Faculty Bibliography

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/CONCLUSIONS: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.

Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding.

BACKGROUND:Persistent cognitive symptoms have been reported following COVID-19 hospitalization. We investigated the relationship between demographics, social determinants of health (SDOH) and cognitive outcomes 6-months after hospitalization for COVID-19. METHODS:We analyzed 6-month follow-up data collected from a multi-center, prospective study of hospitalized COVID-19 patients. Demographic and SDOH variables (age, race/ethnicity, education, employment, health insurance status, median income, primary language, living arrangements, and pre-COVID disability) were compared between patients with normal versus abnormal telephone Montreal Cognitive Assessments (t-MOCA; scores<18/22). Multivariable logistic regression models were constructed to evaluate predictors of t-MoCA. RESULTS:Of 382 patients available for 6-month follow-up, 215 (56%) completed the t-MoCA (n = 109/215 [51%] had normal and n = 106/215 [49%] abnormal results). 14/215 (7%) patients had a prior history of dementia/cognitive impairment. Significant univariate predictors of abnormal t-MoCA included older age, ≤12 years of education, unemployment pre-COVID, Black race, and a pre-COVID history of cognitive impairment (all p < 0.05). In multivariable analyses, education ≤12 years (adjusted OR 5.21, 95%CI 2.25-12.09), Black race (aOR 5.54, 95%CI 2.25-13.66), and the interaction of baseline functional status and unemployment prior to hospitalization (aOR 3.98, 95%CI 1.23-12.92) were significantly associated with abnormal t-MoCA scores after adjusting for age, history of dementia, language, neurological complications, income and discharge disposition. CONCLUSIONS:Fewer years of education, Black race and unemployment with baseline disability were associated with abnormal t-MoCA scores 6-months post-hospitalization for COVID-19. These associations may be due to undiagnosed baseline cognitive dysfunction, implicit biases of the t-MoCA, other unmeasured SDOH or biological effects of SARS-CoV-2.

OBJECTIVE:We assessed the effect of visual learning and recall impairment on Victoria Symptom Validity Test (VSVT) accuracy and response latency for Easy, Difficult, and Total Items. METHOD/METHODS:A sample of 163 adult patients administered the VSVT and Brief Visuospatial Memory Test-Revised were classified as valid (114/163) or invalid (49/163) groups via independent criterion performance validity tests (PVTs). Classification accuracies for all VSVT indices were examined for the overall sample, and separately for subgroups based on visual memory functioning. RESULTS:In the overall sample, all indices produced acceptable classification accuracy (areas under the curve [AUCs] ≥ 0.79). When stratified by visual learning/recall impairment, accuracy indices yielded acceptable classification for both the unimpaired (AUCs ≥0.79) and impaired subsamples (AUCs ≥0.75). Latency indices had acceptable classification accuracy for the unimpaired subsample (AUCs ≥0.74), but accuracy and sensitivity dropped for the impaired sample (AUCs ≥0.67). CONCLUSIONS:VSVT accuracy and response latency yielded acceptable classification accuracies in the overall sample, and this effect was maintained in those with and without visual learning/recall impairment for the accuracy indices. Findings indicate that the VSVT is a psychometrically robust PVT with largely invariant cut-scores, even in the presence of bona fide visual learning/recall impairment.

The increased life expectancy of individuals with Down syndrome (DS) is associated with increased prevalence of trisomy 21-linked early-onset Alzheimer's disease (EOAD) and dementia. The aims of this study of 14 brain regions including the entorhinal cortex, hippocampus, basal ganglia, and cerebellum in 33 adults with DS 26-72 years of age were to identify the magnitude of brain region-specific developmental neuronal deficits contributing to intellectual deficits, to apply this baseline to identification of the topography and magnitude of neurodegeneration and neuronal and volume losses caused by EOAD, and to establish age-based staging of the pattern of genetically driven neuropathology in DS. Both DS subject age and stage of dementia, themselves very strongly correlated, were strong predictors of an AD-associated decrease of the number of neurons, considered a major contributor to dementia. The DS cohort was subclassified by age as pre-AD stage, with 26-41-year-old subjects with a full spectrum of developmental deficit but with very limited incipient AD pathology, and 43-49, 51-59, and 61-72-year-old groups with predominant prevalence of mild, moderately severe, and severe dementia respectively. This multiregional study revealed a 28.1% developmental neuronal deficit in DS subjects 26-41 years of age and 11.9% AD-associated neuronal loss in DS subjects 43-49 years of age; a 28.0% maximum neuronal loss at 51-59 years of age; and a 11.0% minimum neuronal loss at 61-72 years of age. A total developmental neuronal deficit of 40.8 million neurons and AD-associated neuronal loss of 41.6 million neurons reflect a comparable magnitude of developmental neuronal deficit contributing to intellectual deficits, and AD-associated neuronal loss contributing to dementia. This highly predictable pattern of pathology indicates that successful treatment of DS subjects in the fourth decade of life may prevent AD pathology and functional decline.

Background Vasospasm is a treatable cause of deterioration following aneurysmal subarachnoid hemorrhage. Cerebral computed tomography perfusion mean transit times have been proposed as a predictor of vasospasm but suffer from well-known technical limitations. We evaluated fully automated, thresholded time-to-maxima of the tissue residue function (T

BACKGROUND:Extended interval dosing (EID; average dosing interval approximately every 6 weeks) of natalizumab is associated with significantly lower risk of progressive multifocal leukoencephalopathy than standard interval dosing (SID; every 4 weeks) in patients with relapsing-remitting multiple sclerosis (MS). Real-world studies, though limited, suggest that natalizumab effectiveness is generally maintained in patients who switch to EID after initiation of stable treatment with SID. MS PATHS (Multiple Sclerosis Partners Advancing Technology and Health Solutions) is a collaborative, multicenter learning health system that generates real-world clinical and MRI data using highly standardized acquisition protocols. We compared MRI outcomes in MS PATHS patients treated with natalizumab EID versus SID. We also compared MRI outcomes in patients treated with natalizumab (EID and/or SID) versus injectable MS platform therapy. METHODS:Natalizumab infusion data from the TOUCH Prescribing Program database and MS PATHS MRI assessment data from seven US sites as of July 23, 2020, were used to identify patients with relapsing-remitting MS who had received natalizumab EID or SID in the interval between two MRI scans (an MRI segment). Patients who received injectable platform MS therapy between two MRI scans were also identified. MRI data were used to determine the incidence rate and odds of developing new or enlarging T2 lesions, annualized percentage change in T2 lesion volume (T2LV), and annualized percentage change in brain parenchymal fraction (BPF). MRI outcomes were compared for 1) natalizumab EID treatment versus natalizumab SID treatment, 2) natalizumab treatment (EID + SID) versus platform therapy, and 3) natalizumab EID versus platform therapy. Propensity score-based weighting or matching were used to balance covariates at the start of MRI segments for all comparisons. RESULTS:The MRI outcomes observed with natalizumab EID treatment did not differ significantly from those observed with natalizumab SID treatment. The odds ratio for any new or enlarging T2 lesion was 1.07 (95% confidence interval [CI]: 0.93, 1.24; p = 0.355), and the rate ratio (95% CI) for new or enlarging T2 lesions was 1.62 (0.93, 2.82; p = 0.090). Differences (95% CI) between EID and SID patients in mean annualized percentage change in T2LV and BPF were 1.56% (-3.77%, 6.90%; p = 0.566) and -0.11% (-0.25%, -0.10%; p = 0.096), respectively. Conversely, when MRI outcomes in natalizumab and platform therapy patients were compared, there were significant differences favoring natalizumab in all assessments: the odds of any new or enlarging T2 lesion (odds ratio: 0.69 [95% CI: 0.64, 0.75]; p<0.001), the incidence rate of new or enlarging T2 lesions (rate ratio: 0.47 [95% CI: 0.37, 0.61]; p<0.001), annualized percentage change (decrease) in T2LV (difference: -3.68% [95% CI: -7.06%, -0.30%]; p = 0.033), and annualized percentage change (increase) in BPF (difference: 0.22% [95% CI: 0.16%, 0.29%]; p<0.001). Results of the subgroup comparison of natalizumab EID patients with platform therapy patients were similar to those of the overall-natalizumab-group-versus-platform-therapy comparison. CONCLUSIONS:The results indicate that natalizumab EID and SID provide comparable real-world effectiveness on quantitative MRI metrics. These data further demonstrate that natalizumab EID can provide superior real-world effectiveness to injectable platform therapy on quantitative MRI metrics.

Objectives: While national medical guidelines recommend genetic testing for all individuals with non-syndromic autism spectrum disorder (ASD), there is underutilization of genetic testing. This study aims to define the perspectives and approaches to initial genetic testing of ASD diagnosticians in order to improve utilization. Methods: A prospective cross-sectional study was conducted using an anonymous online survey distributed to 59 staff neurologists, developmental-behavioral pediatricians, psychologists, post-graduate physician fellows, and nurse practitioners from a single academic medical center. Questions explored knowledge, attitudes, and practices of initial genetic testing for ASD among diagnosticians. Results: Among the 30 respondents (51% response rate), a lack of comprehensive pre-test genetic counseling was identified and forgetfulness was the most prevalent reason for not recommending genetic testing (n = 7/23, 30%). Insurance prior authorization (PA) for genetic testing was a major barrier for clinicians. More than half of respondents (n = 13/25, 52%) reported being uncomfortable with the PA process. Conclusions: Variability in knowledge and practices among ASD diagnosticians regarding genetic testing was identified. Therefore, potential interventions like clinician and administrative staff education, as well as genetic counselor integration into ASD clinics, may be useful to improve genetic testing utilization.