Faculty Bibliography

A transient ischemic attack is an acute neurologic event caused by focal ischemia affecting the brain, eye, or spinal cord, resolving quickly without infarction on magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI). It is a tissue-based diagnosis, highlighting the need for prompt recognition and risk stratification. Evaluation in the emergency department includes detailed history, risk assessment, neurologic examination, and initial noncontrast computed tomography (CT) to rule out other conditions, with MRI DWI as the gold standard for confirming no infarction. Vascular imaging, echocardiography, electrocardiogram (ECG), and laboratories help identify underlying causes. Central retinal artery occlusion (CRAO) requires urgent diagnosis and ophthalmology consultation to prevent permanent vision loss.

We report a case of an 83-year-old female patient who presented with binocular diplopia associated with left periorbital pain. She was diagnosed with a left pupil-involving third nerve palsy initially believed to be either microvascular or aneurysmal. However, negative vascular neuroimaging and further serologic workup suggested the possibility of neurosyphilis. Her clinical course was significant for persistent periorbital pain and a new seventh nerve palsy, despite syphilis treatment, prompting repeat neuroimaging and lumbar puncture. This case highlights the importance of the clinical history, imaging, CSF studies, and repeated workup in distinguishing between infectious and noninfectious causes of cranial neuropathies.

It is well recognized that many pandemic viruses are associated with neurologic complications, most recently with COVID-19. After the outbreak of the COVID-19 pandemic, neurologic surveillance platforms were implemented to characterize the complications of COVID-19. Surveillance platforms are invaluable in providing timely data, informing clinical practice, and directing future research. Lessons learned from recent neurologic surveillance networks include the importance of global and cross-specialty collaboration. It is critical for future surveillance systems to consider these aspects, as it will also serve to improve representation of low and middle-income countries (LMICs) and communities. Trainees played a critical role in the success of neurologic surveillance networks; as frontline health care workers, they were able to provide timely data collection, and their fresh insights are important for future pandemic surveillance system development. In this article, we review the methods of recent neurologic surveillance networks and discuss their strengths and limitations. We explore the outlook for pandemic surveillance platforms and the crucial role global collaboration plays in ensuring that LMICs are represented. We review the role of trainees in pandemic surveillance networks and discuss how it is vital to encourage their continued involvement to ensure that, as future health care leaders, they are prepared to manage future pandemics effectively.

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.

Parsonage-Turner syndrome (PTS) is a spontaneous neuropathy characterized by severe upper extremity pain and muscle denervation and is considered to be a rare disease that is under-recognized. Quantitative MRI (qMRI) characterizes muscle denervation but has not been previously assessed in a longitudinal PTS cohort. The aims of this study are to prospectively and longitudinally characterize qMRI changes in PTS patients at baseline (< 6 months' symptom onset) and at follow-up timepoints (3, 6, and 12 months), to measure associations against electromyography (EMG) and muscle strength, and to predict muscle strength at follow-up. A total of 49 subjects (age = 47.2 ± 14.0 years, 31 M/18 F) underwent 3-Tesla qMRI with T2-mapping, diffusion-based muscle fiber diameter, volumetry, and fat fraction (FF) mapping. Image segmentation of involved muscles was performed by two raters. Linear regression between qMRI metrics and days from symptom onset (DSO) was performed. Pearson's correlation quantified correlations between qMRI metrics, and Kendall's tau assessed correlations between qMRI and EMG and muscle strength. For predictive modeling of muscle strength, a generalized linear model was used, and the coefficient of determination (r²) was compared for combinations of baseline inputs. Regression detected a mean T2 increase of 0.66 ms/week and a mean muscle fiber diameter decrease of 0.96 μm/week within DSO of 100. Muscle fiber diameter correlated with muscle volume (r = 0.850). T2 correlated with EMG (|τ| = 0.34-0.78) and muscle strength (|τ| = 0.40-0.83) in most muscles that could be analyzed. Muscle fiber diameter was correlated to EMG (|τ| = 0.43-0.72) and muscle strength in some muscles (|τ| = 0.39-0.56). The addition of baseline T2 values improved the prediction of muscle strength at 3-month (from r² = 0.57 to 0.67, with -0.057 to -0.068 muscle grade per ms T2), at 6-month (r² = 0.40-0.59, -0.057 to -0.071 grade per ms), and at 12-month follow-up (r² = 0.40-0.62, -0.053 to -0.080 grade per ms). Muscle qMRI measurements in PTS depict muscle denervation and provide complementary characterization of muscle quality for diagnosis and follow-up assessment.

INTRODUCTION/AIMS/OBJECTIVE:There is a lack of information about startle reflex (SR) in primary lateral sclerosis (PLS). This study examined the presence and prevalence of SR in PLS and compared findings with amyotrophic lateral sclerosis (ALS). METHODS:46 PLS and 54 ALS participants were assessed through structured interviews in this cross-sectional study. Fisher's exact test was used to compare reported SR prevalence. Multivariable linear regression was utilized to study associations between disease group and SR frequency in response to sudden stimuli. RESULTS:SR differed markedly between the two groups, with a higher prevalence in PLS (93.5%) than ALS (20.4%; p < 0.001). Among ALS patients, SR was present in all upper motor neuron (UMN)-predominant cases, which accounted for 54.5% of the SR-positive ALS group, but only 10.4% of probable/definite ALS cases. In SR-positive patients, response frequency to sudden stimuli exceeded 60% in both ALS and PLS, most often triggered by auditory stimuli. Younger age, shorter disease duration, and PLS diagnosis were associated with more frequent SR. DISCUSSION/CONCLUSIONS:SR is significantly more common in PLS than in ALS. Notably, UMN-predominant ALS, although limited in number, showed a higher prevalence of SR (6 out of 6, 100%), indicating that predominant UMN involvement may be a key determinant of SR across both conditions. These hypothesis-generating findings suggest that SR may serve as a novel clinical marker in PLS and UMN-predominant ALS, warranting further validation through prospective studies.

Antibiotic-induced microbiome injury, defined as a reduction of ecological diversity and obligate anaerobe taxa, is associated with negative health outcomes in hospitalized patients, and healthy individuals who received antibiotics in the past are at higher risk for autoimmune diseases. Postbiotics contain mixtures of bacterial fermentation metabolites and bacterial cell wall components that have the potential to modulate microbial communities. Yet, it is unknown if a fermentation-derived postbiotic can reduce antibiotic-induced microbiome injury. Here, we present the results from a single-center, randomized placebo-controlled trial involving 32 patients who received an oral, fermentation-derived postbiotic alongside oral antibiotic and probiotic therapy for non-gastrointestinal (GI) infections. At the end of the antibiotic course, patients receiving the postbiotic (n = 16) had significantly higher fecal bacterial alpha diversity (+40%, inverse Simpson index) compared to the placebo group (n = 16), and the treatment was well-tolerated. Analysis of 157 longitudinal fecal samples revealed that this increased diversity was driven by enrichment of health-associated taxa, notably obligate anaerobic Firmicutes, particularly Lachnospiraceae. In contrast, Escherichia/Shigella species, often linked to pathogenicity and antibiotic resistance, were reduced in postbiotic-treated patients at the end of antibiotic treatment and remained lower up to 10 days later. Our findings suggest that postbiotic co-administration during antibiotic therapy may augment health-associated gut microbiome composition and mitigate antibiotic-induced microbiome injury.Trial registration ISRCTN30327931 retrospectively registered.

Hippocampal sharp-wave ripples (SPW-Rs) are high-frequency oscillations critical for memory consolidation. Despite extensive characterization in rodents, their detection in humans is limited by coarse spatial sampling, interictal epileptiform discharges (IEDs), and a lack of consensus on human ripple localization and morphology. Here, we demonstrate that mouse and human hippocampal ripples share spatial, spectral and temporal features, which are clearly distinct from IEDs. In recordings from male APP/PS1 mice, SPW-Rs were distinguishable from IEDs by multiple criteria. Hippocampal ripples recorded during NREM sleep in female and male surgical epilepsy patients exhibited similar narrowband frequency peaks and multiple ripple cycles in the CA1 and subiculum regions. Conversely, IEDs showed a broad spatial extent and wide-band frequency power. We developed a semi-automated, ripple curation toolbox (ripmap) to separate event waveforms by low-dimensional embedding to reduce false-positive rate in selected ripple channels. Our approach improves ripple detection and provides a firm foundation for future human memory research.

The 2024 revised McDonald criteria for multiple sclerosis recognize the optic nerve as a topography for dissemination in space. Optical coherence tomography-derived inter-eye differences in peri-papillary retinal nerve fiber layer or ganglion cell-inner plexiform layer thicknesses (≥6μm or ≥4μm, respectively) are proposed for identifying unilateral optic nerve involvement. However, the value of combining inter-eye difference measures and optimal temporal-quadrant peri-papillary retinal nerve fiber layer inter-eye differences remains unclear. We investigated the diagnostic performance of combined inter-eye differences, optimal temporal-quadrant peri-papillary retinal nerve fiber layer inter-eye differences, and examined the effects of time, prior optic neuritis frequency, sex, and race on inter-eye differences. Retinal optical coherence tomography images from all study participants underwent rigorous quality control. Receiver operating characteristic analyses and area under the receiver operating characteristic curves (AUC) were used to determine optimal inter-eye differences of individual and combined measures to distinguish eyes with, from without, prior optic neuritis in people with multiple sclerosis. Mixed-effects models were used to assess impact of time, prior optic neuritis events, sex, and race on inter-eye differences. An independent multiple sclerosis cohort from a second center was examined for external validation. Among 1854 people with multiple sclerosis, optimal inter-eye difference thresholds for identifying unilateral optic nerve involvement were 6μm for peri-papillary retinal nerve fiber layer (AUC=0.80), 4μm for ganglion cell-inner plexiform layer (AUC=0.83), and 8μm for temporal-quadrant peri-papillary retinal nerve fiber layer (AUC=0.71) thicknesses. Peri-papillary retinal nerve fiber layer inter-eye differences ≥6μm or ganglion cell-inner plexiform layer inter-eye differences ≥4μm yielded 87.6% sensitivity, 70.0% specificity, and 64.0% positive predictive value. Concurrent inter-eye differences at lower thresholds (≥5μm peri-papillary retinal nerve fiber layer, ≥3μm ganglion cell-inner plexiform layer) reduced sensitivity to 72.5%, but improved specificity (86.6%) and positive predictive value (76.7%), while maintaining accuracy and negative predictive value. Temporal-quadrant peri-papillary retinal nerve fiber layer inter-eye differences did not improve diagnostic performance. Over a median of 5.1 years, ganglion cell-inner plexiform layer and peri-papillary retinal nerve fiber layer inter-eye differences remained stable. Prior optic neuritis counts and sex did not affect inter-eye differences. Although Black Americans had higher inter-eye differences than White Americans, optimal thresholds were comparable across races. The validation cohort comprising 254 people with multiple sclerosis confirmed these findings. In conclusion, concurrent peri-papillary retinal nerve fiber layer (≥5μm) and ganglion cell-inner plexiform layer inter-eye differences (≥3μm) improve unilateral optic nerve involvement detection versus either alone (≥6μm or ≥4μm, respectively), while temporal-quadrant peri-papillary retinal nerve fiber layer inter-eye differences offer limited benefit. Inter-eye differences remain stable longitudinally and unaffected by prior optic neuritis frequency.