Faculty Bibliography

Effective mild cognitive impairment (MCI) screening requires accessible testing. This study compared two tests for distinguishing MCI patients from controls: Rapid Automatized Naming (RAN) for naming speed and Low Contrast Letter Acuity (LCLA) for sensitivity to low contrast letters. Two RAN tasks were used: the Mobile Universal Lexicon Evaluation System (MULES, picture naming) and Staggered Uneven Number test (SUN, number naming). Both RAN tasks were administered on a tablet and in a paper/pencil format. The tablet format was administered using the Mobile Integrated Cognitive Kit (MICK) application. LCLA was tested at 2.5% and 1.25% contrast. Sixty-four participants (31 MCI, 34 controls; mean age 73.2 ± 6.8 years) were included. MCI patients were slower than controls for paper/pencil (75.0 vs. 53.6 sec, p < 0.001), and tablet MULES (69.0 sec vs. 50.2 sec, p = 0.01). The paper/pencil SUN showed no significant difference (MCI: 59.5 sec vs. controls: 59.9 sec, p = 0.07), nor did tablet SUN (MCI: 59.3 sec vs. controls: 55.7 sec, p = 0.36). MCI patients had worse performance on LCLA testing at 2.5% contrast (33 letters vs. 36, p = 0.04*) and 1.25% (0 letters vs. 14. letters, p < 0.001). Receiver operating characteristic (ROC) analysis showed similar performance of paper/pencil and tablet MULES in distinguishing MCI from controls (AUC = 0.77), outperforming both SUN (AUC = 0.63 paper, 0.59 tablet) and LCLA (2.5% contrast: AUC = 0.65, 1.25% contrast: AUC = 0.72). The MULES, in both formats, may be a valuable screening tool for MCI.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Disturbances in brain catecholamine activity may be associated with symptoms after exposure to repetitive head impacts (RHIs) or related chronic traumatic encephalopathy (CTE). In this article, we studied CSF catecholamines in former professional and college American football players and examined the relationship with football proxies of RHI exposure, CTE probability, cognitive performance, neuropsychiatric symptoms, and parkinsonism. METHODS:In this observational cross-sectional study, we examined male former American football players, professional ("PRO") or college ("COL") level, and asymptomatic unexposed male ("UE") individuals from the DIAGNOSE CTE Research Project. Catecholamines-norepinephrine (NE) and its metabolite, 3,4-dihydroxyphenylglycol (DHPG), and dopamine (DA) and its precursor, 3,4-dihydroxyphenylalanine (l-DOPA), and metabolite, 3,4-dihydroxyphenylacetic acid (DOPAC)-were measured in CSF with high-performance liquid chromatography and compared across groups with analysis of covariance. Multivariable linear regression models tested the relationship between CSF catecholamines and proxies of RHI exposure (e.g., total years of playing American football), factor scores for cognition, and neurobehavioral dysregulation (explosivity, emotional dyscontrol, impulsivity, affective lability), as well as depressive/anxiety symptoms, measured with the Beck Depression/Anxiety Inventories. CTE probability and parkinsonism were assessed using the National Institute of Neurological Disorders and Stroke consensus diagnostic criteria for traumatic encephalopathy syndrome (TES), and biomarkers were compared among different diagnostic groups. RESULTS:The cohort consisted of 120 former American football players (85 PRO players, 35 COL players) and 35 UE participants (age 45-75). Former players had significantly lower levels of NE (mean difference = -0.114, 95% CI -0.190 to -0.038), l-DOPA (-0.121, 95% CI -0.109 to -0.027), and DOPAC (-0.116, 95% CI -0.177 to -0.054) than UE participants. For NE and DOPAC, these overall group differences were primarily due to differences between the PRO and UE cohorts. No significant differences were found across TES-CTE probability subgroups or TES-parkinsonism diagnostic groups. Within the COL cohort, tested as post hoc analyses, higher CSF NE and l-DOPA were associated with higher neurobehavioral dysregulation factor scores, BAI total score, and worse executive functioning and processing speed. CSF DHPG and DOPAC were associated with impulsivity only in this subgroup. DISCUSSION/CONCLUSIONS:We observed reduced CSF catecholamine concentrations in former elite American football players, although the relationship with degree of RHI exposure and the clinical impact needs further study.

Optic disc swelling, frequently associated with vitamin A toxicity, is infrequently linked to vitamin A deficiency. This report describes a 6-year-old male with autism spectrum disorder (ASD) and avoidant restrictive food intake disorder who presented with xerophthalmia, optic disc swelling, vision changes, and deficiencies in vitamins A, B₁, and iron. The patient's behavioral dysregulation posed important challenges for the evaluation, diagnosis, and treatment of his hypovitaminoses. This case underscores the importance of considering multiple nutritional deficiencies as the etiology of optic disc swelling in pediatric populations with autism spectrum disorder and avoidant restrictive food intake disorder, diagnoses that have increased in frequency. Early recognition and intervention can prevent further complications such as visual loss and improve outcomes.

PURPOSE/OBJECTIVE:We describe a case of bilateral, multiple, branch retinal artery occlusions (BRAO) associated with carotid webs. METHODS:A thorough chart review was conducted for the patient. Relevant literature was systematically reviewed. RESULTS:Eight cases of fibromuscular dysplasia (FMD) associated with retinal artery occlusions have been reported. Two additional cases of FMD with other ocular involvement have been described. No cases of carotid webs associated with retinal artery occlusions were found. CONCLUSION/CONCLUSIONS:Carotid webs, an uncommon variant of FMD, are a recognized causative etiology of arterial, ischemic stroke. The case described here of bilateral, multifocal BRAOs represents a unique manifestation of this variant of FMD. This diagnosis should be considered in the setting of an otherwise unrevealing BRAO workup, as recognition of this association may be sight and life-saving.

BACKGROUND:Visual symptoms are common after concussion. Rapid automatized naming (RAN) tasks are simple performance measures that demonstrate worse time scores in the setting of acute or more remote injury. METHODS:We evaluated the capacity for the Mobile Universal Lexicon Evaluation System (MULES) and Staggered Uneven Number (SUN) testing to be feasibly administered during preseason testing in a cohort of youth ice hockey athletes using a novel computerized app, the Mobile Integrated Cognitive Kit (MICK). Participants from a youth hockey league underwent preseason testing. RESULTS:Among 60 participants, the median age was 13 years (range 6-17). The median best time for the MULES was 49.8 seconds (range = 34.2-141.0) and the median best time for the SUN was 70.1 (range = 36.6-200.0). As is characteristic of timed performance measures, there were learning effects between the first and second trials for both the MULES (median improvement = 10.6 seconds, range = -32.3 to 92.0, P < 0.001, Wilcoxon signed-rank test) and SUN (median improvement = 2.4 seconds, range= -8.0 to 15.1, P = 0.001, Wilcoxon signed-rank test). Age was a predictor of best baseline times, with longer (worse) times for younger participants for MULES (P < 0.001, rs = -0.67) and SUN (P < 0.001, rs = -0.54 Spearman rank correlation). Degrees of learning effect did not vary with age (P > 0.05, rs = -0.2). CONCLUSIONS:Vision-based RAN tasks, such as the MULES and SUN, can be feasibly administered using the MICK app during preseason baseline testing in youth sports teams. The results suggest that more frequent baseline tests are necessary for preadolescent athletes because of the relation of RAN task performance to age.

BACKGROUND:In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs). METHODS:Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance. RESULTS:The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53. CONCLUSIONS:ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.