Meagan Seay1, Omar Akhand2, Matthew S Galetta3, Lucy Cobbs4, Lisena Hasanaj5, Prin Amorapanth6, John-Ross Rizzo7, Rachel Nolan8, Liliana Serrano9, Janet C Rucker10, Steven L Galetta11, Laura J Balcer12. 1. Departments of Neurolog, New York University School of Medicine, New York, NY, USA. Electronic address: meagan.seay@nyumc.org. 2. Departments of Neurolog, New York University School of Medicine, New York, NY, USA. Electronic address: omar.akhand@nyumc.org. 3. Departments of Neurolog, New York University School of Medicine, New York, NY, USA. Electronic address: matthew.galetta@nyumc.org. 4. Departments of Neurolog, New York University School of Medicine, New York, NY, USA. Electronic address: lucy.cobbs@nyumc.org. 5. Departments of Neurolog, New York University School of Medicine, New York, NY, USA. Electronic address: lisena.hasanaj@nyumc.org. 6. Physical Medicine and Rehabilitation, New York University School of Medicine, New York, NY, USA. Electronic address: prin.amorapanth@nyumc.org. 7. Departments of Neurolog, New York University School of Medicine, New York, NY, USA; Physical Medicine and Rehabilitation, New York University School of Medicine, New York, NY, USA. Electronic address: johnross.rizzo@nyumc.org. 8. Departments of Neurolog, New York University School of Medicine, New York, NY, USA. Electronic address: rachel.nolan@nyumc.org. 9. Departments of Neurolog, New York University School of Medicine, New York, NY, USA. Electronic address: liliana.serrano2@nyumc.org. 10. Departments of Neurolog, New York University School of Medicine, New York, NY, USA; Ophthalmology, New York University School of Medicine, New York, NY, USA. Electronic address: janet.rucker@nyumc.org. 11. Departments of Neurolog, New York University School of Medicine, New York, NY, USA; Ophthalmology, New York University School of Medicine, New York, NY, USA. Electronic address: steven.galetta@nyumc.org. 12. Departments of Neurolog, New York University School of Medicine, New York, NY, USA; Population Health, New York University School of Medicine, New York, NY, USA; Ophthalmology, New York University School of Medicine, New York, NY, USA. Electronic address: laura.balcer@nyumc.org.
Abstract
OBJECTIVE: The Mobile Universal Lexicon Evaluation System (MULES) is a test of rapid picture naming that is under investigation for concussion. MULES captures an extensive visual network, including pathways for eye movements, color perception, memory and object recognition. The purpose of this study was to introduce the MULES to visual assessment of patients with MS, and to examine associations with other tests of afferent and efferent visual function. METHODS: We administered the MULES in addition to binocular measures of low-contrast letter acuity (LCLA), high-contrast visual acuity (VA) and the King-Devick (K-D) test of rapid number naming in an MS cohort and in a group of disease-free controls. RESULTS: Among 24 patients with MS (median age 36 years, range 20-72, 64% female) and 22 disease-free controls (median age 34 years, range 19-59, 57% female), MULES test times were greater (worse) among the patients (60.0 vs. 40.0 s). Accounting for age, MS vs. control status was a predictor of MULES test times (P = .01, logistic regression). Faster testing times were noted among patients with MS who had greater (better) performance on binocular LCLA at 2.5% contrast (P < .001, linear regression, accounting for age), binocular high-contrast VA (P < .001), and K-D testing (P < .001). Both groups demonstrated approximately 10-s improvements in MULES test times between trials 1 and 2 (P < .0001, paired t-tests). CONCLUSION: The MULES test, a complex task of rapid picture naming involves an extensive visual network that captures eye movements, color perception and the characterization of objects. Color recognition, a key component of this novel assessment, is early in object processing and requires area V4 and the inferior temporal projections. MULES scores reflect performance of LCLA, a widely-used measure of visual function in MS clinical trials. These results provide evidence that the MULES test can add efficient visual screening to the assessment of patients with MS.
OBJECTIVE: The Mobile Universal Lexicon Evaluation System (MULES) is a test of rapid picture naming that is under investigation for concussion. MULES captures an extensive visual network, including pathways for eye movements, color perception, memory and object recognition. The purpose of this study was to introduce the MULES to visual assessment of patients with MS, and to examine associations with other tests of afferent and efferent visual function. METHODS: We administered the MULES in addition to binocular measures of low-contrast letter acuity (LCLA), high-contrast visual acuity (VA) and the King-Devick (K-D) test of rapid number naming in an MS cohort and in a group of disease-free controls. RESULTS: Among 24 patients with MS (median age 36 years, range 20-72, 64% female) and 22 disease-free controls (median age 34 years, range 19-59, 57% female), MULES test times were greater (worse) among the patients (60.0 vs. 40.0 s). Accounting for age, MS vs. control status was a predictor of MULES test times (P = .01, logistic regression). Faster testing times were noted among patients with MS who had greater (better) performance on binocular LCLA at 2.5% contrast (P < .001, linear regression, accounting for age), binocular high-contrast VA (P < .001), and K-D testing (P < .001). Both groups demonstrated approximately 10-s improvements in MULES test times between trials 1 and 2 (P < .0001, paired t-tests). CONCLUSION: The MULES test, a complex task of rapid picture naming involves an extensive visual network that captures eye movements, color perception and the characterization of objects. Color recognition, a key component of this novel assessment, is early in object processing and requires area V4 and the inferior temporal projections. MULES scores reflect performance of LCLA, a widely-used measure of visual function in MS clinical trials. These results provide evidence that the MULES test can add efficient visual screening to the assessment of patients with MS.
Authors: Shirley Z Wu; Rachel Nolan-Kenney; Nicholas J Moehringer; Lisena F Hasanaj; Binu M Joseph; Ashley M Clayton; Janet C Rucker; Steven L Galetta; Thomas M Wisniewski; Arjun V Masurkar; Laura J Balcer Journal: J Neuroophthalmol Date: 2021-05-17 Impact factor: 4.415