Latent Similarity Identifies Important Functional Connections for Phenotype Prediction.

Objective: Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets.

Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data.

Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods.

Reproducibility of tract-based white matter microstructural measures using the ENIGMA-DTI protocol.

Background: In preparation for longitudinal analyses of white matter development in youths with family histories of substance use disorders (FH+) or without such histories (FH-), we examined the reproducibility and reliability of global and regional measures of fractional anisotropy (FA) values, measured using the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA)-diffusion tensor imaging (DTI) protocol. Highly reliable measures are necessary to detect any subtle differences in brain development.

Methods: First, we analyzed reproducibility data in a sample of 12 healthy young adults (ages 20-28) imaged three times within a week.

Heterochronicity of white matter development and aging explains regional patient control differences in schizophrenia.

Background: Altered brain connectivity is implicated in the development and clinical burden of schizophrenia. Relative to matched controls, schizophrenia patients show (1) a global and regional reduction in the integrity of the brain's white matter (WM), assessed using diffusion tensor imaging (DTI) fractional anisotropy (FA), and (2) accelerated age-related decline in FA values. In the largest mega-analysis to date, we tested if differences in the trajectories of WM tract development influenced patient-control differences in FA.

Heritability of complex white matter diffusion traits assessed in a population isolate.

Introduction: Diffusion weighted imaging (DWI) methods can noninvasively ascertain cerebral microstructure by examining pattern and directions of water diffusion in the brain. We calculated heritability for DWI parameters in cerebral white (WM) and gray matter (GM) to study the genetic contribution to the diffusion signals across tissue boundaries.

Methods: Using Old Order Amish (OOA) population isolate with large family pedigrees and high environmental homogeneity, we compared the heritability of measures derived from three representative DWI methods targeting the corpus callosum WM and cingulate gyrus GM: diffusion tensor imaging (DTI), the permeability-diffusivity (PD) model, and the neurite orientation dispersion and density imaging (NODDI) model.

The common genetic influence over processing speed and white matter microstructure: Evidence from the Old Order Amish and Human Connectome Projects.

Speed with which brain performs information processing influences overall cognition and is dependent on the white matter fibers. To understand genetic influences on processing speed and white matter FA, we assessed processing speed and diffusion imaging fractional anisotropy (FA) in related individuals from two populations. Discovery analyses were performed in 146 individuals from large Old Order Amish (OOA) families and findings were replicated in 485 twins and siblings of the Human Connectome Project (HCP).

Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data.

The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods.