Boeve, Bradley F.

Behavioral neurology & neuropsychiatry (BNNP) is a field that seeks to understand brain–behavior relationships, including fundamental brain organization principles and the many ways that brain structures and connectivity can be disrupted, leading to abnormalities of behavior, cognition, emotion, perception, and social cognition. In North America, BNNP has existed as an integrated subspecialty through the United Council for Neurologic Subspecialties since 2006. Nonetheless, the number of behavioral neurologists across academic medical centers and community settings is not keeping pace with increasing clinical and research demand. In this commentary, we provide a brief history of BNNP followed by an outline of the current challenges and opportunities for BNNP from the behavioral neurologist’s perspective across clinical, research, and educational spheres. We provide a practical guide for promoting BNNP and addressing the shortage of behavioral neurologists to facilitate the continued growth and development of the subspecialty. We also urge a greater commitment to recruit trainees from diverse backgrounds so as to dismantle persistent obstacles that hinder inclusivity in BNNP—efforts that will further enhance the growth and impact of the subspecialty. With rapidly expanding diagnostic and therapeutic approaches across a range of conditions at the intersection of neurology and psychiatry, BNNP is well positioned to attract new trainees and expand its reach across clinical, research, and educational activities.

Abstract Introduction Quantitative rapid eye movement (REM) sleep without atonia (RSWA) recorded during polysomnography (PSG) represents loss of normal REM sleep atonia. RSWA is the neurophysiologic signature of REM sleep behavior disorder (RBD). Visually scored RSWA is a candidate biomarker for synucleinopathies. However, visual RSWA scoring requires expert scoring, limiting widespread application. We comparatively analyzed RSWA in DLB and iRBD using the automated Ferri REM Atonia Index (RAI). Methods We quantitatively analyzed the RAI in 17 DLB and 20 iRBD patients, compared to 20 controls using Hypnolab Software for Sleep Analysis (SWS Soft, Colognola ai Colli, Italy). PSG data were converted to European Data Format (.EDF), and the submentalis surface electromyogram (EMG) signal was notch-filtered at 60 Hertz and rectified. Automatically calculated RAI scores were comparatively analyzed across groups (DLB, iRBD, controls) using Kruskal Wallis H tests for group comparisons, and regression analyses determined relationships between diagnosis, age, and sex. Logistic regression ROC determined optimal RAI for distinguishing iRBD from DLB. When interpreting RAI, lower values represent greater amounts of RSWA, and higher values represent more normal REM atonia. Results Median age was no different between DLB (72.6 years), iRBD (69.4 years), and controls (68.0 years). The DLB group consisted of 16 men and one woman, the iRBD group had 18 men and 2 women, and controls were comprised of 16 men and 4 women. Median RAI was lowest in DLB (0.32), intermediate in iRBD (0.84) and highest in controls (0.96), indicating a gradient in RSWA amounts from DLB patients (highest) to controls (lowest). Regression demonstrated diagnosis independently predicted RAI, controlling for age and sex (adjusted R2=0.64, p=0.0001). The RAI that optimally distinguished iRBD from DLB was 0.48 (specificity 85%, sensitivity 76%, AUC=0.88). Conclusion The RAI demonstrated a clear gradient throughout the Lewy body spectrum; highest (indicating lowest RSWA amounts) in controls, intermediate in iRBD, and lowest (indicating greatest RSWA) in DLB. The RAI is a promising, time efficient and broadly applicable candidate biomarker for DLB and iRBD. Automated quantitative RSWA could be a valuable tool for deep phenotyping throughout the Lewy body disease spectrum. Support (if any) This study was supported by: U01NS100620, R34AG056639, U19AG071754, and P30AG62677.

Abstract Introduction n this cross-validation study, the distributions of sleep biomarker-based neurodegenerative disorder (NDD) risk were evaluated across a spectrum of NDD patients and presumed controls which were independent of the model development dataset. Methods Variables that included age, time-REM, non-REM hypertonia, autonomic-activation index, spindle-duration, atypical-N3, time-supine, sleep-efficiency, relative-theta, and theta/alpha obtained with Sleep Profiler were used to train a 4-class machine learning algorithm that generated probabilities of NDD risk for the groups: Alzheimer’s dementia (AD), Lewy body disorder (LBD), prodromal synucleinopathy (pSYN based on REM sleep behavior disorder patients), and controls (CG). Records with CG output probabilities >70% were labeled Probably-normal, and from 45-70% Likely-normal with indications of a NDD added when the LBD, AD or pSYN risks exceeded approximately 25%. Similar thresholds were used to assign Likely- or Probable- LBD, AD, pSYN or Mixed based on the group probabilities. This cross-validation study was designed to use datasets that were independent of those used to train the classification algorithm. Thus, NDD risk distributions were evaluated in patients with mild cognitive impairment (MCI=71), Parkinson’s disease (PD=16), progressive supranuclear palsy (PSP=21) and control subjects (NC=41) with Mini-Mental State Exam scores>28 and/or normal for MCI by Petersen criteria. Results In the MCI group, NDD risk assignments were Probable-AD=26%, -Mixed=10%, -LBD=1%, Likely-AD= 7%, -Mixed=6%, -LBD=3%, -pSYN=1%, Likely-Normal indications-AD=9%, -pSYN=7%, -Mixed=4%, and Probably-Normal=26% In the PSP group, Probable-LBD=43%, -Mixed=29% and -pSYN=4%, Likely-Mixed=10%, Likely-Normal indications AD=10%, and Probably-Normal=4%. In the PD group, Probable-LBD=13%, -pSYN=13%, -AD=6%, Likely-Mixed=19%, -pSYN=6%, Likely-Normal Indications-pSYN=19%, -AD=12%, Likely-Normal=6% and Probably-Normal=6% In the NC group, 83% were assigned minimal NDD risk with Probably-Normal=70%, Likely-Normal plus indications=13%, but with Likely NDD=13% and Probable NDD=4%. Conclusion NDD risks were disbursed in a manner consistent with the range of severities expected In MCI and PD groups, with 25% exhibiting relatively normal sleep patterns. Over 70% of the PSP patients exhibited probable NDD risk, with patterns similar to those with LBD. The NC NDD risk distributions were consistent with those assigned to the CG used for model development. Support (if any) NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, R34AG056639.

Abstract Introduction In this pilot study, the diagnostic agreement for sleep biomarker-based neurodegenerative disorder (NDD) risk probabilities was evaluated in patients with Alzheimer’s disease dementia (AD), Lewy Body disease (LBD), and isolated REM sleep behavior disorder (iRBD) and controls with a Mini-Mental State Exam scores>28. Methods Sleep biomarkers recorded with the Sleep Profiler (Advanced Brain Monitoring, Inc) were used as inputs to a 4-class machine learning algorithm trained to assign NDD risk probabilities to AD=27, LBD=19, iRBD=15 and controls (CG=58). Input variables included age, time-REM, non-REM hypertonia, autonomic-activation index, spindle-duration, atypical-N3, time-supine, sleep-efficiency, relative-theta, and theta/alpha. Records with CG output probabilities >70% were labeled Probably-normal, and from 45-70% Likely-normal with indications of a NDD added when the LBD, AD or prodromal synucleinopathy (pSYN) risks exceeded approximately 25%. Similar thresholds were used to assign Likely- or Probable- LBD, AD, or pSYN based each of the group probabilities. Classification accuracies were evaluated in AD=37, LBD=19, iRBD=19 and CG=61 records. Longitudinal test-retest reliabilities were assessed for CG (n=45, range 8-56 months), and after six-months for LBD (n=10) and AD (n=9). Results In the AD group, NDD risk assignments were consistent with diagnoses=85%, with Probable-NDD=59%, Likely-NDD=11%, Normal plus AD/Mixed indications=15%, but with 5%=Probably-normal. In the LBD group, assigned risks were consistent with the diagnosis=79%, with Probable-NDD=74%, Normal with pSYN indications=5%, but with 11%=Probably-normal. In the iRBD group, Probable-NDD=42%, Normal plus pSYN indications=26% and Probably-normal=32%. In the CG group, 89% were assigned minimal NDD risks with Probably-normal=67%, Normal plus indications=22%, but with Likely NDD=8% and Probable NDD=3%. The AD test-retest reliability showed the same NDD classifications=78%, with slightly increasing=11% and decreased=11%. In the LBD group, the same NDD classifications were achieved=80%, with trending equivalence=10%, and decreasing severity=20%. The CG test-retest reliability showed the same NDD classifications=76%, with trending equivalence=22%, and increased severity=2%. Conclusion Consistencies between the diagnoses and assigned NDD risks and test-retest classification agreements were approximately 80% in the AD, LBD, and CG groups. In the iRBD group, the NDD risks were disbursed consistent with the range of expected severities. These results needed to be cross-validated in independent NDD datasets. Support (if any) NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, R34AG056639.

Abstract Introduction Meta-analytic studies support that insomnia is a risk factor for cognitive decline and dementia, but limited accounting of confounders reduces their validity. Further, it is unclear whether these associations are driven by specific insomnia phenotypes. In this study, we aimed to assess the relationship between insomnia and longitudinal global cognitive decline in community-dwelling older adults after considering self-reported sleep duration changes and other important characteristics. Methods From the Mayo Clinic Study of Aging (MCSA) cohort, we identified all (age>=50yo) cognitively unimpaired participants at baseline without comorbid neurological disease with at least two previous comprehensive neuropsychological evaluations. Participants with at least two occurrences of insomnia ICD diagnosis (by EMR and Rochester Epidemiological Project data) at least 30 days apart were classified as having insomnia, and those without any instance of diagnosis were considered negative for insomnia. Changes in sleep duration were assessed using the question #16 of BDI-2 and categorized in reduced sleep (yes/no). We fit mixed-effect regression models to assess whether insomnia was associated with standardized global cognitive scores, after adjusting for age, sex, education, APOEe4, composite cardiovascular and metabolic conditions scores, anxiety/depression, reduced sleep, OSA (ICD diagnosis), alcoholism (CAGE≥2), and pain (NSAIDs use). Number of cognitive assessments and time from baseline were included as both fixed and random effects. Multiple interactions (e.g. insomnia*reduced sleep) were included. A backward stepwise procedure maintaining the hierarchical principal for interactions was utilized to reach the most parsimonious models. Results 3063 participants (50.24% males, aged 70±9.73 at baseline) were included. Insomnia alone was not associated with a decrease in global cognition, but when combined with subjective reduction in sleep duration at baseline it was associated with a -0.19 (95% CI: -0.32, -0.07; interaction p=0.01) reduction in global cognitive scores. This was equivalent to approximately 3 additional years of age or 6 cardiometabolic comorbidities at baseline. The effect size was greater than those associated with baseline cognition for having any APOE e4 allele (-0.12) and alcoholism (-0.15). Conclusion Insomnia with reduced sleep duration phenotype is a potentially modifiable risk factor for cognitive decline in older adults. Further studies with objective sleep measures are necessary for objective confirmation. Support (if any) NIA/NIH

Importance Factors associated with clinical heterogeneity in Alzheimer disease (AD) lay along a continuum hypothesized to associate with tangle distribution and are relevant for understanding glial activation considerations in therapeutic advancement. Objectives To examine clinicopathologic and neuroimaging characteristics of disease heterogeneity in AD along a quantitative continuum using the corticolimbic index (CLix) to account for individuality of spatially distributed tangles found at autopsy. Design, Setting, and Participants This cross-sectional study was a retrospective medical record review performed on the Florida Autopsied Multiethnic (FLAME) cohort accessioned from 1991 to 2020. Data were analyzed from December 2022 to December 2023. Structural magnetic resonance imaging (MRI) and tau positron emission tomography (PET) were evaluated in an independent neuroimaging group. The FLAME cohort includes 2809 autopsied individuals; included in this study were neuropathologically diagnosed AD cases (FLAME-AD). A digital pathology subgroup of FLAME-AD cases was derived for glial activation analyses. Main Outcomes and Measures Clinicopathologic factors of heterogeneity that inform patient history and neuropathologic evaluation of AD; CLix score (lower, relative cortical predominance/hippocampal sparing vs higher, relative cortical sparing/limbic predominant cases); neuroimaging measures (ie, structural MRI and tau-PET). Results Of the 2809 autopsied individuals in the FLAME cohort, 1361 neuropathologically diagnosed AD cases were evaluated. A digital pathology subgroup included 60 FLAME-AD cases. The independent neuroimaging group included 93 cases. Among the 1361 FLAME-AD cases, 633 were male (47%; median [range] age at death, 81 [54-96] years) and 728 were female (53%; median [range] age at death, 81 [53-102] years). A younger symptomatic onset (Spearman ρ = 0.39, P < .001) and faster decline on the Mini-Mental State Examination (Spearman ρ = 0.27; P < .001) correlated with a lower CLix score in FLAME-AD series. Cases with a nonamnestic syndrome had lower CLix scores (median [IQR], 13 [9-18]) vs not (median [IQR], 21 [15-27]; P < .001). Hippocampal MRI volume (Spearman ρ = −0.45; P < .001) and flortaucipir tau-PET uptake in posterior cingulate and precuneus cortex (Spearman ρ = −0.74; P < .001) inversely correlated with CLix score. Although AD cases with a CLix score less than 10 had higher cortical tangle count, we found lower percentage of CD68-activated microglia/macrophage burden (median [IQR], 0.46% [0.32%-0.75%]) compared with cases with a CLix score of 10 to 30 (median [IQR], 0.75% [0.51%-0.98%]) and on par with a CLix score of 30 or greater (median [IQR], 0.40% [0.32%-0.57%]; P = .02). Conclusions and Relevance Findings show that AD heterogeneity exists along a continuum of corticolimbic tangle distribution. Reduced CD68 burden may signify an underappreciated association between tau accumulation and microglia/macrophages activation that should be considered in personalized therapy for immune dysregulation.