Life course, genetic, and neuropathological associations with brain age in the 1946 British Birth Cohort: a population-based study.

BACKGROUND: A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain's ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age. METHODS: Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18-90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy. FINDINGS: Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (β=2·3 [95% CI 1·5 to 3·0]) and 69 years (β=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (-1·3 [-2·4 to -0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD. INTERPRETATION: Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility. FUNDING: Alzheimer's Research UK, Medical Research Council Dementia Platforms UK, Selfridges Group Foundation, Wolfson Foundation, Wellcome Trust, Brain Research UK, Alzheimer's Association

Life course, genetic, and neuropathological associations with brain age in the 1946 British Birth Cohort: a population-based study

Background A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain's ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age. Methods Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18–90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy. Findings Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (β=2·3 [95% CI 1·5 to 3·0]) and 69 years (β=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (–1·3 [–2·4 to –0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD. Interpretation Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Effect of Varying Concentrations of Docosahexaenoic Acid on Amyloid Beta (1⁻42) Aggregation: An Atomic Force Microscopy Study.

Healthcare has advanced significantly, bringing with it longer life expectancies and a growing population of elders who suffer from dementia, specifically Alzheimer's disease (AD). The amyloid beta (Aβ) peptide has been implicated in the cause of AD, where the peptides undergo a conformational change and form neurotoxic amyloid oligomers which cause neuronal cell death. While AD has no cure, preventative measures are being designed to either slow down or stop the progression of this neurodegenerative disease. One of these measures involves dietary supplements with polyunsaturated fatty acids such as docosahexaenoic acid (DHA). This omega-3 fatty acid is a key component of brain development and has been suggested to reduce the progression of cognitive decline. However, different studies have yielded different results as to whether DHA has positive, negative, or no effects on Aβ fibril formation. We believe that these discrepancies can be explained with varying concentrations of DHA. Here, we test the inhibitory effect of different concentrations of DHA on amyloid fibril formation using atomic force microscopy. Our results show that DHA has a strong inhibitory effect on Aβ1⁻42 fibril formation at lower concentrations (50% reduction in fibril length) than higher concentrations above its critical micelle concentration (70% increase in fibril length and three times the length of those at lower concentrations). We provide evidence that various concentrations of DHA can play a role in the inhibitory effects of amyloid fibril formation in vitro and help explain the discrepancies observed in previous studies

Multimodal diffusion model for increments of electroencephalogram data

We propose a new strictly stationary strong mixing diffusion model with marginal multimodal (three-peak) distribution and exponentially decaying autocorrelation function for modeling of increments of electroencephalogram data collected from Ugandan children during coma from cerebral malaria. We treat the increments as discrete-time observations and construct a diffusion process where the stationary distribution is viewed as a mixture of three non-central generalized Gaussian distributions and we state some important properties related to the moments of this mixture. We estimate the distribution parameters using the expectation-maximization algorithm, where the added shape parameter is estimated using the higher order statistics approach based on an analytical relationship between the shape parameter and kurtosis. The derived estimates are then used for prediction of subsequent neurodevelopment and cognition of cerebral malaria survivors using the elastic net regression. We compare different predictive models and determine whether additional information obtained from multimodality of the marginal distributions can be used to improve the prediction

C-terminal domain of nonhistone protein HMGB1 as a modulator of HMGB1–DNA structural interactions

The HMGB1 protein (High Mobility Group protein 1) participates in the formation of functionally significant DNA-protein complexes. HMGB1 protein contains two DNA-binding domains and negatively charged C-terminal region. The latter consists of continuous sequence of dicarboxylic amino acids residues. Structural changes in DNA-protein complexes were studied by circular dichroism spectroscopy (CD) and atomic force microscopy (AFM). Natural HMGB1 and recombinant protein HMGB1(A + B) lacked negatively charged C-terminal region were used. The DNA–HMGB1(A + B) complexes demonstrate an unusually high optical activity in 150 mM NaCl solutions. AFM of the latter complexes shows, that at the low concentration of HMGB1 in the complex the protein is distributed along DNA in a random way. Increase of HMGB1 content leads to cooperative interaction and a redistribution of the bound protein molecules on DNA is observed. Based on the data obtained we conclude that protein–protein interactions play a key role in the formation of highly ordered DNA–HMGB1 complexes. It was shown that C-terminal domain modulate the interactions of DNA with HMGB1 protein. We suggest that the C-terminal domain of HMGB1 also modulates the “packing” of HMGB1 molecules on the DNA.Peer Reviewe

Interaction forces of a supported DOPC bilayer in the presence of the general anaesthetic halothane - An atomic force microscopy study

International audienceIn this study atomic force microscopy (AFM) was used to study the effect of halothane on a supported dioleoylphosphatidylcholine ( DOPC) bilayer under conditions of high anaesthetic loading. In a previous study we demonstrated that bilayer restructuring occurs as a result of halothane incorporation. Force measurements using AFM indicate an initial decrease in adhesive forces and compressibility between the bilayer and AFM tip, followed by an increase in adhesion properties as a function of incubation time. This effect is attributed to the location and dynamic redistribution of halothane within the bilayer

Molecular dynamics simulations and Kelvin probe force microscopy to study of cholesterol-induced electrostatic nanodomains in complex lipid mixtures

The molecular arrangement of lipids and proteins within biomembranes and monolayers gives rise to complex film morphologies as well as regions of distinct electrical surface potential, topographical and electrostatic nanoscale domains. To probe these nanodomains in soft matter is a challenging task both experimentally and theoretically. This work addresses the effects of cholesterol, lipid composition, lipid charge, and lipid phase on the monolayer structure and the electrical surface potential distribution. Atomic force microscopy (AFM) was used to resolve topographical nanodomains and Kelvin probe force microscopy (KPFM) to resolve electrical surface potential of these nanodomains in lipid monolayers. Model monolayers composed of dipalmitoylphosphatidylcholine (DPPC), 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1,2-dioleoyl-sn-glycero-3-[phospho-rac-(3-lysyl(1-glycerol))] (DOPG), and cholesterol were studied. It is shown that cholesterol changes nanoscale domain formation, affecting both topography and electrical surface potential. The molecular basis for differences in electrical surface potential was addressed with atomistic molecular dynamics (MD). MD simulations are compared the experimental results, with 100 s of mV difference in electrostatic potential between liquid-disordered bilayer (Ld, less cholesterol and lower chain order) and a liquid-ordered bilayer (Lo, more cholesterol and higher chain order). Importantly, the difference in electrostatic properties between Lo and Ld phases suggests a new mechanism by which membrane composition couples to membrane function