 (16.03)
    OR right 0.57 (0.02) 0.19 (0.02) 19.53 (4.52)
ADC
    Genu 0.77 (0.01) 0.75 (0.04) 8.67 (0.81)
    Splenium 0.78 (0.02) 0.71 (0.05) 10.10 (1.37)
    OR left 0.88 (0.03) 0.29 (0.03) 25.31 (6.97)
    OR right 0.92 (0.03) 0.25 (0.03) 21.10 (7.43)
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