Predicting Alzheimer disease with beta-amyloid imaging: results from the Australian imaging, biomarkers, and lifestyle study of ageing

Objective: Biomarkers for Alzheimer disease (AD) can detect the disease pathology in asymptomatic subjects and individuals with mild cognitive impairment (MCI), but their cognitive prognosis remains uncertain. We aimed to determine the prognostic value of β-amyloid imaging, alone and in combination with memory performance, hippocampal atrophy, and apolipoprotein E ε4 status in nondemented, older individuals. Methods: A total of 183 healthy individuals (age = 72.0 ± 7.26 years) and 87 participants with MCI (age = 73.7 ± 8.27) in the Australian Imaging, Biomarkers, and Lifestyle study of ageing were studied. Clinical reclassification was performed after 3 years, blind to biomarker findings. β-Amyloid imaging was considered positive if the (11) C-Pittsburgh compound B cortical to reference ratio was ≥1.5. Results: Thirteen percent of healthy persons progressed (15 to MCI, 8 to dementia), and 59% of the MCI cohort progressed to probable AD. Multivariate analysis showed β-amyloid imaging as the single variable most strongly associated with progression. Of combinations, subtle memory impairment (Z score = -0.5 to -1.5) with a positive amyloid scan was most strongly associated with progression in healthy individuals (odds ratio [OR] = 16, 95% confidence interval [CI] = 3.7-68; positive predictive value [PPV] = 50%, 95% CI = 19-81; negative predictive value [NPV] = 94%, 95% CI = 88-98). Almost all amnestic MCI subjects (Z score ≤ -1.5) with a positive amyloid scan developed AD (OR = ∞; PPV = 86%, 95% CI = 72-95; NPV = 100%, 95% CI = 80-100). Hippocampal atrophy and ε4 status did not add further predictive value. Interpretations: Subtle memory impairment with a positive β-amyloid scan identifies healthy individuals at high risk for MCI or AD. Clearly amnestic patients with a positive amyloid scan have prodromal AD and a poor prognosis for dementia within 3 years.Christopher C. Rowe, Pierrick Bourgeat, Kathryn A. Ellis, Belinda Brown, Yen Ying Lim, Rachel Mulligan, Gareth Jones, Paul Maruff, Michael Woodward, Roger Price, Peter Robins, Henri Tochon-Danguy, Graeme O’Keefe, Kerryn E. Pike, Paul Yates, Cassandra Szoeke, Olivier Salvado, S. Lance Macaulay, Timothy O’Meara, Richard Head, Lynne Cobiac, Greg Savage, Ralph Martins, Colin L. Masters, David Ames, and Victor L. Villemagn

A Pre-Landing Assessment of Regolith Properties at the InSight Landing Site

This article discusses relevant physical properties of the regolith at the Mars InSight landing site as understood prior to landing of the spacecraft. InSight will land in the northern lowland plains of Mars, close to the equator, where the regolith is estimated to be ≥3--5 m thick. These investigations of physical properties have relied on data collected from Mars orbital measurements, previously collected lander and rover data, results of studies of data and samples from Apollo lunar missions, laboratory measurements on regolith simulants, and theoretical studies. The investigations include changes in properties with depth and temperature. Mechanical properties investigated include density, grain-size distribution, cohesion, and angle of internal friction. Thermophysical properties include thermal inertia, surface emissivity and albedo, thermal conductivity and diffusivity, and specific heat. Regolith elastic properties not only include parameters that control seismic wave velocities in the immediate vicinity of the Insight lander but also coupling of the lander and other potential noise sources to the InSight broadband seismometer. The related properties include Poisson’s ratio, P- and S-wave velocities, Young’s modulus, and seismic attenuation. Finally, mass diffusivity was investigated to estimate gas movements in the regolith driven by atmospheric pressure changes. Physical properties presented here are all to some degree speculative. However, they form a basis for interpretation of the early data to be returned from the InSight mission.Additional co-authors: Nick Teanby and Sharon Keda

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

Predicting Alzheimer disease with β-amyloid imaging: Results from the Australian imaging, biomarkers, and lifestyle study of ageing

Objective Biomarkers for Alzheimer disease (AD) can detect the disease pathology in asymptomatic subjects and individuals with mild cognitive impairment (MCI), but their cognitive prognosis remains uncertain. We aimed to determine the prognostic value of β‐amyloid imaging, alone and in combination with memory performance, hippocampal atrophy, and apolipoprotein E ε4 status in nondemented, older individuals. Methods A total of 183 healthy individuals (age = 72.0 ± 7.26 years) and 87 participants with MCI (age = 73.7 ± 8.27) in the Australian Imaging, Biomarkers, and Lifestyle study of ageing were studied. Clinical reclassification was performed after 3 years, blind to biomarker findings. β‐Amyloid imaging was considered positive if the 11C‐Pittsburgh compound B cortical to reference ratio was ≥1.5. Results Thirteen percent of healthy persons progressed (15 to MCI, 8 to dementia), and 59% of the MCI cohort progressed to probable AD. Multivariate analysis showed β‐amyloid imaging as the single variable most strongly associated with progression. Of combinations, subtle memory impairment (Z score = −0.5 to −1.5) with a positive amyloid scan was most strongly associated with progression in healthy individuals (odds ratio [OR] = 16, 95% confidence interval [CI] = 3.7–68; positive predictive value [PPV] = 50%, 95% CI = 19–81; negative predictive value [NPV] = 94%, 95% CI = 88–98). Almost all amnestic MCI subjects (Z score ≤ −1.5) with a positive amyloid scan developed AD (OR = ∞; PPV = 86%, 95% CI = 72–95; NPV = 100%, 95% CI = 80–100). Hippocampal atrophy and ε4 status did not add further predictive value. Interpretation Subtle memory impairment with a positive β‐amyloid scan identifies healthy individuals at high risk for MCI or AD. Clearly amnestic patients with a positive amyloid scan have prodromal AD and a poor prognosis for dementia within 3 years. Ann Neurol 2013;74:905–91

10 simple rules for a supportive lab environment

The transition to principal investigator (PI), or lab leader, can be challenging, partially due to the need to fulfil new managerial and leadership responsibilities. One key aspect of this role, which is often not explicitly discussed, is creating a supportive lab environment. Here, we present ten simple rules to guide the new PI in the development of their own positive and thriving lab atmosphere. These rules were written and voted on collaboratively, by the students and mentees of Professor Mark Stokes, who inspired this piec