Heritability of Behavioral and Brain Measures in a Large Cohort of Healthy Twin and non-Twin Subjects

Bioinformatics and Computational Biology, University of Minnesota - 2019-12-05Joseph J, Georgopoulos AP, Christova P
This research investigated comprehensively the effects of genetics on behavioral traits, brain structure and function, and their associations in a large cohort of monozygotic (MZ) twins, dizygotic (DZ) twins, non-twin siblings (SIB) and non related (NR) individuals (N = 1206, total) provided by the Human Connectome Project (HCP). All primary measures available are of the highest quality and quantitatively assessed. They include the following for each individual: (a) Measures of behavioral traits in 5 domains (motor, sensory, cognitive, emotion, and personality); (b) volumes of 70 cortical brain areas extracted from high-resolution (0.7 mm isotropic)
Structural Magnetic Resonance Imaging

Structural Magnetic Resonance Imaging (sMRI)

Performed to assess gray-matter volume. The data are acquired using a Philips 3T Achieva XL magnet with a SENSE 8 channel head coil. Approximately 500,000 voxels per brain are analyzed. In the first analysis, the volume of about 100 separate brain regions is calculated using FreeSurfer software (www.surfer.nmr.mgh.harvard. edu). This provides a coarse-grain, volumetric analysis of areas of the brain. In the second analysis, called voxel-based morphometry, the density of each voxel is assessed for a fine-grain analysis of each area.5 Typically, gray-matter volume decreases with age but at rates that are different for different people, for different areas of the brain, and for men and women. In that sense, one can talk about "gray-matter age" versus chronological age. A person may be 68 years old but have the gray-matter volume of a 50-year-old. Defining brain age based on measurements (as contrasted with chronological age) is a pervasive theme in this project.
data; (c) resting-state blood oxygenation level dependent (BOLD) activity of the same areas extracted from long-duration (1200 volumes), fast-acquisition (every 0.72 s), high-resolution (2 mm isotropic) functional MRI (
Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI)

A functional neuroimaging procedure using MRI technology that measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases.[citation needed] The primary form of fMRI uses the blood-oxygen-level dependent (BOLD) contrast, discovered by Seiji Ogawa. This is a type of specialized brain and body scan used to map neural activity in the brain or spinal cord of humans or other animals by imaging the change in blood flow (hemodynamic response) related to energy use by brain cells. Since the early 1990s, fMRI has come to dominate brain mapping research because it does not require people to undergo shots, surgery, or to ingest substances, or be exposed to ionising radiation, etc.
) data; and (d) white matter integrity measures (fractional anisotropy [FA] and mean diffusivity [MD] for 7 brain regions regions) derived from high angular resolution diffusion imaging (HARDI) MRI (
Diffusion Magnetic Resonance Imaging

Diffusion Magnetic Resonance Imaging (dMRI)

The input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture so as to infer 'structural connectivity' between gray matter regions. In dMRI, a critically important goal is to estimate the orientation of white matter fiber bundles as accurately as possible, especially in regions where multiple fiber bundles intersect one another at various angles or where a fiber bundle bends or fans out and splits into multiple trajectories. Improving signal-to-noise ratio (SNR) by minimizing T2 decay during the diffusion encoding period, and accelerating the data acquisition rate without significantly impacting SNR (i.e. increasing SNR per unit time of data acquisition) are key to obtaining more informative dMRI data for tractography analysis.
) data at 1.25 mm spatial resolution and very strong magnetic field gradients at (100 mT/m). Data extraction and preprocessing was performed using a dedicated 704-processor high-performance computer cluster at the
Brain Sciences Center

Brain Sciences Center (BSC)

A research group in collaboration with the Minnesota American Legion, Minneapolis VA Medical Center, and the University of Minnesota.
using Matlab. Univariate and multivariate statistical analyses were carried out in personal computers using Matlab and IBM-SPSS (version 24). These analyses include the following. (a) Computation of common univariate statistics (mean, variance, etc.); (b) computation of intra class correlation (ICC) for each of the 4 genetic groups (MZ, DZ, SIB, NR) and its z-transform [zICC = atanh(ICC)] for each primary measure above; (c) analysis of variance (ANOVA) of zICC across genetic groups for each measure; (d) computation of heritability using Falconer's formula; (e) multidimensional scaling (MDS) and hierarchical tree clustering (HTC) of this heritability for the different data sets (behavioral,
sMRI

Structural Magnetic Resonance Imaging (sMRI)

Performed to assess gray-matter volume. The data are acquired using a Philips 3T Achieva XL magnet with a SENSE 8 channel head coil. Approximately 500,000 voxels per brain are analyzed. In the first analysis, the volume of about 100 separate brain regions is calculated using FreeSurfer software (www.surfer.nmr.mgh.harvard. edu). This provides a coarse-grain, volumetric analysis of areas of the brain. In the second analysis, called voxel-based morphometry, the density of each voxel is assessed for a fine-grain analysis of each area.5 Typically, gray-matter volume decreases with age but at rates that are different for different people, for different areas of the brain, and for men and women. In that sense, one can talk about "gray-matter age" versus chronological age. A person may be 68 years old but have the gray-matter volume of a 50-year-old. Defining brain age based on measurements (as contrasted with chronological age) is a pervasive theme in this project.
,
fMRI

Functional Magnetic Resonance Imaging (fMRI)

A functional neuroimaging procedure using MRI technology that measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases.[citation needed] The primary form of fMRI uses the blood-oxygen-level dependent (BOLD) contrast, discovered by Seiji Ogawa. This is a type of specialized brain and body scan used to map neural activity in the brain or spinal cord of humans or other animals by imaging the change in blood flow (hemodynamic response) related to energy use by brain cells. Since the early 1990s, fMRI has come to dominate brain mapping research because it does not require people to undergo shots, surgery, or to ingest substances, or be exposed to ionising radiation, etc.
,
dMRI

Diffusion Magnetic Resonance Imaging (dMRI)

The input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture so as to infer 'structural connectivity' between gray matter regions. In dMRI, a critically important goal is to estimate the orientation of white matter fiber bundles as accurately as possible, especially in regions where multiple fiber bundles intersect one another at various angles or where a fiber bundle bends or fans out and splits into multiple trajectories. Improving signal-to-noise ratio (SNR) by minimizing T2 decay during the diffusion encoding period, and accelerating the data acquisition rate without significantly impacting SNR (i.e. increasing SNR per unit time of data acquisition) are key to obtaining more informative dMRI data for tractography analysis.
). These analyses yielded substantial new information on the effects of genetics on brain and behavior, and partially elucidated underlying associations among the various diverse measures above. To our knowledge, this is the first such comprehensive study carried out. This presentation serves as Jasmine Joseph's PhD defense. Jasmine is advised by Apostolos P. Georgopoulos, MD PhD and co-advised by .