Multisite Comparison of MRI Defacing Software Across Multiple Cohorts

With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3–85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3–20 years) for afni_refacer and the oldest (44–85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Structure input file

Input file for the program STRUCTUR

Data from: Stream flow alone does not predict population structure of diving beetles across complex tropical landscapes

Recent theoretical advances have hypothesized a central role of habitat persistence on population genetic structure and resulting biodiversity patterns of freshwater organisms. Here, we address the hypothesis that lotic species, or lineages adapted to comparably geologically stable running water habitats (streams and their marginal habitats), have high levels of endemicity and phylogeographic structure due to the persistent nature of their habitat. We use a nextRAD sequencing approach to investigate the population structure and phylogeography of a putatively widespread New Guinean species of diving beetle, Philaccolilus ameliae (Dytiscidae). We find that P. ameliae is a complex of morphologically cryptic, but geographically and genetically well-differentiated clades. The pattern of population connectivity is consistent with theoretical predictions associated with stable lotic habitats. However, in two clades, we find a more complex pattern of low population differentiation, revealing dispersal across rugged mountains and watersheds of New Guinea up to 430 km apart. These results, while surprising, were also consistent with the original formulation of the Habitat Template Concept by Southwood, involving lineage-idiosyncratic evolution in response to abiotic factors. In our system, low population differentiation might reflect a young species in a phase of range expansion utilizing vast available habitat. We suggest that predictions of life history variation resulting from the dichotomy between lotic and lentic organisms require more attention to habitat characterization and microhabitat choice. Our results also underpin the necessity to study fine-scale processes but at a larger geographic scale, as compared to solely documenting macro-ecological patterns, to understand ecological drivers of regional biodiversity. Comprehensive sampling especially of tropical lineages in complex and threatened environments such as New Guinea, remains a critical challenge

Genetix input file

Input file for the program Genetix used to calculate Fs

Bayescan input file

Input file for the program Bayescan used to identify outlier loci that may be undergoing selectio

MrBayes input file

Input file for MrBayes

divingbeetle

VCF file of Phillaccolilus nextRAD datase

Data from: A range-wide genetic bottleneck overwhelms landscape heterogeneity and local abundance in shaping genetic patterns of an alpine butterfly (Lepidoptera: Pieridae: Colias behrii)

Spatial and environmental heterogeneity are major factors in structuring species distributions in alpine landscapes. These landscapes have also been affected by glacial advances and retreats, causing alpine taxa to undergo range shifts and demographic changes. These non-equilibrium population dynamics have the potential to obscure the effects of environmental factors on the distribution of genetic variation. Here we investigate how demographic change and environmental factors influence genetic variation in the alpine butterfly Colias behrii. Data from 14 microsatellite loci provide evidence of bottlenecks in all population samples. We test several alternative models of demography using approximate Bayesian computation (ABC), with the results favoring a model in which a recent bottleneck precedes rapid population growth. Applying independent calibrations to microsatellite loci and a nuclear gene, we estimate that this bottleneck affected both northern and southern populations 531 to 281 years ago, coinciding with a period of global cooling. Using regression approaches, we attempt to separate the effects of population structure, geographical distance and landscape on patterns of population genetic differentiation. Only 40% of the variation in F_ST is explained by these models, with geographical distance and least cost distance among meadow patches selected as the best predictors. Various measures of genetic diversity within populations are also decoupled from estimates of local abundance and habitat patch characteristics. Our results demonstrate that demographic change can have a disproportionate influence on genetic diversity in alpine species, contrasting with other studies that suggest landscape features control contemporary demographic processes in high elevation environments