Search

Summary: Background: Neuroimaging studies often quantify tau burden in standardized brain regions to assess Alzheimer disease (AD) progression. However, this method ignores another key biological process in which tau spreads to additional brain regions. We have developed a metric for calculating the extent tau pathology has spread throughout the brain and evaluate the relationship between this metric and tau burden across early stages of AD. Methods: 445 cross-sectional participants (aged ≥ 50) who had MRI, amyloid PET, tau PET, and clinical testing were separated into disease-stage groups based on amyloid positivity and cognitive status (older cognitively normal control, preclinical AD, and symptomatic AD). Tau burden and tau spatial spread were calculated for all participants. Findings: We found both tau metrics significantly elevated across increasing disease stages (p

Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals. We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

IntroductionQuantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B-based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design.MethodsPittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally.ResultsGlobal amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers.DiscussionAlthough the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.

BackgroundModels of Alzheimer's disease propose a sequence of amyloid β (Aβ) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal.MethodsBetween Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at ClinicalTrials.gov (number NCT00869817) FINDINGS: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aβ deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aβ accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning).InterpretationMutation carriers had elevations in Aβ deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologies varied throughout the brain, suggesting differential regional and temporal vulnerabilities to Aβ, metabolic decline, and structural atrophy, which should be taken into account when using biomarkers in a clinical setting as well as designing and evaluating clinical trials.FundingUS National Institutes of Health, the German Center for Neurodegenerative Diseases, and the Medical Research Council Dementias Platform UK.

D/deaf clients perceive music through their bodies. The music therapist needs various methods to appreciate these sensations. It is necessary to examine the instruments from a vibrational perspective. Also, listening to the client is more important than speaking. This article focuses on how non-verbal methods are more appropriate when working with clients who have a hearing loss. Vibrosensoric approach to music combined with touch-based communication methods (social-haptic communication) enhances a positive client response. As a deafblind music therapist, musical vibrations play an important role for me as well as for the client during a therapy process. Background noise and spoken language can sometimes mask the perception of musical tones from different types of instruments. Speech with music within GIM sessions renders the client unable to follow either music or storyline. This distorts the sounds in hearing aid devices. Therefore, the instrument selection within the therapy session needs to be considered from a vibratory aspect. Musical tones from the instruments can be felt in different parts of the body. In this case, the focus is not on how an instrument sounds but more on the vibrational sensations it produces.

To inform the design of targeted, species-specific surveys of Prairie Warbler (Setophaga discolor) in Ontario, Canada, we used autonomous recording units (ARUs) to document patterns in male vocal activity. We deployed recorders within the territories of breeding males at coastal sites near Georgian Bay, and at inland sites near Kaladar, in June 2015 and 2016, respectively. Although coastal and inland sites are Key words: acoustic, autonomous recording unit, bird song, monitoring, survey design. Pour guider la conception d'enquêtes ciblées et spécifiques à l'espèce de la Paruline des Prés (Setophaga discolor) en Ontario, au Canada, nous avons utilisé des unités d'enregistrement autonomes ou UEA (en anglais, ARU = Autonomous Recording Units) pour documenter les modèles des vocalisations de mâles. Nous avons déployé des enregistreurs dans les territoires de reproduction des mâles aux sites côtiers près de la baie Georgienne et aux sites intérieurs près de Kaladar. respectivement en juin 2015 et 2016. Bien que les sites côtiers et intérieurs soient séparés moins de 250 km, nous avons observé des différences distinctes dans les modèles et la fréquence du chant des mâles. En général, la moyenne de chants par minute des mâles dans les sites intérieurs près de Kaladar était presque le triple de celui des sites côtiers près de la baie Georgienne. L'activité vocale quotidien a culminé peu après le lever du soleil et a diminué tout au long de la journée sur les deux sites. L'activité vocale quotidien était uniformément élevée tout au long de la période du matin aux sites côtiers, tandis que aux sites à l'intérieur, les taux de chant les plus élevés étaient pendant la période du matin après l'aube. L'activité vocale variait selon le jour chronologique aux sites de l'intérieur, avec des sommets distincts au début et à la fin de juin. Le chant aux sites côtiers ne variait pas selon le jour chronologique et était constamment faible tout au long du mois de juin. Bien que nos effectifs de l'échantillon soient petits, avec la répétition minimale, nous discutons nos résultats dans le contexte du succès d'appartement réduit et des sex-ratios biaisés pour les mâles dans de petites populations isolées. En plus de documenter les modèles d'activité vocale pour optimiser les futures enquêtes d'espèces dont la conservation est préoccupante, les UEA peuvent également indiquer des différences démographiques potentielles importantes pour la persistance de la population qui justifient la recherche additionnelle. Mots clés : acoustique, chants d'oiseaux, plan de l'enquête, suivi, unité d'enregistrement autonome., Data collected from auditory surveys, primarily through detections of distinct songs and calls, underpin most evaluations of population status and distribution for breeding terrestrial birds (DeJong and Emlen 1985, Sauer [...]

No-take marine reserves are often located in remote locations far away from human activity, limiting perceived impact on extractive users but also reducing their use for investigating impacts of fishing. This study aimed to establish a benchmark in the distribution of fished species across the Ningaloo Marine Park – Commonwealth (NMP-Commonwealth), and adjacent comparable habitats within the Ningaloo Marine Park - State (NMP-State), in Western Australia to test if there was evidence of an effect of recreational fishing, as no commercial fishing is allowed within either marine park. We also examined whether the remote location of the newly established (2018) No-take Zone (NTZ), in NMP-Commonwealth, limits its use for studying the effects of fishing. Throughout the NMP-Commonwealth and NMP-State, where recreational fishing is permitted, we expected the abundance of recreationally fished fish species to increase with increasing distance to the nearest boat ramp, as a proxy of recreational fishing effort. Conversely, we did not expect the abundance of non-fished species and overall species richness to vary in response to the proxy for human activity. Distance to the nearest boat ramp was found to be a strong predictor of fished species abundance, indicating that the effect of recreational fishing can be detected across the NMP-Commonwealth. The effect of the NTZ on fished species abundance was weakly positive, but this difference across the NTZ is expected to increase over time. Habitat composition predictors were only found to influence species richness and non-fished species abundance. This study suggests a clear footprint of recreational fishing across the NMP-Commonwealth and as a result the new NTZ, despite its remote location, can act as a control in future studies of recreational fishing effects.

[This corrects the article DOI: 10.1371/journal.pone.0152082.].

Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer's Network (DIAN), an autosomal dominant Alzheimer's disease population. Cross-sectional and longitudinal [11C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer's disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted.

Abstract Introduction Quantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B–based and florbetapir‐based amyloid imaging in the same participants from two independent cohorts using a crossover design. Methods Pittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter‐individual variability of the two tracers were compared using multivariate linear models both cross‐sectionally and longitudinally. Results Global amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers. Discussion Although the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.

INTRODUCTION: Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin‐1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre‐/postcodon 200) influences these pathologic features and dementia at different stages. METHODS: Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross‐sectionally evaluated regional Pittsburgh compound B‐positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging‐based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition. RESULTS: Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures. DISCUSSION: We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials. Highlights: Mutation position influences Aβ burden, SVD, and dementia.PSEN1 pre‐200 group had stronger associations between Aβ burden and disease stage.PSEN1 post‐200 group had stronger associations between SVD markers and disease stage.PSEN1 post‐200 group had worse dementia score than pre‐200 in late disease stage.Diffusion tensor imaging‐based SVD markers mediated mutation position effects on dementia in the late stage. [ABSTRACT FROM AUTHOR]

On the iconic Great Barrier Reef (GBR), the cumulative impacts of tropical cyclones, marine heatwaves and regular outbreaks of coral-eating crown-of-thorns starfish (CoTS) have severely depleted coral cover. Climate change will further exacerbate this situation over the coming decades unless effective interventions are implemented. Evaluating the efficacy of alternative interventions in a complex system experiencing major cumulative impacts can only be achieved through a systems modelling approach. We have evaluated combinations of interventions using a coral reef meta-community model. The model consisted of a dynamic network of 3753 reefs supporting communities of corals and CoTS connected through ocean larval dispersal, and exposed to changing regimes of tropical cyclones, flood plumes, marine heatwaves and ocean acidification. Interventions included reducing flood plume impacts, expanding control of CoTS populations, stabilizing coral rubble, managing solar radiation and introducing heat-tolerant coral strains. Without intervention, all climate scenarios resulted in precipitous declines in GBR coral cover over the next 50 years. The most effective strategies in delaying decline were combinations that protected coral from both predation (CoTS control) and thermal stress (solar radiation management) deployed at large scale. Successful implementation could expand opportunities for climate action, natural adaptation and socioeconomic adjustment by at least one to two decades.

Major imaging biomarkers of Alzheimer's disease include amyloid deposition [imaged with [(11)C]Pittsburgh compound B (PiB) PET], altered glucose metabolism (imaged with [(18)F]fluro-deoxyglucose PET), and structural atrophy (imaged by MRI). Recently we published the initial subset of imaging findings for specific regions in a cohort of individuals with autosomal dominant Alzheimer's disease. We now extend this work to include a larger cohort, whole-brain analyses integrating all three imaging modalities, and longitudinal data to examine regional differences in imaging biomarker dynamics. The anatomical distribution of imaging biomarkers is described in relation to estimated years from symptom onset. Autosomal dominant Alzheimer's disease mutation carrier individuals have elevated PiB levels in nearly every cortical region 15 y before the estimated age of onset. Reduced cortical glucose metabolism and cortical thinning in the medial and lateral parietal lobe appeared 10 and 5 y, respectively, before estimated age of onset. Importantly, however, a divergent pattern was observed subcortically. All subcortical gray-matter regions exhibited elevated PiB uptake, but despite this, only the hippocampus showed reduced glucose metabolism. Similarly, atrophy was not observed in the caudate and pallidum despite marked amyloid accumulation. Finally, before hypometabolism, a hypermetabolic phase was identified for some cortical regions, including the precuneus and posterior cingulate. Additional analyses of individuals in which longitudinal data were available suggested that an accelerated appearance of volumetric declines approximately coincides with the onset of the symptomatic phase of the disease.

Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals.We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir.To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume.We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

Abstract Introduction Structural magnetic resonance imaging is a marker of gray matter health and decline that is sensitive to impaired cognition and Alzheimer's disease pathology. Prior work has shown that both amyloid β (Aβ) and tau biomarkers are related to cortical thinning, but it is unclear what unique influences they have on the brain. Methods Aβ pathology was measured with [18F] AV‐45 (florbetapir) positron emission tomography (PET) and tau was assessed with [18F] AV‐1451 (flortaucipir) PET in a population of 178 older adults, of which 123 had longitudinal magnetic resonance imaging assessments (average of 5.7 years) that preceded the PET acquisitions. Results In cross‐sectional analyses, greater tau PET pathology was associated with thinner cortices. When examined independently in longitudinal models, both Aβ and tau were associated with greater antecedent loss of gray matter. However, when examined in a combined model, levels of tau, but not Aβ, were still highly related to change in cortical thickness. Discussion Measures of tau PET are strongly related to gray matter atrophy and likely mediate relationships between Aβ and gray matter.

Abstract Introduction 18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET) is commonly used to estimate neuronal injury in Alzheimer's disease (AD). Here, we evaluate the utility of dynamic PET measures of perfusion using 11C‐Pittsburgh compound B (PiB) to estimate neuronal injury in comparison to FDG PET. Methods FDG, early frames of PiB images, and relative PiB delivery rate constants (PiB‐R1) were obtained from 110 participants from the Dominantly Inherited Alzheimer Network. Voxelwise, regional cross‐sectional, and longitudinal analyses were done to evaluate the correlation between images and estimate the relationship of the imaging biomarkers with estimated time to disease progression based on family history. Results Metabolism and perfusion images were spatially correlated. Regional PiB‐R1 values and FDG, but not early frames of PiB images, significantly decreased in the mutation carriers with estimated year to onset and with increasing dementia severity. Discussion Hypometabolism estimated by PiB‐R1 may provide a measure of brain perfusion without increasing radiation exposure.

Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. Successful use of this tool is limited by the lack of a common standard in the quantification of amyloid imaging data. The Centiloid approach was recently proposed to address this problem and in this work, we report our implementation of this approach and evaluate the impact of differences in underlying image analysis methodologies using both cross-sectional and longitudinal datasets. The Centiloid approach successfully converts quantitative amyloid burden measurements into a common Centiloid scale (CL) and comparable dynamic range. As expected, the Centiloid values derived from different analytical approaches inherit some of the inherent benefits and drawbacks of the underlying approaches, and these differences result in statistically significant (p