Accelerating Heritability, Genetic Correlation, and Genome-Wide Association Imaging Genetic Analyses in Complex Pedigrees.

National and international biobanking efforts led to the collection of large and inclusive imaging genetics datasets that enable examination of the contribution of genetic and environmental factors to human brains in illness and health. High-resolution neuroimaging (~10 voxels) and genetic (10 single nucleotide polymorphic [SNP] variants) data are available in statistically powerful (N = 10) epidemiological and disorder-focused samples. Performing imaging genetics analyses at full resolution afforded in these datasets is a formidable computational task even under the assumption of unrelatedness among the subjects.

Prognostic factors for worsening and improvement in multiple sclerosis using a multistate model.

Background: The long-term disease trajectory of people living with multiple sclerosis (MS) can be improved by initiating efficacious treatment early. More quantitative evidence is needed on factors that affect a patient's risk of disability worsening or possibility of improvement to inform timely treatment decisions.

Methods: We developed a multistate model to quantify the influence of demographic, clinical, and imaging factors on disability worsening and disability improvement simultaneously across the disability spectrum as measured by the Expanded Disability Status Scale (EDSS).

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility.

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented.

Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior.

Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a lesion, plays an important role in understanding aging and inflammatory diseases. We propose a scalable hierarchical Bayesian spatial model, called BLESS, capable of handling binary responses by placing continuous spike-and-slab mixture priors on spatially-varying parameters and enforcing spatial dependency on the parameter dictating the amount of sparsity within the probability of inclusion.

Ventricular volume asymmetry as a novel imaging biomarker for disease discrimination and outcome prediction.

Aims: Disruption of the predictable symmetry of the healthy heart may be an indicator of cardiovascular risk. This study defines the population distribution of ventricular asymmetry and its relationships across a range of prevalent and incident cardiorespiratory diseases.

Methods And Results: The analysis includes 44 796 UK Biobank participants (average age 64.

Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.

Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.

Quality over quantity: powering neuroimaging samples in psychiatry.

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size.

Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples.

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations.

Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components.

The relative contributions of genetic variation and experience in shaping the morphology of the adolescent brain are not fully understood. Using longitudinal data from 11,665 subjects in the ABCD Study, we fit vertex-wise variance components including family effects, genetic effects, and subject-level effects using a computationally efficient framework. Variance in cortical thickness and surface area is largely attributable to genetic influence, whereas sulcal depth is primarily explained by subject-level effects.

Two common and distinct forms of variation in human functional brain networks.

The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions.

A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis.

Objective: Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, potentially composite endpoint at one time point. While such an analytical focus promotes simple and replicable conclusions, it does not necessarily fully capture the multi-faceted effects of a drug in a complex disease setting.

Decentralized Mixed Effects Modeling in COINSTAC.

Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups.

Spatial confidence regions for combinations of excursion sets in image analysis.

The analysis of excursion sets in imaging data is essential to a wide range of scientific disciplines such as neuroimaging, climatology, and cosmology. Despite growing literature, there is little published concerning the comparison of processes that have been sampled across the same spatial region but which reflect different study conditions. Given a set of asymptotically Gaussian random fields, each corresponding to a sample acquired for a different study condition, this work aims to provide confidence statements about the intersection, or union, of the excursion sets across all fields.

FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible.

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.

Associations between cortisol stress responses and limbic volume and thickness in young adults: An exploratory study.

The investigation of the relationship between neural measures of limbic structures and hypothalamic pituitary adrenal axis responses to acute stress exposure in healthy young adults has so far focused in particular on task-based and resting state functional connectivity studies. Thus, the present study examined the association between limbic volume and thickness measures and acute cortisol responses to the psychosocial stress paradigm ScanSTRESS. Using Permutation Analysis of Linear Models controlling for sex, age and total brain volume, the associations between (sex-specific) cortisol increases and human connectome project style anatomical variables of limbic structures (i.

Reciprocal relationships between stress and depressive symptoms: the essential role of the nucleus accumbens.

Background: Stress and depression have a reciprocal relationship, but the neural underpinnings of this reciprocity are unclear. We investigated neuroimaging phenotypes that facilitate the reciprocity between stress and depressive symptoms.

Methods: In total, 22 195 participants (52.

Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background.

Response to the anti-IL17 monoclonal antibody secukinumab is heterogeneous, and not all participants respond to treatment. Understanding whether this heterogeneity is driven by genetic variation is a key aim of pharmacogenetics and could influence precision medicine approaches in inflammatory diseases. Using changes in disease activity scores across 5,218 genotyped individuals from 19 clinical trials across four indications (psoriatic arthritis, psoriasis, ankylosing spondylitis, and rheumatoid arthritis), we tested whether genetics predicted response to secukinumab.

Neuroscience Needs Network Science.

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity.