Clinical Investigative Study Optic Neuritis and the Visual Pathway: Evaluation of Neuromyelitis Optica Spectrum by Resting-State fMRI and Diffusion Tensor MRI Fernanda Cristina Rueda Lopes, MD, MSc, Soniza Vieira Alves-Leon, MD, PhD, Jose Mauricio Godoy, MD, Simone de Souza Batista Scherpenhuijzen, MD, Leticia Fezer, MD, Emerson Leandro Gasparetto, MD, PhD From the Federal University of Rio de Janeiro, Radiology, Rua Presidente Joao ˜ Pessoa, Niteroi, ´ Rio de Janeiro, Brazil (FCRL, ELG); Federal University of Rio de Janeiro, Neurology, Rio de Janeiro, Rio de Janeiro, Brazil (SVA, SdSBS, LF); and State University of Rio de Janeiro, Neurology, Rio de Janeiro, Rio de Janeiro, Brazil (JMG).
ABSTRACT BACKGROUND AND PURPOSE
Optic neuritis (ON) is an acute episode of inflammation in the visual pathway (VP). It may occur as part of a demyelinating disease, which can affect white matter (WM) throughout the VP. Compensatory cortical adaptations may occur following WM damage to maintain visual integrity. Our aim was to investigate whether resting-state functional MRI (rsfMRI) can detect cortical adaptations following ON attacks and to correlate rsfMRI with diffusion tensor imaging (DTI) of WM within the VP. MATERIALS AND METHODS
Neuromyelitis optica spectrum patients were compared to healthy controls at least 6 months after ON onset. DTI and rsfMRI were performed and post-processed using FSL tools (TBSS for DTI and MELODIC for fMRI). RESULTS
Ptients had higher synchronization values than controls in the visual network (3.48 vs. 2.12, P < .05). A weak trend of correlation was revealed between fMRI and structural analysis by DTI using fractional anisotropy (right side: R = −.36, P < .08; left side: R = .075, P < .73).
Keywords: Neuromyelitis optica, diffusion tensor imaging, resting-state functional, MRI, neuritis optica. Acceptance: Received January 20, 2014, and in revised form May 26, 2014. Accepted for publication September 13, 2014. Correspondence: Address correspondence to Fernanda C Rueda Lopes, Federal University of Rio de Janeiro, Radiology, Rua Presidente Joao ˜ Pessoa, 385/704, Niteroi, ´ Rio de Janeiro, Brazil, 24220-330. E-mail: frueda81@ hotmail.com. J Neuroimaging 2015;25:807-812. DOI: 10.1111/jon.12191
CONCLUSIONS
The rsfMRI detected cortical reorganization following ON attack, but WM was considerably preserved in the posterior VP.
Introduction Optic neuritis (ON) is an acute episode of inflammation within the optic nerve, the prechiasmatic portion of the visual pathway (VP). It can occur as the first symptom of a demyelinating disease, including neuromyelitis opitica (NMO), may be recurrent or a single episode.1 The optic radiation, part of the posterior VP,2 crosses the periventricular and subcortical white matter (WM) and can be damaged by demyelinating lesions, which typically affect these areas. Large lesions of the VP may be most disabling, although damage in the normal appearing WM (NAWM), defined as areas without gross evidence of lesions on conventional MRI, may also cause dysfunction.3 Previous studies have shown that visual dysfunction occurs even without prior ON.4 The variability of recovery from ON is likely due to the nature of the inflammatory response (inflammation and edema), the extent of tissue injury (demyelination and axonal loss), and the individual capacity for repair (cortical reorganization).1,5 Several studies of functional recovery after brain injury have suggested that changes in local connectivity, recruitment of ex-
Copyright
isting pathways involving the lateral geniculate nucleus and the middle temporal area, or creation of new pathways may account for recovery of vision in these patients.5 New advanced MRI techniques can detect structural and functional alterations that underlie recovery. Diffusion tensor imaging (DTI) can evaluate the optic radiation WM. The main parameter of DTI is fractional anisotropy (FA), which represents the direction in which water molecules move along WM tracts. Higher FA values are indicative of anisotropic tracts, which may reflect areas of highly oriented fibers, and in such example may reflect WM integrity.1,2,6 Functional MRI (fMRI) evaluates cortical adaptations following an episode of ON.7 The blood oxygen level-dependent (BOLD) contrast technique analyzes a signal change caused by changes in blood flow, blood volume, and oxygen saturation during rest.8 Resting-state fMRI (rsfMRI), performed while subjects rest quietly, records spontaneous low-frequency fluctuations of cerebral BOLD signal in the cerebral cortex, which may be affected during the process of functional compensation.9
◦ 2015 by the American Society of Neuroimaging C
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Journal of Neuroimaging Vol 25 No 5 September/October 2015
Age
30/08/58 20/09/93 14/05/79 21/08/92 06/09/55 19/06/69 04/10/57 16/10/51 30/01/62 22/01/95 09/08/83 28/04/64 03/09/92 21/10/80 06/09/55 25/09/98 06/06/48 26/12/63 06/06/75 11/07/84 22/06/66 30/12/68 17/11/92 18/06/58 22/06/64 16/08/43 09/01/61 18/09/76
Patient
EF JF MS SS ACP AFCP AMNM AMSC CMCP DC DM ECF FC HASB IE JKSS JSA JCS LS LDV LMC MF MJCA MRS MRM PSC RMC SCM
F M F F F F F F F M M F F M F F M M F F F M F F F M F M
Gender (F/M)
8.5 3.0 3.5 1.0 4.5 1.0 1.5 1.0 5.0 3.5 1.0 5.5 3.0 7.5 4.5 2.0 7.5 1.0 5.0 1.0 4,0 2.0 2.0 4.5 7.5 2.0 1.0 1.0
EDSS
6/2 1/2 1/0 1/1 1/1 3/2 0/0 1/1 0/1 PEV+/0 0/1 4/0 0/0 1/1 0/1 0/1 1/1 1/1 1/1 1/0 1/1 2/0 1/1 0/0 0/1 0/PEV+ 1/1 1/0
No. ON Attacks (R/L)
Table 1. Epidemiological and Clinical Parameters of all Evaluated Patients
2007 2009 2010 2010 2006 2009 – 1997 2007 2010 2010 2008 2011 2009 2006 2008 2010 2003 2007 2010 2001 2011 2011 – 1999 – 2009 2009
Last ON Attack
4 1 1 0 2 7 4 0 0 1 1 3 1 3 2 2 1 1 3 1 2 2 1 1 4 2 0 0
No Myelitis Attack
1998 2008 2010 2011 2006 2007 2004 1997 2006 2010 2011 1999 2011 2010 2006 2005 2009 2000 1998 2010 2001 2010 2009 2003 1999 1980 2008 2009
First Symptom
Azathioprine None Azathioprine None Azathioprine None None None Azathioprine Plamapherisis None Azathioprine None Azathioprine Azathioprine Azathioprine Azathioprine None Azathioprine None Azathioprine None None None Azathioprine None Azathioprine None
Treatment
21/10/11 11/10/11 06/10/11 23/11/11 17/08/10 24/08/10 07/11/10 02/02/11 21/03/11 13/04/11 16/03/11 25/07/11 28/12/11 08/09/10 20/11/10 19/01/11 19/01/11 17/09/10 25/08/11 26/01/11 06/07/11 06/08/11 15/09/11 31/08/10 14/09/10 20/09/11 26/01/11 21/09/10
MRI Date
12.2 2.1 2.1 2.9 8.4 −1.1 1.6 2.6 5.1 3.6 0.7 2.7 2.0 0.3 3.3 4.5 5.5 3.8 2.1 4.2 5.0 1.8 2.8 7.9 4.3 5.1 0.8 1.8
Synch Value
.60/.57 .59/.57 .59/.59 .61/.57 .62/.58 .61/.59 .63/.60 .67/.58 .62/.57 .58/.55 .60/.63 .53/.57 .65/.66 .61/.62 .53/.57 .61/.57 .63/.59 .59/.65 .54/.60 .50/.51 .63/.63 .59/.60 .55/.55
FA (R/L)
Our aim was to investigate whether rsfMRI can detect cortical adaptations following ON attacks and how synchronization values extracted from this network correlate with DTI data from the VP.
Materials and Methods Subjects Subjects were 28 patients with neuromyelitis optica spectrum (NMOs), including 3 patients with myelitis and 25 with ON (Table 1) who met inclusion criteria: (1) age between 18 and 70 years; (2) late onset of ON (more than 6 months); (3) myelitis; (4) positive visual-evoked potential. Patients were excluded if they showed lesions on conventional brain MRI following the Barkhof criteria for multiple sclerosis (MS)10 or if they had corticosteroid or acute relapses within the last 8 weeks. There was no limit for disease duration, number of myelitis or ON attacks, or side for ON. Patients were recruited between July 2010 and July 2012 at the Departments of Neurology and Radiology of our University Hospital. Healthy controls included 19 age- and gender-matched subjects (age 44 ± 9.4 years; 13 females and 6 males) who were free of neurological and psychiatric disorders. The institutional review board approved the study, and all subjects gave written informed consent. A total of 23 patients underwent DTI, and all patients underwent rsfMRI.
MRI Acquisition MRI was performed with a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany), using an 8-channel phased-array head coil. The conventional MRI protocol included: sagittal FLAIR images (repetition time [TR] = 9,650 ms; echo time [TE] = 82 ms; inversion time = 2,500 ms; field of view [FOV] = 220 mm; matrix = 226 × 250; 4.5 section thickness with a 10% gap); sagittal 3D GRE T1-weighted image (WI; TR = 2000 ms; TE = 2.94 ms; inversion time = 1,100 ms; FOV = 230 mm; matrix = 232 × 256; 1 mm section thickness, no gap). Diffusion-weighted single-shot echo-planar imaging was acquired with bipolar diffusion gradients applied along 30 noncollinear directions (b = 0 and b = 900 s/mm2 ; TR = 9,500 ms; TE = 87 ms; matrix = 122 × 122; FOV = 230 mm; 73 slices with 2 mm thickness, no gap; 1 average),11 and rsfMRI was acquired within the following parameters: TR = 3,290 ms; TE = 30 ms; flip angle = 85, matrix: 512 × 512; FOV = 100 mm; 124 images with 3 mm thickness and 30% gap; 1 average. Subjects were instructed to keep their eyes closed and to not focus on anything specific during the MRI.
DTI and RsfMRI Analysis Tract-Based Spatial Statistics (TBSS) was used to analyze diffusion data, which is part of FMRIB’s Diffusion Toolbox within FSL 4.1 (http://www.fmrib.ox.ac.uk/fsl).12 After performing eddy current correction and brain extraction, FA images of 23 patients were created by fitting a tensor model to the raw diffusion data. FA data from all subjects, acquired with high definition, so there is no significant interpolation blurring the images, were aligned into a common space using the FNIRT nonlinear registration tool,13,14 which uses a b-spline representation of the registration warp field and intermediate degrees of freedom.14,15 Next, the mean FA image was created
Fig 1. JHU ICBM-DTI-81 White-Matter Labels atlas representation on the axial plane. Voxels located in the visual pathway were selected at the right side (light green in the occipital projection) and in the occipital left side (light blue). and thinned to generate a mean FA skeleton, which represents the center of all tracts common to the group. Each patient’s aligned FA data were then projected onto this skeleton. Additionally, by applying a mask determined by the JHU ICBMDTI-81 White-Matter Labels atlas, voxels located in the VP were selected, including the postchiasmatic portion (Fig 1), and FA values were extracted for all voxels for each patient, creating a mean FA of the VP for each patient. RsfMRI was postprocessed by FMRIB’s Software Library tools (http://www.fmrib.ox.ac.uk/fsl), using the methodology previously described by Roosendaal.16 The functional magnetic resonance images were motion-corrected, and nonbrain tissue was removed. In addition, images were spatially smoothed and temporally filtered. After this preprocessing, functional scans were aligned to high-resolution T1-WI scans and subsequently to Montreal Neurological Institute-152 standard space using nonlinear registration. The aligned data were then temporally concatenated across subjects to create a single 4D data set. This concatenated fMRI data set was decomposed using independent component analysis (ICA)17 to identify large-scale patterns of functional connectivity. Next, betweensubject analysis of the resting data was carried out using a “dual-regression” approach18 that allows for voxel-wise comparisons of resting functional connectivity. The visual network was selected by visual inspection and comparison to earlier studies.17 It was tested voxel-wise for statistically significant group differences with a general linear model using nonparametric permutation testing (5,000 permutations). The spatial maps generated to characterize between-group differences were controlled for multiple comparisons with a corrected threshold of P < .05, using threshold-free cluster enhancement.19 Synchronization values for the visual network were extracted from each patient and used for correlations with structural parameters.
Statistical Analysis Values are presented as means ± SD. FA values were checked for normality using the Kolmogorov-Smirnov test. Differences in FA values between the right and left VPs and between preserved and nonpreserved eyes were calculated using t-tests with P < .05 considered statistically significant. Correlations between structural and functional parameters and clinical information in the 23 patients were calculated using the Pearson
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Fig 2. Visual network functional representation. All patients and controls were considered in the ICA analysis. Occipital areas show increased synchronization, a marker of the visual network.
Fig 3. Axial (A), coronal (B), and sagittal (C) planes of the rsfMRI of patients compared to controls. Red voxels located in the occipital area represent areas of higher synchronization in patients, reflecting cortical compensation.
correlation method. The Statistical Package for the Social Sciences (SPSS) version 17.0 for Windows was used for statistical analysis.
Results Clinical data of the patients are better described in Table 1. Main clinical aspect such as age, gender, expanded disability status scale (EDSS), number of ON/myelitis attacks, disease duration, treatment, date of MRI, synchronization values of visual network, and FA of right and left VP are included. The rsfMRI data set was decomposed into 14 components, and the visual network was clearly detected in subjects and healthy controls, as shown in Figure 2. Considering the multiple comparison corrected maps, there was increased synchronization in the whole occipital cortex in NMOs patients compared to controls (P = .01; Fig 3). The average synchronization value for patients was 3.5 (SD ± 2.7) and for controls was 2.1 (SD ± 1.4; P = .05).
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FA values for the right and left VP had normal distribution. Average FA values of the VP were similar on the right and left sides (.60 ± .03 and .59 ± .03, respectively, P = .57). A comparison between preserved and nonpreserved eyes was also conducted. A total of 29 eyes were affected within 36 episodes of ON, and 17 eyes were preserved. Average FA values were similar for the affected and preserved eyes (.59 ± .03 and .58 ± .11, respectively, P = .50). A total of 20 ON attacks occurred in the right side and 16 on the left side. The structural parameter (FA) from DTI and synchronization values from rsfMRI showed a trend for a weak negative correlation for the right FA in patients, Pearson R = −.36 (P = .08) and a slightly nonstatistical significant positive correlation for the left FA, R = .075 (P = .73).
Discussion This study demonstrated evidence of cortical neuroplasticity after ON episodes in patients with NMOs using rsfMRI. The
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weak correlation of functional data with structural damage measured by DTI might reflect that functional compensation occurs prior to significant WM damage. The large number of axons in the optic nerve, in excess of the critical threshold necessary to maintain function,20 helps to explain the dissociation between optic nerve tissue damage and the degree of visual loss and recovery.7,20–22 Functional compensation may also explain the lack of correlation between structural damage and clinical improvement.7 Cortical adaptation, especially in the lateral occipital complex20 or even in extraoccipital areas7,21,23,24 is crucial for favorable clinical outcomes, which occur mainly during the late course of ON (after 6 months)23,25 or even during the ON attack.8 To our knowledge, this study is the first to demonstrate visual cortical adaptations using rsfMRI in NMOs. It is critical to analyze the brain activity that occurs in the absence of external stimuli, which may reveal a broader spectrum of compensation, as opposed to task-related fMRI, which may only show specific patterns of adaptation.16 There are several possible explanations for the weak correlation between structural (FA values) and functional (synchronization values) parameters. ON usually affects the anterior part of the VP, which was not evaluated by the current DTI approach. This study considered only the posterior projection of the VP, which could be affected in demyelinating diseases, but no lesions were revealed by MRI.22 Furthermore, functional compensation may be triggered by visual field defects, which are mainly caused by anterior VP damage.24 Finally, visual acuity may correlate with DTI parameters originating from extraoccipital areas that are involved in functional compensations, such as the prefrontal and temporal brain regions, but not with the occipital lobe itself.26 The fact that the affected and nonaffected eyes had similar FA values suggests that posterior structural damage was minimal in this cohort. There were 20 episodes of ON at the right side, whereas only 16 were in the left side. Although it is not a significant difference in occurrence, this slight prevalence on the right side may reflect that the structural damage is somehow worse at the this side and the functional compensation is decaying (reflecting the trend for negative correlation in the Pearson analysis). This study was limited to patients in the NMOs, including only spinal cord attacks in the rsfMRI and including different disease durations and clinical courses. Although, relative to controls, patients showed higher synchronization in the occipital area. This could reflect the presence of undetectable VP lesions in NMOs patients (including the patients with only spinal cord attacks), similar to previous findings in MS patients, in which both affected and unaffected optic nerves had different DTI values from controls. Damage in the “unaffected” optic nerve might be caused by slow axonal degeneration in the absence of a clear inflammation process, which could reflect subclinical inflammation, chronic neuronal dysfunction, and transynaptic degeneration.4,26 Abnormal DTI in the VP has already been described for NMO patients with previous ON but no qualitatively visible intracranial lesions, as well as in the NAWM of the VP in MS patients.3 Thus, subtle structural damage occurs in the absence of clinically significant features, and functional reorganization may occur to maintain visual function. In conclusion, rsfMRI is a useful technique to detect occipital cortical adaptation in NMOs patients following ON
attacks. The balance between structural damage and functional cortical compensation is probably one of the most important mechanism to guarantee the clinical outcome of patients, not only after an ON episode, but also beforehand by causing latent WM lesions.
References 1. Naismith RT, Xu J, Tutlam NT, et al. Disability in optic neuritis correlates with diffusion tensor-derived directional diffusivities. Neurology 2009;72:589-594. 2. Zhang Y, Wan SH, Wu GJ, et al. Magnetic resonance diffusion tensor imaging and diffusion tensor tractography of human visual pathway. Int J Ophthalmol 2012;5(4):452-458. 3. Reich DS, Smith SA, Gordon-Lipkin EM, et al. Damage to the optic radiation in multiple sclerosis is associated with retinal injury and visual disability. Arch Neurol 2009;66(8):998-1006. 4. Smith SA, Williams ZR, Ratchford JN, et al. Diffusion tensor imaging of the optic nerve in multiple sclerosis: association with retinal damage and visual disability. Am J Neuroradiol 2011;32(9):16621668. 5. Mascioli G, Salvolini S, Cavola GL, et al. Functional MRI examination of visual pathways in patients with unilateral optic neuritis. Radiol Res Pract 2012;2012:2653061-7. 6. Alexander AL, Lee JE, Lazar M, et al. Diffusion tensor imaging of the brain. Neurotherapeutics 2007;4:316-319. 7. Toosy AT, Werring DJ, Bullmore ET, et al. Functional magnetic resonance imaging of the cortical response to photic stimulation in humans following optic neuritis recovery. Neurosci Lett 2002;330:255-259. 8. Langkilde AR, Frederiksen JL, Rostrup E, et al. Functional MRI of the visual cortex and visual testing in patients with previous optic neuritis. Eur J Neurol 2002;9:277-286. 9. Roosendaal SD, Hulst HE, Vrenken H, et al. Structural and functional hippocampal changes in multiple sclerosis patients with intact memory function. Radiology 2010;255:595-604. 10. Polman CH, Reingold SC, Banwell B, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011;69:292-302. 11. Smith SM, Johansen-Berg H, Jenkinson M, et al. Acquisition and voxelwise analysis of multi-subject diffusion data with tract-based spatial statistics. Nature Protocols 2007;2(3):499-503. 12. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 2004;23(Suppl1):208-219. 13. Andersson JLR, Jenkinson M, Smith S. Non-linear optimisation. FMRIB technical report TR07JA1 from www.fmrib.ox. ac.uk/analysis/techrep 14. Smith SM, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006;31:1487-1505. 15. Rueckert D, Sonoda LI, Hayes C, et al. Non-rigid registration using free-form deformations: application to breast MR images. IEEE T Med Imaging 1999;18(8):712-721. 16. Roosendaal SD, Schoonheim MM, Hulst HE, et al. Resting state networks change in clinically isolated syndrome. Brain 2010;133(Pt 6):1612-1621. 17. Beckmann CF, DeLuca M, Devlin JT, et al. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 2005;360:1001-1013. 18. Beckmann CF, Mackay CE, Filippini N, et al. Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. NeuroImage 2009;47(Suppl 1):S39-S41. 19. Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 2009;44:83-98.
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20. Jenkins TM, Toosy AT, Ciccarelli O, et al. Neuroplasticity predicts outcome of optic neuritis independent of tissue damage. Ann Neurol 2010;67:99-113. 21. Levin N, Orlov T, Dotan S, et al. Normal and abnormal fMRI activation patterns in the visual cortex after recovery from optic neuritis. NeuroImage 2006;33:1161-1168. 22. Kolappan M, Henderson APD, Jenkins TM, et al. Assessing structure and function of the afferent visual pathway in multiple sclerosis and associated optic neuritis. J Neurol 2009;256:305-319. 23. Toosy AT, Hickman SJ, Miszkiel KA, et al. Adaptive cortical plasticity in higher visual areas after acute optic neuritis. Ann Neurol 2005;57:622-633.
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24. Korsholm K, Madsen KH, Frederiksen JL, et al. Cortical neuroplasticity in patients recovering from acute optic neuritis. NeuroImage 2008;42:836-844. 25. Werring DJ, Bullmore ET, Toosy AT, et al. Recovery from optical 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 2000;68:441449. 26. Kolbe SC, Marriott M, van der Walt A, et al. Diffusion tensor imaging correlates of visual impairment in multiple sclerosis and chronic optic neuritis. Invest Ophthalmol Vis Sci 2012;53:825832.
Journal of Neuroimaging Vol 25 No 5 September/October 2015
ABSTRACT BACKGROUND AND PURPOSE
Optic neuritis (ON) is an acute episode of inflammation in the visual pathway (VP). It may occur as part of a demyelinating disease, which can affect white matter (WM) throughout the VP. Compensatory cortical adaptations may occur following WM damage to maintain visual integrity. Our aim was to investigate whether resting-state functional MRI (rsfMRI) can detect cortical adaptations following ON attacks and to correlate rsfMRI with diffusion tensor imaging (DTI) of WM within the VP. MATERIALS AND METHODS
Neuromyelitis optica spectrum patients were compared to healthy controls at least 6 months after ON onset. DTI and rsfMRI were performed and post-processed using FSL tools (TBSS for DTI and MELODIC for fMRI). RESULTS
Ptients had higher synchronization values than controls in the visual network (3.48 vs. 2.12, P < .05). A weak trend of correlation was revealed between fMRI and structural analysis by DTI using fractional anisotropy (right side: R = −.36, P < .08; left side: R = .075, P < .73).
Keywords: Neuromyelitis optica, diffusion tensor imaging, resting-state functional, MRI, neuritis optica. Acceptance: Received January 20, 2014, and in revised form May 26, 2014. Accepted for publication September 13, 2014. Correspondence: Address correspondence to Fernanda C Rueda Lopes, Federal University of Rio de Janeiro, Radiology, Rua Presidente Joao ˜ Pessoa, 385/704, Niteroi, ´ Rio de Janeiro, Brazil, 24220-330. E-mail: frueda81@ hotmail.com. J Neuroimaging 2015;25:807-812. DOI: 10.1111/jon.12191
CONCLUSIONS
The rsfMRI detected cortical reorganization following ON attack, but WM was considerably preserved in the posterior VP.
Introduction Optic neuritis (ON) is an acute episode of inflammation within the optic nerve, the prechiasmatic portion of the visual pathway (VP). It can occur as the first symptom of a demyelinating disease, including neuromyelitis opitica (NMO), may be recurrent or a single episode.1 The optic radiation, part of the posterior VP,2 crosses the periventricular and subcortical white matter (WM) and can be damaged by demyelinating lesions, which typically affect these areas. Large lesions of the VP may be most disabling, although damage in the normal appearing WM (NAWM), defined as areas without gross evidence of lesions on conventional MRI, may also cause dysfunction.3 Previous studies have shown that visual dysfunction occurs even without prior ON.4 The variability of recovery from ON is likely due to the nature of the inflammatory response (inflammation and edema), the extent of tissue injury (demyelination and axonal loss), and the individual capacity for repair (cortical reorganization).1,5 Several studies of functional recovery after brain injury have suggested that changes in local connectivity, recruitment of ex-
Copyright
isting pathways involving the lateral geniculate nucleus and the middle temporal area, or creation of new pathways may account for recovery of vision in these patients.5 New advanced MRI techniques can detect structural and functional alterations that underlie recovery. Diffusion tensor imaging (DTI) can evaluate the optic radiation WM. The main parameter of DTI is fractional anisotropy (FA), which represents the direction in which water molecules move along WM tracts. Higher FA values are indicative of anisotropic tracts, which may reflect areas of highly oriented fibers, and in such example may reflect WM integrity.1,2,6 Functional MRI (fMRI) evaluates cortical adaptations following an episode of ON.7 The blood oxygen level-dependent (BOLD) contrast technique analyzes a signal change caused by changes in blood flow, blood volume, and oxygen saturation during rest.8 Resting-state fMRI (rsfMRI), performed while subjects rest quietly, records spontaneous low-frequency fluctuations of cerebral BOLD signal in the cerebral cortex, which may be affected during the process of functional compensation.9
◦ 2015 by the American Society of Neuroimaging C
807
808
Journal of Neuroimaging Vol 25 No 5 September/October 2015
Age
30/08/58 20/09/93 14/05/79 21/08/92 06/09/55 19/06/69 04/10/57 16/10/51 30/01/62 22/01/95 09/08/83 28/04/64 03/09/92 21/10/80 06/09/55 25/09/98 06/06/48 26/12/63 06/06/75 11/07/84 22/06/66 30/12/68 17/11/92 18/06/58 22/06/64 16/08/43 09/01/61 18/09/76
Patient
EF JF MS SS ACP AFCP AMNM AMSC CMCP DC DM ECF FC HASB IE JKSS JSA JCS LS LDV LMC MF MJCA MRS MRM PSC RMC SCM
F M F F F F F F F M M F F M F F M M F F F M F F F M F M
Gender (F/M)
8.5 3.0 3.5 1.0 4.5 1.0 1.5 1.0 5.0 3.5 1.0 5.5 3.0 7.5 4.5 2.0 7.5 1.0 5.0 1.0 4,0 2.0 2.0 4.5 7.5 2.0 1.0 1.0
EDSS
6/2 1/2 1/0 1/1 1/1 3/2 0/0 1/1 0/1 PEV+/0 0/1 4/0 0/0 1/1 0/1 0/1 1/1 1/1 1/1 1/0 1/1 2/0 1/1 0/0 0/1 0/PEV+ 1/1 1/0
No. ON Attacks (R/L)
Table 1. Epidemiological and Clinical Parameters of all Evaluated Patients
2007 2009 2010 2010 2006 2009 – 1997 2007 2010 2010 2008 2011 2009 2006 2008 2010 2003 2007 2010 2001 2011 2011 – 1999 – 2009 2009
Last ON Attack
4 1 1 0 2 7 4 0 0 1 1 3 1 3 2 2 1 1 3 1 2 2 1 1 4 2 0 0
No Myelitis Attack
1998 2008 2010 2011 2006 2007 2004 1997 2006 2010 2011 1999 2011 2010 2006 2005 2009 2000 1998 2010 2001 2010 2009 2003 1999 1980 2008 2009
First Symptom
Azathioprine None Azathioprine None Azathioprine None None None Azathioprine Plamapherisis None Azathioprine None Azathioprine Azathioprine Azathioprine Azathioprine None Azathioprine None Azathioprine None None None Azathioprine None Azathioprine None
Treatment
21/10/11 11/10/11 06/10/11 23/11/11 17/08/10 24/08/10 07/11/10 02/02/11 21/03/11 13/04/11 16/03/11 25/07/11 28/12/11 08/09/10 20/11/10 19/01/11 19/01/11 17/09/10 25/08/11 26/01/11 06/07/11 06/08/11 15/09/11 31/08/10 14/09/10 20/09/11 26/01/11 21/09/10
MRI Date
12.2 2.1 2.1 2.9 8.4 −1.1 1.6 2.6 5.1 3.6 0.7 2.7 2.0 0.3 3.3 4.5 5.5 3.8 2.1 4.2 5.0 1.8 2.8 7.9 4.3 5.1 0.8 1.8
Synch Value
.60/.57 .59/.57 .59/.59 .61/.57 .62/.58 .61/.59 .63/.60 .67/.58 .62/.57 .58/.55 .60/.63 .53/.57 .65/.66 .61/.62 .53/.57 .61/.57 .63/.59 .59/.65 .54/.60 .50/.51 .63/.63 .59/.60 .55/.55
FA (R/L)
Our aim was to investigate whether rsfMRI can detect cortical adaptations following ON attacks and how synchronization values extracted from this network correlate with DTI data from the VP.
Materials and Methods Subjects Subjects were 28 patients with neuromyelitis optica spectrum (NMOs), including 3 patients with myelitis and 25 with ON (Table 1) who met inclusion criteria: (1) age between 18 and 70 years; (2) late onset of ON (more than 6 months); (3) myelitis; (4) positive visual-evoked potential. Patients were excluded if they showed lesions on conventional brain MRI following the Barkhof criteria for multiple sclerosis (MS)10 or if they had corticosteroid or acute relapses within the last 8 weeks. There was no limit for disease duration, number of myelitis or ON attacks, or side for ON. Patients were recruited between July 2010 and July 2012 at the Departments of Neurology and Radiology of our University Hospital. Healthy controls included 19 age- and gender-matched subjects (age 44 ± 9.4 years; 13 females and 6 males) who were free of neurological and psychiatric disorders. The institutional review board approved the study, and all subjects gave written informed consent. A total of 23 patients underwent DTI, and all patients underwent rsfMRI.
MRI Acquisition MRI was performed with a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany), using an 8-channel phased-array head coil. The conventional MRI protocol included: sagittal FLAIR images (repetition time [TR] = 9,650 ms; echo time [TE] = 82 ms; inversion time = 2,500 ms; field of view [FOV] = 220 mm; matrix = 226 × 250; 4.5 section thickness with a 10% gap); sagittal 3D GRE T1-weighted image (WI; TR = 2000 ms; TE = 2.94 ms; inversion time = 1,100 ms; FOV = 230 mm; matrix = 232 × 256; 1 mm section thickness, no gap). Diffusion-weighted single-shot echo-planar imaging was acquired with bipolar diffusion gradients applied along 30 noncollinear directions (b = 0 and b = 900 s/mm2 ; TR = 9,500 ms; TE = 87 ms; matrix = 122 × 122; FOV = 230 mm; 73 slices with 2 mm thickness, no gap; 1 average),11 and rsfMRI was acquired within the following parameters: TR = 3,290 ms; TE = 30 ms; flip angle = 85, matrix: 512 × 512; FOV = 100 mm; 124 images with 3 mm thickness and 30% gap; 1 average. Subjects were instructed to keep their eyes closed and to not focus on anything specific during the MRI.
DTI and RsfMRI Analysis Tract-Based Spatial Statistics (TBSS) was used to analyze diffusion data, which is part of FMRIB’s Diffusion Toolbox within FSL 4.1 (http://www.fmrib.ox.ac.uk/fsl).12 After performing eddy current correction and brain extraction, FA images of 23 patients were created by fitting a tensor model to the raw diffusion data. FA data from all subjects, acquired with high definition, so there is no significant interpolation blurring the images, were aligned into a common space using the FNIRT nonlinear registration tool,13,14 which uses a b-spline representation of the registration warp field and intermediate degrees of freedom.14,15 Next, the mean FA image was created
Fig 1. JHU ICBM-DTI-81 White-Matter Labels atlas representation on the axial plane. Voxels located in the visual pathway were selected at the right side (light green in the occipital projection) and in the occipital left side (light blue). and thinned to generate a mean FA skeleton, which represents the center of all tracts common to the group. Each patient’s aligned FA data were then projected onto this skeleton. Additionally, by applying a mask determined by the JHU ICBMDTI-81 White-Matter Labels atlas, voxels located in the VP were selected, including the postchiasmatic portion (Fig 1), and FA values were extracted for all voxels for each patient, creating a mean FA of the VP for each patient. RsfMRI was postprocessed by FMRIB’s Software Library tools (http://www.fmrib.ox.ac.uk/fsl), using the methodology previously described by Roosendaal.16 The functional magnetic resonance images were motion-corrected, and nonbrain tissue was removed. In addition, images were spatially smoothed and temporally filtered. After this preprocessing, functional scans were aligned to high-resolution T1-WI scans and subsequently to Montreal Neurological Institute-152 standard space using nonlinear registration. The aligned data were then temporally concatenated across subjects to create a single 4D data set. This concatenated fMRI data set was decomposed using independent component analysis (ICA)17 to identify large-scale patterns of functional connectivity. Next, betweensubject analysis of the resting data was carried out using a “dual-regression” approach18 that allows for voxel-wise comparisons of resting functional connectivity. The visual network was selected by visual inspection and comparison to earlier studies.17 It was tested voxel-wise for statistically significant group differences with a general linear model using nonparametric permutation testing (5,000 permutations). The spatial maps generated to characterize between-group differences were controlled for multiple comparisons with a corrected threshold of P < .05, using threshold-free cluster enhancement.19 Synchronization values for the visual network were extracted from each patient and used for correlations with structural parameters.
Statistical Analysis Values are presented as means ± SD. FA values were checked for normality using the Kolmogorov-Smirnov test. Differences in FA values between the right and left VPs and between preserved and nonpreserved eyes were calculated using t-tests with P < .05 considered statistically significant. Correlations between structural and functional parameters and clinical information in the 23 patients were calculated using the Pearson
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Fig 2. Visual network functional representation. All patients and controls were considered in the ICA analysis. Occipital areas show increased synchronization, a marker of the visual network.
Fig 3. Axial (A), coronal (B), and sagittal (C) planes of the rsfMRI of patients compared to controls. Red voxels located in the occipital area represent areas of higher synchronization in patients, reflecting cortical compensation.
correlation method. The Statistical Package for the Social Sciences (SPSS) version 17.0 for Windows was used for statistical analysis.
Results Clinical data of the patients are better described in Table 1. Main clinical aspect such as age, gender, expanded disability status scale (EDSS), number of ON/myelitis attacks, disease duration, treatment, date of MRI, synchronization values of visual network, and FA of right and left VP are included. The rsfMRI data set was decomposed into 14 components, and the visual network was clearly detected in subjects and healthy controls, as shown in Figure 2. Considering the multiple comparison corrected maps, there was increased synchronization in the whole occipital cortex in NMOs patients compared to controls (P = .01; Fig 3). The average synchronization value for patients was 3.5 (SD ± 2.7) and for controls was 2.1 (SD ± 1.4; P = .05).
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FA values for the right and left VP had normal distribution. Average FA values of the VP were similar on the right and left sides (.60 ± .03 and .59 ± .03, respectively, P = .57). A comparison between preserved and nonpreserved eyes was also conducted. A total of 29 eyes were affected within 36 episodes of ON, and 17 eyes were preserved. Average FA values were similar for the affected and preserved eyes (.59 ± .03 and .58 ± .11, respectively, P = .50). A total of 20 ON attacks occurred in the right side and 16 on the left side. The structural parameter (FA) from DTI and synchronization values from rsfMRI showed a trend for a weak negative correlation for the right FA in patients, Pearson R = −.36 (P = .08) and a slightly nonstatistical significant positive correlation for the left FA, R = .075 (P = .73).
Discussion This study demonstrated evidence of cortical neuroplasticity after ON episodes in patients with NMOs using rsfMRI. The
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weak correlation of functional data with structural damage measured by DTI might reflect that functional compensation occurs prior to significant WM damage. The large number of axons in the optic nerve, in excess of the critical threshold necessary to maintain function,20 helps to explain the dissociation between optic nerve tissue damage and the degree of visual loss and recovery.7,20–22 Functional compensation may also explain the lack of correlation between structural damage and clinical improvement.7 Cortical adaptation, especially in the lateral occipital complex20 or even in extraoccipital areas7,21,23,24 is crucial for favorable clinical outcomes, which occur mainly during the late course of ON (after 6 months)23,25 or even during the ON attack.8 To our knowledge, this study is the first to demonstrate visual cortical adaptations using rsfMRI in NMOs. It is critical to analyze the brain activity that occurs in the absence of external stimuli, which may reveal a broader spectrum of compensation, as opposed to task-related fMRI, which may only show specific patterns of adaptation.16 There are several possible explanations for the weak correlation between structural (FA values) and functional (synchronization values) parameters. ON usually affects the anterior part of the VP, which was not evaluated by the current DTI approach. This study considered only the posterior projection of the VP, which could be affected in demyelinating diseases, but no lesions were revealed by MRI.22 Furthermore, functional compensation may be triggered by visual field defects, which are mainly caused by anterior VP damage.24 Finally, visual acuity may correlate with DTI parameters originating from extraoccipital areas that are involved in functional compensations, such as the prefrontal and temporal brain regions, but not with the occipital lobe itself.26 The fact that the affected and nonaffected eyes had similar FA values suggests that posterior structural damage was minimal in this cohort. There were 20 episodes of ON at the right side, whereas only 16 were in the left side. Although it is not a significant difference in occurrence, this slight prevalence on the right side may reflect that the structural damage is somehow worse at the this side and the functional compensation is decaying (reflecting the trend for negative correlation in the Pearson analysis). This study was limited to patients in the NMOs, including only spinal cord attacks in the rsfMRI and including different disease durations and clinical courses. Although, relative to controls, patients showed higher synchronization in the occipital area. This could reflect the presence of undetectable VP lesions in NMOs patients (including the patients with only spinal cord attacks), similar to previous findings in MS patients, in which both affected and unaffected optic nerves had different DTI values from controls. Damage in the “unaffected” optic nerve might be caused by slow axonal degeneration in the absence of a clear inflammation process, which could reflect subclinical inflammation, chronic neuronal dysfunction, and transynaptic degeneration.4,26 Abnormal DTI in the VP has already been described for NMO patients with previous ON but no qualitatively visible intracranial lesions, as well as in the NAWM of the VP in MS patients.3 Thus, subtle structural damage occurs in the absence of clinically significant features, and functional reorganization may occur to maintain visual function. In conclusion, rsfMRI is a useful technique to detect occipital cortical adaptation in NMOs patients following ON
attacks. The balance between structural damage and functional cortical compensation is probably one of the most important mechanism to guarantee the clinical outcome of patients, not only after an ON episode, but also beforehand by causing latent WM lesions.
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