Article

Diffusion imaging protocol effects on genetic associations

Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA.
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 05/2012; DOI: 10.1109/ISBI.2012.6235712
Source: PubMed

ABSTRACT Large multi-site image-analysis studies have successfully discovered genetic variants that affect brain structure in tens of thousands of subjects scanned worldwide. Candidate genes have also associated with brain integrity, measured using fractional anisotropy in diffusion tensor images (DTI). To evaluate the heritability and robustness of DTI measures as a target for genetic analysis, we compared 417 twins and siblings scanned on the same day on the same high field scanner (4-Tesla) with two protocols: (1) 94-directions; 2mm-thick slices, (2) 27-directions; 5mm-thickness. Using mean FA in white matter ROIs and FA 'skeletons' derived using FSL, we (1) examined differences in voxelwise means, variances, and correlations among the measures; and (2) assessed heritability with structural equation models, using the classical twin design. FA measures from the genu of the corpus callosum were highly heritable, regardless of protocol. Genome-wide analysis of the genu mean FA revealed differences across protocols in the top associations.

Download full-text

Full-text

Available from: Katie Mcmahon, Jul 06, 2015
0 Followers
 · 
130 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate single-nucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20-30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained ≈ 6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.
    Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 04/2012; 37(9):2012-9. DOI:10.1038/npp.2012.49 · 7.83 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Knowledge of the properties of white matter fiber tracts isa crucial and necessary step toward a precise understanding of the functional architecture of the living human brain. Previously, this knowledge was severely limited, as it was difficult to visualize these structures or measure their functions in vivo. The HCP has recently generated considerable interest because of its potential to explore connectivity and its relationship with genetics and behavior. For neuroscientists and the lay public alike, the ability to assess, measure, and explore this wealth of layered information concerning how the brain is wired is a much sought after prize.The navigation of the human connectome and the discovery of how it is affected through genetics, and in a range of neurological and psychiatric diseases, have far reaching implications. From a range of ongoing connectomics related activities, the systematic characterization of brain connectedness and the resulting functional aspects of such connectivity will not only realize the work of Ramón y Cajal and others, but will also greatly expand our understanding of the brain, the mind, and what it is to be truly human. The similarities and differences that mark normal diversity will help us to understand variation among people and set the stage to chart genetic influences on typical brain development and decline during aging. What is more, an understanding of how brains might become disordered will shed light on autism, schizophrenia, Alzheimer’s, and other diseases that exact a tremendous and terrible social and economic toll.
    Neurosurgery 07/2012; 71(1):1-5. DOI:10.1227/NEU.0b013e318258e9ff · 3.03 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The quest to map brain connectivity is being pursued worldwide using diffusion imaging, among other techniques. Even so, we know little about how brain connectivity measures depend on the magnetic field strength of the scanner. To investigate this, we scanned ten healthy subjects at 7 and 3 Tesla - using 128-gradient high-angular resolution diffusion imaging (HARDI). For each subject and scan, whole-brain tractography was used to estimate connectivity between 113 cortical and subcortical regions. We examined how scanner field strength affects (1) the signal-to-noise ratio of the non-diffusion-sensitized reference images (b0); (2) DTI-derived fractional anisotropy (FA), mean, radial and axial diffusivity (MD/RD/AD), in atlas-defined regions; (3) whole-brain tractography; (4) the 113x113 brain connectivity maps; and (5) five commonly-used network topology measures. We also assessed effects of the multi-channel reconstruction methods (sum-of-squares, SOS, at 7T; adaptive recombine, AC, at 3T). At 7T with SOS, the b0 images had 18.3% higher SNR than with 3T-AC. FA was similar for most ROIs derived from an online DTI atlas (ICBM81), but higher at 7T in the cerebral peduncle and internal capsule. MD, AD and RD were lower at 7T for most ROIs. The apparent fiber density between some subcortical regions was greater at 7T-SOS than 3T-AC, with a consistent connection pattern overall. Suggesting the need for caution, the recovered brain network was apparently more "efficient" at 7T, which cannot be biologically true as the same subjects were assessed. Care is needed when comparing network measures across studies, and when interpreting apparently discrepant findings.
    12/2012; DOI:10.1089/brain.2012.0114