Faculty Bibliography

Brainstem nuclei dysfunction is implicated in sudden unexpected death in epilepsy. In animal models, deficient serotonergic activity is associated with seizure-induced respiratory arrest. In humans, glia are decreased in the ventrolateral medullary pre-Botzinger complex that modulate respiratory rhythm, as well as in the medial medullary raphe that modulate respiration and arousal. Finally, sudden unexpected death in epilepsy cases have decreased midbrain volume. To understand the potential role of brainstem nuclei in sudden unexpected death in epilepsy, we evaluated molecular signalling pathways using localized proteomics in microdissected midbrain dorsal raphe and medial medullary raphe serotonergic nuclei, as well as the ventrolateral medulla in brain tissue from epilepsy patients who died of sudden unexpected death in epilepsy and other causes in diverse epilepsy syndromes and non-epilepsy control cases (n = 15-16 cases per group/region). Compared with the dorsal raphe of non-epilepsy controls, we identified 89 proteins in non-sudden unexpected death in epilepsy and 219 proteins in sudden unexpected death in epilepsy that were differentially expressed. These proteins were associated with inhibition of EIF2 signalling (P-value of overlap = 1.29 × 10^-8, z = -2.00) in non-sudden unexpected death in epilepsy. In sudden unexpected death in epilepsy, there were 10 activated pathways (top pathway: gluconeogenesis I, P-value of overlap = 3.02 × 10^-6, z = 2.24) and 1 inhibited pathway (fatty acid beta-oxidation, P-value of overlap = 2.69 × 10^-4, z = -2.00). Comparing sudden unexpected death in epilepsy and non-sudden unexpected death in epilepsy, 10 proteins were differentially expressed, but there were no associated signalling pathways. In both medullary regions, few proteins showed significant differences in pairwise comparisons. We identified altered proteins in the raphe and ventrolateral medulla of epilepsy patients, including some differentially expressed in sudden unexpected death in epilepsy cases. Altered signalling pathways in the dorsal raphe of sudden unexpected death in epilepsy indicate a shift in cellular energy production and activation of G-protein signalling, inflammatory response, stress response and neuronal migration/outgrowth. Future studies should assess the brain proteome in relation to additional clinical variables (e.g. recent tonic-clonic seizures) and in more of the reciprocally connected cortical and subcortical regions to better understand the pathophysiology of epilepsy and sudden unexpected death in epilepsy.

The utility of the Victoria Symptom Validity Test (VSVT) as a performance validity test (PVT) has been primarily established using response accuracy scores. However, the degree to which response latency may contribute to accurate classification of performance invalidity over and above accuracy scores remains understudied. Therefore, this study investigated whether combining VSVT accuracy and response latency scores would increase predictive utility beyond use of accuracy scores alone. Data from a mixed clinical sample of 163 patients, who were administered the VSVT as part of a larger neuropsychological battery, were analyzed. At least four independent criterion PVTs were used to establish validity groups (121 valid/42 invalid). Logistic regression models examining each difficulty level revealed that all VSVT measures were useful in classifying validity groups, both independently and when combined. Individual predictor classification accuracy ranged from 77.9 to 81.6%, indicating acceptable to excellent discriminability across the validity indices. The results of this study support the value of both accuracy and latency scores on the VSVT to identify performance invalidity, although the accuracy scores had superior classification statistics compared to response latency, and mean latency indices provided no unique benefit for classification accuracy beyond dimensional accuracy scores alone.

Although mRNA vaccine responses following previous coronavirus disease 2019 (COVID-19) infection have not been assessed in trials, it has been shown that serological evidence of previous COVID-19 generates strong humoral and cellular responses to one dose of mRNA vaccine. We describe a patient with prior COVID-19 infection who developed acute transient encephalopathy with elevated inflammatory markers within 24 h of her first injection of Moderna COVID-19 vaccine. A 69-year-old cognitively normal woman presented with intermittent inattention, disorientation, left/right confusion, weakness, gait instability, and decreased speech. Head CT, brain MRI and MRA, complete blood count, liver enzymes, hepatitis B serology, ammonia, thyroid function, vitamin B12, and pulse oximetry were normal. Electroencephalography performed 48 h after symptom onset showed diffuse triphasic waves, diffuse theta and delta slowing, and no posterior dominant rhythm. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG was positive and inflammatory markers were elevated. On day 5 post-vaccine, she returned to her baseline, without neurological sequelae. The reported patient likely developed a transient inflammatory encephalopathy associated with an abnormal immunologic reaction to one dose of COVID-19 vaccine, in the setting of remote COVID-19 infection (1 year prior), SARS-CoV-2 IgG-positivity, and multiple comorbidities. Physicians should be alert to possible postvaccination reactogenicity in individuals with SARS-CoV-2 IgG-positivity, including risk of neuro-inflammation.

Objective/UNASSIGNED:Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods/UNASSIGNED:This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. Results/UNASSIGNED:The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Conclusions/UNASSIGNED:Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

Early-onset parkinsonism (EO parkinsonism), defined as subjects with disease onset before the age of 40 or 50 years, can be the main clinical presentation of a variety of conditions that are important to differentiate. Although rarer than classical late-onset Parkinson's disease (PD) and not infrequently overlapping with forms of juvenile onset PD, a correct diagnosis of the specific cause of EO parkinsonism is critical for offering appropriate counseling to patients, for family and work planning, and to select the most appropriate symptomatic or etiopathogenic treatments. Clinical features, radiological and laboratory findings are crucial for guiding the differential diagnosis. Here we summarize the most important conditions associated with primary and secondary EO parkinsonism. We also proposed a practical approach based on the current literature and expert opinion to help movement disorders specialists and neurologists navigate this complex and challenging landscape.

Background: The importance of informant history in the diagnosis of mild cognitive impairment (MCI) has been emphasized. Yet, there is limited literature looking into the agreement of self- and informant- reported memory loss in MCI due to Alzheimer's disease (AD) versus cerebrovascular disease (CVD). We investigated the hypothesis that rates of concordance and discordance differ based on MCI etiology.
Method(s): Retrospective clinical data was gathered from the National Alzheimer's Coordinating Center dataset. First visits with a clinical diagnosis of MCI with a primary etiology of AD without CVD (AD MCI) or CVD without AD (CVD MCI) were included for analysis. Self- and informant-based report of memory loss were gleaned from the questions "does the subject report a decline in memory (relative to previously attained abilities?)" and "does the co-participant report a decline in subject's memory (relative to previously attained abilities?)." We excluded participants with missing self-reported or co-participant information on cognitive decline. Statistical analysis was performed using Chi-squared tests.
Result(s): A total of 1917 AD MCI and 213 CVD MCI visits from unique participants were included in the study. Compared to CVD MCI, AD MCI had a higher incidence of visits where both participant and informant noted memory loss (67.8% vs. 54.0%, p<0.0001). Compared to AD MCI, CVD MCI featured more disagreement between participant and informant, both in cases where the participant denied memory loss (17.8% vs. 12.0%, p = 0.0137) and where the informant denied memory loss (21.6% vs. 11.4%, p < 0.0001). Incidence of both participant and informant denying memory loss were similar between AD MCI and CVD MCI (8.9% vs. 6.6%, p = 0.2580).
Conclusion(s): Self- and collateral-based report of memory loss are more discordant in CVD MCI versus AD MCI. This reinforces the importance of eliciting a history from both patients and reliable informants, especially in patients with vascular risk factors and comorbidities