Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models.

Erratum: Separating Clinical and Subclinical Depression by Big Data Informed Structural Vulnerability Index and Its impact on Cognition: ENIGMA Dot Product.

In the PSB article published in Biocomputing 2022: Proceedings of the Pacific Symposium, pp. 133-143; doi: 10.1142/9789811250477_0013 (https://www.

Mapping gray and white matter volume abnormalities in early-onset psychosis: an ENIGMA multicenter voxel-based morphometry study.

Introduction: Regional gray matter (GM) alterations have been reported in early-onset psychosis (EOP, onset before age 18), but previous studies have yielded conflicting results, likely due to small sample sizes and the different brain regions examined. In this study, we conducted a whole brain voxel-based morphometry (VBM) analysis in a large sample of individuals with EOP, using the newly developed ENIGMA-VBM tool.

Methods: 15 independent cohorts from the ENIGMA-EOP working group participated in the study.

Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles.

Background: Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements.

Methods: We designed a model that parametrically estimates the timing of migration from animal tracking data.

Search for Dark Photons in Rare Z Boson Decays with the ATLAS Detector.

A search for events with a dark photon produced in association with a dark Higgs boson via rare decays of the standard model Z boson is presented, using 139  fb^{-1} of sqrt[s]=13  TeV proton-proton collision data recorded by the ATLAS detector at the Large Hadron Collider. The dark boson decays into a pair of dark photons, and at least two of the three dark photons must each decay into a pair of electrons or muons, resulting in at least two same-flavor opposite-charge lepton pairs in the final state. The data are found to be consistent with the background prediction, and upper limits are set on the dark photon's coupling to the dark Higgs boson times the kinetic mixing between the standard model photon and the dark photon, α_{D}ϵ^{2}, in the dark photon mass range of [5, 40] GeV except for the ϒ mass window [8.

Combined Measurement of the Higgs Boson Mass from the H→γγ and H→ZZ^{*}→4ℓ Decay Channels with the ATLAS Detector Using sqrt[s]=7, 8, and 13 TeV pp Collision Data.

A measurement of the mass of the Higgs boson combining the H→ZZ^{*}→4ℓ and H→γγ decay channels is presented. The result is based on 140  fb^{-1} of proton-proton collision data collected by the ATLAS detector during LHC run 2 at a center-of-mass energy of 13 TeV combined with the run 1 ATLAS mass measurement, performed at center-of-mass energies of 7 and 8 TeV, yielding a Higgs boson mass of 125.11±0.

Sibling Relationship and Behavioral Adjustment in Families of Disabled Children: Cross-Lagged Associations.

Cross-lagged panel designs were used to examine longitudinal and potential (bi)directional relationships between primary caregiver reported sibling relationship quality and the behaviors of children with intellectual disability (n = 297) and their closest in age siblings. The behavioral and emotional problems of the child with intellectual disability positively predicted sibling conflict over time. When accounting for control variables, this relationship was no longer present.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size.

Peptidylarginine deiminase 2 citrullinates MZB1 and promotes the secretion of IgM and IgA.

Introduction: MZB1 is an endoplasmic reticulum residential protein preferentially expressed in plasma cells, marginal zone and B1 B cells. Recent studies on murine B cells show that it interacts with the tail piece of IgM and IgA heavy chain and promotes the secretion of these two classes of immunoglobulin. However, its role in primary human B cells has yet to be determined and how its function is regulated is still unknown.

Cortical microstructural associations with CSF amyloid and pTau.

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau and Aβ burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.

Long-Term Outcomes After Abdominal Radiation for Wilms Tumor: A 20-Year Experience.

Background: As radiation therapy (RT) for Wilms tumor (WT) evolves with more conformal techniques, it is necessary to evaluate patterns of failure and toxicity. We sought to determine the rate of local failure (LF) after abdominal RT in WT, specifically focusing on those with contained rupture treated with whole abdominal and pelvic RT (WAPRT) vs flank RT. Secondary objectives were to determine overall survival (OS), distant failure (DF), and late toxicities.

Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting.

White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model - a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings.

Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection.

Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as convolutional neural networks (CNNs) have increasingly been applied to brain images to perform diagnostic and prognostic tasks by learning robust features. Vision transformers (ViT) - a new class of deep learning architectures - have emerged in recent years as an alternative to CNNs for several computer vision applications.

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs.

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images.

Predictive Modeling Of Alzheimer's Disease Prognosis Using Anatomical & Diffusion MRI.

Mild cognitive impairment (MCI) is an intermediate stage between healthy aging and Alzheimer's disease (AD), and AD is a progressive neurodegenerative disorder that affects around 50 million people worldwide. As new AD treatments begin to be developed, one key goal of AD research is to predict which individuals with MCI are most likely to progress to AD over a given interval (such as 2 years); if successful, these individuals could be preferentially enrolled in drug trials that aim to slow AD progression. Here we benchmarked a range of MCI-to-AD predictive models including linear regressions, support vector machines, and random forests, using predictors from anatomical and diffusion-weighted brain MRI, age, sex, APOE genotype and standardized clinical scores.

Along-Tract Statistical Mapping of Microstructural Abnormalities in Bipolar Disorder: A Pilot Study.

Investigating brain circuitry involved in bipolar disorder (BD) is key to discovering brain biomarkers for genetic and interventional studies of the disorder. Even so, prior research has not provided a fine-scale spatial mapping of brain microstructural differences in BD. In this pilot diffusion MRI dataset, we used BUndle ANalytics (BUAN)-a recently developed analytic approach for tractography-to extract, map, and visualize the profile of microstructural abnormalities on a 3D model of fiber tracts in people with BD (N=38) and healthy controls (N=49), and investigate along-tract white matter (WM) microstructural differences between these groups.