Citations

Abstract Introduction In this pilot study, we explored sleep biomarker risk probabilities for different neurodegenerative disorder (NDD) phenotypes across a spectrum of NDD patients, compared with controls. Methods We analyzed a cohort of patients with different NDD phenotypes who underwent in-home recordings with Sleep Profiler, including Lewy body disease (LBD=20), Alzheimer’s disease dementia (AD=29), and isolated REM sleep behavior disorder (iRBD=19). Controls with an MMSE>28 (CG=61) and patients with Parkinson disease (PD=16) and mild-cognitive-impairment (MCI=41) also participated. We developed a machine-learning classifier that assigned NDD probabilities for LBD, AD, iRBD, and CG. The input variables included: time-REM, non-REM hypertonia, autonomic-activation index, spindle-duration, atypical-N3, time-supine, sleep-efficiency, relative-theta, and theta/alpha. Probabilities >50% were assigned “likely”, and for CG>=50% and a NDD group probability 20-50%, the assignment was normal “plus”. Probability assignments were then made for the NDD and CG groups, then further applied to the PD and MCI patient groups. Results The CG group participants were assigned Normal-Likely=74%, Normal+AD=11%, Normal+iRBD=5%, iRBD-Likely=5%, and AD-Likely=5%. LBD patient distributions were LBD-Likely=70%, iRBD-Likely=5%, AD-Likely=5%, Normal+LBD=5%, Normal+iRBD=5%, Normal+AD=5%, and Normal-Likely=5%. AD group distributions were AD-Likely=71%, LBD-Likely=4%, Normal+AD=14%, Normal+iRBD=4%, Normal-Likely=7%. iRBD patients were characterized with iRBD-Likely=37%, LBD-Likely=5%, AD-Likely=5%, Normal+iRBD=27%, Normal-Likely=26%. PD patients were assigned iRBD-Likely=29%, LBD-likely=14%, AD-Likely=14%, Normal+iRBD=21%, Normal+AD=7%, Normal-Likely=14%. MCI distributions were AD-Likely=45%, LBD-Likely=8%, iRBD-Likely=5%, Normal+AD=21%, Normal-Likely=13%. Conclusions For LBD, AD and CG groups, correct risk assignments were >70% while gross misclassifications were <10%. Classification patterns for PD, MCI and iRBD were disbursed in a manner consistent with the range of severities expected in each group.

Abstract Introduction Atypical N3 sleep (AN3=delta waves with limited theta and sigma) has been associated with ICU delirium and sepsis and averaged 25% of sleep time in Japanese ICU patients. We were interested in exploring whether AN3 might be a marker of cerebral dysfunction in ambulatory patients across a range of neurodegenerative disorders, including those with a dementia diagnosis. Methods After ethics review and with informed consent, patients with Lewy body disease (DLB/PDD: n=20,male=90%,age=70 + 6.2), Alzheimers disease dementia (AD: n=29,male=79%,age=75 + 6.7), Parkinson disease (PD: n=16,male=69%,age=67 + 8.7), mild cognitive impairment (MCI: n=41,male=63%,age=70 + 8.5), isolated REM sleep behavior disorder (iRBD: n=19,male=74%,age=64 + 9.6) and a control group (CG: n=61,male=47%,age=65 + 8.3) were studied with the Sleep Profiler and auto-detected AN3 computed. Between-group comparisons were assessed with Mann-Whitney U and Chi-square tests. Results The mean percentages of sleep time with AN3 were significantly greater in DLB/PDD (8 + 12.3) vs. PD (4 + 10.8), AD (2 + 3.7), MCI (2 + 2.3), iRBD (1 + 1.6), and CG (1 + 2.4)(all p<0.02). The proportions of records with abnormal AN3 (>5% of sleep time) were significantly greater in those with DLB/PDD=35% vs. MCI=10%, iRBD=5% and CG=5% (all p<0.05), but not AD=17% and PD=13%. Conclusions Further investigations are needed to determine if abnormal AN3 may be a biomarker that distinguishes patients with Lewy body spectrum diseases from alternative pathologies and/or could be helpful in monitoring the side effects of CNS-acting medications

Abstract Introduction Symptoms associated with autonomic nervous system dysfunction are common to patients with neurodegenerative disorders (NDD) resulting from the accumulation of α-synuclein in the neurons and glial cells. In this study we investigate whether a novel biomarker, the autonomic activation index, can discriminate these potentially affected NDD groups. Methods After ethics review and with informed consent, patients with Lewy body disease (DLB/PDD: n=20%), Alzheimer’s disease dementia (AD: n=27), Parkinson disease (PD: n=14), mild cognitive impairment (MCI: n=37), isolated REM sleep behavior disorder (iRBD: n=15) and a control group (CG: n=58) were studied with the Sleep Profiler. AAI was based on a 6 BPM increase in pulse rate compared to the previous/subsequent 10th second. Between-group comparisons were assessed with Mann-Whitney U and Chi-square tests. Results The AAI values for the DLB/PDD patients were decreased compared to the AD, MCI (both p<0.03), and CG (p<0.0005), while the PD was less than AD and MCI (both < 0.05) and CG (p<0.002). The proportions of cases with abnormal AAI values (<10 events/h) was greatest in DLB/PDD patients, and greater than in ADem (p<0.02), MCI (p<0.02) and CG (p<0.0001), while AAI in PD and iRBD was greater than in CG (p<0.007 and 0.05 respectively). Conclusions These findings suggest that the AAI can be used to objectively confirm subjective complaints of autonomic dysfunction symptoms in patients with synucleinopathies. Longitudinal studies are needed determine factors that influence changes in the AAI.

Abstract Introduction In 2015, Lee et al. observed sleeping position influenced the efficiency of glymphatic clearance in rats. In 2018, Levendowski et al. reported supine sleep was independently associated with neurodegenerative disorders (NDD)(odds ratio=3.7). This update further evaluates the comparative frequency of the supine sleep position across a spectrum of cognitive impairment, including patients diagnosed with a Parkinsonian spectrum disorder (PSD), Alzheimer’s disease dementia (AD) and mild cognitive impairment (MCI), compared with a control group (CG) of patients without a known cognitive disorder. Methods After ethics review and with informed consent, a control group (CG: n=170), PSD (n=36), AD (n=29) and MCI group (n=41) were studied in-home with the Sleep Profiler; 89% across two-nights. Between-group comparisons were assessed with Mann-Whitney U and Chi-square tests. Results The hours of supine sleep were greater in PSD=3.0 + 1.9, AD=3.2 + 2.0 and MCI=3.0 + 2.0 vs. CG=1.9 + 1.8 (all p<0.002). The proportion of patients with >2 hours of supine sleep were PSD=69%, AD=69% and MCI=59% vs. CG=39%, with odds ratios of PSD=3.6, AD=3.5 and MCI=2.2 (all p<0.001). Conclusions Here we provide further evidence for a relatively strong association between supine sleep and neurodegenerative disease including PSD, AD and MCI cohorts. The nature of this association remains unclear from cross sectional study designs. Future prospective studies should test if there is a causal link between supine sleep and subsequent development of neurodegeneration.

Abstract There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer’s disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer’s disease pathology (behavioural Alzheimer’s disease) and 28 had amnestic Alzheimer’s disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as ‘eigenbrains’. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer’s disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer’s disease, then amnestic Alzheimer’s disease. Dysexecutive Alzheimer’s disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer’s disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer’s disease, temporo-parietal areas in amnestic Alzheimer’s disease and frontotemporal areas in bvFTD and behavioural Alzheimer’s disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer’s disease patient for behavioural Alzheimer’s disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Abstract Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer’s disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging’s use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

Background: Sleep disturbances are common in parkinsonian disorders; however, whether sleep disorders affect individuals with early-onset parkinsonism and whether they differ from individuals with typical-onset parkinsonism is unknown. Objective: To compare the prevalence and incidence of sleep disorders before and after parkinsonian motor symptom onset between individuals with early onset parkinsonism (age ≤50 at motor symptom onset) and typical-onset parkinsonism (age >50 at motor symptom onset). Methods: We used a population-based, 1991 to 2015 incident-cohort study of parkinsonism including 38 patients with early-onset and 1,001 patients with typical-onset parkinsonism. Presence or absence and type of sleep disorder as well as the relationship between motor and sleep symptoms were abstracted from the medical records. Rates of sleep disorders before and after onset of parkinsonism were compared with logistic regression and Cox proportional hazards models. Results: The prevalence of sleep disorders prior to the onset of parkinsonism in early vs. typical parkinsonism (24% vs. 16% p = 0.19) and incidence of sleep disorders after parkinsonism onset (5.85 cases per 100 person-years vs. 4.11 cases per 100 person-years; HR 1.15 95% CI: 0.74–1.77) were similar between the two groups. Early-onset parkinsonism had a higher risk for developing post-motor insomnia compared with typical-onset parkinsonism (HR 1.73, 95% CI: 1.02–2.93); the risk for developing all other sleep disorders considered was similar between groups. Conclusion: Sleep disorders are common in individuals with early-onset parkinsonism and occur with similar frequency to those with typical-onset parkinsonism, except for insomnia, which was more frequent in the early-onset group.

AbstractINTRODUCTIONDelay in diagnosis of posterior cortical atrophy (PCA) syndrome is common, and the lack of familiarity with assessment tools for identifying visual cortical dysfunction is a contributing factor. We propose recommendations for the approach to the evaluation of PCA clinical features during the office visit, the neuropsychological evaluation, and the research setting. A recommended screening battery for eye clinics is also proposed.METHODSRecommendations were developed using results from a web‐based survey of members of Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) Atypical Alzheimer's Disease Professional Interest Area (PIA), literature review, and consensus by the PCA assessment working party of the Atypical Alzheimer's Disease PIA.RESULTSSurvey results revealed robust agreement for assessment tool preferences for PCA features, and many respondents indicated that they reserve assessment tools for use only when PCA is suspected. For some PCA features, curated tools were preferred over validated battery tools, particularly for the office visit. Consensus recommendations superseded survey preferences for two core cognitive features within the 2017 PCA diagnostic criteria.DISCUSSIONThese consensus recommendations provide an evaluation framework for PCA clinical features and can facilitate timely and accurate recognition and diagnosis of PCA. Broader use of these tools should be sought, and development and validation of novel PCA clinical outcome assessments are needed to improve our understanding of atypical AD and other dementias and support the inclusion of those with PCA in treatment trials.