Objective:

To investigate the relationship of cognitive-visual performance, as measured by MULES, and retinal integrity and afferent visual function as quantified by OCT and LCLA, in patients with MS and NMOSD

Background:

Performance on the Mobile Universal Lexicon Evaluation System (MULES), a rapid automatized naming (RAN) task engaging visual and cognitive network pathways, has demonstrated impairment in neurological conditions such as multiple sclerosis (MS), concussion, Parkinson’s and Alzheimer’s diseases. Optical coherence tomography (OCT) and low-contrast letter acuity (LCLA) have demonstrated changes in retinal structure and visual function in MS and neuro-myelitis optica spectrum disorder (NMOSD).

Design/Methods:

The MULES test, consisting of 54 color photographs of various objects (fruits, animals, and random objects), OCT and high- and low-contrast letter acuity (LCLA) testing were administered to 54 people with MS, 15 with NMOSD, and 15 healthy controls (HC). People with MS included those with clinically isolated syndrome, relapsing remitting MS (RRMS) and progressive MS. OCT imaging of the peripapillary retinal nerve fiber layer (pRNFL) and the macular ganglion cell + inner plexiform layer (GCIPL) were performed. Average pRNFL and GCIPL thickness between both eyes of participants, as well as inter-eye thickness differences, were calculated.

Results:

Greater MULES time scores, indicating worse performance, were associated with lower average pRNFL thickness in NMOSD (p=0.004, linear regression accounting for age), lower average GCIPL thickness in NMOSD (p=0.019) and higher inter-eye GCIPL thickness differences in RRMS (p=0.003) and HC (p=0.045). Longer MULES times were also associated with worse binocular high- and low-contrast letter acuity scores in both MS and NMOSD (p<0.05).

Conclusions:

The MULES test of rapid picture naming enhances visual screening when evaluating patients with MS and NMOSD. Greater degrees of retinal neurodegeneration were associated with worse RAN task performance. Inter-eye GCIPL difference is the best OCT predictor of worse RAN task performance in MS.