Faculty Bibliography

Understanding the effects of solid boundaries on turbulent fluctuations remains a long-standing challenge. Available data on mean-square fluctuations in these flows show apparent contradiction with classical scaling. We had earlier proposed an alternative model based on the principle of bounded dissipation. Despite its putative success, a conclusive outcome requires much higher Reynolds numbers than are available at present, or can be expected to be available in the near future. However, the model can be validated satisfactorily even within the Reynolds number range already available by considering high-order moments and their distributions in the wall-normal direction. Expressions for high-order moments of streamwise velocity fluctuation [Formula: see text] are derived in the form [Formula: see text], where the superscript [Formula: see text] indicates the wall unit normalization, and brackets stand for averages over time and the homogeneous plane normal to the wall, [Formula: see text] is an integer, [Formula: see text] and [Formula: see text] are constants independent of the friction Reynolds number [Formula: see text], and [Formula: see text] is the distance away from the wall, normalized by the flow thickness [Formula: see text]. In particular, [Formula: see text] according to the "linear q-norm Gaussian" process, where [Formula: see text] and [Formula: see text] are flow-independent constants. Excellent agreement is found between this formula and the available data in boundary layers, pipes, and channels for [Formula: see text]. For fixed [Formula: see text], the present formulation leads to the bounded state [Formula: see text] as [Formula: see text]. This work demonstrates the success of the present model in describing the behavior of fluctuations in wall flows.

OBJECTIVE:This study was undertaken to investigate diagnostic delay in adolescent onset focal epilepsy, including reasons for longer delays and associated morbidities. METHODS:Secondary analysis was done using enrollment data from the Human Epilepsy Project, a multi-institutional cohort including 34 sites in the USA, Canada, Finland, Austria, and Australia (2012-2017). Participants were aged 11-64 years at enrollment and within 4 months of treatment initiation for newly diagnosed focal epilepsy. Participants with seizure onset at age ≤ 21 years were evaluated. Data included seizure diaries documenting onset, frequency, and characteristics of seizures, reasons for diagnostic delays, and prediagnosis morbidities, including injuries, suicidal ideation, and self-injurious behaviors. RESULTS: = 7.04, p = .008). SIGNIFICANCE/CONCLUSIONS:This study highlights significant delays in diagnosing adolescent onset focal epilepsy, especially in cases with nonmotor seizures. These delays, often due to lack of recognition by patients and health care providers, are linked to more frequent seizures, higher injury rates, and increased suicidal ideation and self-injury. Early recognition and diagnosis may mitigate adverse outcomes and improve quality of life for adolescents with epilepsy.

Documentation, coding, and billing (claims submission) are foundational to neurologic practice in the United States, enabling accurate reimbursement, effective communication, and data-driven advancements in patient care, research, and education. Neurologists navigate complex regulatory frameworks and evolving payer guidelines, requiring meticulous attention to diagnostic coding, evaluation and management (E/M) services, and procedure-specific requirements. This chapter examines critical aspects of neurologic billing and coding, including ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) for diagnostic accuracy, updated E/M guidelines emphasizing medical decision-making and time, and new telemedicine codes. It highlights the best practices for procedure coding and the use of digital health technologies. The challenges posed by prior authorization are explored, alongside potential solutions like artificial intelligence-driven tools and policy reform. By prioritizing precision, compliance, and technological adaptation, neurologists can enhance patient outcomes, support practice sustainability, and contribute to the broader goals of equitable, efficient, and innovative neurologic care.

BACKGROUND AND IMPORTANCE/BACKGROUND:Rhino-orbital cerebral mucormycosis (ROCM) is an aggressive fungal infection involving the paranasal sinuses, orbit, and intracranial cavity, with a propensity for vascular invasion. This can lead to complications such as internal carotid artery (ICA) thrombosis and occlusion, presenting major neurosurgical challenges. Although surgical debridement and antifungal therapy are the mainstays of treatment, cases with significant neurovascular involvement require specialized intervention. We report a case of ROCM with severe flow-limiting ICA stenosis treated by direct extracranial-intracranial bypass. CLINICAL PRESENTATION/METHODS:tA 65-year-old man with diabetes presented with progressive left-sided blindness and facial numbness. Imaging revealed a left orbital mass extending into the paranasal sinuses and intracranially. Empiric antifungal therapy was started. Pathology confirmed Rhizopus species. Despite extensive surgical debridement and antifungal therapy, the patient developed progressive severe cavernous ICA stenosis, leading to watershed territory strokes. To restore cerebral perfusion, protect from distal emboli, and prepare for potential aggressive debridement, a flow-replacing direct (superficial temporal artery-middle cerebral artery (M2)) bypass was performed, and the supraclinoid carotid was trapped. Intraoperative angiography confirmed robust flow through the bypass. The patient was discharged on antifungal therapy and aspirin. At 6-month follow-up, the patient was neurologically intact with an modified Rankin Scale score of 1. Computed tomography angiography and transcranioplasty Doppler ultrasonography confirmed good flow through the bypass. CONCLUSION/CONCLUSIONS:In addition to antifungal therapy and surgical debridement, superficial temporal artery-middle cerebral artery bypass can be a lifesaving intervention in the management of ROCM with severe cerebrovascular compromise. This case highlights the critical role of cranial bypass in preserving cerebral perfusion in patients with flow-limiting ROCM-associated ICA invasion.

Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer's disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer's Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70-77% accuracy and 61-83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.

The International League Against Epilepsy (ILAE) has updated the operational classification of epileptic seizures, building upon the framework established in 2017. This revision, informed by the implementation experience, involved a working group appointed by the ILAE Executive Committee. Comprising 37 members from all ILAE regions, the group utilized a modified Delphi process, requiring a consensus threshold of more than two thirds for any proposal. Following public comments, the Executive Committee appointed seven additional experts to the revision task force to address and incorporate the issues raised, as appropriate. The updated classification maintains four main seizure classes: Focal, Generalized, Unknown (whether focal or generalized), and Unclassified. Taxonomic rules distinguish classifiers, which are considered to reflect biological classes and directly impact clinical management, from descriptors, which indicate other important seizure characteristics. Focal seizures and those of unknown origin are further classified by the patient's state of consciousness (impaired or preserved) during the seizure, defined operationally through clinical assessment of awareness and responsiveness. If the state of consciousness is undetermined, the seizure is classified under the parent term, that is, the main seizure class (focal seizure or seizure of unknown origin). Generalized seizures are grouped into absence seizures, generalized tonic-clonic seizures, and other generalized seizures, now including recognition of negative myoclonus as a seizure type. Seizures are described in the basic version as with or without observable manifestations, whereas an expanded version utilizes the chronological sequence of seizure semiology. This updated classification comprises four main classes and 21 seizure types. Special emphasis was placed on ensuring translatability into languages beyond English. Its aim is to establish a common language for all health care professionals involved in epilepsy care, from resource-limited areas to highly specialized centers, and to provide accessible terms for patients and caregivers.

Curbs serve as vital borders that delineate safe pedestrian zones from potential vehicular traffic hazards. Curbs also represent a primary spatial hazard during dynamic navigation with significant stumbling potential. Such vulnerabilities are particularly exacerbated for persons with blindness and low vision (PBLV). Accurate visual-based discrimination of curbs is paramount for assistive technologies that aid PBLV with safe navigation in urban environments. Herein, we investigate the efficacy of curb segmentation for foundation models. We introduce the largest curb segmentation dataset to date to benchmark leading foundation models. Our results show that state-of-the-art foundation models face significant challenges in curb segmentation. This is due to their low precision and recall with poor performance distinguishing curbs from curb-like objects or non-curb areas, such as sidewalks. In addition, the best-performing model averaged a 3.70-s inference time, underscoring problems in providing real-time assistance. In response, we propose solutions including filtered bounding box selections to achieve more accurate curb segmentation. Overall, despite the immediate flexibility of foundation models, their application for practical assistive technology applications still requires refinement. This research highlights the critical need for specialized datasets and tailored model training to address navigation challenges for PBLV and underscores implicit weaknesses in foundation models.

BACKGROUND AND PURPOSE/OBJECTIVE:Cerebrovascular reactivity (CVR) is a widely studied biomarker of cerebral hemodynamics, commonly used in risk stratification and treatment planning in patients with steno-occlusive disease (SOD). Conventional use relies on normalization of estimates to contralateral hemisphere reference values, which is unsuitable for bilateral or indeterminate distributions of disease. We report upon a custom data-driven approach leveraging random forest classifiers (RFc) to identify candidate voxels for normalization in order to facilitate interrogation outside conditions of known unilateral SOD MATERIALS AND METHODS: We retrospectively analyzed 16 patients with unilateral SOD who underwent acetazolamide-augmented BOLD-MRI and DSC perfusion. Three RFc models were trained using leave-one-out cross-validation (LOOCV) to identify candidate voxels brain-wide whose CVR were within 10% of the normal hemispheric median: i. all voxels; ii. gray matter only; and iii. white matter only. Model input features included time-to-maximum (Tmax), mean transit time (MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV) from contemporaneous DSC. The median model-predicted reference CVR (CVRref) was compared to ground-truth medians in LOOCV, and its impact on threshold-based volumetric classification of CVR reduction assessed. RESULTS:RFc models effectively predicted ground-truth CVR voxels, achieving median absolute percent differences of 12.8% (IQR: 5.0%-18.9%) using all voxels, 11.3% (IQR: 9.3%-16.1%) for gray matter, and 9.8% (IQR: 4.4%-16.9%) for white matter. Volumetric estimates of CVR reduction across thresholds for the models revealed excellent agreement between ground-truth and model estimates without statistically significant differences (p>0.01), excepting lowest white matter CVR thresholds. Model use in a small pilot deployment of bilateral SOD cases demonstrated the potential utility, enabling voxel-wise CVR assessment without reliance on contralateral reference. CONCLUSIONS:We present a novel data-driven approach for normalizing CVR maps in patients with bilateral or indeterminate SOD. Using an RFc, our method provides an individualized, brain-wide reference CVR, expanding the utility of CVR estimates beyond the typical constraints of unilateral disease, and with potential application to other, similarly constrained scenarios such as for SPECT or PET hemodynamic studies. ABBREVIATIONS/BACKGROUND:CVR = cerebrovascular reactivity; RFc = random forest classifier; SOD = steno-occlusive disease.

Desmosomes are transmembrane protein complexes that contribute to cell-cell adhesion in epithelia and other tissues. Here, we report the discovery of frequent genetic alterations in the desmosome in human cancers, with the strongest signal seen in cutaneous melanoma, where desmosomes are mutated in more than 70% of cases. In primary but not metastatic melanoma biopsies, the burden of coding mutations in desmosome genes is associated with a strong reduction in desmosome gene expression. Analysis by spatial transcriptomics and protein immunofluorescence suggests that these decreases in expression occur in keratinocytes in the microenvironment rather than in primary melanoma cells. In further support of a microenvironmental origin, we find that desmosome gene knockdown in keratinocytes yields markedly increased proliferation of adjacent melanoma cells in keratinocyte and melanoma cocultures. Similar increases in melanoma proliferation are observed in media preconditioned with desmosome-deficient keratinocytes. Thus, gradual accumulation of desmosome mutations in neighboring cells may prime melanoma cells for neoplastic transformation.