Faculty Bibliography

BACKGROUND AND OBJECTIVES/OBJECTIVE:We sought to determine clinical significance of neuronal septin autoimmunity and evaluate for potential IgG effects. METHODS:Septin-IgGs were detected by indirect immunofluorescence assays (IFAs; mouse tissue and cell based) or Western blot. IgG binding to (and internalization of) extracellular septin epitopes were evaluated for by live rat hippocampal neuron assay. The impact of purified patient IgGs on murine cortical neuron function was determined by recording extracellular field potentials in a multielectrode array platform. RESULTS:Septin-IgGs were identified in 23 patients. All 8 patients with septin-5-IgG detected had cerebellar ataxia, and 7 had prominent eye movement disorders. One of 2 patients with co-existing septin-7-IgG had additional psychiatric phenotype (apathy, emotional blunting, and poor insight). Fifteen patients had septin-7 autoimmunity, without septin-5-IgG detected. Disorders included encephalopathy (11; 2 patients with accompanying myelopathy, and 2 were relapsing), myelopathy (3), and episodic ataxia (1). Psychiatric symptoms (≥1 of agitation, apathy, catatonia, disorganized thinking, and paranoia) were prominent in 6 of 11 patients with encephalopathic symptoms. Eight of 10 patients with data available (from 23 total) improved after immunotherapy, and a further 2 patients improved spontaneously. Staining of plasma membranes of live hippocampal neurons produced by patient IgGs (subclasses 1 and 2) colocalized with pre- and post-synaptic markers. Decreased spiking and bursting behavior in mixed cultures of murine glutamatergic and GABAergic cortical neurons produced by patient IgGs were attributable to neither antigenic crosslinking and internalization nor complement activation. INTERPRETATION/CONCLUSIONS:Septin-IgGs are predictive of distinct treatment-responsive autoimmune central nervous system (CNS) disorders. Live neuron binding and induced electrophysiologic effects by patient IgGs may support septin-specific pathophysiology. ANN NEUROL 2022.

OBJECTIVE:The objectives of this study were to define the clinical and biochemical spectrum of spinal muscular atrophy with progressive myoclonic epilepsy (SMA-PME) and to determine if aberrant cellular ceramide accumulation could be normalized by enzyme replacement. METHODS:Clinical features of 6 patients with SMA-PME were assessed by retrospective chart review, and a literature review of 24 previously published cases was performed. Leukocyte enzyme activity of acid ceramidase was assessed with a fluorescence-based assay. Skin fibroblast ceramide content and was assessed by high performance liquid chromatography, electrospray ionization tandem mass spectroscopy. Enzyme replacement was assessed using recombinant human acid ceramidase (rhAC) in vitro. RESULTS:The six new patients showed the hallmark features of SMA-PME, with variable initial symptom and age of onset. Five of six patients carried at least one of the recurrent SMA-PME variants observed in two specific codons of ASAH1. A review of 30 total cases revealed that patients who were homozygous for the most common c.125C > T variant presented in the first decade of life with limb-girdle weakness as the initial symptom. Sensorineural hearing loss was associated with the c.456A > C variant. Leukocyte acid ceramidase activity varied from 4.1%-13.1% of controls. Ceramide species in fibroblasts were detected and total cellular ceramide content was elevated by 2 to 9-fold compared to controls. Treatment with rhAC normalized ceramide profiles in cultured fibroblasts to control levels within 48 h. INTERPRETATION/CONCLUSIONS:This study details the genotype-phenotype correlations observed in SMA-PME and shows the impact of rhAC to correct the abnormal cellular ceramide profile in cells.

OBJECTIVE:To examine the prevalence of orofacial pain and associated factors in Chinese older adults at the end of life. METHODS:This cross-sectional study included 1646 participants (65 years or older) in their last year of life from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We used the 6-month prevalence questions to measure two specific orofacial pain symptoms: toothache, and jaw or facial pain. Logistic regression analyses were used to examine factors, such as socioeconomic status, health behaviours and chronic diseases, that were associated with these two orofacial pain symptoms in the last year of life. RESULTS:The 6-month prevalence estimates for toothache and jaw pain or facial pain for older adults in the last year of life were 14.1% and 4.5% respectively. Higher socioeconomic status was associated with lower odds of toothache and jaw pain or facial pain. Smoking was associated with high odds of toothache. Participants who brushed their teeth at least once a day were more likely to have toothache and jaw or facial pain than those who did not. Having any chronic conditions was associated with higher odds of toothache and jaw or facial pain. Older adults who had at least one tooth were more likely to have jaw or facial pain than those without any teeth. CONCLUSION/CONCLUSIONS:A considerable proportion of Chinese older adults in their last year of life reported toothache and/or jaw pain or facial pain. These findings suggest that appropriate measures need to be taken to address the oral health needs in these vulnerable individuals, especially those of low socioeconomic status and chronic conditions.

Background: Pepinemab (VX15/2503) is a humanized IgG4 monoclonal antibody that blocks the binding of semaphorin 4D (SEMA4D) to its plexin receptors. SEMA4D is upregulated in neurons during Huntington's Disease (HD) and alzheimer's Disease (AD) progression and triggers astrocytes that express plexin-B1/B2 receptor to undergo reactive gliosis with concomitant loss of normal astrocyte functions1. Drivers of glial cell activation represent novel targets to modify progression of neurodegenerative pathology. Blocking antibody to SEMA4D has been shown to reduce neurodegenerative processes in the SIGNAL-HD (NCT02481674) Phase 2 trial2 as well as in preclinical models of HD and AD. These studies provided clinical rationale for the ongoing Phase 1/2 SIGNAL-AD study (NCT04381468).
Objective(s): Present the updated safety, efficacy, and biomarker data from the completed SIGNAL-HD trial2. In addition, describe how neuroimaging and subgroup analysis of the clinical HD results provide further rationale for investigation in AD, and present the trial design, enrollment status, and updated blinded safety data for the Phase 1b/2a double-blind, randomized, placebO'Controlled SIGNAL-AD trial.
Method(s): The SIGNAL-HD phase 2 study included 301 subjects with late prodromal (LP) and early manifest (EM) HD. Subjects were treated with monthly infusions of pepinemab for at least 18 months and evaluated for safety and a variety of clinical parameters including cognition (HD-CAB). Imaging endpoints included structural MRI to assess brain atrophy and FDG-PET to assess brain metabolism. The SIGNAL-AD study is in progress and is planned to enroll up to 40 subjects with early AD treated for approximately 1 year. Objectives include safety, change in brain metabolism via FDG-PET, and clinical endpoints including the alzheimer's Disease Assessment Scale - cognition (ADAS-cog) and Clinical Dementia Rating Scale - sum of boxes (CDR-SB).
Result(s): In SIGNAL-HD, pepinemab was well-tolerated and was shown to cross the BBB at a concentration sufficient to engage its target. While co-primary efficacy outcome measures did not achieve statistical significance in this study, multiple exploratory and post-hoc measures indicated significant cognitive benefit and were supported by pre-specified FDG-PET imaging that indicated significant reversal of decline in metabolic activity (p>=0.05) in 15/26 brain regions of interest. Treatment effects were observed in EM but not LP subjects. In 179 EM subjects, a treatment benefit was observed in 6/6 components of the HD-CAB cognitive assessment battery, with a significant treatment effect on the HD-CAB composite index (p=0.007). Post-hoc analysis of the HD-CAB results showed pepinemab treatment preserved the ability of EM subjects to learn from experience during sequential administration of HD-CAB and that the cognitive treatment benefit was greater in subjects that were more cognitively impaired at baseline, as judged by Montreal Cognitive Assessment (MoCA) score <26 vs. >=26. The largest metabolic decline in HD is observed in caudate and putamen. It is, therefore, striking that a treatment effect on FDG-PET SUVR was not observed in caudate and putamen of either EM or LP subjects. Since degeneration of medium spiny neurons in striatum is an early event in prodromal HD that continues following motor diagnosis, this could account for reduced glucose utilization that is not SEMA4D-dependent and, therefore, not affected by pepinemab treatment. Our data support an important glial contribution to glucose utilization in other brain regions that is reduced by reactive gliosis and restored by pepinemab treatment. This suggests distinct early and late stages of pathology during disease progression. The ongoing blinded SIGNAL-AD trial has enrolled approximately half of the 40 planned subjects, and top line data for a full year of randomized, double-blind treatment is anticipated in Q1 2024. It will be of particular interest to determine whether metabolic changes in the entorhinal cortex, a region of early degeneration in AD, are less SEMA4D-dependent than for other cortical regions that degenerate somewhat later in disease progression.
Conclusion(s): SIGNAL-HD showed a favorable safety profile and positive trends in cognition and imaging endpoints that encourage continued development in both HD and AD. The Phase 1b/2a study in AD (SIGNAL-AD), is currently enrolling and initial blinded safety review has suggested pepinemab is well tolerated in AD as well

Gradient-based analyses have contributed to the description of cerebellar functional neuroanatomy. More recently, functional gradients of the cerebellum have been used as a multi-purpose tool for neuroimaging research. Here, we provide an overview of the many practical applications of cerebellar functional gradient analyses. These practical applications include examination of intra-cerebellar and cerebellar-extracerebellar organization; transformation of functional gradients into parcellations with discrete borders; projection of functional gradients calculated within cerebellar structures to other extracerebellar structures; interpretation of cerebellar neuroimaging findings using qualitative and quantitative methods; detection of differences in patient populations; and other more complex practical applications of cerebellar gradient-based analyses. This review may serve as an introduction and catalog of options for neuroscientists who wish to design and analyze imaging studies using functional gradients of the cerebellum.

In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leveraged 496 clinical MRI exams collected retrospectively at a single clinical site from patients referred for any reason. This diagnostic dataset was enriched to have 259 hydrocephalus cases. A 3D convolutional neural network was trained on 16 manually segmented exams (ten hydrocephalus) and subsequently used to automatically segment the remaining 480 exams and extract volumetric anatomical features. A linear classifier of these features was trained on 240 exams to detect cases of hydrocephalus that required treatment with surgical intervention. Performance was compared to four neuroradiologists on the remaining 240 exams. Performance was also evaluated on a separate screening dataset of 451 exams collected from a routine clinical population to predict the consensus reading from four neuroradiologists using images alone. The pipeline was also tested on an external dataset of 31 exams from a 2nd clinical site. The most discriminant features were the Magnetic Resonance Hydrocephalic Index (MRHI), ventricle volume, and the ratio between ventricle and brain volume. At matching sensitivity, the specificity of the machine and the neuroradiologists did not show significant differences for detection of hydrocephalus on either dataset (proportions test, p > 0.05). ROC performance compared favorably with the state-of-the-art (AUC 0.90-0.96), and replicated in the external validation. Hydrocephalus cases requiring treatment can be detected automatically from MRI in a heterogeneous patient population based on quantitative characterization of brain anatomy with performance comparable to that of neuroradiologists.

Automatic action identification from video and kinematic data is an important machine learning problem with applications ranging from robotics to smart health. Most existing works focus on identifying coarse actions such as running, climbing, or cutting vegetables, which have relatively long durations and a complex series of motions. This is an important limitation for applications that require identification of more elemental motions at high temporal resolution. For example, in the rehabilitation of arm impairment after stroke, quantifying the training dose (number of repetitions) requires differentiating motions with sub-second durations. Our goal is to bridge this gap. To this end, we introduce a large-scale, multimodal dataset, StrokeRehab, as a new action-recognition benchmark that includes elemental short-duration actions labeled at a high temporal resolution. StrokeRehab consists of high-quality inertial measurement unit sensor and video data of 51 stroke-impaired patients and 20 healthy subjects performing activities of daily living like feeding, brushing teeth, etc. Because it contains data from both healthy and impaired individuals, StrokeRehab can be used to study the influence of distribution shift in action-recognition tasks. When evaluated on StrokeRehab, current state-of-the-art models for action segmentation produce noisy predictions, which reduces their accuracy in identifying the corresponding sequence of actions. To address this, we propose a novel approach for high-resolution action identification, inspired by speech-recognition techniques, which is based on a sequence-to-sequence model that directly predicts the sequence of actions. This approach outperforms current state-of-the-art methods on StrokeRehab, as well as on the standard benchmark datasets 50Salads, Breakfast, and Jigsaws.