The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease

Contact with Counter-Stereotypical Women Predicts Less Sexism, Less Rape Myth Acceptance, Less Intention to Rape (in Men) and Less Projected Enjoyment of Rape (in Women)

Intergroup contact—(positive) interactions with people from different social groups—is a widely researched and strongly supported prejudice-reducing mechanism shown to reduce prejudice against a wide variety of outgroups. However, no known previous research has investigated whether intergroup contact can also reduce sexism against women. Sexism has an array of negative outcomes. One of the most detrimental and violent ones is rape, which is both justified and downplayed by rape myth acceptance. We hypothesised that more frequent, higher quality contact with counter-stereotypical women would predict lower levels of sexism and thus less rape myth acceptance (in men) and less sexualised projected responses to rape (in women). Two studies using online surveys with community samples supported these hypotheses. In Study 1, 170 male participants who experienced more positive contact with counter-stereotypical women reported less intention to rape. Similarly, in Study 2, 280 female participants who experienced more positive contact with counter-stereotypical women reported less projected sexual arousal at the thought of being raped. Thus, the present research is the first known to show that contact could be a potential tool to combat sexism, rape myth acceptance, intentions to rape in men, and sexualisation of rape by women

Causal inference on neuroimaging data with mendelian randomisation

While population-scale neuroimaging studies offer the promise of discovery and characterisation of subtle risk factors, massive sample sizes increase the power for both meaningful associations and those attributable to confounds. This motivates the need for causal modelling of observational data that goes beyond statements of association and towards deeper understanding of complex relationships between individual traits and phenotypes, clinical biomarkers, genetic variation, and brain-related measures of health. Mendelian randomisation (MR) presents a way to obtain causal inference on the basis of genetic data and explicit assumptions about the relationship between genetic variables, exposure and outcome. In this work, we provide an introduction to and overview of causal inference methods based on Mendelian randomisation, with examples involving imaging-derived phenotypes from UK Biobank to make these methods accessible to neuroimaging researchers. We motivate the use of MR techniques, lay out the underlying assumptions, introduce common MR methods and focus on several scenarios in which modelling assumptions are potentially violated, resulting in biased effect estimates. Importantly, we give a detailed account of necessary steps to increase the reliability of MR results with rigorous sensitivity analyses

A Bayesian Hierarchical Spatial Point Process Model for MS Subtype Classification

Poster submitted to the 2015 Organization for Human Brain Mapping (OHBM) in Hawaii, 14-18 June

Alcohol consumption and telomere length: observational and Mendelian randomisation approaches

Alcohols impact on telomere length, a proposed marker of biological age, is unclear. We performed the largest observational study to date and compared findings with Mendelian randomization (MR) estimates. Two-sample MR used data from a recent genome-wide association study (GWAS) of telomere length. Genetic variants were selected on the basis of associations with alcohol consumption and alcohol use disorder (AUD). Non-linear MR employed UK Biobank individual data. MR analyses suggest a causal relationship between alcohol and telomere length: both genetically predicted alcohol traits were inversely associated with telomere length. 1 S.D. higher genetically-predicted log-transformed alcoholic drinks weekly had a -0.07 S.D. effect on telomere length (95% confidence interval [CI]:-0.14 to -0.01); genetically-predicted AUD -0.06 S.D. effect (CI:-0.10 to -0.02). Results were consistent across methods and independent from smoking. Non-linear analyses indicated a potential threshold relationship between alcohol and telomere length. Our findings have implications for potential aging-related disease prevention strategies

Alcohol consumption and telomere length: Mendelian randomization clarifies alcohol’s effects

Alcohol’s impact on telomere length, a proposed marker of biological aging, is unclear. We performed the largest observational study to date (in n = 245,354 UK Biobank participants) and compared findings with Mendelian randomization (MR) estimates. Two-sample MR used data from 472,174 participants in a recent genome-wide association study (GWAS) of telomere length. Genetic variants were selected on the basis of associations with alcohol consumption (n = 941,280) and alcohol use disorder (AUD) (n = 57,564 cases). Non-linear MR employed UK Biobank individual data. MR analyses suggested a causal relationship between alcohol traits, more strongly for AUD, and telomere length. Higher genetically-predicted AUD (inverse variance-weighted (IVW) β = −0.06, 95% confidence interval (CI): −0.10 to −0.02, p = 0.001) was associated with shorter telomere length. There was a weaker association with genetically-predicted alcoholic drinks weekly (IVW β = −0.07, CI: −0.14 to −0.01, p = 0.03). Results were consistent across methods and independent from smoking. Non-linear analyses indicated a potential threshold relationship between alcohol and telomere length. Our findings indicate that alcohol consumption may shorten telomere length. There are implications for age-related diseases

Alcohol consumption and telomere length: Mendelian randomization clarifies alcohol's effects.

Funder: UK Medical Research Council (G1001354 & MR/K013351/1) European Commission (Horizon 2020 732592)Funder: Wellcome Trust WINFunder: US Department of Veterans Affairs (I01CX001849)Funder: Li Ka Shing Centre for Health Information and Discovery Wellcome Trust 100309/Z/12/ZFunder: National Institute for Health Research Cambridge Biomedical Research Centre Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (204623/Z/16/Z)Funder: Leicester Cardiovascular Biomedical Research CentreAlcohol's impact on telomere length, a proposed marker of biological aging, is unclear. We performed the largest observational study to date (in n = 245,354 UK Biobank participants) and compared findings with Mendelian randomization (MR) estimates. Two-sample MR used data from 472,174 participants in a recent genome-wide association study (GWAS) of telomere length. Genetic variants were selected on the basis of associations with alcohol consumption (n = 941,280) and alcohol use disorder (AUD) (n = 57,564 cases). Non-linear MR employed UK Biobank individual data. MR analyses suggested a causal relationship between alcohol traits, more strongly for AUD, and telomere length. Higher genetically-predicted AUD (inverse variance-weighted (IVW) β = -0.06, 95% confidence interval (CI): -0.10 to -0.02, p = 0.001) was associated with shorter telomere length. There was a weaker association with genetically-predicted alcoholic drinks weekly (IVW β = -0.07, CI: -0.14 to -0.01, p = 0.03). Results were consistent across methods and independent from smoking. Non-linear analyses indicated a potential threshold relationship between alcohol and telomere length. Our findings indicate that alcohol consumption may shorten telomere length. There are implications for age-related diseases

Amplitudes of resting-state functional networks - investigation into their correlates and biophysical properties

Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks' spontaneous fluctuations may be associated with individuals' clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former