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. Author manuscript; available in PMC: 2013 May 1.
Published in final edited form as: Vision Res. 2012 Mar 29;60C:51–60. doi: 10.1016/j.visres.2012.03.012

Arterial Spin Labeling fMRI Measurements of Decreased Blood Flow in Primary Visual Cortex Correlates with Decreased Visual Function in Human Glaucoma

Robert O Duncan 1, Pamela A Sample 2, Christopher Bowd 2, Robert N Weinreb 2, Linda M Zangwill 2
PMCID: PMC3340501  NIHMSID: NIHMS367169  PMID: 22465941

Abstract

Purpose

Altered metabolic activity has been identified as a potential contributing factor to the neurodegeneration associated with primary open angle glaucoma (POAG). Consequently, we sought to determine whether there is a relationship between the loss of visual function in human glaucoma and resting blood perfusion within primary visual cortex (V1).

Methods

Arterial spin labeling (ASL) functional magnetic resonance imaging (fMRI) was conducted in ten participants with POAG. Resting cerebral blood flow (CBF) was measured from dorsal and ventral V1. Behavioral measurements of visual function were obtained using standard automated perimetry (SAP), short-wavelength automated perimetry (SWAP), and frequency-doubling technology perimetry (FDT). Measurements of CBF were compared to differences in visual function for the superior and inferior hemifield.

Results

Differences in CBF between ventral and dorsal V1 were correlated with differences in visual function for the superior versus inferior visual field. A statistical bootstrapping analysis indicated that the observed correlations between fMRI responses and measurements of visual function for SAP (r = 0.49), SWAP (r = 0.63), and FDT (r = 0.43) were statistically significant (all p < 0.05).

Conclusions

Resting blood perfusion in human V1 is correlated with the loss of visual function in POAG. Altered CBF may be a contributing factor to glaucomatous optic neuropathy, or it may be an indication of post-retinal glaucomatous neurodegeneration caused by damage to the retinal ganglion cells.

Keywords: Functional MRI, Brain imaging, Optic nerve, Degeneration, Visual function testing, Visual pathway

INTRODUCTION

The glaucomas, the second leading cause of worldwide blindness, are a group of progressive optic neuropathies that are characterized by a gradual loss of retinal ganglion cells and a progressive neurodegeneration of the optic nerve. Left untreated, there can be irreversible vision loss and eventual blindness. Although elevated intraocular pressure is a leading risk factor, the pathophysiology of the neuronal degeneration of the glaucomas remains unknown. Several factors other than altered intraocular pressure have been identified as potentially contributing to the disease (Weinreb & Khaw, 2004), including alterations in ocular perfusion and alterations in the ocular and systemic microcirculation (Harris, 2009).

In addition to the retinal ganglion cells, the glaucomas also can have damage to post-retinal mechanisms, including the lateral geniculate nucleus of the thalamus (LGN) and the primary visual cortex (V1). Neuronal degeneration of the LGN and activity changes in V1 have been discovered using experimental models of glaucoma in primates (Crawford, Harwerth, Smith, Mills & Ewing, 2001, Crawford, Harwerth, Smith, Shen & Carter-Dawson, 2000, Vickers, Hof, Schumer, Wang, Podos & Morrison, 1997, Weber, Chen, Hubbard & Kaufman, 2000, Yucel, Zhang, Gupta, Kaufman & Weinreb, 2000, Yucel, Zhang, Weinreb, Kaufman & Gupta, 2001, Yucel, Zhang, Weinreb, Kaufman & Gupta, 2003). Metabolic activity is also greatly reduced within V1 ocular dominance columns that receive input from the glaucomatous eye (Brooks, Kallberg, Cannon, Komaromy, Ollivier, Malakhova, Dawson, Sherwood, Kuekuerichkina & Lambrou, 2004, Crawford et al., 2001, Crawford et al., 2000).

Neuroimaging methods have been used to monitor glaucomatous changes in human brain morphology and function in vivo (Duncan, 2010). Studies using traditional MRI methods (e.g., T1-weighted imaging) have reported a decrease in the volume of anatomically distinct visual areas including the optic nerve, LGN, and V1 in human glaucoma (Boucard, Hernowo, Maguire, Jansonius, Roerdink, Hooymans & Cornelissen, 2009, Brodsky, Glasier & Creel, 1993, Fujita, Tanaka, Takanashi, Hirabuki, Abe, Yoshimura & Nakamura, 2001, Gupta, Greenberg, de Tilly, Gray, Polemidiotis & Yucel, 2009, Iwata, Patronas, Caruso, Podgor, Remaley, Kupfer & Kaiser-Kupfer, 1997, Kashiwagi, Okubo & Tsukahara, 2004, Kitsos, Zikou, Bagli, Kosta & Argyropoulou, 2009, Lagreze, Gaggl, Weigel, Schulte-Monting, Buhler, Bach, Munk & Bley, 2009). Diffusion tensor imaging has also been used to measure glaucomatous degeneration of the optic nerve (Garcia, de Bazelaire & Alsop, 2005, Xu, Sun, Naismith, Snyder, Cross & Song, 2008). While anatomical imaging techniques have proven useful for quantifying cortical thickness and volume in post-retinal structures, these methods are limited because they cannot measure neuronal function or blood flow, which may be better indicators of the neuropathology of the glaucomas. Furthermore, anatomical techniques are not ideal for comparing cortical data to visual function data because regions of interest within visual cortex can only be localized reliably using functional activity. fMRI, on the other hand, can be used to localize regions of interest throughout V1 (Duncan, Sample, Weinreb, Bowd & Zangwill, 2007a, Duncan, Sample, Weinreb, Bowd & Zangwill, 2007b), which is important considering the variability of visual areas between individuals (Duncan & Boynton, 2003).

While the traditional blood oxygen level dependent (BOLD) fMRI technique is preferred for defining regions of interest in the brain based on neuronal activity, it is difficult to measure glaucomatous neurodegeneration using BOLD. Visual stimulation must be used to elicit neuronal activity in BOLD experiments. Therefore, it is difficult to differentiate whether a reduction in the cortical fMRI signal of glaucoma patients is due to cortical neurodegeneration or damage to the optic disk. Furthermore, the BOLD technique measures the relative change in neuronal activity associated with two brain states, and therefore BOLD does not directly measure ischemic injury in quantitative units.

Arterial spin labeling (ASL) is a non-invasive MRI method that measures absolute CBF in ml/100g/min (Detre, Leigh, Williams & Koretsky, 1992). ASL can measure resting CBF in the absence of visual stimulation. Compared to the BOLD technique, ASL demonstrates less inter-subject variability, and ASL tends to be more robust in the presence of baseline vascular changes (Aguirre, Detre, Zarahn & Alsop, 2002, Brown, Eyler Zorrilla, Georgy, Kindermann, Wong & Buxton, 2003, Stefanovic, Warnking, Rylander & Pike, 2006, Tjandra, Brooks, Figueiredo, Wise, Matthews & Tracey, 2005, Wang, Aguirre, Kimberg, Roc, Li & Detre, 2003). It also has been suggested that the ASL signal may be more localized to brain parenchyma than BOLD (Kim, 1995, Luh, Wong, Bandettini, Ward & Hyde, 2000). Therefore, ASL is a better choice for measuring the resting perfusion state of brain tissue in human glaucoma.

The objective of this study was to compare ASL fMRI measurements of CBF from V1 to standard measures of visual function in human primary open angle glaucoma, the most prevalent of the glaucomas.

2. METHODS

2.1 Subjects

Participants with primary open angle glaucoma (POAG) were selected from the ongoing longitudinal Diagnostic Innovations in Glaucoma Study (DIGS), conducted at the Hamilton Glaucoma Center at the University of California at San Diego (UCSD). The DIGS study is prospectively designed to assess structure and function in glaucoma. Informed consent was obtained from all participants after the nature and procedures of the study were explained. The Institutional Review Board of the University of California at San Diego approved the study, which follows the tenets of the Declaration of Helsinki.

2.1.1 Inclusion Criteria for DIGS

Participants underwent complete ophthalmologic examinations including slitlamp biomicroscopy, intraocular pressure measurement, and dilated stereroscopic fundus examination. Simultaneous stereoscopic photographs were obtained for all participants and had to be of adequate quality for the subject to be included. All participants had open angles, a best corrected acuity of 20/40 or better, a spherical refraction within and inclusive of ± 5.0 D (transposition allowed), and cylinder correction within ± 3.0 D. A family history of glaucoma was allowed.

2.1.2 Exclusion Criteria for DIGS

We excluded all participants with non-glaucomatous secondary causes of elevated intraocular pressure (IOP) (e.g., iridocyclitis, trauma), other intraocular eye disease, other diseases affecting the visual field (e.g., pituitary lesions, demyelinating diseases, HIV+ or AIDS, or diabetic retinopathy), with medications known to affect visual field sensitivity, or with problems other than glaucoma affecting color vision (as assessed by the Farnsworth D-15 color vision test).

2.1.3 For This Report

Glaucomatous optic neuropathy was defined for this report based on the appearance of a glaucomatous optic disk and by a repeatable abnormal SAP result in at least one eye. Ten participants with reliable visual field results on three tests of visual function were included from DIGS. Reliable visual fields were defined as ≤ 25% false positives, false negatives, and fixation losses. Participants also had a statistically significant superior-inferior visual hemifield asymmetry on at least two consecutive tests in one eye for SAP, as indicated by the Glaucoma Hemifield Test in the StatPac analysis package included with the visual field analyzer (Carl Zeiss Meditec, Dublin, CA). All tests of visual function were done in randomized order and completed within a 3-month period. Participants were also screened for standard MRI exclusion criteria: no conditions/medications known to affect cerebral metabolism, no metal in the body that could not be removed, and no history of claustrophobia. Participants were selected on the basis of consecutive visual field testing using the Glaucoma Hemifield Test and not MR imaging.

2.2 Evaluation of Stereophotographs

Evaluation of stereophotographs has been described in detail elsewhere (Sample, Medeiros, Racette, Pascual, Boden, Zangwill, Bowd & Weinreb, 2006). Evaluation of structural damage to the optic disk was based on assessment of simultaneous stereoscopic optic disk photographs (Nidek Stereo Camera Model 3-DX, Nidek Inc, Palo Alto, CA). Two experienced graders evaluated the photographs, and each grader was masked to the subject’s identity, the other test results, and the other grade. All included photographs were judged to be of good quality. Discrepancies between the two graders were resolved either by consensus or by a third experienced grader. Glaucomatous optic disks were defined as having either asymmetric vertical cup-to-disk ratio > 0.2, rim thinning, notching, excavation, disk hemorrhages, or nerve fiber layer defects.

2.3 Psychophysical Tests of Function

Visual fields were collected using SAP, SWAP, and FDT. Details of these tests have been presented previously (Boden, Pascual, Medeiros, Aihara, Weinreb & Sample, 2005, Martinez, Sample & Weinreb, 1995, Racette, Medeiros, Zangwill, Ng, Weinreb & Sample, 2008, Racette & Sample, 2003, Sample et al., 2006). All measurements were conducted within the central 30 degrees of the visual field and required fixation by the participant. Proper refraction was provided for each device. All tests required a 3mm or larger pupil. Dilation was used if necessary. Lids of eyes with potential ptosis were taped to reduce artifacts. The untested eye was occluded with an eye patch.

2.3.1 SAP

SAP utilizes a small (0.47 degree), 200-ms flash of white light as the target presented on a dim background (10 cd/m2 or 31.5 asb). The target was randomly presented to 54 locations within the central 24 degrees of visual field using a Humphrey Visual Field Analyzer II (Carl Zeiss Meditec, Dublin, CA), which used the 24-2 protocol (software version 3.4.7) and the Swedish Interactive Thresholding Algorithm (SITA) testing algorithm. The two locations just above and below the blind spot were not included in the analysis.

2.3.2 SWAP

SWAP was measured with the same perimeter, software version, and protocol as SAP-SITA. SWAP utilizes a 440 nm, narrow band, 1.8-degree target at 200-ms duration on a bright 100 cd/m2 yellow background to selectively test the short-wavelength sensitive cones and their connections.

2.3.3 FDT

FDT perimetry (24-2) was measured on a commercially available device, the Matrix perimeter, which was developed by Welch-Allyn (Skaneateles, NY) and marketed by Carl Zeiss Meditec. The Matrix FDT uses the Zippy Estimation by Sequential Testing (ZEST) thresholding algorithm.(Turpin, McKendrick, Johnson & Vingrys, 2002) FDT measures the contrast required to detect a counterphase flickering grating that spans five degrees of visual angle. The sinusoidal grating has a spatial frequency of 0.5 cyc/deg and a temporal frequency of 18 Hz.

2.3.4 Analyses and Visualization of the Visual Field Data

Because area V1 receives binocular input, measurements of resting CBF are predicted to correlate best with binocular visual function. Consequently, the visual function data from each eye were combined. Visual field parameters were derived by comparing each participant’s data to the manufacturer's normative database. For each participant, the pattern deviation (PD) values from the left and right eye were averaged for each test location based on their proximity to the center of fixation. The PD values indicate the deviation in decibels from the age-corrected normal values for each test location within the visual field. Increasingly negative PD scores indicate a greater deviation from normal vision due to glaucoma. PD values were selected rather than mean deviation (MD) or total deviation (TD) values because they are a less sensitive but more selective indicator of the pattern of vision loss associated with glaucoma. The mean PD value for each test location was compared to the normative database and a p-value was assigned accordingly. The p-values served only to assist in the visualization of the data. All subsequent data analysis was conducted using the mean PD values. The two most eccentric nasal and temporal points were included in our visualization of the data but not in the final statistical analysis.

2.4 General MRI Methodology

Each subject participated in a single one-hour scanning session that included anatomical, BOLD, and ASL scans. FMR images were acquired at the Center for Functional Magnetic Resonance Imaging at UCSD using a General Electric 3.0 Tesla HD Signa Excite scanner with an 8-channel brain coil. Visual stimuli were presented using fiber optic goggles (Avotec Inc., Stuart, FL). The general specifications of the visual presentation system follow: field of view = 30H × 23V degrees; focus +/−6 D; maximum luminance = 28.9 cd/m2; resolution = 1024H × 768V, 60 Hz refresh rate. Visual stimuli were generated using the Psychophysics Toolbox (Brainard, 1997, Pelli, 1997) for Matlab (Mathworks, Natick, MA) on a MacBook Pro computer (Apple, Cupertino, CA).

Standard anatomical and BOLD fMRI scanning protocols were used to conduct retinotopic mapping in each participant. A high-resolution (1×1×1 mm voxel) reference volume was obtained for each subject using a T1-weighted, Fast Spoiled Gradient Echo pulse sequence (FSPGR). Retinotopic mapping was obtained for each subject using a low-bandwidth echo planar imaging (EPI) pulse sequence lasting 260 s (TR = 2 s, TE = 30 ms, flip angle = 90°, 28 axial slices, FOV = 20×20 cm). The first ten temporal frames (20 s) were discarded to avoid magnetic saturation effects.

Cortical flattening techniques and methods for projecting functional data onto a flattened representation of visual cortex have been described in detail elsewhere (Duncan & Boynton, 2003). The occipital pole was flattened initially, dorsal and ventral V1 were defined using standard retinotopic analysis methods, and V1 was re-flattened to minimize local distortions caused by flattening. Regions of interest corresponding to dorsal and ventral V1 were redefined within the flattened representations. These regions of interest were then projected back to the 3D reference volume. Finally, the anatomical images and regions of interest were resampled to match the resolution of the ASL fMRI data.

2.5 Retinotopy Stimuli

Standard retinotopic mapping was conducted to delineate the borders of dorsal and ventral V1. The details of this procedure have been described elsewhere (Duncan et al., 2007a, Duncan et al., 2007b). During retinotopic mapping, participants viewed binocular images of either an expanding ring or a rotating wedge made from contrast-reversing checkerboards (100% contrast; 8 Hz flicker). In addition to the rings and wedges, the horizontal and vertical meridians were mapped using alternating “hourglass” and “bow tie” shaped checkerboard patterns. Meridian-mapping stimuli were composed of two mirror-symmetric, triangular regions spanning 90° of polar angle about the meridian. The parameters of the contrast-reversing checkerboards (1×1° squares, 8 Hz flicker freq., 100% contrast on a mean gray background) were selected from values known to elicit a maximum BOLD response from V1 (DeYoe, Bandettini, Neitz, Miller & Winans, 1994, Engel, Glover & Wandell, 1997, Engel, Rumelhart, Wandell, Lee, Glover, Chichilnisky & Shadlen, 1994, Sereno, Dale, Reppas, Kwong, Belliveau, Brady, Rosen & Tootell, 1995, Tootell, Hadjikhani, Vanduffel, Liu, Mendola, Sereno & Dale, 1998).

Eye movements were monitored via an infrared camera in the visual presentation system (iView dark-pupil eye tracking software, SMI, Teltow, Germany). Eye traces were processed according to previously developed protocols (Krauzlis & Miles, 1996). Deviations in eye position beyond 3 deg of visual angle were flagged. Analysis revealed that the direction of fixation breaks was spatially distributed and not associated with viewing through the glaucomatous or fellow eye (χ2, all p > 0.10). It is important to note that fixation losses only add noise to the fMRI signal during retinotopic mapping. Thus, fixation losses cannot account for a correlation between visual function loss and measurements of CBF, which were obtained in the absence of visual stimulation.

2.6 ASL Methodology

2.6.1 Data Acquisition

Measurements of CBF were obtained using a modified flow-sensitive inversion recovery (FAIR) pulse sequence with single-shot spiral read out (Wong, Buxton & Frank, 1998). Twenty contiguous axial slices were collected from V1 (5×5 mm resolution and 1 mm spacing). Three scans were used: The ASL-FAIR scan collects perfusion-weighted data, and the CSF and MinContrast scans are required to quantify the perfusion data. The global inversion pulse in standard FAIR was replaced by a spatially selective inversion pulse, which extended 10 cm in either direction outside the imaging slab. A 200 mm tagging slab was placed 10mm below the most inferior slice. QUIPSS II saturation pulses were applied, and the TI1 and TI2 parameters were chosen to satisfy the following two criteria (Wong et al., 1998): (1) TI1 is less than the natural temporal bolus width δ and (2) TI2-TI1 is greater than the longest transit delay Δt. The pulse sequence used a single shot spiral acquisition, collecting 50 tag+control image pairs (TI1=600 ms, TI2=1600 ms, TR=2.5 s, TE=minimum, FOV = 22×22 cm). The ASL-FAIR scan lasted 4 min 10 s and the two calibration scans lasted 1 min each.

During each ASL scan, a dark cloth was used to cover the eyes of the participants, and the room lights were attenuated so that participants were not subjected to visible light. Because fMRI activity in visual cortex is suppressed when the eyes are closed (Goldman, Stern, Engel & Cohen, 2002), participants were instructed to keep their eyes open during the duration of the scan. Compliance was monitored using an infrared camera within the eye tracking goggles. Because the eye tracker uses infrared light, we were able to monitor eye position without exposing participants to visible light. A liberal fixation-tolerance window was used in the iView software (20° visual angle). Thus, participants could freely move their eyes in the dark, but an alarm would sound if they closed their eyes for longer than one second.

2.6.2 Data Analysis

ASL data analysis methods have been described in detail elsewhere (Lu, Perthen, Duncan, Zangwill & Liu, 2008). The first four images of each scan were excluded to avoid magnetic saturation effects. Each ASL scan was motion corrected and then registered to the first functional run using AFNI software (Cox, 1996). Functional scans were aligned to the anatomical volume using the scanner coordinates of each volume. CBF responses were computed by subtracting the control and tag image series. If odd indices correspond to control images and even indices correspond to tag images, then the surround subtraction over an image acquisition time series y[n], n=0,1,2,… produces the perfusion weighted time series: {y[1]−(y[0]+y[2])/2, (y[1]+y[3])/2 − y[2], …} (Liu & Wong, 2005). Mean CBF was computed for voxels within the regions of interest corresponding to dorsal and ventral V1. The mean CBF for each voxel was averaged across four scans in the same fMRI scanning session for each participant.

2.6.3 Comparing Measurements of CBF to Visual Fields

Measurements of resting CBF from V1 were compared to measurements of visual function obtained with SAP, SWAP and FDT. For each test of visual function, difference scores (ΔPDMEAN) were computed, one for the left and right hemifield, to reveal any asymmetry that might exist between the superior versus inferior quadrants. Thus, each participant generated six ΔPDMEAN values, two for each test of visual function. ΔPDMEAN was computed by subtracting the mean of all PD values in the inferior visual quadrant from the mean of the superior quadrant. Positive ΔPDMEAN values indicate better visual function for the superior versus inferior visual quadrant. Negative ΔPDMEAN values indicate better visual function for the inferior visual quadrant. The mean resting CBF was computed for voxels within dorsal and ventral V1. Difference scores (ΔCBFMEAN) were computed for each hemisphere by subtracting the mean dorsal CBF from the mean ventral CBF. Positive ΔCBFMEAN values indicate greater resting CBF for ventral versus dorsal V1 in each hemisphere. PD and CBF difference scores for both hemispheres in all participants were compared using simple linear regression.

3. RESULTS

3.1 Data From a Single Subject

Visual field data for participant 4 are presented in Figure 1. Data from SAP, SWAP and FDT are presented in each row. Data from the right and left eyes are presented in the two left columns. Data in the right column indicate the average PD value across both eyes for each test location. The data for each test location is shaded to indicate the PD (or mean PD) value in dB. The legend indicates how these measurements compare to data from the normative database for each test. The p-values serve only to make this presentation of the data intuitive to those accustomed to viewing printouts from standard perimeters. The mean PD values for SAP, SWAP and FDT denote that this participant had severe visual loss in the inferior hemifield of the left eye. Statistical bootstrapping revealed that the asymmetry was significant for each test (all p < 0.05). It is important to note, however, that a significant asymmetry for all three tests of visual function was not a necessary criterion for inclusion in this study.

Figure 1. Visual field data for a single subject.

Figure 1

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SAP, SWAP and FDT data are presented for Participant 4. Data from the right (OD) and left (OS) eyes are presented with the average PD value (in dB) across both eyes (OU) for each test location. Shading and p-values indicate how data compare to standard normative databases obtained from monocular viewing. Participant 4 had severe visual loss in the inferior hemifield of the left eye (all p < 0.05).

CBF data for participant 4 are presented in Figure 2. Anatomical images for several axial slices through V1 are displayed in each panel using a grayscale map. The color map superimposed upon the anatomical images indicates the CBF values for active voxels in ml/100g/min. Each row of panels is composed of axial slices that contained active voxels for dorsal and ventral V1 in either hemisphere. The data indicate that resting CBF for participant 4 is greater in ventral V1 compared to dorsal V1. This asymmetry in CBF is consistent with the asymmetry in visual function observed in Figure 1. The relative loss of visual function in the inferior hemifield of participant 4 is accompanied by a relative decrease in CBF within dorsal V1.

Figure 2. CBF data for a single subject.

Figure 2

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Axial slices through V1 are displayed for Participant 4. The color map indicates CBF values for voxels in ml/100g/min. CBF data was constrained to the four regions of interest (dorsal-left (DL), dorsal-right (DR), ventral-left (VL) and ventral-right (VR)). Consistent with this participant’s asymmetry in visual function, resting CBF in ventral V1 is greater than dorsal V1.

3.2 Measurements of CBF Correlate with Visual Function

Data for superior and inferior visual fields and dorsal and ventral cortex appear in Table 1. Visual field data for both eyes (OU) and CBF measurements from V1 are presented for all ten participants in Figure 3. Yellowish-red pixels correspond to voxels where measurements of resting CBF were relatively large. Bluish-purple pixels indicate relatively smaller measurements of CBF. To conserve space, the axial slice with the highest mean CBF value is presented for each region of interest. However, data analysis was conducted on all active voxels within dorsal and ventral V1.

Table 1.

Subject data for measurements of visual function and CBF

SAP* SWAP* FDT* CBF**
Subject Age Left Right Left Right Left Right Right Left
1 59 0.46 2.69 −2.69 1.73 −1.11 5.70 23.55 29.03
2 50 −4.62 −2.62 −4.35 −1.35 −2.30 −6.59 −14.93 53.71
3 73 −10.38 −4.27 −13.62 −5.12 −5.56 −8.19 16.38 50.97
4 59 2.15 1.92 2.73 3.31 3.44 5.41 −6.50 19.36
5 59 −6.46 −12.04 −0.81 −10.08 −0.37 −19.85 6.11 82.31
6 75 3.50 −1.81 −0.81 −0.38 −0.33 2.26 −4.77 27.30
7 73 0.88 1.35 0.65 1.46 1.30 1.78 6.73 47.20
8 54 1.31 −1.85 −5.46 −7.96 −7.96 −6.22 129.10 50.00
9 66 3.88 1.73 8.35 0.92 −1.00 3.44 −42.29 −22.94
10 56 −2.35 −2.04 −0.85 0.38 0.63 −2.22 −74.19 27.40
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*

Δ PD (dB) from left and right hemifield

**

Δ CBF (ml/100g/min) from right and left hemisphere

Figure 3. Visual field data for all participants.

Figure 3

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CBF measurements and visual field data from both eyes (OU) are presented. The three columns at left indicate the mean pattern deviation (PDMEAN) for each test of visual function (SAP, SWAP and FDT). The gray scale and p-values demonstrate how the PDMEAN compares to the normative database. The four columns at right present mean CBF (CBFMEAN) for each cortical region of interest (dorsal-left (DL), dorsal-right (DR), ventral-left (VL) and ventral-right (VR)). Yellowish-red pixels correspond to voxels with large CBF values. Bluish-purple pixels indicate relatively smaller measurements of CBF. The axial slice with the highest mean CBF value is presented for each region of interest, but data analysis was conducted on all active voxels.

Measurements of CBF were compared to visual fields for all participants (Figure 4). Each data point represents the asymmetry in visual fields (ΔPDMEAN) and CBF (ΔCBFMEAN). There was a positive correlation between ΔPDMEAN and ΔCBFMEAN for SAP (r = 0.49; p < 0.05) and SWAP (r = 0.63; p < 0.05). There was a borderline significant correlation (r = 0.43; p = 0.059) between ΔCBFMEAN and ΔPDMEAN for FDT. While definitive conclusions cannot be based on this analysis for FDT, the consistency between the three tests of visual function is notable. Specifically, participants with large superior-inferior visual field asymmetries also possessed large differences in CBF measurements from ventral versus dorsal V1, and vice versa. Simple linear regression did not reveal a correlation between mean CBF and age (r = −0.21, F(1,8) = 0.39, p = 0.55).

Figure 4. Comparisons between measurements of CBF and visual fields.

Figure 4

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Data points represent visual field asymmetry (ΔPDMEAN) and the difference in CBF for dorsal and ventral V1 (ΔCBFMEAN). Large superior-inferior visual field asymmetries were accompanied by large ventral-dorsal CBF asymmetries and vice versa. A statistical bootstrapping procedure showed the positive correlations for SAP (r = 0.49), SWAP (r = 0.63), and FDT (r = 0.43) were significant (all p < 0.05).

There was relatively low power to detect an association between ΔCBFMEAN and ΔCBFMEAN for FDT due to the small number of subjects enrolled in the study. Consequently, a statistical “bootstrapping” method was used to determine whether the correlations observed between CBF responses and visual fields was significant (Henderson, 2005, Hesterberg, Moore, Monaghan, Clipson & Epstein, 2005). Statistical bootstrapping is well suited to small samples sizes because it reduces the assumptions made regarding the population distribution. The ΔCBFMEAN for a given hemisphere was randomly paired with the ΔPDMEAN from another subject. For each sample of random pairings, a correlation coefficient was computed. This process was repeated 10,000 times. To determine the statistical significance (i.e., p-value) of the originally observed correlation statistic, the number of randomly generated correlations that exceeded the observed correlation statistic was divided by the total number of random correlations. The bootstrapping approach reveals that ΔCBFMEAN was significantly correlated with ΔPDMEAN for FDT as well as for SAP and SWAP (all p < 0.05).

The bootstrapping approach was also used to determine whether the observed correlations could be attributed to between-subject variability rather than within-subject, retinotopic differences in visual function. The ΔCBFMEAN values from each region of interest (i.e., dorsal-left (DL), dorsal-right (DR), ventral-left (VL) and ventral-right (VR)) were randomly paired with the ΔPDMEAN values from each visual quadrant for each participant. For each sample of random pairings, a correlation coefficient was computed. This process was repeated 10,000 times and a p-value was computed. For all three tests of visual function, the observed correlations between ΔCBFMEAN and ΔPDMEAN were significantly greater than the distribution of random correlations (all p < 0.05). Therefore, it is unlikely that between-subject variability could account for the observed correlations, and CBF is correlated with visual function at the level of retinotopy.

4. DISCUSSION

There are few studies of cortical metabolism or cerebral blood flow in the human glaucomas (Duncan, 2010). To our knowledge, the current report is the first to quantify the relationship between measurements of visual field loss and resting cortical blood perfusion in human glaucoma. CBF was relatively lower for regions of V1 that correspond to glaucomatous regions of the visual field. The results from this study suggest that the ASL fMRI can be used to measure CBF throughout the visual system in human glaucoma, and cortical metabolism is likely to be affected in human POAG. It is recognized, however, that a causal relationship between glaucoma and decreased CBF cannot be established without a control group that demonstrates visual field loss not resulting from glaucoma. Future studies will compare CBF in patients with glaucoma to patients with Ischemic Optic Neuropathy (ION).

4.1 Measurement of Metabolism and Blood Flow in Human Glaucoma

The experimental glaucomas may stem from different etiologies than human glaucoma. Consequently, human studies are preferred to determine the contribution of metabolism and blood flow to POAG. There is a large body of data obtained from fundus imaging techniques that implicate ocular blood perfusion in glaucoma (Flammer & Mozaffarieh, 2008, Grover & Budenz, 2011, He, Vingrys, Armitage & Bui, 2011, Leske, 2009, Schmidl, Garhofer & Schmetterer, 2011, Venkataraman, Flanagan & Hudson, 2010, Yanagi, Kawasaki, Wang, Wong, Crowston & Kiuchi, 2011). Fundus imaging techniques can measure blood flow via the pupil, but these methods rely upon optical transparency and clear media. Neuroimaging can be used to study anatomical structure and neurovascular coupling throughout the visual pathway without such limitations.

Previous neuroimaging studies have provided indirect evidence for altered cerebral metabolism in glaucoma. T1-weighted MRI reveals that normal tension glaucoma patients have an increased incidence of cerebral infarcts, white matter hyperintensities, subcortical ischemia, and corpus collosum atrophy (Acaroglu, Kansu & Dogulu, 2001, Ong, Farinelli, Billson, Houang & Stern, 1995, Stroman, Stewart, Golnik, Cure & Olinger, 1995). Decreased visual function in various retinal pathologies was correlated with a wider calcarine sulcus (Kitajima, Korogi, Hirai, Hamatake, Ikushima, Sugahara, Shigematsu, Takahashi & Mukuno, 1997). Ischemic changes throughout the brain correlate with decreased visual function in normal tension glaucoma (Suzuki, Tomidokoro, Araie, Tomita, Yamagami, Okubo & Masumoto, 2004). And POAG patients demonstrate optic nerve atrophy, degeneration of the optic chiasm and V1, microvascular injury, and a higher concentration of white matter hyperintensities in the cortex and optic chiasm (Kitsos et al., 2009).

Single-voxel proton magnetic resonance spectroscopy (1H-MRS) can detect concentrations of the metabolites N-acetylaspartate (NAA), creatine (Cr), and choline (Cho), which are markers of neuronal integrity, metabolism and cell death. Surprisingly, 1H-MRS revealed normal metabolite concentrations (NAA, Cr, and Cho) in V1 for patients with POAG (Boucard, Hoogduin, van der Grond & Cornelissen, 2007). Conversely, 1H-MRS revealed abnormal metabolite concentrations in a rodent model of glaucoma (Chan, So & Wu, 2009). Cho levels were significantly lower in the glaucomatous regions of visual cortex compared to healthy control regions. Chan and colleagues speculate that the report of Boucard et al. may not have detected any changes in metabolites because of insufficient field strength. Metabolite changes might have been too slow to detect, or the sample size might have been too small to detect a difference.

Reduced cortical blood flow was reported using 123I-IMP SPECT in a 22-year-old patient with normal tension glaucoma (Yoshida, Sugiyama, Sugasawa, Nakajima, Ikeda & Utsunomiya, 2006). The patient’s visual fields progressed without changes to IOP. Seven out of 31 normal tension glaucoma patients demonstrated cerebral blood flow patterns similar to Alzheimer’s Disease even though none were diagnosed with Alzheimer’s disease (Sugiyama, Utsunomiya, Ota, Ogura, Narabayashi & Ikeda, 2006). Visual field defects progressed more rapidly and cerebral blood flow was lower in these patients. The effects of a neuroprotective agent used to treat Alzheimer’s disease (donepezil hydrochloride) were recently studied in this patient population (Yoshida, Sugiyama, Utsunomiya, Ogura & Ikeda). All five patients demonstrated significant improvement on visual fields and measures of blood flow after only six months of treatment. These results suggest that normal tension glaucoma is largely vascular in origin, and it may share a common mechanism with Alzheimer’s disease. These studies and recent measurements of blood perfusion in the rat retina using ASL implicate abnormal cerebral blow flow in glaucoma (Li, Cheng & Duong, 2008, Shen, Ren, Cheng, Fisher & Duong, 2005).

4.2 Measurements of Cerebral Metabolism using ASL fMRI

The calibrated BOLD technique is a non-invasive neuroimaging protocol that compares ASL fMRI measurements of CBF and measurements of the BOLD signal to obtain a quantitative estimate of oxidative metabolism (CMRO2) (Davis, Kwong, Weisskoff & Rosen, 1998, Hoge, Atkinson, Gill, Crelier, Marrett & Pike, 1999). Hence, calibrated BOLD may be the best method for measuring CMRO2 changes in human glaucoma in vivo.

We recently demonstrated that CMRO2 could be measured from the LGN in healthy volunteers using physiological noise reduction and background suppression methods (Lu et al., 2008). Background suppression of fatty tissue enhances the SNR of the ASL fMRI signal. The use of retrospective physiological noise reduction methods can significantly enhance the ASL signal in the visual cortex as well (Restom, Behzadi & Liu, 2006). However, we had difficulty implementing the calibrated BOLD technique in the current experiment. The addition of physiological measurement devices to the visual display goggles and the claustrophobic MRI environment was too challenging for our volunteers. Therefore, we decided to measure resting CBF in the absence of visual stimulation as a means to infer decreased cortical metabolism/function. Future modifications to standard physiological measuring devices may make calibrated BOLD more feasible for use in patient populations. We are aware that only the calibrated BOLD technique gives a true measure of CRMO2.

4.3 Conclusions

Resting blood perfusion in human V1 is correlated with the loss of visual function in POAG. Altered CBF may be a contributing factor to glaucomatous optic neuropathy or it may be an indication of post-retinal glaucomatous neurodegeneration caused by damage to the retinal ganglion cells. In addition, the fMRI techniques developed in this report can be used to study human glaucoma in vivo and may lead to new approaches for diagnosis and treatment. A larger study of ocular hypertensive patients is required to determine whether compromised cerebral blood flow predicts glaucomatous neurodegeneration.

Highlights.

  1. Arterial spin labeling was conducted in ten participants with glaucoma.

  2. Visual function was compared to measurements of cerebral blood flow.

  3. Resting blood flow is correlated with the loss of visual function in glaucoma.

  4. Altered cerebral blood flow may contribute to glaucomatous optic neuropathy.

ACKNOWLEDGEMENTS

We would like to acknowledge Drs. Thomas T. Liu, Kun Lu, and Joanna E. Perthen for their excellent guidance and technical assistance.

Sponsored by:

  • The Glaucoma Foundation, Grant in Aid (ROD)

  • NIH EY11008 (LMZ)

  • NIH EY008208 (PAS)

ROLE OF FUNDING SOURCES

Funding sources did not play a role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

DISCLOSURE STATEMENT

Financial (F), consulting (C), and honoraria (H):
  • Linda M. Zangwill: F; Carl Zeiss Meditec Inc., Heidelberg Engineering GmbH, Optovue Inc., Topcon Medical Systems Inc. R: Heidelberg Engineering
  • Christopher Bowd: Pfizer, Inc.
  • Robert N. Weinreb: F; Carl Zeiss Meditec, Heidelberg Engineering GmbH, Novartis, Optovue., Topcon Medical Systems. C; Alcon, Allergan, Carl Zeiss Meditec, Optovue, Pfizer, Merck
  • Pamela A. Sample: F; Carl Zeiss Meditec Inc, Haag-Streit

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