A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment ...efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset (769 participants in training set, 171 in test set). It is able to distinguish autism from control with hold-out accuracy of 65.0% (over baseline 51.6%) using functional images with personal characteristic data when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (889 participants in training set, 222 in test set). These results outperform all previously presented methods on both datasets. To our knowledge, this is the first demonstration of a single automated learning process that can produce classifiers for distinguishing patients vs. controls from brain imaging data with above-chance accuracy on large datasets for two different psychiatric illnesses (ADHD and autism). Working toward clinical applications requires robustness against real-world conditions, including the substantial variability that often exists among data collected at different institutions. It is therefore important that our algorithm was successful with the large ADHD-200 and ABIDE datasets, which include data from hundreds of participants collected at multiple institutions. While the resulting classifiers are not yet clinically relevant, this work shows that there is a signal in the (f)MRI data that a learning algorithm is able to find. We anticipate this will lead to yet more accurate classifiers, over these and other psychiatric disorders, working toward the goal of a clinical tool for high accuracy differential diagnosis.
The chemistry and physics of charged interfaces is regulated by the structure of the electrical double layer (EDL). Herein we quantify the average thickness of the Stern layer at the silica (SiO2) ...nanoparticle/aqueous electrolyte interface as a function of NaCl concentration following direct measurement of the nanoparticles’ surface potential by X‐ray photoelectron spectroscopy (XPS). We find the Stern layer compresses (becomes thinner) as the electrolyte concentration is increased. This finding provides a simple and intuitive picture of the EDL that explains the concurrent increase in surface charge density, but decrease in surface and zeta potentials, as the electrolyte concentration is increased.
The electrical double layer (EDL) is presented in a simple and intuitive picture that explains the concurrent increase in surface charge density, but decrease in surface and zeta potentials, as electrolyte concentration is increased.
Genomewide association studies (GWAS) have proven a powerful hypothesis-free method to identify common disease-associated variants. Even quite large GWAS, however, have only at best identified ...moderate proportions of the genetic variants contributing to disease heritability. To provide cost-effective genotyping of common and rare variants to map the remaining heritability and to fine-map established loci, the Immunochip Consortium has developed a 200,000 SNP chip that has been produced in very large numbers for a fraction of the cost of GWAS chips. This chip provides a powerful tool for immunogenetics gene mapping.
This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity ...features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject's fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data-exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.
The ventral tegmental area (VTA) and nucleus accumbens (NAc) are essential for learning about environmental stimuli associated with motivationally relevant outcomes. The task of signalling such ...events, both rewarding and aversive, from the VTA to the NAc has largely been ascribed to dopamine neurons. The VTA also contains GABA (γ-aminobutyric acid)-releasing neurons, which provide local inhibition and also project to the NAc. However, the cellular targets and functional importance of this long-range inhibitory projection have not been ascertained. Here we show that GABA-releasing neurons of the VTA that project to the NAc (VTA GABA projection neurons) inhibit accumbal cholinergic interneurons (CINs) to enhance stimulus-outcome learning. Combining optogenetics with structural imaging and electrophysiology, we found that VTA GABA projection neurons selectively target NAc CINs, forming multiple symmetrical synaptic contacts that generated inhibitory postsynaptic currents. This is remarkable considering that CINs represent a very small population of all accumbal neurons, and provide the primary source of cholinergic tone in the NAc. Brief activation of this projection was sufficient to halt the spontaneous activity of NAc CINs, resembling the pause recorded in animals learning stimulus-outcome associations. Indeed, we found that forcing CINs to pause in behaving mice enhanced discrimination of a motivationally important stimulus that had been associated with an aversive outcome. Our results demonstrate that VTA GABA projection neurons, through their selective targeting of accumbal CINs, provide a novel route through which the VTA communicates saliency to the NAc. VTA GABA projection neurons thus emerge as orchestrators of dopaminergic and cholinergic modulation in the NAc.
Journal clubs have typically been held within the walls of academic institutions and in medicine have served the dual purpose of fostering critical appraisal of literature and disseminating new ...findings. In the last decade and especially the last few years, online and virtual journal clubs have been started and are flourishing, especially those harnessing the advantages of social media tools and customs. This article reviews the history and recent innovations of journal clubs. In addition, the authors describe their experience developing and implementing NephJC, an online nephrology journal club conducted on Twitter.
Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population ...and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.
Five Years of GWAS Discovery VISSCHER, Peter M; BROWN, Matthew A; MCCARTHY, Mark I ...
American journal of human genetics,
01/2012, Volume:
90, Issue:
1
Journal Article
Peer reviewed
Open access
The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting ...variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section.
The New Tree of Eukaryotes Burki, Fabien; Roger, Andrew J.; Brown, Matthew W. ...
Trends in ecology & evolution (Amsterdam),
January 2020, 2020-01-00, 20200101, 2020, Volume:
35, Issue:
1
Journal Article
Peer reviewed
Open access
For 15 years, the eukaryote Tree of Life (eToL) has been divided into five to eight major groupings, known as ‘supergroups’. However, the tree has been profoundly rearranged during this time. The new ...eToL results from the widespread application of phylogenomics and numerous discoveries of major lineages of eukaryotes, mostly free-living heterotrophic protists. The evidence that supports the tree has transitioned from a synthesis of molecular phylogenetics and biological characters to purely molecular phylogenetics. Most current supergroups lack defining morphological or cell-biological characteristics, making the supergroup label even more arbitrary than before. Going forward, the combination of traditional culturing with maturing culture-free approaches and phylogenomics should accelerate the process of completing and resolving the eToL at its deepest levels.
The eukaryote Tree of Life (eToL) represents the phylogeny of all eukaryotic lineages, with the vast bulk of this diversity comprising microbial ‘protists’. Since the early 2000s, the eToL has been summarized in a few (five to eight) ‘supergroups’. Recently, this tree has been deeply remodeled due mainly to the maturation of phylogenomics and the addition of numerous new ‘kingdom-level’ lineages of heterotrophic protists.The current eToL is derived almost exclusively from molecular phylogenies, in contrast to earlier models that were syntheses of molecular and other biological data.The supergroup model for the eToL has become increasingly abstract due to the absence of known shared derived characteristics for the new supergroups.Culture-based studies, not higher-throughput methods, have been responsible for most of the new major lineages recently added to the eToL.
Polygenic risk scores Brown, Matthew A.
Seminars in arthritis and rheumatism,
February 2024, 2024-Feb, 2024-02-00, 20240201, Volume:
64
Journal Article
Peer reviewed
Polygenic risk scores (PRS) estimate an individual's genetic risk for a disease or trait compared to a matched population. For many rheumatic diseases PRS have been developed that have discriminatory ...capacity better than some widely used biomarkers, and in some cases are the most discriminatory tests available. These PRS assessments do not rely on the disease being present or its activity level, making them highly valuable for predicting disease development or enabling early diagnosis. However, most PRS to date have been developed in research settings, and in populations of European-ancestry. Further studies are required to assess their utility in clinical settings, in relation to existing tests, and in non-European populations. Such studies are underway, and it is likely in the near future these tests will become widely available, with significant benefits for the practice of medicine.
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