Skip to main content
NCBI home page
Human Brain Mapping logoLink to Human Brain Mapping
. 2009 Aug 6;31(2):276–286. doi: 10.1002/hbm.20863

Dissecting structure–function interactions in acute optic neuritis to investigate neuroplasticity

Thomas Jenkins 1,✉, Olga Ciccarelli 1, Ahmed Toosy 1, Katherine Miszkiel 2, Claudia Wheeler‐Kingshott 1,3, Daniel Altmann 4, Laura Mancini 2, Steve Jones 5, Gordon Plant 6,7, David Miller 3,6, Alan Thompson 1,6
PMCID: PMC6870769  PMID: 19662659

Abstract

Structural MRI, electrophysiology, and functional MRI (fMRI) elucidate different aspects of damage and repair in demyelinating diseases. We combined them to investigate why patients with optic neuritis (ON) exhibit a wide variation in severity of acute visual loss, with the following objectives: (1) To determine how structural and electrophysiological changes in the anterior and posterior visual pathways contribute to acute visual loss. (2) To combine these data with fMRI, to investigate whether cortical activity modulates visual acuity. The visual system of 28 patients with acute unilateral ON was assessed. Linear regression modeling was used to identify parameters associated with acute visual loss, and to determine whether fMRI activity was associated with vision, after accounting for structural and electrophysiological predictors, age, and gender. Optic nerve lesion length and visual evoked potential (VEP) amplitude were associated with visual loss. Bilateral activation in the extra‐striate occipital cortex correlated directly with vision, after adjusting for optic nerve lesion length, VEP amplitude, and demographic characteristics. These data suggest that acute visual loss is associated with the extent of inflammation and conduction block in the optic nerve, but not with pathology in the optic radiations or occipital cortex. The association of better vision with greater fMRI responses, after accounting for factors which reduce afferent input, suggests a role for adaptive neuroplasticity within the association cortex of the dorsal stream of higher visual processing. Longitudinal studies will clarify whether different extra‐striate cortical regions play a role in adaptive plasticity in the acute and chronic stages of injury. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.

Keywords: Cuneus, dorsal stream, plasticity, MRI, diffusion

INTRODUCTION

Understanding mechanisms of damage and repair in demyelinating diseases, such as multiple sclerosis (MS), is important to identify targets for therapeutic intervention, but represents a challenge because of complex interactions between brain structure and function. Acute idiopathic demyelinating optic neuritis (ON) is a disease of the optic nerve, which is often associated with MS. It is a useful model for studying damage and repair during an acute attack of demyelination, as inflammation in the optic nerve has similar pathological and clinical characteristics as elsewhere in the central nervous system.

A striking clinical characteristic of ON is the wide variability in severity of acute visual loss. Some people experience only mild blurring, while others are unable to perceive light. It is unclear whether this depends solely on the degree of structural damage within the optic nerve itself, or if it is influenced by other factors, such as pathology elsewhere in the visual pathways, or compensatory brain responses. In this study, we attempt to dissect the structural and functional contributions underlying this variability in acute clinical deficit, by combining structural MRI, electrophysiology, and functional MRI (fMRI) in vivo in a group of patients with acute ON.

These techniques have previously been applied individually to clarify aspects of pathology in the optic nerve in ON. Acute edema [Hickman et al., 2004a], inflammation [Hickman et al., 2004b; Kupersmith et al., 2002; Youl et al., 1991], and possibly demyelination [Brusa et al., 2001; Halliday et al., 1972] are associated with visual loss. More recently, quantitative MRI techniques have permitted investigation of pathological mechanisms and physiological responses in greater detail. For example, diffusion‐based tractography [Basser and Pierpaoli, 1996; Ciccarelli et al., 2008; Mori et al., 1999] is particularly sensitive to damage in white matter tracts, and fMRI [Ogawa et al., 1990] allows assessment of cortical responses to visual stimuli in disease, providing insights into neuroplastic reorganization. Combining these techniques, and the complementary perspectives they offer, provides an opportunity to elucidate further the complex structure–function interactions that occur during acute ON.

Although disease mechanisms have been characterized in the optic nerve itself, the impact of changes in the rest of the visual system resulting from acute ON is less clear, and it has not been investigated in depth. Posterior visual pathway involvement and occipital cortical responses may contribute to the clinical picture, either by sustaining or modulating damage. For example, patients with clinically isolated ON commonly have inflammatory lesions on brain MRI, which often involve the optic radiations [Hornabrook et al., 1992], and may affect vision [Plant et al., 1992]. In addition, optic nerve inflammation may have secondary consequences for the posterior pathways. Previous studies have demonstrated abnormalities of the optic radiations [Ciccarelli et al., 2005], and occipital cortex [Audoin et al., 2006], in the months following ON; it remains unclear when these changes first appear.

The functional response of the brain to an attack of ON appears to be complex. A generalized reduction in fMRI response to visual stimuli occurs, which correlates with visual acuity [Korsholm et al., 2007; Langkilde et al., 2002; Rombouts et al., 1998; Toosy et al., 2005]. This can be explained by a reduced afferent input, secondary to active inflammation in the anterior pathway. However, it appears that fMRI responses reflect more than the magnitude of visual input to primary visual cortex. Activation has been identified in extra‐striate areas, thought to represent neuroplastic reorganization, which may contribute to visual recovery [Korsholm et al., 2007; Levin et al., 2006; Toosy et al., 2005; Werring et al., 2000]. The timing of these changes remains somewhat uncertain, but at least some of them appear to occur soon after the initial insult.

It is not known whether cortical responses to visual stimuli can modulate visual acuity during the acute stage, or whether they are just consequent to it. For example, a region of fMRI activity which correlates with visual acuity could be interpreted in two ways. It may be that patients with better vision have a greater afferent input to occipital cortex, and hence greater fMRI activity. Alternatively, increased fMRI activity may reflect a neuroplastic response of the gray matter that is actively contributing to preservation of vision. These two possibilities are difficult to disentangle, but elucidating this is important when considering how the brain responds to injury.

We attempted to address this important issue by combining structural MRI, electrophysiology, and fMRI in a cohort of patients with acute ON. The objectives of the study were twofold: first, to identify which structural and electrophysiological measures in the afferent pathways were associated with acute visual loss. Second, to use these structural and electrophysiological measures to inform a further analysis, in which fMRI data were also entered. This strategy aimed to separate the effects of a reduced afferent input from any cortical influence on vision. We hypothesized that any fMRI activity correlating with vision, independent from measures of structural and electrophysiological integrity of the optic nerve, would represent a neuroplastic modulatory effect.

In a model such as this, it is important that the structural assessment is comprehensive to include any potential influences on vision from the posterior, as well as the anterior pathways. To this end, the combination of optic nerve, optic radiation, and visual cortical structural imaging, together with visual fMRI, represents the most detailed MRI assessment of the visual system in ON to date.

METHODS

Subjects

We recruited 28 consecutive patients attending the Moorfields Eye Hospital with a typical acute unilateral ON. This was defined as a painful unilateral loss of vision, which progresses over a few days to 2 weeks, and is clinically isolated, i.e. no other neurological symptoms are present [Hickman et al., 2002]. Ten healthy age and sex matched controls were also studied. Patients with a diagnosis of MS, bilateral ON, or other chronic neurological conditions were excluded. The presence of brain inflammatory lesions was not considered to be an exclusion criterion. Treatment with steroids was noted. Patients were invited for clinical assessment, visual evoked potentials (VEPs), structural and functional MRI, on the same day, within a month from symptom onset. All subjects gave informed written consent. The study was approved by the local Ethics Committee.

Clinical Assessment

On the day of scanning, the patients' best corrected visual acuity, using glasses and pinhole correction if necessary, was measured using a retro‐illuminated Early Treatment Diabetic Retinopathy Study Chart. Scores were recorded as the 4 meters logarithm of the minimum angle of resolution (logMAR) acuity [Ferris et al., 1982]. Higher logMAR scores indicate worse vision. When no letters could be correctly identified, a score of 1.7 was assigned [Optic Neuritis Study Group, 1991].

Electrophysiological Assessment

Each patient was invited to have whole‐field pattern‐reversal VEPs on the day of scanning, with parameters that have been described in previous studies [Brusa et al., 2001]. The amplitude of the response and the latency of the P100 waveform were calculated.

MRI acquisition and Analysis

Two 1.5T GE Medical Systems scanners (Milwaukee, WI) were used to acquire MRI data, using an 8‐channel receive‐only head‐coil. The scanners were chosen based on their dedicated setup. Structural imaging of the optic nerves and brain was performed in all subjects with a 1.5‐T Signa Echo speed MRI system, with a maximum gradient strength of 33 mT m−1, while fMRI was performed, on the same day, on a 1.5‐T Signa Excite whole‐body MRI system, with a gradient strength of 22 mT m−1.

Structural MRI Imaging

Optic nerves

  • 1

    A coronal‐oblique fast spin‐echo sequence (TR 2,300 ms, TE 68 ms, 2 excitations, echo train length 8, matrix size 512 × 384, field of view (FOV) 24 cm × 18 cm, 16 contiguous 3 mm slices) was acquired to calculate lesion length, which was determined by an experienced neuroradiologist (KM), blinded to image identity and side affected, by multiplying the number of consecutive slices of optic nerve returning abnormal signal by the slice thickness. The intraobserver coefficient of variation, which is equal to the ratio of the standard deviation to the mean, multiplied by 100%, and was calculated on eight patients, was 2.8%.

  • 2

    Post triple‐dose gadolinium‐enhanced coronal‐oblique fat‐saturated T1‐weighted spin echo was acquired in patients only (TR 600 ms, TE 20 ms, 1 excitation, matrix size 256 × 192, FOV 24 cm × 18 cm, 16 contiguous 3 mm slices).

  • 3

    Coronal‐oblique FLAIR imaging (TR 2,500 ms, TE 12.7 ms, TI 995 ms, 6 excitations, echo train length 6, matrix size 512 × 384, FOV 24 cm × 18 cm, 16 contiguous 3 mm slices) was performed to obtain the optic nerve cross‐sectional area, which was calculated by a single blinded observer (TJ), from five contiguous slices anterior from the orbital apex [Hickman et al., 2001], using a semi‐automated contouring technique [Plummer, 1992]. The intraobserver coefficient of variation was 4.6%. To account for normal inter‐individual variability, the ratio of affected to fellow nerve area was calculated.

Optic radiations

  • 1

    Axial‐oblique dual‐echo fast spin‐echo of the whole brain (TR 2,000 ms, TE 17 ms/102 ms, echo train length 8, matrix size 256 × 256, FOV 24 cm × 18 cm, 28 contiguous 5 mm slices) were used by TJ to calculate the optic radiation lesion load, after lesions were identified by KM, using standard anatomical landmarks. The intraobserver coefficient of variation was 2.6%.

  • 2

    Diffusion tensor imaging (DTI) of the optic radiations and occipital lobe was obtained using an optimised single‐shot, cardiac‐gated, diffusion‐weighted echo‐planar imaging sequence (TR 10, RR ∼11–13 s, TE 82 ms, 1 excitation, matrix size 96 × 96 (reconstructed to 128 × 128), FOV 22 cm × 22 cm, in‐plane resolution 2.3 mm × 2.3 mm (reconstructed to 1.7 mm × 1.7 mm), 30 contiguous 2.3 mm slices, parallel to the AC‐PC line, diffusion gradients applied along 61 directions [Cook et al., 2007], b max = 1,200 s/mm2, and seven interleaved non‐diffusion‐weighted (b0) scans, acquisition time 10–15 min, depending on cardiac cycle. One additional b0 volume was acquired, covering the whole brain (60 slices, extended from the original 30), to assist coregistration of partial brain diffusion data to whole brain fMRI data, which was necessary for tractography. Head motion and eddy‐current induced distortions were corrected and the diffusion tensor was then calculated on a pixel‐by‐pixel basis, using FSL tools (http://www.fmrib.ox.ac.uk/fsl/).

The optic radiations were reconstructed using the probabilistic tractography algorithm provided by FSL (http://www.fmrib.ox.ac.uk/fsl/fdt/fdt_probtrackx.html [Behrens et al., 2003a, b]. The mean fractional anisotropy (FA) within each tractography‐derived optic radiation was obtained in each subject. Our tractography analysis, including definition of seed regions, is explained in detail in the Supporting Information and shown in Figure 1.

Figure 1.

Figure 1

Open in a new tab

(A) The LGN group activation clusters, overlaid onto a coronal slice of the MNI152 T1‐weighted template, and corrected at voxel‐level P < 0.05. (B) The reconstructed LGN seed‐masks at their original locations in native diffusion space in green, and following a 10 voxel lateral shift in blue, within the apex of Meyer's loop, in one of the control subjects. The seed masks are overlaid onto an axial slice of the FA map, with the eigenvector map superimposed in red. (C) The results of tractography for the same control subject. The target region is shown in white, the exclusion mask in gray, and the reconstructed optic radiations in the red color scale, which indicates the connectivity values.

Visual cortex

Three‐dimensional fast prepared spoiled gradient recall (3D‐FSPGR) of the whole brain was acquired (TR 14.3 ms, TE 5.1 ms, 1 excitation, matrix size 256 × 128, FOV 31 cm × 31 cm, 156 contiguous 1.2 mm slices). The images were analyzed with FreeSurfer software (http://surfer.nmr.mgh.harvard.edu), in which they were reconstructed as 1 mm × 1 mm × 1 mm axial images, and the brain extracted. The skull‐strip was assessed visually in all cases, and manual correction performed if necessary, by a single observer (TJ), blinded to image identity. Fully automated cortical parcellation was then performed, and pericalcarine volume, surface area, and cortical thickness estimates obtained [Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 1999, 2004].

An SPM segmentation‐based methodology was used to extract whole brain gray matter fractional volume [Chard et al., 2002], which was quantified using in‐house software (http://www.nmrgroup.ion.ucl.ac.uk/atrophy/).

Functional MRI Imaging

The fMRI data for this study were acquired in four scanning sessions, using a T2*‐weighted echo‐planar imaging sequence. Thirty‐eight axial‐oblique slices covering the whole brain and parallel to the AC‐PC line were acquired (TR 3,950 ms, TE 50 ms, matrix size 64 × 64, FOV 20 cm, slice thickness 2 mm, 1 mm gap).

The visual stimulation paradigm comprised eight epochs, each of 16 s, of flickering checkerboard stimulation, alternated with eight epochs, each of 16 s, of gray background, presented on a projection screen. Subjects wore transparent plano chromatic filter goggles, with one green and one red filter (Haag‐Streit, UK). The checkerboards were also green and red, so that the green checkerboard was invisible through the red filter, and likewise for the red checkerboard through the green filter. This was to allow monocular stimulation while testing both eyes within the same run and to facilitate attention and fixation of a central cross (further details on the screen and luminance of each checkerboard are given in the Supporting Information). Subjects were instructed to fixate a central cross, and asked to press a button when it changed to a # symbol [Jenkins et al., 2008]. The patients responded correctly to the cross changing 74% of the time, and controls 97% of the time, indicating generally good attention and fixation. Each experiment consisted of two sessions, and the orientation of the goggles was reversed in between, to swap the red and green filters.

After these sessions, the subjects had two further sessions of stimulation using black and white checkerboards, one for each eye, with the fellow eye patched. Each session consisted of eight 16‐s alternating epochs of stimulation and rest. These data were used to define the seed region for tractography.

Statistical parametric mapping software was used (SPM5, Wellcome Department of Cognitive Neurology, London, United Kingdom). Each fMRI series was realigned, normalized to MNI stereotactic space, and smoothed, using an 8 mm isotropic Gaussian kernel. Realignment parameters and time derivatives were entered as covariates into the general linear model, together with the time‐points at which the subjects pressed the button during the task to maintain attention.

For each subject, first level fixed effect contrasts were specified, for affected and fellow eyes individually (1 0 and 0 1, respectively), combining epochs of stimulation through the red and green filters. Controls right and left eyes were pseudorandomized to maintain exactly the same methodology as the patients. The subsequent contrast images, representing the main effect of stimulation for each eye, were entered into the second level regression models, described in the next section.

Statistical Analysis

Statistical analysis was performed using Stata‐9.2 (StataCorp, TX), and variables considered significant at P < 0.05, unless otherwise stated.

Structural and Functional Differences Between Patients and Controls

Two‐tailed unpaired t‐tests were used to compare patients' affected and fellow eye parameters to controls (i.e. optic nerve lesion length and area, optic radiation lesion load and FA, pericalcarine cortical volume, surface area and thickness, VEP amplitude and latency). For the controls, optic nerve area, VEP amplitude, and VEP latency were averaged for the two eyes, to maximize use of the available data. The group means, standard deviation (SD), and P value were reported for each t‐test.

As there were no differences between the right and left optic radiations (using the Wilcoxon signed‐rank test), in either the patient or control groups, mean FA values of the right and left optic radiation were calculated, and compared between patients and controls.

Differences in pericalcarine cortical volume, surface area and thickness, and whole brain gray matter volume, between patients and controls were calculated using unpaired t‐tests. A multivariable regression approach was used to adjust for age and gender. Pericalcarine volumetric comparisons were adjusted for whole brain gray matter volume. Group differences were quantified by dividing the patient group parameter by the control parameter and multiplying by 100%.

Voxel‐wise differences in fMRI activity between patients and controls were compared for affected and fellow eyes, using SPM5. A second‐level two‐sample t‐test model was used to identify any regions where patients activated more than controls (contrast 1 −1), or vice versa (−1 1). The resulting statistical parametric maps were thresholded at cluster‐level P < 0.05 (corrected). Regions of significant activation were localized, using the SPM5 Anatomy toolbox [Eickhoff et al., 2005].

Associations Between Structural and Electrophysiological Measures and Vision

Separate linear regression models were estimated to identify any imaging, electrophysiological or demographic variables that were associated with logMAR visual acuity. LogMAR visual acuity scores were always the dependent variable, and the following variables were entered individually as predictors: fast spin‐echo and gadolinium‐enhanced optic nerve lesion length, optic nerve area, optic radiation lesion load and mean FA, pericalcarine cortical volume, surface area and thickness, VEP amplitude and latency, age, gender, side affected and the number of days from presentation to assessment. Acuity in the fellow eye was always entered into the model to correct for any normal intersubject variability.

Associations Between fMRI Responses and Vision

To identify areas of brain functional activity associated with visual acuity, after adjusting for structural damage in the visual system, conduction deficit and demographic characteristics, any significant variables from the previous analysis were entered into a whole brain voxel‐based multiple regression analysis in SPM5, together with the fMRI data, affected eye visual acuity scores, age, gender, and side of stimulation. Positive and inverse associations between visual acuity in the affected eye and fMRI activity were assessed, by specifying 1 and −1 contrasts respectively, with zeros for the other variables. Separate models were specified, in turn, for the affected and fellow eye fMRI data.

RESULTS

Patient Characteristics

The mean age of the patients was 32 years (SD 7) and 23 were female. The control group was age and sex matched, with a mean age of 30 years (SD 3) and 8 females.

In the patient group, the median duration of symptoms was 22 days (range 7–34). Patients' mean logMAR score was 0.72 (SD 0.69) for the affected eye and −0.05 (SD 0.10) for the fellow eye. ON affected the left eye in 14 patients and the right eye in the other 14. Seven patients (25%) were treated with steroids, three prior to MRI assessment and four after the initial scans.

Gadolinium‐enhanced scans were obtained for 26 of the 28 patients, with the remaining two patients declining an intravenous injection.

In one patient, DTI data was unavailable, and, in one more, the connectivity of one of the reconstructed optic radiations was too low to survive thresholding. These data were omitted from further analysis.

With regard to the timing of assessments, all were performed on the same day in 19/28 patients, and within 2 days of each other in 25/28. In two patients, the structural scans (and, in addition, VEP in one of these two) were performed 5 days after the fMRI and clinical assessment. In one patient, the fMRI scan could not be performed until 21 days after the other assessments.

Structural and Functional Differences Between Patients and Controls

Differences in structural and electrophysiological measures between patients and controls are summarized in Table I.

Table I.

MRI and VEP parameters from the anterior and posterior visual pathways in patients and controls

Patients Controls P value
Anterior pathway structure
  T2 optic nerve lesion length (mm) 21.6 ± 10.4 – NA
  Gad‐enhanced segment lesion length (mm) 23.5 ± 13.3 – NA
 Affected optic nerve area (mm2) 14.6 ± 3.2 12.8 ± 1.4 (mean of L and R healthy eye) NS
 Fellow optic nerve area (mm2) 12.3 ± 1.3 12.8 ± 1.4 (mean of L and R healthy eye) NS
Posterior pathway structure
  Optic radiation lesion load (mm2) 63 ± 117 – NA
 Mean FA optic radiations 0.35 ± 0.03 0.36 ± 0.03 NS
  Pericalcarine cortical volume (cm3) 36.30 ± 9.81 42.32 ±7.22 0.047
 Pericalcarine surface area (mm2) 24.40 ± 4.83 27.80 ± 5.32 NS
 Pericalcarine cortical thickness (mm) 1.61 ± 0.13 1.63 ± 0.10 NS
 Whole brain grey matter volume (cm3) 715 ± 54 740 ± 53 NS
Electrophysiology
  Affected VEP amplitude (μV) 5.30 ± 3.90 9.70 ± 3.34 (mean of L and R healthy eye) 0.003
 Fellow VEP amplitude (μV) 12.00 ± 4.4 9.70 ± 3.34 (mean of L and R healthy eye) NS
  Affected VEP latency (ms) 120 ± 18 95 ± 6 (mean of L and R healthy eye) <0.001
 Fellow VEP latency (ms) 95 ± 7 95 ± 6 (mean of L and R healthy eye) NS
Open in a new tab

Means ± standard deviations are reported. Significant between‐group differences are highlighted in bold font. Gad, gadolinium; FA, fractional anisotropy; NA, not applicable; NS, nonsignificant; VEP, visual evoked potential; L, left; R, right.

Optic nerves, optic radiations, and visual cortex

T2 lesions were visible in the patients' affected optic nerve in 96% of cases, and in one case a segment of high T2 signal was seen in the fellow optic nerve.

Contrast enhancement was detected in 92% of affected optic nerves, and no fellow nerve enhanced.

The patients' affected optic nerves were swollen during the acute phase compared with the fellow eye (19% greater area, P < 0.001), and the difference from controls was of borderline significance (patients 14% greater area, P = 0.088). There were no differences in the area of patients' fellow nerves compared with controls.

Seventy‐five percent of patients had asymptomatic brain lesions and in 50% the optic radiations were involved. The brain lesions fulfilled the MRI criteria for diagnosis of dissemination in space [McDonald et al., 2001;Polman et al., 2005] in four cases (14%). There were no significant differences in FA of the optic radiations between the patient and control groups.

No optic nerve abnormalities or brain lesions were found in controls

The volume of the pericalcarine cortex was 14% smaller in patients than controls (P = 0.047). The mean whole brain gray matter volume was 3% less in patients, which was not statistically significant (P = 0.220). The difference in pericalcarine cortical volumes retained significance after correction for age and gender (P = 0.049), and retained borderline significance after adjustment for whole brain gray matter volume (P = 0.080). Pericalcarine cortical surface area was 12% smaller in patients, which was of borderline significance before (P = 0.071) and after (P = 0.067) adjustment for age and gender. There was no difference in the pericalcarine cortical thickness between patients and controls.

Cerebral cortical function

Differences in fMRI activity between patients and controls are summarized in Table II. For the affected eye, patients with ON exhibited lower fMRI activation than controls within the bilateral visual cortex. There were no areas where patients with ON affected eyes activated more than controls.

Table II.

Regions of differential fMRI activation between patients and controls

fMRI response Patients > controls Controls > patients
Eye tested Fellow Affected
Region (MNI coordinates) Right precentral gyrus (48, −16, 56) Bilateral visual cortex (−24, −82, −14)
Spatial extent (voxels) 576 3208
Voxel‐level t‐score 3.70 5.64
Cluster‐level P value 0.041 <0.001
Open in a new tab

The location, Montreal Neurological Institute (MNI) coordinates, spatial extent, global maximal t‐score, and cluster‐level P value are reported.

For the fellow eye, patients with ON activated more than controls in a region centered on the right precentral gyrus, extending into the right postcentral gyrus. There were no areas where controls activated more than patients, with respect to the fellow eye.

Associations Between Structural and Electrophysiological Measures and Vision

Among all the parameters tested, fast spin‐echo lesion length, enhancing lesion length, and VEP amplitude were found to be significantly associated with visual acuity (Table III).

Table III.

Structural, electrophysiological, and demographic associations with visual acuity

Predictor Partial correlation coefficient (r) P value
Fast spin‐echo optic nerve lesion length 0.55 0.001
Gad‐enhanced optic nerve lesion length (n = 26) 0.47 0.010
Optic nerve cross‐sectional area 0.35 NS
Optic radiation lesion load −0.01 NS
Optic radiation fractional anisotropy (n = 27) 0.21 NS
Pericalcarine volume −0.07 NS
Pericalcarine surface area −0.15 NS
Pericalcarine thickness 0.18 NS
VEP amplitude −0.84 <0.001
VEP latency (n = 23, 5 flat responses) −0.08 NS
Age −0.09 NS
Gender −0.06 NS
Side affected 0 NS
Number of days from presentation −0.08 NS
Open in a new tab

Partial correlation coefficients are reported, with the associated P value. Only significant P values are reported. The sample size was 28, unless otherwise stated. Significant variables are highlighted in bold font. NS, nonsignificant; VEP, visual evoked potential.

A more positive logMAR score, indicating worse vision, was associated with longer lesions and smaller VEP amplitudes. None of the optic radiation or occipital cortical parameters was associated with visual acuity. Therefore, fast spin‐echo and gadolinium‐enhancing measures of lesion length and VEP amplitude were entered into the next step of the analysis (i.e. the whole brain voxel‐based SPM5 multivariable regression model).

Associations Between fMRI Responses and Vision

There was a significant association between greater fMRI response to stimulation of the affected eye and better vision (i.e. lower logMAR) in the bilateral visual association cortex of the cuneus, when adjusting for lesion length, VEP amplitude, age, gender, and side of ON (see Fig. 2). The association suggested that better vision correlated with greater fMRI activity, in a manner that could not be explained by structural, electrophysiological, or demographic associations.

Figure 2.

Figure 2

Open in a new tab

Statistical parametric maps showing group correlations between fMRI response and visual acuity, after correcting for age, gender, side affected, gadolinium‐enhanced lesion length, fast spin‐echo lesion length, and VEP amplitude. A correlation is seen in the region of the cuneus bilaterally, where better visual acuity is associated with a greater fMRI response. The graph plots logMAR visual acuity against the mean corrected fMRI response (approximate percentage BOLD signal change), at the peak voxel. The statistical parametric maps are thresholded at cluster level P < 0.05 (corrected), and the scale bar indicates the voxel level t‐scores. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

No associations were identified between vision in the patients' affected eye and fellow eye fMRI activity

DISCUSSION

The most important finding of this study is the association between acute visual acuity, and extra‐striate cortical activation, located in the cuneus, part of the dorsal stream of visual processing. This was identified following a voxel‐based whole brain multivariable regression analysis, after taking into account structural imaging and electrophysiological measures that reflect of the effects of pathology on both the anterior and posterior visual pathway. We argue that this may represent an influence of neuroplasticity on acute visual function.

In this section, first the differences between patients and controls will be discussed, and then the associations between vision, structure, and function in patients, together with a discussion of neuroplasticity in ON.

Structural and Functional Differences Between Patients and Controls

We found abnormal structural imaging and electrophysiological measures in the acute stage of ON, which confirm the presence of optic nerve inflammation and edema, and consequent conduction delay and block. Half the patients had incidental lesions in the optic radiations, but optic radiation FA did not differ between patients and controls, suggesting that tract integrity is preserved. However, there was a suggestion of volume loss in the patients' pericalcarine cortex. This novel finding appeared to be partly independent of whole brain grey matter volume loss, which has been reported in clinically isolated syndromes [Anderson et al., 2007]. The smaller calcarine cortex could be due to trans‐synaptic degeneration, but this explanation is not in agreement with a normal integrity of the subserving optic radiations. Therefore, we hypothesize that this may reflect a generalized susceptibility of the visual system to damage from demyelinating disease. It is known that the optic nerve is particularly vulnerable, as ON is the presenting symptom of MS in approximately 20% of patients [Sorensen et al., 1999], optic radiation lesions are common [Hornabrook et al., 1992] and it is conceivable that the occipital gray matter may also be susceptible to damage.

In the direct voxel‐wise comparison of fMRI responses, controls' eyes activated more in the visual cortex than patients' affected eyes. This can be explained by a reduced afferent input in patients, and it is consistent with the findings of previous studies. There was no evidence of differential activation in the cuneus between patients and controls, which might be anticipated if adaptive plastic changes were genuinely occurring in this region. However, the sensitivity of the voxel‐based whole brain analysis approach to detect such an effect may be limited in patients with ON; these patients have structural damage in the optic nerve which reduces the afferent input, resulting in a generally small BOLD signal in all visual areas.

With regard to the region of differential activation between groups identified in the right precentral gyrus, this was of somewhat borderline statistical significance, and could represent a noise spike artefact. Alternatively, it could relate to the motor task, in which the subjects pressed a button when the fixation‐cross changed, although the times of motor responses were entered as covariates into the analysis. It could reflect an abnormal spatial and temporal profile of motor processing in the patient group, and abnormal motor processing occurs in MS [Rocca et al., 2005], although whether it occurs in clinically isolated ON is unknown. Future studies could correct for possible motor‐task related activation by introducing a “control” condition during the fMRI experiment, such as pressing a button while watching the gray background.

Associations Between Structural and Electrophysiological Measures and Vision

We found that the severity of acute visual loss in ON was associated with measures of the extent of optic nerve inflammation (lesion length) and conduction block (VEP amplitude). This is in line with previous experimental and clinical imaging, electrophysiological and pathological studies, which have demonstrated that inflammation and demyelination contribute to conduction block and clinical deficit [Smith and McDonald, 1999]. Our results confirm that acute visual loss is related to the magnitude of the insult locally within the optic nerve, rather than in the posterior visual pathways.

Associations Between fMRI Responses and Vision

We detected an association between greater fMRI activity in the cuneus and better vision, when correcting for the optic nerve inflammation and conduction block. This suggests a possible adaptive neuroplasticity in this extra‐occipital cortex in the acute stage of the disease. The cuneus is an area of visual association cortex, outside primary visual cortex, which is part of the dorsal processing stream, involved in spatial localization of objects in the physical world. Although our study did not include a retinotopic mapping sequence to identify the borders between hierarchical visual areas within individual subjects, the cuneus generally corresponds to Brodmann areas 18 and 19, with the region adjacent to the parieto‐occipital sulcus encompassing dorsal area V3. This area is sensitive to coherent motion stimuli [Braddick et al., 2001], and receives major, excitatory feed‐forward projections from lower areas, such as V2 [Anderson and Martin, 2005].

Extra‐striate activation has been reported before in several areas: the insula, claustrum, thalami, corpus striatum, orbitofrontal and lateral temporal cortices, and the posterior parietal cortex, which is also part of the dorsal stream [Werring et al., 2000]. More recent longitudinal MRI studies have suggested that adaptive plasticity might occur during the acute stage of ON. Both lower visual areas, such as the LGN [Korsholm et al., 2007] and higher visual areas, such as the lateral occipital complexes [Toosy et al., 2005], located in the ventral processing stream [Goodale and Milner, 1992], have been implicated. At present, it remains unclear whether the location of extra‐striate neuroplastic reorganization is important, in terms of the relative ability of neurons in different regions to adapt their usual function in response to pathological insult (cross‐modal reassignment). Location might be important if neuroplastic processes, such as dendritic arborization or synaptic plasticity [Johansen‐Berg, 2007], were facilitated in certain regions, through a mechanism of reorganization of existing, but previously inactive, neural networks. The location of such networks could be patient‐specific or common across groups of patients. Alternatively, location may not be important, and reorganization may represent nonspecific map expansion from adjacent primary visual areas, in response to a reduced afferent input. The finding of novel regions, such as the cuneus, and previous reports implicating a variety of different areas, may suggest that either nonspecific map expansion, or relatively subject‐specific cross‐modal reassignment is more likely than a population‐level common “emergency” anatomical network. Interestingly, a subsequent longitudinal study on the same patient cohort which used a region‐of‐interest analysis has found that the baseline activation in the cuneus in patients was not associated with visual acuity at 1 year [unpublished data]. Further, longitudinal studies on larger cohorts are needed to confirm the role of the extra‐striate, associative cortices in modulating clinical function in response to pathology in both the acute stage and long‐term phase. It is possible that different regions play a role in adaptive plasticity during different stages of the injury.

Our study has some limitations. The patient cohort included a small proportion of people in whom structural and functional scans were separated by more than 2 days, and acute ON is a time of dynamic pathophysiological change. In particular, in one patient the scans were separated by a much longer period. In addition, there was some heterogeneity within the cohort in the time from symptom onset to assessment. To investigate the influence of these factors, the SPM5 multivariable regression model was respecified, first omitting the subject with a delay, and second, including an additional regressor, representing days from symptom onset. In both cases, significant associations between vision and fMRI activity in the cuneus remained (results not shown).

In addition, the influence of steroids on the fMRI response remains unknown, but their influence on the electrophysiological parameters has been reported [Trauzettel‐Klosinski et al., 1995]. However, in this study, only three patients had steroids before their imaging and neurophysiological assessment. We did not find any differences in the fMRI response and VEPs amplitude and latency between patients treated with steroids and those untreated (results not shown).

While this study incorporated, to our knowledge, the most comprehensive structural assessment of the visual pathways to date in ON, it is possible that an influence of optic tract pathology was missed, as it was not assessed. Diffusion‐based tractography of the optic tract may be possible, but would necessitate longer scan‐times. For this reason, it was unfeasible to include either optic nerve or optic tract DTI in this study. Prohibitive scan‐times remain a limiting factor when aiming for a comprehensive MRI assessment of the visual system. In the future, this may be addressed with improving technology, such as higher strength magnetic fields.

Finally, we applied a two‐stage analysis strategy to dissect structure–function relationships. This helped to limit the number of variables in the final analysis. Future studies may incorporate a larger sample size, which would allow a more complete assessment of all structural variables in a regression model with brain fMRI response.

In conclusion, an improved understanding of adaptive neuroplasticity, occurring during the acute episode of ON, which may act to modulate clinical deficit, is important, as it may be a potential target for future therapeutic intervention.

Supporting information

Additional Supporting Information may be found in the online version of this article.

Supporting Information Materials.

Click here for additional data file. (49.5KB, doc)

REFERENCES

  1. Anderson JC, Martin KA ( 2005): Connection from cortical area V2 to V3 A in macaque monkey. J Comp Neurol 488: 320–330. [DOI] [PubMed] [Google Scholar]
  2. Anderson VM, Fernando KT, Davies GR, Rashid W, Frost C, Fox NC, Miller DH ( 2007): Cerebral atrophy measurement in clinically isolated syndromes and relapsing remitting multiple sclerosis: A comparison of registration‐based methods. J Neuroimaging 17: 61–68. [DOI] [PubMed] [Google Scholar]
  3. Audoin B, Fernando KT, Swanton JK, Thompson AJ, Plant GT, Miller DH ( 2006): Selective magnetization transfer ratio decrease in the visual cortex following optic neuritis. Brain 129: 1031–1039. [DOI] [PubMed] [Google Scholar]
  4. Basser PJ, Pierpaoli C ( 1996): Microstructural and physiological features of tissues elucidated by quantitative‐diffusion‐tensor MRI. J Magn Reson B 111: 209–219. [DOI] [PubMed] [Google Scholar]
  5. Behrens TE, Johansen‐Berg H, Woolrich MW, Smith SM, Wheeler‐Kingshott CA, Boulby PA, Barker GJ, Sillery EL, Sheehan K, Ciccarelli O, Thompson AJ, Brady JM, Matthews PM ( 2003a): Non‐invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci 6: 750–757. [DOI] [PubMed] [Google Scholar]
  6. Behrens TE, Woolrich MW, Jenkinson M, Johansen‐Berg H, Nunes RG, Clare S, Matthews PM, Brady JM, Smith SM ( 2003b): Characterization and propagation of uncertainty in diffusion‐weighted MR imaging. Magn Reson Med 50: 1077–1088. [DOI] [PubMed] [Google Scholar]
  7. Braddick OJ, O'Brien JM, Wattam‐Bell J, Atkinson J, Hartley T, Turner R ( 2001): Brain areas sensitive to coherent visual motion. Perception 30: 61–72. [DOI] [PubMed] [Google Scholar]
  8. Brusa A, Jones SJ, Plant GT ( 2001): Long‐term remyelination after optic neuritis: A 2‐year visual evoked potential and psychophysical serial study. Brain 124: 468–479. [DOI] [PubMed] [Google Scholar]
  9. Chard DT, Parker GJ, Griffin CM, Thompson AJ, Miller DH ( 2002): The reproducibility and sensitivity of brain tissue volume measurements derived from an SPM‐based segmentation methodology. J Magn Reson Imaging 15: 259–267. [DOI] [PubMed] [Google Scholar]
  10. Ciccarelli O, Toosy AT, Hickman SJ, Parker GJ, Wheeler‐Kingshott CA, Miller DH, Thompson AJ ( 2005): Optic radiation changes after optic neuritis detected by tractography‐based group mapping. Hum Brain Mapp 25: 308–316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ciccarelli O, Catani M, Johansen‐Berg H, Clark C, Thompson A ( 2008) Diffusion‐based tractography in neurological disorders: Concepts, applications, and future developments. Lancet Neurol 7: 715–727. [DOI] [PubMed] [Google Scholar]
  12. Cook PA, Symms M, Boulby PA, Alexander DC ( 2007): Optimal acquisition orders of diffusion‐weighted MRI measurements. J Magn Reson Imaging 25: 1051–1058. [DOI] [PubMed] [Google Scholar]
  13. Dale AM, Fischl B, Sereno MI ( 1999): Cortical surface‐based analysis. I. Segmentation and surface reconstruction. Neuroimage 9: 179–194. [DOI] [PubMed] [Google Scholar]
  14. Eickhoff SB, Stephan KE, Mohlberg H, Grefkes C, Fink GR, Amunts K, Zilles K ( 2005): A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25: 1325–1335. [DOI] [PubMed] [Google Scholar]
  15. Ferris FL III, Kassoff A, Bresnick GH, Bailey I ( 1982): New visual acuity charts for clinical research. Am J Ophthalmol 94: 91–96. [PubMed] [Google Scholar]
  16. Fischl B, Dale AM ( 2000): Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 97: 11050–11055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fischl B, Sereno MI, Dale AM ( 1999): Cortical surface‐based analysis. II. Inflation, flattening, and a surface‐based coordinate system. Neuroimage 9: 195–207. [DOI] [PubMed] [Google Scholar]
  18. Fischl B, van der KA, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM ( 2004): Automatically parcellating the human cerebral cortex. Cereb Cortex 14: 11–22. [DOI] [PubMed] [Google Scholar]
  19. Goodale MA, Milner AD ( 1992): Separate visual pathways for perception and action. Trends Neurosci 15: 20–25. [DOI] [PubMed] [Google Scholar]
  20. Halliday AM, McDonald WI, Mushin J ( 1972): Delayed visual evoked response in optic neuritis. Lancet 1: 982–985. [DOI] [PubMed] [Google Scholar]
  21. Hickman SJ, Brex PA, Brierley CM, Silver NC, Barker GJ, Scolding NJ, Compston DA, Moseley IF, Plant GT, Miller DH ( 2001): Detection of optic nerve atrophy following a single episode of unilateral optic neuritis by MRI using a fat‐saturated short‐echo fast FLAIR sequence. Neuroradiology 43: 123–128. [DOI] [PubMed] [Google Scholar]
  22. Hickman SJ, Dalton CM, Miller DH, Plant GT ( 2002): Management of acute optic neuritis. Lancet 360: 1953–1962. [DOI] [PubMed] [Google Scholar]
  23. Hickman SJ, Toosy AT, Jones SJ, Altmann DR, Miszkiel KA, MacManus DG, Barker GJ, Plant GT, Thompson AJ, Miller DH ( 2004a): A serial MRI study following optic nerve mean area in acute optic neuritis. Brain 127: 2498–2505. [DOI] [PubMed] [Google Scholar]
  24. Hickman SJ, Toosy AT, Miszkiel KA, Jones SJ, Altmann DR, MacManus DG, Plant GT, Thompson AJ, Miller DH ( 2004b): Visual recovery following acute optic neuritis—A clinical, electrophysiological and magnetic resonance imaging study. J Neurol 251: 996–1005. [DOI] [PubMed] [Google Scholar]
  25. Hornabrook RS, Miller DH, Newton MR, MacManus DG, du Boulay GH, Halliday AM, McDonald WI ( 1992): Frequent involvement of the optic radiation in patients with acute isolated optic neuritis. Neurology 42: 77–79. [DOI] [PubMed] [Google Scholar]
  26. Jenkins TM, Mancini L, Toosy AT, Ciccarelli O, Plant GT, Miller DH, Thompson AJ ( 2008): A quadrant‐specific monocular visual functional MRI paradigm designed to minimize attention and loss‐of‐fixation biases. In ISMRM 16th Scientific Meeting and Exhibition Abstract, 3rd–9th May 2008, Toronto, Ontario, Canada, 2460.
  27. Johansen‐Berg H ( 2007): Structural plasticity: Rewiring the brain. Curr Biol 17: R141–R144. [DOI] [PubMed] [Google Scholar]
  28. Korsholm K, Madsen KH, Frederiksen JL, Skimminge A, Lund TE ( 2007): Recovery from optic neuritis: An ROI‐based analysis of LGN and visual cortical areas. Brain 130: 1244–1253. [DOI] [PubMed] [Google Scholar]
  29. Kupersmith MJ, Alban T, Zeiffer B, Lefton D ( 2002): Contrast‐enhanced MRI in acute optic neuritis: Relationship to visual performance. Brain 125: 812–822. [DOI] [PubMed] [Google Scholar]
  30. Langkilde AR, Frederiksen JL, Rostrup E, Larsson HB ( 2002): Functional MRI of the visual cortex and visual testing in patients with previous optic neuritis. Eur J Neurol 9: 277–286. [DOI] [PubMed] [Google Scholar]
  31. Levin N, Orlov T, Dotan S, Zohary E ( 2006): Normal and abnormal fMRI activation patterns in the visual cortex after recovery from optic neuritis. Neuroimage 33: 1161–1168. [DOI] [PubMed] [Google Scholar]
  32. McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, McFarland HF, Paty DW, Polman CH, Reingold SC, Sandberg‐Wollheim M, Sibley W, Thompson A, van den NS, Weinshenker BY, Wolinsky JS ( 2001): Recommended diagnostic criteria for multiple sclerosis: Guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 50: 121–127. [DOI] [PubMed] [Google Scholar]
  33. Mori S, Crain BJ, Chacko VP, van Zijl PC ( 1999): Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45: 265–269. [DOI] [PubMed] [Google Scholar]
  34. Ogawa S, Lee TM, Kay AR, Tank DW ( 1990): Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA 87: 9868–9872. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Optic Neuritis Study Group ( 1991): The clinical profile of optic neuritis. Experience of the Optic Neuritis Treatment Trial. Optic Neuritis Study Group. Arch Ophthalmol 109: 1673–1678. [DOI] [PubMed] [Google Scholar]
  36. Plant GT, Kermode AG, Turano G, Moseley IF, Miller DH, MacManus DG, Halliday AM, McDonald WI ( 1992): Symptomatic retrochiasmal lesions in multiple sclerosis: Clinical features, visual evoked potentials, and magnetic resonance imaging. Neurology 42: 68–76. [DOI] [PubMed] [Google Scholar]
  37. Plummer DL ( 1992): DispImage: A display and analysis tool for medical images. Rivista di Neuroradiologia 5: 489–495. [Google Scholar]
  38. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, Lublin FD, Metz LM, McFarland HF, O'Connor PW, Sandberg‐Wollheim M, Thompson AJ, Weinshenker BG, Wolinsky JS ( 2005): Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol 58: 840–846. [DOI] [PubMed] [Google Scholar]
  39. Rocca MA, Colombo B, Falini A, Ghezzi A, Martinelli V, Scotti G, Comi G, Filippi M ( 2005): Cortical adaptation in patients with MS: A cross‐sectional functional MRI study of disease phenotypes. Lancet Neurol 4: 618–626. [DOI] [PubMed] [Google Scholar]
  40. Rombouts SA, Lazeron RH, Scheltens P, Uitdehaag BM, Sprenger M, Valk J, Barkhof F ( 1998): Visual activation patterns in patients with optic neuritis: An FMRI pilot study. Neurology 50: 1896–1899. [DOI] [PubMed] [Google Scholar]
  41. Smith KJ, McDonald WI ( 1999): The pathophysiology of multiple sclerosis: The mechanisms underlying the production of symptoms and the natural history of the disease. Philos Trans R Soc Lond B Biol Sci 354: 1649–1673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Sorensen TL, Frederiksen JL, Bronnum‐Hansen H, Petersen HC ( 1999): Optic neuritis as onset manifestation of multiple sclerosis: A nationwide, long‐term survey. Neurology 53: 473–478. [DOI] [PubMed] [Google Scholar]
  43. Toosy AT, Hickman SJ, Miszkiel KA, Jones SJ, Plant GT, Altmann DR, Barker GJ, Miller DH, Thompson AJ ( 2005): Adaptive cortical plasticity in higher visual areas after acute optic neuritis. Ann Neurol 57: 622–633. [DOI] [PubMed] [Google Scholar]
  44. Trauzettel‐Klosinski S, Diener HC, Dietz K, Zrenner E ( 1995): The effect of oral prednisolone on visual evoked potential latencies in acute optic neuritis monitored in a prospective, randomized, controlled study. Doc Ophthalmol 91: 165–179. [DOI] [PubMed] [Google Scholar]
  45. Werring DJ, Bullmore ET, Toosy AT, Miller DH, Barker GJ, MacManus DG, Brammer MJ, Giampietro VP, Brusa A, Brex PA, Moseley IF, Plant GT, McDonald WI, Thompson AJ ( 2000): Recovery from optic neuritis is associated with a change in the distribution of cerebral response to visual stimulation: A functional magnetic resonance imaging study. J Neurol Neurosurg Psychiatry 68: 441–449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Youl BD, Turano G, Miller DH, Towell AD, MacManus DG, Moore SG, Jones SJ, Barrett G, Kendall BE, Moseley IF ( 1991): The pathophysiology of acute optic neuritis. An association of gadolinium leakage with clinical and electrophysiological deficits. Brain 114 ( Part 6): 2437–2450. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional Supporting Information may be found in the online version of this article.

Supporting Information Materials.

Click here for additional data file. (49.5KB, doc)

Articles from Human Brain Mapping are provided here courtesy of Wiley

RESOURCES