Faculty Bibliography

BACKGROUND AND OBJECTIVES/OBJECTIVE:The aim of this study was to identify distinct inflammatory response subtypes in patients with c-NORSE by analyzing their cytokine profiles. Insights into underlying mechanisms were sought to understand the pathophysiology and guide personalized therapies to improve patient outcomes. METHODS:Sixty-two patients with c-NORSE were included. A comprehensive panel of 96 cytokines was analyzed in serum samples. Patients were clustered based on their cytokine profiles using the Louvain algorithm, an unsupervised graph-based clustering method. The identified clusters of patients were compared regarding cytokine levels and clinical features. Protein pathway analysis was used to explore the biological relevance of the inflammatory markers within each cluster. Patients with c-NORSE were compared with control patients (n = 18) and patients with other forms of refractory SE (n = 45). RESULTS:Compared with controls, patients with c-NORSE exhibited significant differences in 33 cytokines. Pathway analysis revealed dysregulations in chemotaxis and neutrophil recruitment and migration, highlighting the importance of innate immunity in patients with c-NORSE. Within the c-NORSE cohort, 3 clusters of patients emerged: cluster A, lacking specific inflammatory markers; cluster B, with a much stronger innate-immunity cytokine-driven inflammatory response compared with clusters A and C; and cluster C, defined by dysregulated autoimmune processes. Notably, patients in cluster B showed a statistically significant elevation of innate immune-related proinflammatory cytokines associated with leukocyte recruitment and degranulation. By contrast, those in cluster C showed activation of Janus kinase signal transducer and activator of transcription (JAK-STAT) pathways, suggesting autoimmune mechanisms. Patients in clusters B and C demonstrated varied responses to immunotherapies, with cluster C patients showing favorable outcomes after multiple immunotherapies. DISCUSSION/CONCLUSIONS:The identification of distinct inflammatory subgroups in c-NORSE suggests that variations in the underlying immune mechanisms contribute to differential treatment responses. These findings underscore the importance of personalized therapeutic strategies, potentially targeting specific inflammatory pathways, to optimize clinical outcomes in this challenging condition.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Older chronological age is associated with decreased multiple sclerosis (MS) relapse rates and increased risk of progressive disease. Measurement of biological age may be more precise than birthdate in understanding these aging effects. In addition to normal aging, MS-related accelerated aging may contribute. Measurement of biological age in adults may be confounded by the effects of natural aging and age-related comorbidities. Examining age extremes can be informative, and demonstrating accelerated biological aging in children would support a hypothesis of MS driving premature aging. We sought to compare epigenetic age in participants with pediatric-onset MS (POMS) and age-similar controls. METHODS:We performed a multicenter case-control analysis of epigenetic age in a prospectively collected set of whole blood DNA samples and clinical data. Quantitative methylation scores were derived for approximately 850,000 cytosine-phosphate-guanine (CpG) sites. Epigenetic age was calculated based on 4 established epigenetic clock algorithms. Epigenetic age and age acceleration residual (AAR) were compared between participants with POMS and age-similar controls using multivariate regression analysis, adjusted for demographic variables. RESULTS:= 0.004). DISCUSSION/CONCLUSIONS:We observed greater point estimates of epigenetic age in participants with POMS compared with healthy controls in all epigenetic clocks tested. This difference was statistically significant for the Hannum and PhenoAge clocks after multivariable modeling. These results are consistent with those of studies in adult MS and suggest that accelerated aging may be present even in the youngest people living with MS.

Neuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across the occipital cortex and are usually largest when the cortex is inactive. However, recent studies measuring visual receptive fields have reported that local alpha power increases when cortex is excited by visual stimulation. This contrasts with the expectation that alpha oscillations are associated with cortical inactivity. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We hypothesized that stimulus-driven local increases in alpha power result from a mixture of two effects: a reduction in alpha oscillatory power and a simultaneous increase in broadband power. To test this, we implemented a model to separate these components. The two components were then independently fit by population receptive field (pRF) models. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power but are several times larger and exhibit the opposite effect: alpha oscillatory power decreases in response to stimuli within the receptive field, reinforcing the link between alpha oscillations and cortical inactivity, whereas broadband power increases. The results demonstrate that alpha suppression in the human visual cortex can be precisely tuned, but that to measure these effects, it is essential to separate the oscillatory signal from broadband power changes. Finally, we show how the large size and the negative valence of alpha pRFs can explain key features of exogenous visual attention.

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.

Sleep disturbances are prominent across neurodevelopmental disorders (NDDs) and may reflect specific abnormalities in brain development and function. Overnight polysomnography (PSG) allows for detailed investigation of sleep architecture, offering a unique window into neurocircuit function. Analysis of existing pediatric PSGs from clinical studies could enhance the availability of sleep studies in pediatric patients with NDDs towards a better understanding of mechanisms underlying abnormal development in NDDs. Here, we introduce and characterize a retrospective collection of 1527 clinical pediatric overnight PSGs across five different sites. We first developed an automated stager trained on independent pediatric sleep data, which yielded better performance compared to a generic stager trained primarily on adults. Using consistent staging across cohorts, we derived a panel of EEG micro-architectural features. This unbiased approach replicated broad trajectories previously described in typically developing sleep architecture. Further, we found sleep architecture disruptions in children with Down's Syndrome (DS) that were consistent across independent cohorts. Finally, we built and evaluated a model to predict age from sleep EEG metrics, which recapitulated our previous findings of younger predicted brain age in children with DS. Altogether, by creating a resource pooled from existing clinical data we expanded the available datasets and computational resources to study sleep in pediatric populations, specifically towards a better understanding of sleep in NDDs. This Retrospective Analysis of Sleep in Pediatric (RASP) cohorts dataset, including staging annotation derived from our automated stager, is deposited at https://sleepdata.org/datasets/rasp.

The proposed Neuronal α-Synuclein Disease Integrated Staging System (NSD-ISS) was recently applied to early Parkinson's disease (PD) cohorts. We applied this research framework to the BioFIND study cohort, which includes more moderately advanced PD participants with clinically established PD. Disease durations within each ISS stage were highly variable. Cognitive and non-motor anchors had little weight in determining staging. The analysis highlights strengths and limitations of NSD-ISS to guide further refinement of an integrated staging system.

Sentence production is the uniquely human ability to transform complex thoughts into strings of words. Despite the importance of this process, language production research has primarily focused on single words. It remains a largely untested assumption that the principles of word production generalize to more naturalistic utterances like sentences. Here, we investigate this using high-resolution neurosurgical recordings (ECoG) and an overt production experiment where ten patients produced six words in isolation (picture naming) and in sentences (scene description). We trained machine learning classifiers to identify the unique brain activity patterns for each word during picture naming, and used these patterns to decode which words patients were processing while they produced sentences. Our findings confirm that words share cortical representations across tasks, but reveal a division of labor within the language network. In sensorimotor cortex, words were consistently activated in the order in which they were said in the sentence. However, in prefrontal cortex, the order in which words were processed depended on the syntactic structure of the sentence. In non-canonical sentences (passives), we further observed a spatial code for syntactic roles, with subjects selectively encoded in inferior frontal gyrus (IFG) and objects selectively encoded in middle frontal gyrus (MFG). We suggest that these complex dynamics of prefrontal cortex may impose a subtle pressure on language evolution, potentially explaining why nearly all the world's languages position subjects before objects.

The transformer architecture has revolutionized bioinformatics and driven progress in the understanding and prediction of the properties of biomolecules. To date, most biosequence transformers have been trained on a single omic-either proteins or nucleic acids and have seen incredible success in downstream tasks in each domain with particularly noteworthy breakthroughs in protein structural modeling. However, single-omic pre-training limits the ability of these models to capture cross-modal interactions. Here we present OmniBioTE, the largest open-source multi-omic model trained on over 250 billion tokens of mixed protein and nucleic acid data. We show that despite only being trained on unlabelled sequence data, OmniBioTE learns joint representations consistent with the central dogma of molecular biology. We further demonstrate that OmbiBioTE achieves state-of-the-art results predicting the change in Gibbs free energy (∆G) of the binding interaction between a given nucleic acid and protein. Remarkably, we show that multi-omic biosequence transformers emergently learn useful structural information without any a priori structural training, allowing us to predict which protein residues are most involved in the protein-nucleic acid binding interaction. Lastly, compared to single-omic controls trained with identical compute, OmniBioTE demonstrates superior performance-per-FLOP and absolute accuracy across both multi-omic and single-omic benchmarks, highlighting the power of a unified modeling approach for biological sequences.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Dural arteriovenous fistula (dAVF) surgery is a microsurgical procedure that requires confirmation of obliteration using formal cerebral angiography, but the lack of intraoperative angiogram or need for postoperative angiogram in some settings necessitates a search for alternative, less invasive methods to verify surgical success. This study evaluates the use of indocyanine green videoangiography FLOW 800 hemodynamic intraoperatively during cranial and spinal dAVF obliteration to confirm obliteration and predict surgical success. METHODS:A retrospective analysis was conducted using indocyanine green videoangiography FLOW 800 to intraoperatively measure 4 hemodynamic parameters-Delay Time, Speed, Time to Peak, and Rise Time-across venous drainage regions of interest pre/post-dAVF obliteration. Univariate and multivariate statistical analyses to evaluate and visualize presurgical vs postsurgical state hemodynamic changes included nonparametric statistical tests, logistic regression, and Bayesian analysis. RESULTS:A total of 14 venous drainage regions of interest from 8 patients who had successful spinal or cranial dAVF obliteration confirmed with intraoperative digital subtraction angiography were extracted. Significant hemodynamic changes were observed after dAVF obliteration, with median Speed decreasing from 13.5 to 5.5 s-1 (P = .029) and Delay Time increasing from 2.07 to 7.86 s (P = .020). Bayesian logistic regression identified Delay Time as the strongest predictor of postsurgical state, with a 50% increase associated with 2.16 times higher odds of achieving obliteration (odds ratio = 4.59, 95% highest density interval: 1.07-19.95). Speed exhibited a trend toward a negative association with postsurgical state (odds ratio = 0.62, 95% highest density interval: 0.26-1.42). Receiver operating characteristic-area under the curve analysis using logistic regression demonstrated a score of 0.760, highlighting Delay Time and Speed as key features distinguishing preobliteration and postobliteration states. CONCLUSION/CONCLUSIONS:Our findings demonstrate that intraoperative FLOW 800 analysis reliably quantifies and visualizes immediate hemodynamic changes consistent with dAVF obliteration. Speed and Delay Time emerged as key indicators of surgical success, highlighting the potential of FLOW 800 as a noninvasive adjunct to traditional imaging techniques for confirming dAVF obliteration intraoperatively.