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Item does not contain fulltext, Sleep plays a crucial role in memory consolidation. Recent data in rodents and young adults revealed that fast spindle band power fluctuates at a 0.02-Hz infraslow scale during non-rapid eye movement (NREM) sleep. These fluctuations result from a periodic temporal clustering of spindles and may modulate sleep maintenance and memory consolidation. With age, sleep undergoes substantial changes but age-related changes in spindle clustering have never been investigated. Polysomnography data were collected in 147 older (mean age ± SD: 69.3 ± 4.1 years) and 32 young-middle aged (34.5 ± 10.9 years) adults. Sleep-dependent memory consolidation was assessed in a subsample of 57 older adults using a visuospatial memory task. We analyzed power fluctuations in fast spindle frequency band, detected fast spindles, and quantified their clustering during the night separating encoding and retrieval. Fast spindle band power fluctuated at a 0.02-Hz infraslow scale in young-middle aged and older adults. However, the proportion of clustered fast spindles decreased non-linearly with age (p < .001). This effect was not mediated by NREM sleep fragmentation. The clustering level of fast spindles modulated their characteristics (p < .001). Finally, the mean size of spindle clusters was positively associated with memory consolidation (p = .036) and negatively with NREM sleep micro-arousal density (p = .033). These results suggest that clusters of fast spindles may constitute stable sleep periods promoting off-line processes such as memory consolidation. We emphasize the relevance of considering spindle dynamics, obviously impaired during aging, to understand the impact of age-related sleep changes on memory. Clinical Trial Information: Name: Study in Cognitively Intact Seniors Aiming to Assess the Effects of Meditation Training (Age-Well). URL: https://clinicaltrials.gov/ct2/show/NCT02977819?term=Age-Well&draw=2&rank=1. See STROBE_statement_AGEWELL.doc in supplementary material. Registration

Contains fulltext : 248802.pdf (Publisher’s version ) (Closed access)

Image-derived phenotypes (IDPs) from multimodal MRI sequences constitute an important resource that allows the characterization of brain alterations in the early stages of Alzheimer diseases and other neurodegenerative conditions. Here, we showed the computation of multimodal IDPs from the European Prevention of Alzheimer Dementia (EPAD) cohort and assessed their relationship with non-imaging markers of neurodegeneration. We demonstrated the clinical relevance of IDPs to uncover early brain alteration in AD by showing expected association with non-imaging data.

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer’s Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI pre- processing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features — i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features — Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) — were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

Contains fulltext : 232542.pdf (Publisher’s version ) (Open Access), INTRODUCTION: Non-pharmacological treatments (NPTs) have the potential to improve meaningful outcomes for older people at risk of, or living with dementia, but research often lacks methodological rigor and continues to produce mixed results. METHODS: In the current position paper, experts in NPT research have specified treatment targets, aims, and ingredients using an umbrella framework, the Rehabilitation Treatment Specification System. RESULTS: Experts provided a snapshot and an authoritative summary of the evidence for different NPTs based on the best synthesis efforts, identified main gaps in knowledge and relevant barriers, and provided directions for future research. Experts in trial methodology provide best practice principles and recommendations for those working in this area, underscoring the importance of prespecified protocols. DISCUSSION: We conclude that the evidence strongly supports various NPTs in relation to their primary targets, and discuss opportunities and challenges associated with a unifying theoretical framework to guide future efforts in this area.

Altres ajuts: Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea, "Una manera de hacer Europa", La "Marató TV3" grant (20141210 to J.F.), Alzheimer's disease and related dementias (ADRDs) are a global crisis facing the aging population and society as a whole. With the numbers of people with ADRDs predicted to rise dramatically across the world, the scientific community can no longer neglect the need for research focusing on ADRDs among underrepresented ethnoracial diverse groups. The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART; alz.org/ISTAART) comprises a number of professional interest areas (PIAs), each focusing on a major scientific area associated with ADRDs. We leverage the expertise of the existing international cadre of ISTAART scientists and experts to synthesize a cross-PIA white paper that provides both a concise "state-of-the-science" report of ethnoracial factors across PIA foci and updated recommendations to address immediate needs to advance ADRD science across ethnoracial populations.

Alzheimer's disease and related dementias (ADRDs) are a global crisis facing the aging population and society as a whole. With the numbers of people with ADRDs predicted to rise dramatically across the world, the scientific community can no longer neglect the need for research focusing on ADRDs among underrepresented ethnoracial diverse groups. The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART; alz.org/ISTAART) comprises a number of professional interest areas (PIAs), each focusing on a major scientific area associated with ADRDs. We leverage the expertise of the existing international cadre of ISTAART scientists and experts to synthesize a cross‐PIA white paper that provides both a concise "state-of-the-science" report of ethnoracial factors across PIA foci and updated recommendations to address immediate needs to advance ADRD science across ethnoracial populations.

Research increasingly suggests that subjective cognitive decline (SCD) in older adults, in the absence of objective cognitive dysfunction or depression, may be a harbinger of non-normative cognitive decline and eventual progression to dementia. Little is known, however, about the key features of self-report measures currently used to assess SCD. The Subjective Cognitive Decline Initiative (SCD-I) Working Group is an international consortium established to develop a conceptual framework and research criteria for SCD (Jessen et al., 2014, Alzheimers Dement 10, 844-852). In the current study we systematically compared cognitive self-report items used by 19 SCD-I Working Group studies, representing 8 countries and 5 languages. We identified 34 self-report measures comprising 640 cognitive self-report items. There was little overlap among measures-approximately 75% of measures were used by only one study.Wide variation existed in response options and item content. Items pertaining to the memory domain predominated, accounting for about 60% of items surveyed, followed by executive function and attention, with 16% and 11% of the items, respectively. Items relating to memory for the names of people and the placement of common objects were represented on the greatest percentage of measures (56% each). Working group members reported that instrument selection decisions were often based on practical considerations beyond the study of SCD specifically, such as availability and brevity of measures. Results document the heterogeneity of approaches across studies to the emerging construct of SCD.We offer preliminary recommendations for instrument selection and future research directions including identifying items and measure formats associated with important clinical outcomes.

The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the subpopulations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotelling's T2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS).