Vision as a piece of the head trauma puzzle

Abstract

Approximately half of the brain's circuits are involved in vision and control of eye movements. Therefore, visual dysfunction is a common symptom of concussion, the mildest form of traumatic brain injury (TBI). Photosensitivity, vergence dysfunction, saccadic abnormalities, and distortions in visual perception have been reported as vision-related symptoms following concussion. Impaired visual function has also been reported in populations with a lifetime history of TBI. Consequently, vision-based tools have been developed to detect and diagnose concussion in the acute setting, and characterize visual and cognitive function in those with a lifetime history of TBI. Rapid automatized naming (RAN) tasks have provided widely accessible and quantitative measures of visual-cognitive function. Laboratory-based eye tracking approaches demonstrate promise in measuring visual function and validating results from RAN tasks in patients with concussion. Optical coherence tomography (OCT) has detected neurodegeneration in patients with Alzheimer's disease and multiple sclerosis and may provide critical insight into chronic conditions related to TBI, such as traumatic encephalopathy syndrome. Here, we review the literature and discuss the future directions of vision-based assessments of concussion and conditions related to TBI.

Fig. 1. Major cortical and subcortical pathways involved in saccadic generation.

Eye fields (FEF). Striate cortex (SC). Dorsolateral prefrontal cortex (DLPC). Supplementary eye fields (SEF). Fastigial nucleus (FN). Substantia nigra pars reticulata (SNPR). Nucleus reticularis tegmenti pontis (NRTP). Brainstem Gaze Centers (BGC). Cerebellum Oculomotor Vermis (V). Reprinted with permission from: Galetta et al. [44].

Fig. 2. Screenshots of the Mobile Integrated Cognitive Kit (MICK) app.

This app includes digitized versions of the MULES (upper two panels) and SUN tests (lowest panel), tailored for tablet devices to allow for greater accessibility of testing. The app is currently available for research-based testing. Trained professionals who perform testing are able to assess performance on the MULES and SUN tests in a user-friendly manner with additional features such as timers and voice recordings. Versions of the app that can be used on smartphone-based platforms are also under development. Reprinted with permission from: Park et al. [32].

Fig. 3. Box plot showing MULES and SUN time scores with learning effects (n = 59).

Lines in the boxes represent medians; boxes delineate the interquartile range (25th to 75th percentiles). Whiskers represent ranges of observations; outliers are represented by dots. No significant differences were noted between MICK app-based MULES and SUN vs. the previously developed paper/ pencil versions. These analyses accounted for whether MICK app versions or paper/ pencil versions were presented first (P = 0.45 for MULES, P = 0.50 for SUN, linear regression models). Percentage learning effects (difference between two trials divided by best time for each test) were significantly greater for the paper/pencil version compared with the MICK app only for the MULES (17.8% vs. 11.5%, P = 0.03, linear regression accounting simultaneously for which platform was presented first). MULES learning effects were significantly greater than those for SUN, as demonstrated in previous studies [32, 36]. Reprinted with permission from: Park et al. [32].