Longitudinal stability of inter-eye differences in optical coherence tomography measures for identifying unilateral optic nerve lesions in multiple sclerosis.

INTRODUCTION: Optical coherence tomography (OCT)-derived peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell+inner plexiform layer (GCIPL) thickness inter-eye differences (IEDs) are robust measurements for identifying clinical history acute ON in people with MS (PwMS). This study investigated the utility and durability of these measures as longitudinal markers to identify optic nerve lesions. METHODS: Prospective, multi-center international study of PwMS (with/without clinical history of ON) and healthy controls. Data from two sites in the International MS Visual System Consortium (IMSVISUAL) were analyzed. Mixed-effects models were used to compare inter-eye differences based on MS and acute ON history. RESULTS: Average age of those with MS (n = 210) was 39.1 ± 10.8 and 190 (91%) were relapsing-remitting. Fifty-nine (28.1%) had a history of acute unilateral ON, while 9/210 (4.3%) had >1 IB episode. Median follow-up between OCT scans was 9 months. By mixed-effects modeling, IEDs were stable between first and last visits within groups for GCIPL for controls (p = 0.18), all PwMS (p = 0.74), PwMs without ON (p = 0.22), and PwMS with ON (p = 0.48). For pRNFL, IEDs were within controls (p = 0.10), all PwMS (p = 0.53), PwMS without ON history (p = 0.98), and PwMS with history of ON (p = 0.81). CONCLUSION: We demonstrated longitudinal stability of pRNFL and GCIPL IEDs as markers for optic nerve lesions in PwMS, thus reinforcing the role for OCT in demonstrating optic nerve lesions.

Normative Data and Conversion Equation for Spectral-Domain Optical Coherence Tomography in an International Healthy Control Cohort.

BACKGROUND: Spectral-domain (SD-) optical coherence tomography (OCT) can reliably measure axonal (peripapillary retinal nerve fiber layer [pRNFL]) and neuronal (macular ganglion cell + inner plexiform layer [GCIPL]) thinning in the retina. Measurements from 2 commonly used SD-OCT devices are often pooled together in multiple sclerosis (MS) studies and clinical trials despite software and segmentation algorithm differences; however, individual pRNFL and GCIPL thickness measurements are not interchangeable between devices. In some circumstances, such as in the absence of a consistent OCT segmentation algorithm across platforms, a conversion equation to transform measurements between devices may be useful to facilitate pooling of data. The availability of normative data for SD-OCT measurements is limited by the lack of a large representative world-wide sample across various ages and ethnicities. Larger international studies that evaluate the effects of age, sex, and race/ethnicity on SD-OCT measurements in healthy control participants are needed to provide normative values that reflect these demographic subgroups to provide comparisons to MS retinal degeneration. METHODS: Participants were part of an 11-site collaboration within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. SD-OCT was performed by a trained technician for healthy control subjects using Spectralis or Cirrus SD-OCT devices. Peripapillary pRNFL and GCIPL thicknesses were measured on one or both devices. Automated segmentation protocols, in conjunction with manual inspection and correction of lines delineating retinal layers, were used. A conversion equation was developed using structural equation modeling, accounting for clustering, with healthy control data from one site where participants were scanned on both devices on the same day. Normative values were evaluated, with the entire cohort, for pRNFL and GCIPL thicknesses for each decade of age, by sex, and across racial groups using generalized estimating equation (GEE) models, accounting for clustering and adjusting for within-patient, intereye correlations. Change-point analyses were performed to determine at what age pRNFL and GCIPL thicknesses exhibit accelerated rates of decline. RESULTS: The healthy control cohort (n = 546) was 54% male and had a wide distribution of ages, ranging from 18 to 87 years, with a mean (SD) age of 39.3 (14.6) years. Based on 346 control participants at a single site, the conversion equation for pRNFL was Cirrus = -5.0 + (1.0 × Spectralis global value). Based on 228 controls, the equation for GCIPL was Cirrus = -4.5 + (0.9 × Spectralis global value). Standard error was 0.02 for both equations. After the age of 40 years, there was a decline of -2.4 µm per decade in pRNFL thickness ( P < 0.001, GEE models adjusting for sex, race, and country) and -1.4 µm per decade in GCIPL thickness ( P < 0.001). There was a small difference in pRNFL thickness based on sex, with female participants having slightly higher thickness (2.6 µm, P = 0.003). There was no association between GCIPL thickness and sex. Likewise, there was no association between race/ethnicity and pRNFL or GCIPL thicknesses. CONCLUSIONS: A conversion factor may be required when using data that are derived between different SD-OCT platforms in clinical trials and observational studies; this is particularly true for smaller cross-sectional studies or when a consistent segmentation algorithm is not available. The above conversion equations can be used when pooling data from Spectralis and Cirrus SD-OCT devices for pRNFL and GCIPL thicknesses. A faster decline in retinal thickness may occur after the age of 40 years, even in the absence of significant differences across racial groups.

The Role of Optical Coherence Tomography Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis.

BACKGROUND AND OBJECTIVES: Recent studies have suggested that intereye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell + inner plexiform (GCIPL) thickness by spectral domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history. METHODS: Participants were from 11 sites within the International Multiple Sclerosis Visual System consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with a history of ON among PwMS. Receiver operating characteristic (ROC) curve analysis was performed on a training data set (2/3 of cohort) and then applied to a testing data set (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT. RESULTS: Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs controls. This composite score performed best, with area under the curve (AUC) = 0.89 (95% CI 0.85-0.93), sensitivity = 81%, and specificity = 80%. The composite score ROC curve performed better than any of the individual measures from the model (p < 0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC = 0.77, 95% CI 0.71-0.83, sensitivity = 68%, specificity = 77%). SVM analysis performed comparably with standard logistic regression models. DISCUSSION: A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with a history of unilateral ON. SVM performed as well as standard statistical models for these classifications. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared with clinical criteria.

The MICK (Mobile integrated cognitive kit) app: Digital rapid automatized naming for visual assessment across the spectrum of neurological disorders.

OBJECTIVE: Rapid automatized naming (RAN) tasks have been utilized for decades to evaluate neurological conditions. Time scores for the Mobile Universal Lexicon Evaluation System (MULES, rapid picture naming) and Staggered Uneven Number (SUN, rapid number naming) are prolonged (worse) with concussion, mild cognitive impairment, multiple sclerosis and Parkinson's disease. The purpose of this investigation was to compare paper/pencil versions of MULES and SUN with a new digitized format, the MICK app. METHODS: Participants (healthy office-based volunteers, professional women's hockey players), completed two trials of the MULES and SUN tests on both platforms (tablet, paper/pencil). The order of presentation of the testing platforms was randomized. Between-platform variability was calculated using the two-way random-effects intraclass correlation coefficient (ICC). RESULTS: Among 59 participants (median age 32, range 22-83), no significant differences were observed for comparisons of mean best scores for the paper/pencil versus MICK app platforms, counterbalanced for order of administration (P = 0.45 for MULES, P = 0.50 for SUN, linear regression). ICCs for agreement between the MICK and paper/pencil tests were 0.92 (95% CI 0.86, 0.95) for MULES and 0.94 (95% CI 0.89, 0.96) for SUN, representing excellent levels of agreement. Inter-platform differences did not vary systematically across the range of average best time score for either test. CONCLUSION: The MICK app for digital administration of MULES and SUN demonstrates excellent agreement of time scores with paper/pencil testing. The computerized app allows for greater accessibility and scalability in neurological diseases, inclusive of remote monitoring. Sideline testing for sports-related concussion may also benefit from this technology.

Exploration of Rapid Automatized Naming and Standard Visual Tests in Prodromal Alzheimer Disease Detection.

BACKGROUND: Visual tests in Alzheimer disease (AD) have been examined over the last several decades to identify a sensitive and noninvasive marker of the disease. Rapid automatized naming (RAN) tasks have shown promise for detecting prodromal AD or mild cognitive impairment (MCI). The purpose of this investigation was to determine the capacity for new rapid image and number naming tests and other measures of visual pathway structure and function to distinguish individuals with MCI due to AD from those with normal aging and cognition. The relation of these tests to vision-specific quality of life scores was also examined in this pilot study. METHODS: Participants with MCI due to AD and controls from well-characterized NYU research and clinical cohorts performed high and low-contrast letter acuity (LCLA) testing, as well as RAN using the Mobile Universal Lexicon Evaluation System (MULES) and Staggered Uneven Number test, and vision-specific quality of life scales, including the 25-Item National Eye Institute Visual Function Questionnaire (NEI-VFQ-25) and 10-Item Neuro-Ophthalmic Supplement. Individuals also underwent optical coherence tomography scans to assess peripapillary retinal nerve fiber layer and ganglion cell/inner plexiform layer thicknesses. Hippocampal atrophy on brain MRI was also determined from the participants' Alzheimer disease research center or clinical data. RESULTS: Participants with MCI (n = 14) had worse binocular LCLA at 1.25% contrast compared with controls (P = 0.009) and longer (worse) MULES test times (P = 0.006) with more errors in naming images (P = 0.009) compared with controls (n = 16). These were the only significantly different visual tests between groups. MULES test times (area under the receiver operating characteristic curve [AUC] = 0.79), MULES errors (AUC = 0.78), and binocular 1.25% LCLA (AUC = 0.78) showed good diagnostic accuracy for distinguishing MCI from controls. A combination of the MULES score and 1.25% LCLA demonstrated the greatest capacity to distinguish (AUC = 0.87). These visual measures were better predictors of MCI vs control status than the presence of hippocampal atrophy on brain MRI in this cohort. A greater number of MULES test errors (rs = -0.50, P = 0.005) and worse 1.25% LCLA scores (rs = 0.39, P = 0.03) were associated with lower (worse) NEI-VFQ-25 scores. CONCLUSIONS: Rapid image naming (MULES) and LCLA are able to distinguish MCI due to AD from normal aging and reflect vision-specific quality of life. Larger studies will determine how these easily administered tests may identify patients at risk for AD and serve as measures in disease-modifying therapy clinical trials.

Role for OCT in detecting hemi-macular ganglion cell layer thinning in patients with multiple sclerosis and related demyelinating diseases.

OBJECTIVE: Investigations have found associations of homonymous thinning of the macular ganglion cell/ inner-plexiform layer (GCIPL) with demyelinating lesions in the post-chiasmal visual pathway among patients with multiple sclerosis (MS). Retinal thinning may also occur through retrograde trans-synaptic degeneration, a process by which lesions in post-geniculate visual pathway structures lead to thinning of the GCIPL across thalamic synapses. The purpose of our study was to determine the frequency of homonymous hemimacular thinning that occurs in association with post-chiasmal visual pathway demyelinating lesions in patients with MS and other demyelinating diseases. METHODS: Adult patients with demyelinating diseases (MS, neuromyelitis optica spectrum disorder [NMOSD], myelin oligodendrocyte glycoprotein antibody disease (anti-MOG)) who were participants in an ongoing observational study of visual pathway structure and function were analyzed for the presence of hemimacular GCIPL thinning on OCT scans. Brain MRI scans were examined for the presence of post-geniculate visual pathway demyelinating lesions. RESULTS: Among 135 participants in the visual pathway study, 5 patients (3.7%) had homonymous hemimacular GCIPL thinning. Eleven patients (8.1%) had a whole+half pattern of GCIPL thinning, characterized by hemimacular thinning in one eye and circumferential macular thinning in the contralateral eye. All but one patient with homonymous hemimacular thinning had demyelinating lesions in the post-geniculate visual pathway; however, these lesions were located in both cerebral hemispheres. CONCLUSION: Homonymous hemimacular thinning in the GCIPL by OCT is associated with post-chiasmal visual pathway demyelinating lesions but it appears to be a relatively uncommon contributor to GCIPL loss. Patients with this pattern of GCIPL often fail to complain of hemifield visual loss. Future studies with prospective and detailed MR imaging may be able to more closely associate demyelinating lesions in anatomically appropriate regions of the post-chiasmal visual pathways with homonymous hemimacular thinning.

The SUN test of vision: Investigation in healthy volunteers and comparison to the mobile universal lexicon evaluation system (MULES).

OBJECTIVE: Tests of rapid automatized naming (RAN) have been used for decades to evaluate neurological conditions. RAN tests require extensive brain pathways involving visual perception, memory, eye movements and language. To the extent that different naming tasks capture varied visual pathways and related networks, we developed the Staggered Uneven Number (SUN) test of rapid number naming to complement existing RAN tests, such as the Mobile Universal Lexicon Evaluation System (MULES). The purpose of this investigation was to determine values for time scores for SUN, and to compare test characteristics between SUN and MULES. METHODS: We administered the SUN and MULES tests to healthy adult volunteers in a research office setting. MULES consists of 54 color photographs; the SUN includes 145 single- and multi-digit numbers. Participants are asked to name each number or picture aloud. RESULTS: Among 54 healthy participants, aged 33 ± 13 years (range 20-66), the average SUN time score was 45.2 ± 8.3 s (range 30-66). MULES test times were 37.4 ± 9.9 s (range 20-68). SUN and MULES time scores did not differ by gender, but were greater (worse) among older participants for MULES (rs = 0.43, P = .001). Learning effects between first and second trials were greater for the MULES; participants improved (reduced) their time scores between trials by 5% on SUN and 16% for MULES (P < .0001, Wilcoxon signed-rank test). CONCLUSION: The SUN is a new vision-based test that complements presently available picture- and number-based RAN tests. These assessments may require different brain pathways and networks for visual processing, visual memory, language and eye movements.

Rapid picture naming in Parkinson's disease using the Mobile Universal Lexicon Evaluation System (MULES).

OBJECTIVE: The Mobile Universal Lexicon Evaluation System (MULES) is a test of rapid picture naming that captures extensive brain networks, including cognitive, language and afferent/efferent visual pathways. MULES performance is slower in concussion and multiple sclerosis, conditions in which vision dysfunction is common. Visual aspects captured by the MULES may be impaired in Parkinson's disease (PD) including color discrimination, object recognition, visual processing speed, and convergence. The purpose of this study was to compare MULES time scores for a cohort of PD patients with those for a control group of participants of similar age. We also sought to examine learning effects for the MULES by comparing scores for two consecutive trials within the patient and control groups. METHODS: MULES consists of 54 colored pictures (fruits, animals, random objects). The test was administered in a cohort of PD patients and in a group of similar aged controls. Wilcoxon rank-sum tests were used to determine statistical significance for differences in MULES time scores between PD patients and controls. Spearman rank-correlation coefficients were calculated to examine the relation between MULES time scores and PD motor symptom severity (UPDRS). Learning effects were assessed using Wilcoxon rank-sum tests. RESULTS: Among 51 patients with PD (median age 70 years, range 52-82) and 20 disease-free control participants (median age 67 years, range 51-90), MULES scores were significantly slower (worse performance) in PD patients (median 63.2 s, range 37.3-296.3) vs. controls (median 53.9 s, range 37.5-128.6, P = .03, Wilcoxon rank-sum test). Slower MULES times were associated with increased motor symptom severity as measured by the Unified Parkinson's Disease Rating Scale, Section III (rs = 0.37, P = .02). Learning effects were greater among patients with PD (median improvement of 14.8 s between two MULES trials) compared to controls (median 7.4 s, P = .004). CONCLUSION: The MULES is a complex test of rapid picture naming that captures numerous brain pathways including an extensive visual network. MULES performance is slower in patients with PD and our study suggests an association with the degree of motor impairment. Future studies will determine the relation of MULES time scores to other modalities that test visual function and structure in PD.