Faculty Bibliography

Individuals with neurofibromatosis type 1 (NF1) are prone to the evolution of neurodevelopmental symptomatology including motor delays, learning disabilities, autism, and attention deficits. Caused by heterozygous germline mutations in the NF1 gene, this monogenic condition offers unique opportunities to study the genetic etiologies for neurodevelopmental disorders and the mechanisms that underlie their formation. Although numerous small animal models have been generated to elucidate the causes of these alterations, there is little consensus on how to align preclinical observations with clinical outcomes, harmonize findings across species, and consolidate these insights to chart a cohesive path forward. Capitalizing on expertise from clinicians; human, animal, and cellular model research scientists; and bioinformatics researchers, the first Cognition and Behavior in NF1 (CABIN) meeting was convened at the Banbury Center of Cold Spring Harbor Laboratory in October 2024. This Perspective summarizes the state of our understanding and a proposed plan for future investigation and exploration to improve the quality of life of those with NF1.

Increasing evidence points to a pivotal role of immune processes in the pathogenesis of Alzheimer disease, which is the most prevalent neurodegenerative and dementia-causing disease of our time. Multiple lines of information provided by experimental, epidemiological, neuropathological and genetic studies suggest a pathological role for innate and adaptive immune activation in this disease. Here, we review the cell types and pathological mechanisms involved in disease development as well as the influence of genetics and lifestyle factors. Given the decade-long preclinical stage of Alzheimer disease, these mechanisms and their interactions are driving forces behind the spread and progression of the disease. The identification of treatment opportunities will require a precise understanding of the cells and mechanisms involved as well as a clear definition of their temporal and topographical nature. We will also discuss new therapeutic strategies for targeting neuroinflammation, which are now entering the clinic and showing promise for patients.

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and other comorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

OBJECTIVE:This study assesses whether longitudinal quantitative pupillometry predicts neurological deterioration after large middle cerebral artery (MCA) stroke and determines how early changes are detectable. METHODS:This prospective, single-center observational cohort study included patients with large MCA stroke admitted to Boston Medical Center's intensive care unit (2019-2024). Associations between time-to-neurologic deterioration and quantitative pupillometry, including Neurological Pupil Index (NPi), were assessed using Cox proportional hazards models with time-dependent covariates adjusted for age, sex, and Alberta Stroke Program Early CT Score. Models using dilation velocity were compared with partial likelihood ratio tests. Pupillometric changes over 2-h intervals in the 12 h preceding deterioration were analyzed with linear mixed-effects modeling and Tukey's test. Matched referents (age, sex, stroke side, follow-up duration) were used for comparison. Optimal thresholds were identified using the Youden Index. RESULTS:Among 71 patients (mean age 66.5 years; 59.2% women), 32 (45.1%) experienced deterioration. A 1-unit decrease in NPi was associated with a higher hazard of deterioration (hazard ratio 2.46; 95% confidence interval 1.68-3.61). Dilation velocity improved model performance compared to NPi alone. NPi was significantly lower at 0-2 h (3.81 vs. 4.38, p = 0.001) and 2-4 h (3.71 vs. 4.38, p < 0.001) before deterioration compared to 10-12 h prior. Optimal thresholds were 4.01 for NPi, 0.49 mm/s for dilation velocity, and -0.15 change in NPi over 12 h. INTERPRETATION/CONCLUSIONS:Quantitative pupillometry predicts neurological deterioration in MCA stroke, with declines detectable up to 12 h prior. Dilation velocity shows promise as a novel biomarker. ANN NEUROL 2025.

This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals across 100 h of speech production and comprehension as participants engaged in open-ended real-life conversations. We extracted low-level acoustic, mid-level speech and contextual word embeddings from a multimodal speech-to-text model (Whisper). We developed encoding models that linearly map these embeddings onto brain activity during speech production and comprehension. Remarkably, this model accurately predicts neural activity at each level of the language processing hierarchy across hours of new conversations not used in training the model. The internal processing hierarchy in the model is aligned with the cortical hierarchy for speech and language processing, where sensory and motor regions better align with the model's speech embeddings, and higher-level language areas better align with the model's language embeddings. The Whisper model captures the temporal sequence of language-to-speech encoding before word articulation (speech production) and speech-to-language encoding post articulation (speech comprehension). The embeddings learned by this model outperform symbolic models in capturing neural activity supporting natural speech and language. These findings support a paradigm shift towards unified computational models that capture the entire processing hierarchy for speech comprehension and production in real-world conversations.

This study investigated the potential of combining multiple MR parameters to enhance the characterization of myelin in the mouse brain. We collected ex vivo multiparametric MR data at 7 T from control and Gli1^-/- mice; the latter exhibit enhanced myelination at Postnatal Day 10 (P10) in the corpus callosum and cortex. The MR data included relaxivity, magnetization transfer, and diffusion measurements, each targeting distinct myelin properties. This analysis was followed by and compared to myelin basic protein (MBP) staining of the same samples. Although a majority of the MR parameters included in this study showed significant differences in the corpus callosum between the control and Gli1^-/- mice, only T₂, T₁/T₂, and radial diffusivity (RD) demonstrated a significant correlation with MBP values. Based on data from the corpus callosum, partial least square regression suggested that combining T₂, T₁/T₂, and inhomogeneous magnetization transfer ratio could explain approximately 80% of the variance in the MBP values. Myelin predictions based on these three parameters yielded stronger correlations with the MBP values in the P10 mouse brain corpus callosum than any single MR parameter. In the motor cortex, combining T₂, T₁/T₂, and radial kurtosis could explain over 90% of the variance in the MBP values at P10. This study demonstrates the utility of multiparametric MRI in improving the detection of myelin changes in the mouse brain.

While mostly de novo truncating variants in SCAF4 were recently identified in 18 individuals with variable neurodevelopmental phenotypes, knowledge on the molecular and clinical spectrum is still limited. We assembled data on 50 novel individuals with SCAF4 variants ascertained via GeneMatcher and personal communication. With detailed evaluation of clinical data, in silico predictions and structural modeling, we further characterized the molecular and clinical spectrum of the autosomal dominant SCAF4-associated neurodevelopmental disorder. The molecular spectrum comprises 25 truncating, eight splice-site and five missense variants. While all other truncating variants were classified as pathogenic/likely pathogenic, significance of one C-terminal truncating variant, one splice-site variant and the missense variants remained unclear. Three missense variants in the CTD-interacting domain of SCAF4 were predicted to destabilize the domain. Twenty-three variants occurred de novo, and variants were inherited in 13 cases. Frequent clinical findings were mild developmental delay with speech impairment, seizures, and skeletal abnormalities such as clubfoot, scoliosis or hip dysplasia. Cognitive abilities ranged from normal IQ to severe intellectual disability (ID), with borderline to mild ID in the majority of individuals. Our study confirms the role of SCAF4 variants in neurodevelopmental disorders and further delineates the associated clinical phenotype.

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.

Mobile applications are widely used in epilepsy, although their impact on clinical effectiveness (CE) and their feasibility, acceptability, and usability (FAU) remain unclear. We conducted a systematic review investigating CE and FAU of epilepsy mobile applications using MEDLINE and Embase from database inception to June 21, 2024. We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting standards. The protocol was registered on PROSPERO (CRD42019134848). In duplicate, we determined study quality using the Newcastle-Ottawa Quality Assessment Scale (NOQAS) and the Joanna Briggs Critical Appraisal Checklist (to determine eligibility for inclusion), risk of bias using the Cochrane Risk of Bias tool, and usability study quality using the 15-point Silva scale. We identified 8953 studies, of which 20 were included. Twelve (60.0%) addressed CE, nine (45.0%) acceptability, five (25.0%) usability, and eight (40.0%) feasibility. Five (25.0%) evaluated CE and FAU. Studies comprised prospective cohort (n = 9, 45.0%), pilot (n = 3, 15.0%), randomized controlled trial (n = 7, 35.0%), and pre/post (n = 1, 5.0%) designs. Most apps were used for self-management or to enhance education or communication between patients and providers. Cohort studies demonstrated fair quality (median NOQAS score = 5, interquartile range [IQR] = 5.0-5.8), whereas of seven randomized controlled trials, four (57.1%) had some concern for bias. Usability studies demonstrated high quality (median Silva score = 10, IQR = 10-11). Apps were predominantly intended for patient use (n = 9, 75.0%). Symptom reporting and medication management were the most common app targets in both CE and FAU studies (n = 8, 66.7%; n = 9, 69.2%), although FAU studies more frequently used monitoring or tracking (n = 10, 76.9%) and reminder setting (n = 10, 76.9%) than CE apps (n = 7, 58.3%). Investigations of application use most commonly studied CE and patient-facing apps. Additional high-quality evidence is necessary to evaluate the CE and FAU of app use in epilepsy to work toward the standardization of FAU metrics and development of implementation guidelines.

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.