Boeve, Bradley F.

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.

ObjectiveMicrotubule‐associated protein tau (MAPT) mutations cause frontotemporal lobar degeneration, and novel biomarkers are urgently needed for early disease detection. We used task‐free functional magnetic resonance imaging (fMRI) mapping, a promising biomarker, to analyze network connectivity in symptomatic and presymptomatic MAPT mutation carriers.MethodsWe compared cross‐sectional fMRI data between 17 symptomatic and 39 presymptomatic carriers and 81 controls with (1) seed‐based analyses to examine connectivity within networks associated with the 4 most common MAPT‐associated clinical syndromes (ie, salience, corticobasal syndrome, progressive supranuclear palsy syndrome, and default mode networks) and (2) whole‐brain connectivity analyses. We applied K‐means clustering to explore connectivity heterogeneity in presymptomatic carriers at baseline. Neuropsychological measures, plasma neurofilament light chain, and gray matter volume were compared at baseline and longitudinally between the presymptomatic subgroups defined by their baseline whole‐brain connectivity profiles.ResultsSymptomatic and presymptomatic carriers had connectivity disruptions within MAPT‐syndromic networks. Compared to controls, presymptomatic carriers showed regions of connectivity alterations with age. Two presymptomatic subgroups were identified by clustering analysis, exhibiting predominantly either whole‐brain hypoconnectivity or hyperconnectivity at baseline. At baseline, these two presymptomatic subgroups did not differ in neuropsychological measures, although the hypoconnectivity subgroup had greater plasma neurofilament light chain levels than controls. Longitudinally, both subgroups showed visual memory decline (vs controls), yet the subgroup with baseline hypoconnectivity also had worsening verbal memory and neuropsychiatric symptoms, and extensive bilateral mesial temporal gray matter decline.InterpretationNetwork connectivity alterations arise as early as the presymptomatic phase. Future studies will determine whether presymptomatic carriers' baseline connectivity profiles predict symptomatic conversion. ANN NEUROL 2023;94:632–646

AbstractSynucleinopathies‐related disorders such as Lewy body dementia (LBD) and isolated/idiopathic REM sleep behavior disorder (iRBD) have been associated with neuroinflammation. In this study, we examined whether the human leukocyte antigen (HLA) locus plays a role in iRBD and LBD. In iRBD, HLA‐DRB1*11:01 was the only allele passing FDR correction (OR = 1.57, 95% CI = 1.27–1.93, p = 2.70e‐05). We also discovered associations between iRBD and HLA‐DRB1 70D (OR = 1.26, 95%CI = 1.12–1.41, p = 8.76e‐05), 70Q (OR = 0.81, 95%CI = 0.72–0.91, p = 3.65e‐04) and 71R (OR = 1.21, 95%CI = 1.08–1.35, p = 1.35e‐03). Position 71 (pomnibus = 0.00102) and 70 (pomnibus = 0.00125) were associated with iRBD. Our results suggest that the HLA locus may have different roles across synucleinopathies.

AbstractIn Lewy body dementia (LBD), disturbances of sleep and/or arousal including insomnia, excessive daytime sleepiness, rapid eye movement (REM) sleep behavior disorder, obstructive sleep apnea, and restless leg syndrome are common. These disorders can each exert a significant negative impact on both patient and caregiver quality of life; however, their etiology is poorly understood. Little guidance is available for assessing and managing sleep disorders in LBD, and they remain under‐diagnosed and under‐treated. This review aims to (1) describe the specific sleep disorders which occur in LBD, considering their putative or potential mechanisms; (2) describe the history and diagnostic process for these disorders in LBD; and (3) summarize current evidence for their management in LBD and consider some of the ongoing and unanswered questions in this field and future research directions.

AbstractBackgroundWhite matter signal abnormalities (WMSA) are frequently observed on magnetic resonance imaging (MRI) scans of people with frontotemporal dementia (FTD). We hypothesized that carriers of mutations in progranulin (GRN+), chromosome 9 open reading frame 72 (C9orf72+) and microtubule associated protein tau (MAPT+) have increased rates of progression of WMSA compared to non‐carrier individuals prior to onset of dementia, and that different mutations may have different rates of WMSA accumulation.MethodParticipants were recruited through the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Study. We selected participants with Clinical Dementia Rating scale (CDR) scores of 0 and 0.5. If a participant developed FTD during the follow‐up, we only analyzed observations collected before the conversion. WMSA were defined as hyperintensities on FLAIR MRI. Total WMSA volumes were normalized to the respective intracranial volumes. We used a linear mixed effects model, with random intercepts and slopes, to compare the baseline burden and the longitudinal progression of total WMSA volumes between mutation carriers and controls. The model was adjusted for baseline age, sex, education, baseline Montreal Cognitive Assessment (MoCA) scores, and MRI acquisition sites.ResultWe analyzed MRI data from 199 participants who had at least two MRI visits (46 C9orf72+, 17 GRN+, 35 MAPT+, 101 non‐carrier controls; up to five years of follow‐up). We did not find significant group differences in baseline demographic features, except that MAPT+ was younger than non‐carrier controls (average age: C9orf72+ = 47, GRN+ = 56, MAPT+ = 40, controls = 47). Baseline WMSA volumes were not significantly different between mutation carriers and controls. Longitudinally, the rates of WMSA volume increase in C9orf72+ and MAPT+ were comparable to controls; whereas, GRN+ had higher rates of WMSA volume increases compared to controls (p = 0.01).ConclusionWMSA may accumulate at higher rates in GRN+ prior to the onset of FTD, which is consistent with the frequent presence of white matter diseases observed in symptomatic FTD patients with GRN mutations. Future work is warranted to investigate the association between white matter structural changes and cognitive/psychiatric changes in FTD mutation carriers.

AbstractBackgroundDementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, after Alzheimer’s disease (AD). While alpha‐synuclein pathology is the hallmark of DLB, AD and cerebrovascular pathologies are common in DLB patients. However, the contribution of these pathologies towards neurodegeneration in DLB is largely unknown. We investigated associations of amyloid‐beta, tau, and cerebrovascular biomarkers with regional gray matter (GM) volume in DLB patients and cognitively unimpaired (CU) controls.MethodWe included 30 DLB patients (69.3±10.2 years old, 87% men) and 100 CU individuals balanced on age and sex (Table 1). We used 11C‐Pittsburgh Compound‐B (PiB) and 18F‐Flortaucipir positron emission tomography (PET) to assess amyloid‐beta and tau in‐vivo. We used structural MRI to assess white matter hyperintensity (WMH) volume, a marker of cerebrovascular lesion load, and regional GM volume (a marker of neurodegeneration). We used correlations and ANCOVA in the whole dataset and structural equation modelling in the DLB patients to investigate associations of WMH volume and regional amyloid‐beta and tau PET standardized uptake value ratios (SUVRs) with regional GM volume.ResultDLB patients showed lower GM volume across all cortical and subcortical regions except for cuneus, putamen, and pallidum (Fig.1). Greater WMH volume was associated with lower volume in the medial and orbital frontal cortices, insula, fusiform cortex, and thalamus in DLB patients (Fig.2). Greater PiB SUVR was associated with lower volume in the inferior temporal cortex, while Flortaucipir SUVR did not correlate with GM volume (Fig.2). Structural equation modelling showed that higher age and positivity for the APOE e4 genotype were significant predictors of greater WMH, and WMH in turn was a significant predictor of GM volume in medial and orbital frontal cortices, insula, and inferior temporal cortex (Fig.3). In contrast, we observed two distinct paths for the fusiform cortex, with age having an effect through PiB and Flortaucipir SUVRs on the one path, and through WMH on the other path (Fig.3).ConclusionDLB patients have widespread cortical atrophy, most of which likely is influenced by alpha‐synuclein pathology. Although amyloid‐beta, tau, and cerebrovascular pathologies often coexist in DLB, their contributions to neurodegeneration seem to be region specific.

AbstractBackgroundNeuropsychiatric symptoms (NPS) often occur during the prodromal stage of frontotemporal dementia (FTD), as evidenced by the presence of early psychiatric diagnosis in approximately 50% of bvFTD patients prior to their dementia diagnosis. We hypothesized that carriers of mutations in progranulin (GRN+), chromosome 9 open reading frame 72 (C9orf72+) and microtubule associated protein tau (MAPT+) have increased baseline burden and rates of progression of NPS compared to non‐carrier individuals prior to onset of dementia symptoms based on the Clinical Dementia Rating (CDR) scale.MethodParticipants were recruited through the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Study. NPS were assessed with Neuropsychiatric Inventory‐Questionnaire (NPI‐Q), Geriatric Depression Scale (GDS), Interpersonal Reactivity Index (IRI), Social Behaviour Checklist (SBOCL), and Behavioral Inhibition Scale (BIS) annually for up to five years. We selected participants with CDR scores of 0 and 0.5. If a participant’s CDR changed to above 0.5 during the follow‐up, we only analyzed observations collected before the conversion. We used mixed effects models to compare the baseline burden and the longitudinal progression of NPS measures between carriers and controls, adjusted for baseline age, sex, education, and baseline Montreal Cognitive Assessment (MoCA) scores.ResultWe analyzed data from 607 participants (111 C9orf72+, 47 GRN+, 67 MAPT+ and 382 non‐carrier controls). There were no significant group differences in baseline demographic features, except that MAPT+ was significantly younger than other groups (average age: C9orf72+ = 45, GRN+ = 53, MAPT+ = 41, controls = 54). Baseline NPS scores were not significantly different between carriers and controls, except that C9orf72+ had significantly lower GDS compared to controls (p = 0.002). Longitudinally, GRN+ (p = 0.02) and MAPT+ (p = 0.009) had significantly higher rates of NPI‐Q increases compared to controls, while the progression of NPI‐Q in C9orf72+ was comparable to controls. The rates of GDS, IRI, SBOCL and BIS progression were not significantly different between carriers and controls.ConclusionPrior to the onset of dementia, GRN+ and MAPT+ have a faster progression of NPS, as measured by NPI‐Q, compared to controls. Future work will investigate whether different mutation carriers are affected by specific type/cluster of symptoms.